annexes: impacts of comprehensive climate and energy policy options on the u.s. economy

Impacts of Comprehensive Climate and Energy Policy Options on the U.S. Economy 73

The methodologies summarized in this Annex are used to extrapolate the quantification results (greenhouse gas [GHG] reduction potentials and per-ton cost/savings, i.e., cost-effectiveness) for the 23 “super options” analyzed by the Center for Climate Strategies (CCS) in 16 states. The 23 policies and measures in the 16 states’ action plans are extrapolated to the rest of the states. The 16 states with CCS-analyzed action plans include: Alaska, Arkansas, Arizona, Colorado, Florida, Iowa, Maryland, Michigan, Minnesota, Montana, New Mexico, North Carolina, Pennsylvania, South Carolina, Vermont, and Washington (“existing states”). The methodology was applied to each of the remaining 34 U.S. states (“new states”) to derive both the reduction potentials and cost-effectiveness of each individual super option.

I. Summary

In Section II of this Annex, three methods were evaluated to extrapolate the quantification results of the Energy Supply (ES), Residential Commercial, and Industrial (RCI), and Agriculture, Forestry, and Waste Management (AFW) super options in the 16 existing states where CCS had done analysis for climate action plans to the remaining non-Southern Governors’ Association (SGA) states. These methods are known as Analogous State, Simple-Average, and Weighted-Average. Of the three, the Weighted-Average Method was selected because of its superior performance. For every new state, this method utilizes the data from all of the 16 existing states in the extrapolation calculation for each super option. By applying this approach, rather than using a simple average, higher weights are assigned to existing states that share similarities with the new state. Also, this approach captures most of the major strengths of the Analogous State Method, but does not place the onus of comparison on any single state. It may still place too much emphasis on existing states that are different from the new state, but this is moderated somewhat in that different weights are used for each set of options.

The Weighted-Average Method develops a mathematical approach to quantify the similarities among individual states. The similarity measurement is established based on how each state ranks relative to others in terms of key factors affecting the reduction potentials and cost/savings of various mitigation/sequestration policy options. Theoretically, among the three alternative methods, the Weighted-Average Method can best capture the underlying factors that explain the variances in the quantification results of the super options among the 16 existing states.

In Section III, the VISION model is described, which was used to analyze the GHG reduction potentials and cost/savings associated with the implementation of the super options from the Transportation and Land Use (TLU) sector. The VISION model is a peer-reviewed spreadsheet scenario modeling tool that was developed by the United States Department of Energy (USDOE) (Ward, 2008). The VISION model is updated on an annual basis. The most recent version of the Voluntary Innovative Sector Initiatives (VISION) model was used for the analysis of the TLU sector.

In Section IV, two versions of national cost curves (full stakeholder implementation version and congressional target implementation version) are developed based on the extrapolation analysis of the GHG reduction potentials and cost-effectiveness of super options from the existing states to the remaining states in the U.S.

annex a

» Estimation Methodology for GHG Reduction Potentialand Cost-Effectiveness of Super Options

74 Johns Hopkins University and Center for Climate Strategies

II. Extrapolation Methodology for the ES, RCI, and AFW Super Options

Definitions and Descriptions of the Methods Used

A. Analogous State Method

In this method, we identify the analogous state from the 16 existing states to be used as the extrapolation basis for each of the new states in the cost curve extrapolation. An analogous state is not necessarily a neighboring state of the new state. It is more important that the analogous state and the new state share similar characteristics that are key factors affecting the GHG reduction potentials and costs/savings associated with the reduction. For each new state/territory, the analogous state is identified for each of the three sectors (Energy Supply [ES]. Residential, Commercial, and Industrial [RCI], and Agriculture, Forestry, and Waste Management [AFW]), since the key factors affecting the reductions and cost/savings are quite different in various sectors. Appendix A lists sectoral key factors. Appendix B gives an example of how the analogous state for the Demand Side Management Programs option for an example new state is determined.

After the analogous state is selected (see the details in Appendix B), the extrapolation to the new states is performed through the following steps:

1. For the ES options, the ratio of total emissions from the Power sector between the new state and the analogous state is computed.1 For the RCI options, the ratio of emissions from the Commercial and Industrial sectors between the new state and the analogous state is computed for the Combined Heat and Power option. Emissions from the Residential, Commercial, and Industrial sectors are computed for the other RCI options.2 For the AFW options, the ratio of applicability between the new and analogous states is computed (for example, the variable used to determine the applicability of the Livestock Manure option is the estimated total state population of dairy cattle, beef cattle, and swine. The variable used to determine the applicability of the Reforestation/Afforestation option is the total acres of combination of agricultural land and other non-forest, non-urban land).

2. The new state over the analogous state ratio of total sectoral emissions or the ratio of applicability is multiplied by the emission reduction potentials of the super option in the analogous state to get the estimate of the GHG reduction potentials in the new state. (For example, if the ratio of the estimated total population of dairy cattle, beef cattle, and swine in the new state and the analogous state is 2:1, the estimated GHG reduction potentials of the Livestock Manure option in the new state are two times that in the analogous state.)

3. The cost-effectiveness (i.e., per-ton cost/saving of GHG removed) of each super option in the new state is assumed to be same as in the analogous state.

B. Simple-Average Method

In this method, instead of using data from one analogous state to do the extrapolation, for each option, data from all the 16 existing states are utilized in the extrapolation analysis for the new states. In contrast to the Weighted-Average Method to be described below, this method treats the data from the 16 existing states equally in the extrapolation calculation for the new state.

1. For the super options of Carbon Capture and Sequestration and Coal Plant Efficiency Improvements, geological CO2 storage resource and total electricity generation from coal are used , respectively (instead of total emissions from the Power sector) in this extrapolation step.2. For High Performance Buildings, Appliance Standards, and Building Codes options, 100% of the emissions from the Residential and Commercial sectors is included. For the Industrial sector, only 9.4% of the sectoral emissions is included. This is because, according to the EIA 2002 Energy Consumption by Manufacturers report, approximately 9.4% of the industrial energy use in the U.S. is for heating, ventilating and air conditioning (HVAC), lighting, and other facility support (see http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html). For the Demand Side Management option, total emissions from the Industrial sector are included, since this option would cover reductions in emissions from various industrial processes.


The extrapolation steps are:

1. For the ES and RCI super options, the ratio of emissions from the relevant sector between the new state and each of the 16 existing states is computed, while for the AFW options, the ratio of applicability between the new and each of the 16 existing states is computed.

2. The new state over the existing state ratio of total sectoral emissions or the ratio of applicability is multiplied by the emission reduction potentials of the super option in the existing state to get the estimate of the GHG reduction potentials in the new state. This calculation is made 16 times, each time using the numbers from one existing state as the extrapolation basis.

3. The simple average of the 16 estimates of GHG reduction potentials computed in step 2 is calculated as the final estimate for the new state.

4. The simple average of the cost-effectiveness of the 16 states is computed as the estimate for the new state.

5. Please note for options that are not recommended in all the 16 states, simple average is calculated by averaging the numbers from those states that included the options in their state climate action plan recommendations.

C. Weighted-Average Method

Similar to the Simple-Average Method, data from all 16 existing states are utilized in the extrapolation computation for any given new state. The difference is that weights for the 16 existing states are computed based on several key sectoral factors and state characteristics, so that existing states that share similarities with the new state will be given a higher weight in the extrapolation analysis. Appendix B contains an example of how the weights for the Demand Side Management Program option are computed for an example new state.

After the calculation of the weights for the 16 existing states (see Appendix B for details), the extrapolation steps are:

1. For the ES and RCI super options, the ratio of emissions from the relevant sector between the new state and each of the 16 existing states is computed, while for the AFW options, the ratio of applicability between the new and each of the 16 existing states is computed.

2. The new state over the existing state ratio of sectoral emissions or the ratio of applicability is multiplied by the emission reduction potentials of the super option in the existing state to get the estimate of the GHG reduction potentials in the new state. This calculation is made 16 times, each time using the numbers from one existing state as the extrapolation basis.

3. The weighted average of the 16 estimates of GHG reduction potentials computed in step 2 is calculated as the final estimate for the new state.

4. The weighted average of the cost-effectiveness of the 16 states is computed as the estimate for the new state.

5. Again, for options that are not recommended in all the 16 states, weighted average is calculated by averaging just the numbers from those states that included the options in their state climate action plan recommendations.

Sample Calculations for the Three Alternative Approaches

In this section, we use the RCI super option Demand Side Management Programs as an example to illustrate how the three alternative extrapolation methods work. Table A-1 lists the options from the 16 existing states on Demand Side Management Programs.


