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 energy-efficient 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
|
Pollutant (Benchmark) |
Benchmark
Emissions |
Fraction
Reduced |
2007
Emission Cap |
|
SO2
(tons in Title IV) |
8.95 million tons |
75% |
2.24 million tons |
|
NOx
(tons in 1997) |
6.04 million tons |
75% |
1.51 million tons |
|
Hg
(tons in 1999) |
48 tons |
90% |
4.8 tons |
|
C
(metric tons in 1990) |
475 million metric tons |
- |
475 million metric tons |
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 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 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.
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 kilowatt-hours 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
|
Indicator |
Scenario A Standard
Technology Case |
Scenario B
Advanced Technology Case |
Scenario C CEF Moderate
Case |
Scenario D CEF
Advanced Case |
|
Primary
Energy |
0% |
-2.5% |
-3.4% |
-6.3% |
|
Electricity
Sales |
0% |
-2.4% |
-5.9% |
-6.8% |
|
Carbon
Emissions |
0% |
-5.0% |
-7.4% |
-10.7% |
|
NOx
Emissions |
0% |
-2.6% |
-5.4% |
-8.1% |
|
|
|
|
|
|
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)
|
Scenario |
2002 |
2005 |
2010 |
2015 |
|
Scenario A |
0.0 |
0.0 |
0.0 |
0.0 |
|
Scenario B |
0.8 |
1.6 |
2.7 |
2.9 |
|
Scenario C |
1.2 |
2.3 |
4.3 |
4.8 |
|
Scenario D |
2.1 |
3.9 |
5.2 |
5.5 |
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
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)
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 demand-side 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
|
Analytical
Scenario |
Electricity End Use Demand (Billion Kilowatt-hours) |
Natural Gas Use in
Electricity Generation (Quads) |
Coal Use in Electricity
Generation (Quads) |
Electricity Expenditures
(Billion 1999 Dollars) |
Personal Consumption (Billion 1999 Dollars) |
Investment (Billion 1999 Dollars) |
Gross Domestic Product (Billion 1999 Dollars) |
|
Reference
|
4,346 |
8.3 |
22.3 |
269.4 |
8,902.0 |
3,042.4 |
13,211.7 |
|
A.
Standard Tech |
4,156 |
9.3 |
14.6 |
353.9 |
8,870.9 |
3,067.3 |
13,204.3 |
|
B.
High Tech |
4,112 |
8.9 |
15.0 |
337.4 |
8,873.7 |
3,067.0 |
13,209.5 |
|
C.
Mod CEF |
4,070 |
8.2 |
15.6 |
323.0 |
8,881.7 |
3,066.8 |
13,218.9 |
|
D.
Adv CEF |
4,025 |
7.7 |
15.9 |
308.9 |
8,889.2 |
3,066.7 |
13,227.2 |
|
|
|
|
|
|
|
|
|
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 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
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