Table A-1. List of the Updated State Options on Demand Side Management Programs

Option Option Description2020 GHG

Reductions (MMtCO2e)

Cost–Effectiveness ($/tCO2e)

AK: ESD-2/4/6 Energy Efficiency for Residential, Commercial, and Industrial Customers, 2% per year 1.07 –$74.01

AR: RCI-2b Utility and Non-Utility Demand Side Management (DSM) and Energy Efficiency for Electricity 2.39 –$5.70

AZ: Combination of RCI-1 and RCI-8*

RCI-1: Efficiency Goals, Funds, Incentives, and Programs

RCI-8: Electricity Pricing Strategies

9.58 –$42.49

CO: Combination of RCI-1, RCI-7, and RCI-5b*

RCI-1: Expand DSM Programs of All Electric and Gas Utilities

RCI-5b: Inverted Block Rates to Fund Energy Efficiency (Less Stringent targets)

RCI-7: Electricity Smart Metering with Time-of-Use Rates and In-Home or In-Office Displays for All Residential, Commercial, and Industrial Consumers

8.40 –$62.78

FL: ESD-12 DSM/Energy Efficiency Programs, Funds, or Goals for Electricity 17.24 –$51.32

IA: Combination of EEC-2, EEC-5, EEC-12*

EEC-2: Demand Side Management/Energy Efficiency Programs for Natural Gas

EEC-5: Incentive Mechanisms for Achieving Energy Efficiency

EEC-12: Demand Side Management/Energy Efficiency Programs for Electricity

5.17 –$26.74

MD: RCI-2 Demand Side Management for Electricity and Natural Gas 4.48 –$63.70

MI: RCI-2

RCI-2: Existing Building Energy Efficiency Incentives, Assistance, Certification and Financing (MI RCI-2 is split 53% and 47% between DSM option and High Performance Buildings option)

15.29 –$30.27

MN: RCI-1 RCI-1 Maximize Savings from the Utility Conservation Improvement Program (CIP) 9.51 –$72.96

MT: Combination of RCII-1, RCII-2, RCII-10, RCII-11, and RCII-13*

RCII-1: Expand Energy Efficiency (EE) Funds

RCII-2: Market Transformation and Technology Development Programs

RCII-10: Industrial Energy Audits and Recommended Measure Implementation

RCII-11: Low-Income Energy Efficiency Programs

RCII-13: Metering Technologies with Opportunity for Load Management and Choice

2.43 –$21.03

NC: Combination of RCI-1, RCI-2, and RCI-11*

RCI-1: Demand Side Management Programs for the RCI Sectors—Recommended Case: “Top-Ten States” EE Investment

RCI-2: Expand Energy Efficiency Funds

RCI-11: Residential, Commercial, and Industrial Energy and Emissions Technical Assistance and Recommended Measure Implementation

17.86 –$43.99


Option Option Description2020 GHG

Reductions (MMtCO2e)

Cost–Effectiveness ($/tCO2e)

NM: Combination of RCI-1, RCI-2, RCI-3, RCI-6, and RCI-9*

RCI-1: Electricity DSM (over/above BAU)

RCI-2: Natural Gas DSM (over/above BAU)

RCI-3: Regional Market Transformation Alliance

RCI-6: Rate Design

RCI-9: Government Agency Requirements and Goals (Including Procurement)—Focus on Operations

1.69 –$37.98

PA: Combination of RCI-10, RCI-11, Ind-2, and Elec-3*

RCI-10: Demand Side Management—NG

RCI-11: Demand Side Management—Electricity

Ind-2: Industrial DSM

Elec-3: Stabilize Load Growth

20.90 –$13.99

SC: Combination of RCI-1 and RCI-2*

RCI-1: Demand Side Management (DSM)/Energy Efficiency Programs, Funds, or Goals for Electricity

RCI-2: Demand Side Management (DSM)/ Energy Efficiency Programs, Funds, or Goals for Natural Gas, Propane, and Fuel Oil

7.35 –$41.08

VT: ESD-1 ESD-1: Evaluation and Continuation / Expansion of Existing DSM for Electricity and Natural Gas 0.98 –$44.65

WA: Combination of RCI-1 and RCI-5*

RCI-1: Demand Side Management Programs Energy Efficiency Programs, Funds, or Goals for Natural Gas, Propane, and Fuel Oil

RCI-5: Rate Structures and Technologies to Promote Reduced GHG Emissions

1.57 –$50.80

* The overlaps between these options are adjusted before combining them in the table. The cost-effectiveness is the weighted average of these options (using GHG emission reductions as weights). AK = Alaska; AZ = Arizona; CO = Colorado; IA = Iowa; MI = Michigan; MN = Minnesota; MT = Montana; NM = New Mexico; PA = Pennsylvania; VT = Vermont; WA = Washington; DSM = demand side management; BAU = Business As Usual; EE = energy efficiency; NG = natural gas; ESD = Energy Supply and Demand; RCI =Residential, Commercial, and Industrial; RCIII = Residential, Commercial, Institutional,and Industrial; GHG = greenhouse gas; MMtCO2e = million metric tons of carbon dioxide equivalent; $/tCO2e = dollars per metric ton of carbon dioxide equivalent (reduced).

Table A-2 presents an example of the extrapolation results of the Demand Side Management Programs option to a hypothetical new state using the three alternative approaches. In the calculation, we assume:

1. For the new state, the weights computed for the 16 existing states (please see the method elaborated in Appendix B at the end of this Annex) are:

New State Weights Computed for Existing States

Alaska 4%

Arkansas 13%

Arizona 5%

Colorado 4%

Florida 4%

Iowa 6%

Maryland 3%

Table A-1, continued from previous page


New State Weights Computed for Existing States

Michigan 5%

Minnesota 4%

Montana 6%

North Carolina 6%

New Mexico 7%

Pennsylvania 6%

South Carolina 19%

Vermont 3%

Washington 5%

2. The ratios of the total emissions from the RCI sector between the new state and the 16 existing states are:

New State Ratio of Total Emissions from the RCI Sector

Alaska 3:1

Arkansas 2:1

Arizona 1.7:1

Colorado 1.3:1

Florida 1:2

Iowa 1.4:1

Maryland 1.3:1

Michigan 1.6:1

Minnesota 1.2:1

Montana 5.5:1

North Carolina 1:1.3

New Mexico 2.2:1

Pennsylvania 1:1.8

South Carolina 1.5:1

Vermont 14:1

Washington 2.5:1

Some states had slight variants on the sector names. For example, Alaska, Florida, and Vermont developed Energy Supply and Demand (ESD) policies. While most states used RCI, Iowa termed this sector Energy Efficiency and Conservation (EEC).

Table A-2. Illustrative Extrapolation Analysis Results of the Demand Side Management Programs Option Using the Three Alternative Approaches

Method 2020 GHG Reductions (MMtCO2e)

Cost-Effectiveness ($/tCO2e) Notes

Analogous State Method 11.03 –$41.08 In the Analogous State Method, South Carolina is identified as the analogous state to the new state.

Simple-Average Method 10.23 –$42.72

Weighted-Average Method 9.92 –$37.42

GHG = greenhouse gas; MMtCO2e = million metric tons of carbon dioxide equivalent; $/tCO2e = dollars per metric ton of carbon dioxide equivalent (reduced emissions).


Comparison of the Three Alternative Approaches

A. Analogous State Method

Pros:

»» Easy to understand.

»» Less time-consuming than the Weighted-Average Method.

»» Utilizes the sectoral key factors in the selection of the analogous state.

Cons:

»» May not seem to adequately consider political issues.

»» For each option, only data from one existing state are utilized in the extrapolation. If this state is not adequately representative of the new state, then the results will be biased.

B. Simple-Average Method

Pros:

»» Easy to understand.

»» Least time-consuming method among the three.

»» For each option, data from all the 16 existing states are utilized in the calculation.

Cons:

»» Sectoral key factors are not used in this method.

»» For each option, all the new states will have the same cost-effectiveness, which would be the average of those of the 16 existing states.

C. Weighted-Average Method

Pros:

»» For each option, data from all the 16 existing states are utilized in the calculation.

»» This method utilizes the sectoral key factors in the weightings computation, which potentially can capture some of the similarities and differences across the states.

Cons

»» Most time-consuming method.

We also need to acknowledge that additional variation exists that cannot be readily captured by any of the methods. This variation is a natural outcome of the stakeholder-driven processes that allow for local knowledge, as well as variation in the policy design and goals among states. An example of this in the RCI sector would be that, in some existing states, energy-efficient measures in new construction might be combined in the Demand Side Management super option, while in other states these measures might fall into the High Performance Buildings super option. However, this becomes a non-issue when looking at total RCI emission reduction potentials (all RCI super options together) among new and existing states.

In sum, the Weighted-Average Method was selected from the three alternative extrapolation approaches to use in this study. The Weighted-Average Method develops a mathematical approach to quantify the similarities among individual states. The similarity measurement is established based on how each state ranks relative to others in terms of key factors affecting the reduction potentials and cost/savings of various mitigation/sequestration policy options. Theoretically, among the three alternative methods, the Weighted-Average Method can best capture the underlying factors that explain the variances in the quantification results of the super options among the 16 analyzed states. The only drawback of the Weighted-Average Method is that, even with weighting, it may still place too much emphasis on existing states that are different from the new state. This is moderated somewhat in that different weights are used for each set of options from different sectors.


III. Extrapolation Methodology for the TLU Super Options, Including Description of the USDOE VISION Model

Analysis of the Scenario to Transition to Lower-Carbon Methods of Moving Goods (Covering Anti-Idling and Freight Mode Shift)

To quantify the GHG emission reductions and cost-effectiveness of a transition to low-carbon methods of moving goods, three policies were examined:

1. Encouraging truck stop electrification (TSE);

2. Promoting the use of plug-in trailer refrigeration units (TRUs); and

3. Encouraging increased use of shuttle rail to move goods.

The effects of encouraging TSE were calculated by estimating the number of expected TSE units during the policy analysis period (i.e., 2009 to 2020), the GHG reductions attributed to a TSE unit relative to traditional engine idling, and the cost of expanding TSE units on a per-unit basis. The 2009 count of TSE units was estimated using information from USDOE.3 The number of truck stops is assumed to increase at the same growth rate as TSE units in New York. The compound annual growth rate for TSE units in New York was estimated in a recently completed New York State Energy Research and Development Authority (NYSERDA) study. GHG emissions relative to traditional idling practices and TSE unit costs were gleamed from a 2004 Transportation Research Board (TRB) study.4

The number of TRUs was estimated by scaling the number of TRUs in New York, according to the same recently completed NYSERDA study, by the population ratio of the two states. Plugged-in TRU GHG emissions relative to traditional idling practices and TRU unit costs were gleaned from a 2004 TRB study.5 The analysis utilizes a perpetual inventory of TRUs that enter and exit the TRU population as old units are phased out and new units are purchased over time.

The effects of encouraging increased use of freight rail diversion were estimated from a national-level estimate of the impacts of freight rail diversion. The share of the estimated GHG reduction and cost estimates were scaled using the current share of national rail freight movement, which is estimated to be 1.3% of all national rail-transported freight and available rail lines.

Annual percentage reductions in carbon dioxide-equivalent (CO2e) emissions are applied to a baseline forecast of GHG emissions for New Jersey to determine the reduction in CO2e emissions. Omitted gasoline and ethanol sales are multiplied by a scaling factor and forecasted U.S. fuel prices. TRU and TSE program costs are calculated by multiplying the cost of a TRU or TSE unit by the number of TRUs and TSEs expected to be sold over time minus the fuel savings expected from introducing the new technology. The number of TSEs sold is based on a growth rate assigned to the number of TSEs. The number of TRUs is scaled down from the number of TRUs in New York based on the population ratio of the two states.

Rail freight diversion is estimated by scaling down the national-level costs of rail freight diversion based on the current share of rail freight that is transported through a state according to Freight Analysis Framework estimates. To calculate the costs and levels of rail diversion that might be realized, a credible source is the American Association of State Highway and Transportation Officials’ (AASHTO’s) Freight-Rail Bottom Line Report.6 The report addresses concerns about the capacity of the nation’s freight transportation system, especially the freight-rail system, to keep pace with the expected growth of the economy over the next 20 years. The report finds that relatively small public investments in the nation’s freight railroads can be leveraged into relatively large public benefits for the nation’s highway infrastructure, highway users, and freight shippers.

3. U.S. Department of Energy, http://www.afdc.energy.gov/afdc/locator/tse/state.4. Transportation Research Board. 2004. “Long-Haul Tractor Idling Alternative.” Table 1. See http://epa.gov/smartway/documents/dewitt-study.pdf.5. Ibid.6. American Association of State Highway and Transportation Officials. Transportation Invest in America Freight-Rail Bottom Line Report. Available at: http://freight.transportation.org/doc/FreightRailReport.pdf.

http://www.afdc.energy.gov/afdc/locator/tse/state

http://freight.transportation.org/doc/FreightRailReport.pdf


Analysis of Scenarios Describing Vehicle Efficiency Incentives and Greater Use of Biofuels

For these two scenarios, the VISION model was utilized as the central analytical tool to measure the emissions and costs impacts of both a higher-biofuels-usage scenario and a scenario in which effective incentives resulted in a more fuel-efficient light duty fleet.

The VISION model was developed by USDOE to provide estimates of the potential energy use, oil use, and carbon emission impacts of advanced light and heavy duty highway vehicle technologies and alternative fuels from the present through the year 2100.7 The VISION model uses vehicle survival and age-dependent use characteristics to project total light and heavy vehicle stock, total vehicle miles traveled (VMT), and total energy use by technology and fuel type by year, given market penetration and vehicle fuel economy assumptions developed exogenously. Total carbon emissions for on-highway vehicles by year are also estimated because life-cycle carbon coefficients for various fuels are included in VISION. These coefficients are consistent with the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model, which was also developed by USDOE.

The VISION model considers a set of input parameters. Default parameter values taken from USDOE’s Annual Energy Outlook (AEO) reference case are in place. The default parameter values are regularly updated, usually on an annual basis, to reflect the most recent AEO reference case, which was 2009 for this analysis. All input values can be changed by the user to customize the simulation. Input parameters include:

»» Car market penetration and fuel economy ratio;

»» Light truck market penetration and fuel economy ratio;

»» Light truck share of total light duty vehicle market;

»» Fuel type (including hydrogen and ethanol via multiple production pathways) and price;

»» VMT, including growth rate and elasticity to the cost of driving;

»» Heavy vehicle fuel economy, market share, and alternative fuel use; and

»» Light vehicle cost.

The model generates output values, by vehicle type (car, light truck, and heavy truck). The output totals include (Ward, 2008):

»» Energy use by fuel type (oil, compressed natural gas, Fischer-Tropsch diesel, biodiesel, methanol, hydrogen, electricity, ethanol, and other fuels);

»» Full-fuel-cycle carbon emissions (million metric tons [MMt] of CO2e);

»» Full-fuel-cycle GHG emissions (MMtCO2e);

»» Fuel expenditures (billion 2005$);

»» Fuel expenditures as a percentage of gross domestic product; and

»» Light vehicle miles per gallon gasoline equivalent (energy).

VISION is a well-documented, well-used, and well-published model. A 2006 American Association of State Highway and Transportation Officials and Transportation Research Board study recommended the use of the VISION tool for state-level and multi-state analyses. The VISION model is the most widely used tool for state-level analyses of transportation energy and climate emissions scenarios. In addition to the SGA region, the states where VISION has been used include California, New Mexico, Colorado, New York, and New Jersey. The northeastern states are developing a regionally tailored version of the VISION tool.

7. VISION is a public access tool that may be found at : http://www.transportation.anl.gov/modeling_simulation/VISION/index.html.


VISION can be tailored to specific states or multi-state regions when locally specific data are available to replace any default data.

VISION is an open-source, fully transparent tool, so many researchers have used and adapted it to their own needs, replacing some market assumptions (e.g., vehicle demand growth, alternative-fueled vehicle cost) with their own assumptions for different analyses. USDOE’s Argonne National Laboratory continues to maintain and update the model, and the key developers are readily available to answer questions. VISION is not a behavioral model. Rather, it is a complex accounting system that allows users to modify assumptions as needed. It provides a perpetual inventory of the vehicle fleet through 2100 by type, size, efficiency, technology, and use.

In order to generate estimates of the costs and impacts of a biofuels scenario, projected national trends of increased biofuels consumption by light duty and heavy duty vehicles were magnified from a baseline projection to a projection of 20% consumption by volume in 2020. (The baseline projection is roughly half the scenario projection.) In addition, market penetration of vehicles capable of using biofuels in the light duty market was also manipulated in line with fuel consumption trend adjustments. Those inputs were then utilized in the VISION model to estimate scenario costs for vehicles and fuels, as well as estimates for emissions from the light duty and heavy duty vehicle fleets.

In order to generate estimates of the costs and GHG reduction potentials of a vehicle purchase incentives scenario, estimates of the impacts of such a program were drawn from Greene et al.’s 2005 research on vehicle efficiency purchase incentives. The Greene study’s estimates of efficiency impacts were used as inputs in VISION to estimate scenario costs for vehicles and fuels, as well as estimates for emissions from the light duty vehicle fleet.

Analysis of Scenarios Describing Application of Smart Growth Principles and Greater Reliance on Public Transit

The VISION model was also used for the travel activity analyses. Since the amount and types of travel activity are important factors in the determination of fuel use, the VISION model was used as a complement to option-specific spreadsheet analyses for TLU-5: Smart Growth/ Land Use and for TLU-6: Transit. The complementary use of VISION with the VMT-related options included three specific uses that are particularly noteworthy:

»» Use of VISION to contribute to energy and GHG outputs under baseline forecast conditions,

»» Use of life-cycle GHG emissions factors to convert reductions in VMT to reductions in GHG potential, and

»» Use of VISION to run alternative scenarios with higher (baseline) VMT and lower (with TLU-5 and TLU-6) VMT reduction, so as to account for overlap between the VMT-related measures and other measures.

The option-specific spreadsheet analyses and parameters for analyses of smart growth and transit were taken from the spreadsheets and policy designs developed for the CCS states with completed state plans. The option-specific spreadsheets and parameters for smart growth incorporate the state-specific factors in terms of the baseline conditions for auto use and public transportation use, and in terms of their baseline VMT that may be associated with land development patterns.

The spreadsheet scenarios take into account the specific economic and transportation characteristics of each state, while extrapolating from the CCS state results on the basis of the policy descriptions and policy designs that reflect the stakeholder consensus and recommendations in the state plans. In this way, the national results represent an extrapolation of the results from the CCS state plans, adjusted for the key factors described and state-specific characteristics.


The analyses of the Transportation sector took into account key factors and state characteristics that included:

A. Population growth and density,

B. Energy consumption by the Transportation sector,

C. Shares of urban, suburban, and rural population,

D. Forecasted VMT levels and growth,

E. Shares of use of public transportation, and

F. Current and projected fleet mixes for passenger, heavy duty, and freight vehicles.

The discussion below shows step-by-step descriptions where the above key factors and state characteristics are factored in by showing the letters (A through F) following the step where these factors were taken into account. In addition to the factors applied, the initial judgments on the feasibility of VMT goals as determined by stakeholders in the CCS states with completed plans took into account many local and state-specific factors that are reflected in the goal levels.

Following is a step-by-step description of the smart growth and transit options spreadsheet analysis:

»» Use VISION 2008 (most recent VISION available), results with updated AEO 2009 fuel forecast for baseline VMT forecast consistent with adopted federal Corporate Average Fuel Economy standards. (Factor B)

»» Factor in state-specific motor vehicle registrations and state-specific motor vehicle use for the states. (Factor F)

»» Estimate the baseline state-specific VMT forecasts. (Factor D)

»» Compare the VISION state-specific baseline VMT forecasts with the U.S. Department of Transportation Federal Highway Administration state shares of VMT in order to confirm state shares of VMT. (Factor D)

»» Factor in light duty vehicle shares as a portion of all vehicles. (Factor F)

»» Factor in urban VMT shares as a portion of total VMT. (Factor C)

»» Use stakeholder-approved spreadsheet results to estimate 2020 result of percentage reductions in VMT associated with smart growth. (Factor A)

»» Obtain the state-specific transit use data from the national transit database. (Factor E)

»» Obtain the state-specific urban population, service area population, and population density data from the national transit database. (Factor A)

»» Calculate the potential VMT reduction for 2020 associated with increased transit ridership as a multiplicative factoring up of already existing state-specific transit ridership for each state, factoring in the national transit database data on urban population, service area population, and population density for each state. (Factors A, C, and E)

»» Convert from potential VMT reduction to GHG reduction using VISION light duty vehicle 2020 emissions coefficient factor. (Factors B and F)

»» Conduct cumulative GHG estimation analysis for 2010–2020 using linear ramp-up for the 11-year period. (Factors A through F)

The VISION tool was further used for analyses of potential overlap between strategies. The different amounts of VMT that were associated with the travel activity scenarios were used in alternative runs of the VISION model in order to estimate the impact of combined options related to vehicles, fuels, and travel activity taken together. The results of these analyses were used to reduce “stand-alone” estimates in order to take into account any potential double counting.


IV. Two Versions of the National Cost Curve

After the extrapolation of the GHG reduction potential and cost-effectiveness of each individual super option to the new states, the extrapolation analysis is also undertaken for the existing states for any “missing” (un-recommended) super options in the original state action plan. Table A-3 indicates the super options that were recommended in the state action plans for each of the 16 existing states.

Based on the extrapolation results, the national cost curve is developed by aggregating the 50 states’ data. For each super option, the national-level GHG reduction potential is computed as the sum of reduction potential in each individual state. The cost-effectiveness is computed as the weighted average of the cost-effectiveness of the 50 states, using GHG reduction potential as the weighting.

Two versions of national cost curve are developed in this study:

»» Full Stakeholder Implementation: In this scenario, it is assumed that all 23 super options are fully implemented in all the 50 states. The total GHG reduction potential of the 23 super options will help the country reduce 42.1% of the baseline emissions in year 2020.

»» Congressional Target Implementation: In this scenario, we proportionally scale back the reduction efforts of the 23 super options so that the aggregate reductions will meet the 2020 emission reduction target (or goal) specified in the Kerry-Lieberman bill exactly. The scale-back factor is computed for the C&T sector and non-C&T sector separately so that both the scaled-back efforts in the cap-and-trade (C&T) sector and in the non-C&T sector can achieve the 2020 emission reduction target or goal exactly. The scale-back factors for the C&T sector and the non-C&T sector are 62% and 27%, respectively. The former is applied to all non-AFW options, while the latter is applied to all AFW options. The cost-effectiveness (per-ton costs) of each super option is assumed to be same as in the Full Stakeholder Implementation scenario.


Table A-3. Recommendation Status of the 23 Super Options in the 16 Existing States

Super Options AK AR AZ CO FL IA MD MI MN MT NC NM PA SC VT WA Total

RPS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14

Nuclear 1 1 1 1 1 1 1 7

CCSR 1 1 1 1 4

Coal Plant Efficiency Improvements 1 1 1 1 1 1 6

DSM 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 16

High Performance Buildings 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14

Appliance Standards 1 1 1 1 1 1 1 1 1 1 1 11

Building Codes 1 1 1 1 1 1 1 1 1 1 1 1 1 13

CHP 1 1 1 1 1 1 1 1 1 1 1 1 1 13

Crop Production Practices 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14

Livestock Manure 1 1 1 1 1 1 1 1 1 1 1 1 1 13

Forest Retention 1 1 1 1 1 1 1 1 1 1 10

Reforestation/Afforestation 1 1 1 1 1 1 1 1 1 1 10

Urban Forestry 1 1 1 1 1 1 1 1 1 1 1 11

MSW Source Reduction 1 1 1 1 1 1 6

Enhanced Recycling of MSW 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 16

MSW LFG Management 1 1 1 1 1 1 1 1 1 1 10

Vehicle Purchase Incentives 1 1 1 1 1 1 1 1 1 1 1 1 1 13

Renewable Fuel Standard 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14

Smart Growth/Land Use 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 16

Transit 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15

Anti-Idling Technologies and Practices 1 1 1 1 1 1 1 1 1 1 1 1 12

Mode Shift from Truck to Rail 1 1 1 1 1 1 1 1 1 1 1 11

Total 9 20 14 16 20 18 19 20 14 15 20 16 21 18 13 16

“1” in the table represents that the state-recommended the super option. CCSR = carbon capture and storage or reuse; CHP = combined heat and power; DSM = demand side management; LFG = landfill gas; MSW = municipal solid waste; RPS = renewable portfolio standard.Sources: Center for Climate Strategies (CCS). 2009. Southern Regional Economic Assessment of Climate Policy Options and Review of Economic Stud-ies of Climate Policy. Available at: http://www.climatestrategies.us/template.cfm?FrontID=6081. Ward, J. VISION 2008 User’s Guide. Washington, DC: U.S. Department of Energy. October 2008.

http://www.climatestrategies.us/template.cfm?FrontID=6081


Appendix A. List of Key Factors

Heat and Power Generation Sector

1. Percentage of Electricity Generated by Coal

2. CO2 Intensity of Electricity Generated

3. Per-Capita Electricity Consumption

4. Weighted-Average Delivered Electricity Price

5. Net Electricity Trade Index [= production / (consumption + losses)]

6. Projected Wind Capacity Installations

7. Per-Capita Hydropower Nameplate Capacity

8. Solar Power Potential by Sun Index

9. Ratio of CO2 Storage Resource to 2020 Total CO2 Emissions (will only be used in Carbon Capture and Storage or Reuse option)

Residential, Commercial, Industrial, and Institutional Sectors

1. Per-Capita Income

2. Per-Capita Electricity Consumption

3. Net Electricity Trade Index [= production / (consumption + losses)]

4. Weighted-Average Delivered Electricity Price

5. Weighted-Average Natural Gas Price

6. Percentage of Gross Domestic Product From Manufacturing Sector

7. Percentage of Energy Consumption by Residential & Commercial Sectors

8. Percentage of Energy Consumption by Industrial Sector

9. Sum of Heating- and Cooling-Degree Days

10. American Council for an Energy-Efficient Economy (ACEEE) State Scoring on Energy Efficiency (scoring for certain aspects will be used for relevant super options, e.g., Building Code Score will only be used for the building codes super option)

11. Per-Capita Commercial/Industrial Combined Heat and Power (CHP) Potential (will only be used in the CHP super option)

Agriculture Sector

1. Harvested Cropland

2. Estimated Cropland Without No-Till

3. Synthetic Nitrogen Fertilizer Applied

4. Total Populations of Dairy Cattle and Hogs

Forestry Sector

1. Percentage of Total Land Forested

2. State-Level Acres of Forest

3. Combination of Agricultural Land and Other Non-forest Non-urban Land (pertains to Reforestation/Afforestation options)

4. Total Urban Land Area (pertains to urban forestry option)


Waste Sector

1. Municipal Solid Waste Landfilled (in tons of landfilled waste)

2. Waste in Place at Uncontrolled Landfills with Landfill Gas-to-Energy Potential

In addition to the above key factors, we examined some important assumptions used in quantification of the existing states, such as RCI Avoided Fuel Costs, Avoided Electricity Cost, RCI Cost of Energy Efficiency, and Avoided CO2 per megawatt-hour. Through comparison, we found that these important assumptions are relatively homogenous across the 16 existing states, so that no additional manipulation of these assumptions is required.

Transportation and Land Use Sectors

1. Population Growth and Density

2. Energy Consumption by Transportation Sectors

3. Shares of Urban, Suburban, and Rural Population

4. Forecasted VMT Levels and Growth

5. Shares of Use of Public Transportation

6. Current and Projected Fleet Mixes for Passenger, Heavy Duty, and Freight Vehicles

Appendix B. Method to Compute the Weights of the 16 Existing States

Table A-4 presents data on the key variables for the Demand Side Management Programs option, along with the state rankings in terms of each key variable.

Table A-5 computes for each variable the absolute value of the ranking difference between an example new state and each of the 16 existing states. The ranking differences of all the variables are then added up in the Sum of Ranking Difference column.

In the Analogous State Method, since South Carolina has the lowest sum of ranking differences (45) across the 10 key variables with the example new state, South Carolina is selected as the analogous state for the new state for the Demand Side Management Programs option.

In the Weighted-Average Method, the Inverse of the Sum of Ranking Difference is computed for each existing state, as shown in the second-to-last column in Table A-5. The sum in this column in this case is 0.115. Then each of the inverse numbers and their sum (0.115) in this column are used to compute the weights, as shown in the last column of Table A-5. Intuitively, this approach assigns higher weights to existing states that have comparatively less differences with the new state. These weights will then be used in the 16-state Weighted-Average Method to estimate the GHG reduction potentials and cost-effectiveness of the Demand Side Management Programs option for the new state. This mathematical approach attempts to quantify similarities between individual states by comparing how each state ranks relative to others. For example, for electricity-exporting states, the weighting methodology allows for estimates of GHG reduction supplies to be more heavily weighted with data from states like New Mexico and Montana, the largest electricity exporters in the existing 16-state database.


Table A-4. Key Factors for the Demand Side Management Programs Option

States

Per Capita Income ($)

Per Capita Electricity

Consumption (megawatt-hours)

Net Electricity Trade Index

[= production / (consumption +

losses)]

Weighted-Average Delivered Electricity

Price (cents/kilowatt-hour)

Weighted-Average

Natural Gas Price ($/MMBtu)

% GDP from Manufacturing

Sector

Value Ranking Value Ranking % Ranking Value Ranking Value Ranking % Ranking

States Without Action Plans

AL 32,419 44 20.3 3 1.39 8 7.57 28 9.8 24 17.22% 10

CA 43,641 8 7.3 50 0.72 44 12.80 10 8.7 38 9.81% 33

CT 56,272 1 9.7 40 0.94 31 16.45 2 11.0 15 13.35% 18

DE 40,519 18 14.2 24 0.61 48 11.35 13 10.8 17 7.39% 40

GA 33,499 40 15.4 16 0.94 31 7.86 25 11.5 8 10.88% 26

HI 42,055 14 8.3 47 0.99 25 21.29 1 26.8 1 1.71% 50

ID 33,074 43 16.9 11 0.44 50 5.07 50 9.5 27 9.86% 31

IL 42,347 13 11.5 36 1.25 12 8.46 21 9.9 23 12.43% 21

IN 34,605 36 17.5 9 1.06 18 6.50 42 9.2 30 25.03% 1

KS 38,820 22 14.6 23 1.14 16 6.84 39 9.1 32 15.15% 14

KY 30,824 47 22.2 2 0.97 29 5.84 47 10.9 16 18.43% 6

LA 35,100 32 17.5 8 0.85 38 8.39 22 7.6 45 18.25% 7

MA 51,254 3 8.8 45 0.77 43 15.16 4 12.2 6 9.54% 35

ME 36,457 29 9.0 44 1.20 13 14.59 5 8.9 36 11.05% 25

MO 33,964 38 14.8 21 0.99 25 6.56 41 12.3 5 13.48% 17

MS 28,541 50 16.5 13 0.92 33 8.03 23 8.6 39 14.96% 15

ND 39,870 20 18.7 6 2.30 3 6.42 43 7.6 44 9.08% 37

NE 39,150 21 16.2 14 1.05 19 6.28 46 9.0 34 11.84% 23

NH 43,623 9 8.5 46 1.87 4 13.98 6 10.3 21 10.86% 27

NJ 51,358 2 9.4 42 0.69 46 13.01 9 11.3 13 9.23% 36

NV 41,182 17 15.2 19 0.85 38 9.99 16 8.1 42 4.37% 46

NY 48,753 4 7.7 48 1.00 23 15.22 3 11.4 10 6.04% 43

OH 36,021 31 14.1 25 0.89 35 7.91 24 11.4 12 17.83% 8

OK 34,997 33 15.7 15 1.18 14 7.29 31 8.1 43 10.75% 28

OR 36,297 30 13.5 28 1.05 19 7.02 36 9.0 33 18.69% 5

RI 41,368 16 7.4 49 0.87 37 13.12 8 10.7 18 9.82% 32

SD 38,661 24 13.7 27 0.53 49 6.89 38 8.9 37 9.57% 34

TN 33,395 42 17.9 7 0.82 41 7.07 35 11.6 7 16.10% 12

TX 37,083 28 15.1 20 1.02 21 10.11 15 7.0 47 12.98% 19

UT 31,944 45 11.5 37 1.45 7 6.41 44 7.2 46 11.86% 22

VA 41,727 15 14.8 22 0.65 47 7.12 34 11.5 9 8.59% 38

WI 37,767 26 12.8 32 0.80 42 8.48 20 10.2 22 20.32% 3

WV 29,385 49 18.8 5 2.48 2 5.34 48 11.2 14 10.73% 29

WY 48,608 5 30.6 1 2.56 1 5.29 49 7.0 48 3.06% 48

States With Action Plans

AK 44,039 7 9.6 41 0.97 29 13.28 7 5.8 50 1.99% 49

AR 30,177 48 16.9 10 1.02 21 6.96 37 9.0 34 17.37% 9

AZ 34,335 37 13.2 31 1.33 10 8.54 18 8.4 40 7.85% 39

CO 42,985 11 11.1 38 0.99 25 7.76 27 6.9 49 6.40% 42

FL 38,417 25 13.2 29 0.89 35 10.33 14 9.1 31 4.80% 45

IA 37,402 27 15.2 17 1.00 23 6.83 40 9.5 28 20.76% 2

MD 46,471 6 11.7 35 0.70 45 11.50 12 13.3 2 5.56% 44


States

Per Capita Income ($)


Consumption (megawatt-hours)



losses)]

Weighted-Average Delivered Electricity

Price (cents/kilowatt-hour)

Weighted-Average

Natural Gas Price ($/MMBtu)

% GDP from Manufacturing

Sector

Value Ranking Value Ranking % Ranking Value Ranking Value Ranking % Ranking

MI 34,949 34 10.7 39 0.98 28 8.53 19 9.6 26 16.14% 11

MN 43,037 10 13.2 30 0.84 40 7.44 29 9.3 29 12.83% 20

MT 34,644 35 16.6 12 1.50 5 7.13 33 9.6 25 4.05% 47

NC 33,735 39 15.2 18 0.90 34 7.83 26 12.3 4 19.48% 4

NM 33,430 41 11.7 34 1.50 5 7.44 29 8.4 41 6.59% 41

PA 40,140 19 12.2 33 1.37 9 9.08 17 11.4 10 13.64% 16

SC 31,103 46 19.3 4 1.16 15 7.18 32 10.3 20 16.10% 13

VT 38,686 23 9.3 43 1.30 11 12.04 11 12.7 3 11.40% 24

WA 42,857 12 13.8 26 1.12 17 6.37 45 10.5 19 9.91% 30

States

% Energy Consumption by Residential & Commercial

Sectors

% Energy Consumption by Industrial Sectors

Sum of Heating- & Cooling-Degree Days

ACEEE Utility and Public Benefits Efficiency Programs and Policies Score

(Maximum Possible Points Is 20)

% Ranking % Ranking Value Ranking Value Ranking

States Without Action PlansAL 32% 42 45% 9 4,537 43 0.0 42

CA 37% 33 23% 37 1,968 50 14.5 3

CT 56% 3 14% 45 6,863 16 15.5 2

DE 40% 25 35% 18 6,012 27 0.0 42

GA 40% 24 29% 28 4,463 46 1.5 31

HI 24% 47 21% 40 4,561 42 8.5 14

ID 39% 31 36% 16 6,578 19 10.0 10

IL 43% 17 30% 27 7,328 14 3.0 25

IN 30% 44 47% 6 6,563 20 2.5 28

KS 39% 32 35% 17 6,423 22 1.0 33

KY 30% 43 46% 8 5,604 32 3.0 25

LA 16% 50 64% 1 4,288 48 0.0 42

MA 54% 5 14% 46 6,407 23 12.5 6

ME 39% 28 32% 24 7,672 9 6.5 18

MO 46% 11 23% 38 5,991 29 0.0 42

MS 32% 40 37% 15 4,483 44 0.0 42

ND 28% 45 49% 4 9,280 2 0.5 38

NE 42% 20 31% 25 7,407 12 0.5 38

NH 52% 7 15% 44 7,927 7 7.5 17

NJ 45% 14 17% 42 6,048 26 10.0 10

NV 40% 26 27% 33 6,094 25 8.5 14

NY 60% 1 12% 50 7,405 13 12.5 6

OH 40% 27 34% 20 6,253 24 5.5 21

OK 34% 36 38% 13 5,015 38 0.0 42

OR 43% 16 26% 34 4,764 41 13.5 4

RI 58% 2 12% 49 6,468 21 10.0 10

SD 43% 15 24% 36 8,503 4 0.5 38

TN 39% 29 33% 23 5,138 36 1.0 33

TX 25% 46 50% 3 4,430 47 3.0 25

UT 39% 30 28% 29 6,696 18 6.5 18

VA 46% 12 22% 39 5,308 35 1.0 33

WI 41% 21 35% 19 7,712 8 10.0 10



States


Sectors

% Energy Consumption by Industrial Sectors

Sum of Heating- & Cooling-Degree Days

ACEEE Utility and Public Benefits Efficiency Programs and Policies Score

(Maximum Possible Points Is 20)

% Ranking % Ranking Value Ranking Value Ranking

WV 32% 41 46% 7 5,984 30 0.0 42

WY 21% 48 53% 2 7,569 10 0.0 42

States With Action PlansAK 17% 49 48% 5 8,574 3 0.0 42

AR 34% 37 41% 10 5,021 37 1.0 33

AZ 49% 8 15% 43 5,395 34 4.0 23

CO 42% 18 27% 32 6,823 17 8.0 16

FL 52% 6 13% 48 4,085 49 2.5 28

IA 34% 38 41% 11 7,484 11 10.5 9

MD 55% 4 13% 47 5,618 31 5.5 21

MI 45% 13 28% 30 7,176 15 0.5 38

MN 41% 23 31% 26 9,931 1 13.5 4

MT 34% 35 38% 14 8,001 6 6.0 20

NC 47% 10 26% 35 4,780 40 2.0 30

NM 33% 39 33% 21 5,571 33 4.0 23

PA 41% 22 33% 22 5,994 28 1.0 33

SC 35% 34 38% 12 4,467 45 1.5 31

VT 48% 9 19% 41 8,154 5 19.0 1

WA 42% 19 27% 31 4,970 39 12.0 8

ACEEE = American Council for an Energy-Efficient Economy; $/MMBtu = cost per million British thermal unit; GDP = gross domestic product.

Table A-5. Ranking Differences of Relevant Variables for the Demand Side Management Programs Option: An Example of a New State Versus 16 Existing States

State Per Capita Income ($)


Consumption (megawatt-

hours)



losses)]

Weighted-Average Delivered

Electricity Price (cents/kilowatt-

hour)

Weighted-Average

Natural Gas Price

($/MMBtu)

% GDP From Manufacturing

Sector


Sectors

Example New State

AK 37 38 21 21 26 39 7

AR 4 7 13 9 10 1 5

AZ 7 28 2 10 16 29 34

CO 33 35 17 1 25 32 24

FL 19 26 27 14 7 35 36

IA 17 14 15 12 4 8 4

MD 38 32 37 16 22 34 38

MI 10 36 20 9 2 1 29

MN 34 27 32 1 5 10 19

MT 9 9 3 5 1 37 7

NC 5 15 26 2 20 6 32

NM 3 31 3 1 17 31 3

PA 25 30 1 11 14 6 20

SC 2 1 7 4 4 3 8

VT 21 40 3 17 21 14 33

WA 32 23 9 17 5 20 23



State

% Energy Consumption by Industrial

Sectors

Sum of Heating- & Cooling-

Degree Days

ACEEE Utility and Public Benefits

Efficiency Programs and Policies Score

Sum of Ranking Difference

Inverse of Sum of Ranking Difference

Weights

Example New State

AK 4% 40 0 233 0.004 4%

AR 1% 6 9 65 0.015 13%

AZ 34% 9 19 188 0.005 5%

CO 23% 26 26 242 0.004 4%

FL 39% 6 14 223 0.004 4%

IA 2% 32 33 141 0.007 6%

MD 38% 12 21 288 0.003 3%

MI 21% 28 4 160 0.006 5%

MN 17% 42 38 225 0.004 4%

MT 5% 37 22 135 0.007 6%

NC 26% 3 12 147 0.007 6%

NM 12% 10 19 130 0.008 7%

PA 13% 15 9 144 0.007 6%

SC 3% 2 11 45 0.022 19%

VT 32% 38 41 260 0.004 3%

WA 22% 4 34 189 0.005 5%

Total 0.115 100.0%

$/MMBtu = dollars per million British thermal unit; ACEEE = American Council for an Energy-Efficient Economy; GDP = gross domestic product.

References

Center for Climate Strategies (CCS). 2009. Southern Regional Economic Assessment of Climate Policy Options and Review of Economic Studies of Climate Policy. Available at: http://www.climatestrategies.us/template.cfm?FrontID=6081.

Greene, David L., Patterson, Philip D., Singh, Margaret, and Li, Jia. September 2005. “Feebates, Rebates and Gas Guzzler Taxes: A Study of Incentives for Increased Fuel Economy.” Energy Policy 33(14): 1901-1902.

Ward, J. October 2008. VISION 2008 User’s Guide. Washington, DC: U.S. Department of Energy.

Data Sources

American Association of State Highway and Transportation Officials. Transportation Invest in America Freight-Rail Bottom Line Report. Available at: http://freight.transportation.org/doc/FreightRailReport.pdf.

American Council for an Energy-Efficient Economy. 2008. The 2008 State Energy Efficiency Scorecard. www.aceee.org/pubs/e086_es.pdf.

Central Intelligence Agency. 2009. The World Factbook. www.cia.gov/library/publications/the-world-factbook/rankorder/2004rank.html.

ONSITE SYCOM Energy Corporation. 2000. The Market and Technical Potential for Combined Heat and Power in the Commercial/Institutional Sector, pp.57–58. www.eere.energy.gov/de/pdfs/chp_comm_market_potential.pdf.

Transportation Research Board. 2004. “Long-Haul Tractor Idling Alternative.” Table 1. See http://epa.gov/smartway/documents/dewitt-study.pdf.

U.S. Bureau of Economic Analysis. 2009. Gross Domestic Product by State. www.bea.gov/regional/gsp.


http://www.aceee.org/pubs/e086_es.pdf

http://www.aceee.org/pubs/e086_es.pdf

https://www.cia.gov/library/publications/the-world-factbook/rankorder/2004rank.html



http://www.eere.energy.gov/de/pdfs/chp_comm_market_potential.pdf

http://www.eere.energy.gov/de/pdfs/chp_comm_market_potential.pdf


U.S. Bureau of Economic Analysis and Bureau of the Census. 2008. State Personal Income 2008. www.bea.gov/newsreleases/regional/spi/spi_newsrelease.htm.

U.S. Bureau of the Census, Population Division. 2004. State Interim Population Projections by Age and Sex: 2004–2030. www.census.gov/population/www/projections/projectionsagesex.html.

U.S. Bureau of the Census. 2009. Heating- and Cooling-Degree Days. www.census.gov/compendia/statab/tables/09s0379.xls.

U.S. Department of Energy, http://www.afdc.energy.gov/afdc/locator/tse/state.

U.S. Department of Energy, Energy Information Administration. 2005. 2002 Energy Consumption by Manufacturers. http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html.

U.S. Department of Energy, Energy Information Administration. 2008. State Energy Data System. www.eia.doe.gov/emeu/states/hf.jsp?incfile=sep_sum/plain_html/sum.

U.S. Department of Energy, Energy Information Administration. 2009. Electric Power Annual 2007. www.eia.doe.gov/cneaf/electricity/epa/epa_sprdshts.html.

U.S. Department of Energy, Energy Information Administration. 2009. State Electric Profile. www.eia.doe.gov/cneaf/electricity/st_profiles/e_profiles_sum.html.

http://www.bea.gov/newsreleases/regional/spi/spi_newsrelease.htm

http://www.bea.gov/regional/gsp/

http://www.bea.gov/regional/gsp/

http://www.census.gov/population/www/projections/projectionsagesex.html

http://www.census.gov/population/www/projections/projectionsagesex.html

http://www.census.gov/compendia/statab/tables/09s0379.xls






http://www.eia.doe.gov/emeu/states/hf.jsp?incfile=sep_sum/plain_html/sum

http://www.eia.doe.gov/emeu/states/hf.jsp?incfile=sep_sum/plain_html/sum

http://www.eia.doe.gov/cneaf/electricity/epa/epa_sprdshts.html. 5

http://www.eia.doe.gov/cneaf/electricity/epa/epa_sprdshts.html. 5

http://www.eia.doe.gov/cneaf/electricity/st_profiles/e_profiles_sum.html


This Annex provides brief descriptions of the 23 super options that are the basis of this study. To provide some context on the selection of the super options, the following is a brief discussion of the work the Center for Climate Strategies (CCS) has undertaken in over 20 states to facilitate stakeholder-based, state-level climate action plans.

State Climate Action Plan Processes

The identification, design and analysis of policy option recommendations in the states’ action planning processes involved preliminary fact finding through the development of a draft inventory and forecast of greenhouse gas (GHG) emissions, and a draft inventory and catalog of existing and planned actions that reduce emissions in each state, combined with actions considered or undertaken in one or more other U.S. states (over 300 actions in all sectors). Next, stakeholder advisory groups engaged in joint fact-finding and policy development processes that involved the following sequential steps and stakeholder decisions:

1. Expansion of the initial state’s catalog of actions to fill gaps and provide a full range of potential actions of relevance to the state.

2. Narrowing of the catalog of actions to a set of top 10 or so draft policy options for each sector based on screening criteria that included GHG reduction potential, cost-effectiveness, co-benefits or costs, and feasibility considerations.

3. Development of draft policy design parameters for each individual policy option (timing, level of effort, coverage of implementing parties, etc.).

4. Modifications of inventory and forecast estimates if/as needed.

5. Identification of preferred data sources, methods, and assumptions for analysis.

6. Identification of preferred or potentially applicable policy implementation tools.

7. Development of estimated GHG reduction potential and costs/savings per metric ton of GHG removed for specific individual policy options.

8. Identification and qualitative or quantitative assessment of co-benefits and costs for specific individual policy options.

9. Development of estimated GHG reduction potential and costs or savings per metric ton of GHG removed for all policy options combined.

10. Final approval of individual policy option recommendations and related planning goals.

11. Development of final report language.

12. Transmittal of the final report to the convening body, typically the governor’s office.

Selection of 23 “Super Options”

More than 900 specific policy options have been identified in the development of state climate action plans. Due to the limitations of this project, the authors could not reanalyze all of these policy options. Instead, a list of 23 so-called “super options” was proposed and evaluated following review and approval

annex b

» Super Mitigation Options Descriptions


by the 18 governors’ offices of the Southern Governors’ Association.1 These super options are actually categories or groupings of more specific policies that have been or could be implemented at the state or local level. They were chosen because they typically (1) have the greatest GHG reduction potential; (2) are gateway options, sometimes with limited near-term reduction potential but holding great promise in later years (carbon capture and storage or reuse, nuclear); or (3) are highly cost-effective and important for other reasons (state lead by example).

Because each state process was conducted independently and focused on individual state needs, and because they were stakeholder-driven and conducted at different times over the past few years, differences exist between their designs, final outcomes and results. However, the states also share many common issues and characteristics; therefore, the results also overlap substantially in key policy areas. For this study, analyses of recommended policy options from 16 states2 with consensus-based stakeholder processes where used.

To ensure consistency of analytical methods, assumptions and data sources across all 23 super options in all 16 state plans, the results of the state plans were updated using methods that addressed:

»» The effects of the recession on assumed levels of economic growth and other economy-driven assumptions,

»» The effects of changes in fuel prices, and

»» The impacts of recent state or federal actions on assumed future levels of GHG emissions in the absence of the proposed new GHG reduction policies.

Please refer to the separate document Annex A for a detailed discussion of the methodology used in the extrapolation process. Following are brief descriptions of the super options by sector.

Energy Supply ES-1. Renewable Portfolio Standard

A Renewable Portfolio Standard (RPS) is a requirement that utilities must supply a certain, generally fixed percentage of electricity from an eligible renewable energy source(s). About 20 states currently have an RPS in place. In some cases, utilities may also meet their portfolio requirements by purchasing Renewable Energy Certificates (RECs) from eligible renewable energy projects. With REC “trading,” it may be beneficial to consider a variety of renewable resources.

ES-2. Nuclear

Nuclear power has historically been a low-GHG source of electric power. No new nuclear power plants have come on line in the United States since 1996. The Energy Policy Act of 2005 included provisions encouraging the construction of new nuclear units. There are currently nine new plant applications on file with the Nuclear Regulatory Commission. The current Administration has been supportive of nuclear expansion, emphasizing its importance in maintaining a diverse energy supply and its reputation for producing electricity with negligible pollutant emissions during operation. Congress has also offered significant financial subsidies for new nuclear plants in an effort to jump-start the industry, including limitations on liability for nuclear accidents.

1. This national scale-up project is in part an outgrowth of work CCS performed for the SGA. The vetting of the 23 super options through those governors’ offices was performed as part of that effort. The final SGA report can be found at http://www.climatestrategies.us/template.cfm?FrontID=6081.2 . The 16 states in study are Alaska, Arkansas, Arizona, Colorado, Florida, Iowa, Maryland, Michigan, Minnesota, Montana, New Mexico, North Carolina, Pennsylvania, South Carolina, Vermont, and Washington.




ES-3. Carbon Capture and Storage or Reuse

Carbon capture and storage or reuse (CCSR) is a process that includes separation of carbon dioxide (CO2) from industrial and energy-related sources, transport to a storage location, and permanent or long-term storage in isolation from the atmosphere. Ideally, the CO2 from large point sources such as power plants can be compressed and transported for storage in geological formations, in the ocean, and in mineral carbonates, or can be used in industrial processes. Captured carbon can also be used for enhanced recovery of oil and gas. The net reduction of emissions to the atmosphere through CCSR depends on the fraction of CO2 captured, the relative increase in CO2 production resulting from loss in the overall efficiency of power plants that capture carbon, energy used for transport and storage, any leakage from transport, and the fraction of CO2 retained in storage over the long term.

ES-4. Coal Plant Efficiency Improvements

Efficiency improvements refer to increasing generation efficiency at power stations through incremental improvements at existing plants (e.g., more efficient boilers and turbines, improved control systems, or combined-cycle technology). Repowering existing power plants refers to switching to lower- or zero-emitting fuels at existing plants, or for new capacity additions, including use of biomass or natural gas in place of coal or oil. Policies to encourage efficiency improvements and repowering of existing plants could include incentives or regulations as described in other options, with adjustments for financing opportunities and emission rates of existing plants.

Residential, Commercial, and Industrial RCI-1. Demand Side Management (DSM)

This policy requires that a certain percentage of energy sales (electricity, natural gas, fuel oils) be achieved through demand side management (DSM) efficiency measures. This policy focuses on increasing investment in electricity DSM efforts through programs run by utilities or other load-serving entities, energy efficiency funds, and/or energy efficiency goals. The policy design includes two key and linked dimensions: achievable/desirable energy savings as mentioned above, as well as policy/administrative mechanisms to achieve these savings. Policy and administrative mechanisms that might be applied include regulator-verified savings targets, public benefit charges, portfolio standards, energy trusts, integrated resource planning, performance-based incentives, decoupling of rates and revenues, and appropriate rate treatment for efficiency.

RCI-2a. High Performance Buildings—Private Sector

This policy provides incentives and targets to encourage or induce the owners and developers of new and existing buildings and facilities to improve the efficiency of their buildings, along with provisions for raising targets periodically and providing resources to building industry professionals to help achieve the desired building performance. This policy can include elements to encourage the improvement and review of energy-use goals over time, and flexibility in contracting arrangements to promote integrated energy- and resource-efficient design and construction.

RCI-2b. High Performance Buildings—Government Lead by Example

Recognizing that governments should “lead by example,” this option provides targets to improve the energy efficiency of existing state and local government buildings, existing buildings being renovated, and new buildings under construction. This option could include improved design and construction for government-owned institutional buildings, such as schools and universities. The proposed targets are typically much higher than code standards for new state-funded and other government buildings. Potential elements of this policy include: requiring that energy efficiency be a criterion in procurement of energy-using equipment and systems; requiring improvements in the operation of buildings and other facilities; requiring audits of energy performance and operations of state and other government buildings; energy star procurement requirements; review of efficiency goals over time, and development of flexibility in contracting arrangements to encourage integrated energy-efficient design and construction.


RCI-3. Appliance Standards

Appliance and electronic equipment efficiency standards reduce the market cost of energy efficiency improvements by incorporating technological advances into base appliance models, thereby creating economies of scale. Appliance and electronic equipment efficiency standards can be implemented at the state level for equipment not covered by federal standards, or where higher-than-federal standard efficiency requirements are appropriate.3 Regional coordination for state appliance and electronic equipment standards can be used to avoid concerns that retailers or manufacturers may (1) resist supplying equipment to one state that has advanced standards, or (2) focus sales of lower-efficiency models on states with less stringent efficiency standards.

RCI-4. Building Codes

Building energy codes specify minimum energy efficiency requirements for new buildings or for existing buildings undergoing a major renovation. Given the long lifetime of most buildings, amending state and/or local building codes to include minimum energy efficiency requirements and periodically updating energy efficiency codes could provide long-term GHG savings from commercial, residential, institutional, industrial and government facilities. Implementation of building energy codes, particularly when much of the building occurs outside of urban centers, can require additional resources.

RCI-5. Combined Heat and Power

Combined heat and power (CHP) systems reduce fossil fuel use and GHG emissions through the improved efficiency of the CHP systems (relative to separate heat and power technologies), and by avoiding transmission and distribution losses associated with moving power from central power stations located far from where the electricity is used.

Transportation and Land Use Vehicles TLU-1. Vehicle Purchase Incentives, Including Pay as You Drive

Federal, state and local governments can provide incentives for public and private vehicle fleets to include low-GHG vehicles. An example is the federal “Cash for Clunkers” program, which encouraged consumers to trade in older, less fuel-efficient vehicles for new vehicles that get better fuel economy by providing a credit of either $3,500 or $4,500. The state may pass necessary legislation to allow, encourage and support the provision of pay-as-you-drive auto insurance, possibly including state support for additional pilot programs. This measure converts vehicle insurance from a relatively fixed annual amount (which varies little by mileage), to a mostly mileage-based rate.

FuelsTLU-2. Renewable Fuel Standard (Biofuels Goals)

Adopt standards that require a certain amount or percentage of fuel sold within the state to be a renewable fuel (e.g., ethanol or biodiesel). This percentage can gradually increase over time. The state can help facilitate the transition to renewable fuels by regulating quality standards for fuel blends.

Vehicle Miles Traveled—Travel ActivityTLU-3. Smart Growth/Land Use

Provide state funding, information dissemination, and technical assistance to facilitate the adoption of smart growth planning processes, models and tools by local and regional jurisdictions. Smart growth planning, modeling, and tools are methods of development that reduce sprawl and maximize environmental, fiscal, and economic resources. This form of planning and modeling often incorporates other planning tools, such as mixed-use, open-space protection and transit-oriented development.

3. In recent years, Arizona, Oregon, and Washington, among other states, adopted state standards for several appliances; this led to the inclusion of standards for these appliances in the Energy Policy Act of 2005 federal energy bill.


TLU-4. Transit

Improve existing transit service (e.g., expanded hours or coverage of bus service, higher-frequency bus routes, investments in rail transit) to generate greater use of public transit and a reduction in automobile travel. Fund enhanced promotion and marketing of transit to achieve greater use of public transit. Create new public transit infrastructure (e.g., rail lines, bus rapid transit routes); greater use of public transit and reduction in automobile travel can be achieved by expanding public transit infrastructure.

Freight/Goods Movement-Related MeasuresTLU-5. Anti-Idling Technologies and Practices

Vehicle idling can be reduced by enforcing anti-idling ordinances and/or encouraging the use of alternatives to idling. Many states and local governments have adopted idling regulations for trucks and buses. Alternatives to long-term truck idling include the use of technologies, such as automatic engine shut-down/start-up system controls, direct-fired heaters, auxiliary power units, and truck stop electrification.

TLU-6. Mode Shift from Truck to Rail

This option focuses on strategies to encourage more use of rail freight, for example through improvements to railroad infrastructure and railyards. In many cases, carrying freight by rail rather than trucks can reduce emissions and fuel consumption, while also reducing congestion on major roadways. Shifting freight from trucks to rail also decreases impacts on highway infrastructure, and may reduce truck-related idling and emissions of particulate matter.

Agriculture, Forestry, and Waste ManagementAgricultureAFW-1a. Crop Production Practices—Soil Carbon Management

The amount of carbon stored in the soil can be increased by the adoption of such practices as conservation, no-till cultivation, and crop rotation. Reducing summer fallow and increasing winter cover crops are complementary practices that reduce the need for conventional tillage. In addition, the application of biochar (i.e., charcoal) may also increase soil carbon content and stabilize soil carbon. By reducing mechanical soil disturbance, these practices reduce the oxidation of soil carbon compounds and allow more stable aggregates to form. Other benefits include reduced wind and water erosion, reduced fuel consumption, and improved wildlife habitat.

AFW-1b. Crop Production Practices—Nutrient Management

Improve the efficiency of fertilizer use and other nitrogen-based soil amendments through implementation of management practices and Generally Accepted Agricultural Management Practices. Excess nitrogen not metabolized by plants can leach into groundwater and/or be emitted to the atmosphere as nitrous oxide (N2O). Better nutrient utilization can lead to lower N2O emissions from runoff.

AFW-2. Livestock Manure—Anaerobic Digestion and Methane Utilization

Reduce the amount of methane emissions from livestock manure by installing manure digesters on livestock operations. Energy from the manure digesters is used to create heat or power, which offsets fossil fuel–based energy production and the associated GHG emissions. Farmers may consider new technologies as well, such as plasma arc technology. The joint U.S. Department of Agriculture/U.S. Environmental Protection Agency AgSTAR program maintains extensive information on manure management technologies, including anaerobic digestion. More information may be found at: http://www.epa.gov/agstar/.

http://www.epa.gov/agstar/


ForestryAFW-3. Forest Retention

Reduce the rate at which existing forests are cleared and converted to developed uses. Much of the carbon stored in forest biomass and soils can be lost as a result of such a land-use conversion. Easements can be used toward this end, as well as conservation programs.

AFW-4. Reforestation/Afforestation

Establish forests on land that has not historically been forested (e.g., agricultural land — “afforestation”). Promote forest cover and associated carbon stocks by regenerating or establishing forests in areas with little or no present forest cover (“reforestation”). In addition, implement such practices as soil preparation, erosion control, and stand stocking to ensure conditions that support forest growth.

AFW-5. Urban Forestry

Maintain and improve the health and longevity of trees in urban and residential areas to protect and enhance the carbon stored in tree biomass. Indirect emission reductions may also occur by reducing heating and cooling needs as a result of planting shade trees. Promote use of software programs that can be used by cities and communities to track urban forestry. The policy design needs to be sensitive to greenbelt taxing issues.

Waste ManagementAFW-6. MSW Source Reduction

Reduce the volume of waste from Residential, Commercial, and Government sectors through programs that reduce the generation of wastes. Reducing generation at the source reduces both landfill emissions and upstream production emissions.

AFW-7. Enhanced Recycling of Municipal Solid Waste

Increase recycling and reduce waste generation in order to limit GHG emissions associated with landfill methane generation and with the production of raw materials. Increase recycling programs, create new recycling programs, provide incentives for the recycling of construction materials, develop markets for recycled materials, and increase average participation and recovery rates for all existing recycling programs.

AFW-8. MSW Landfill Gas Management

Use the renewable energy created at landfills by anaerobic digestion (methane) to make electric power, space heat, or liquefied natural gas. Encourage smaller landfills that do not fall under environmental protection regulations (i.e., new-source performance standards) to capture and flare methane gas. Flares are used to safely combust toxic and volatile gases from landfills, and they convert methane gas, which has a relatively high global warming potential, to CO2.


The Regional Economic Models, Inc (REMI) Policy Insight Plus (PI+) is a structural economic forecasting and policy analysis model. It integrates input-output, computable general equilibrium, econometric and economic geography methodologies. The model is dynamic, with forecasts and simulations generated on an annual basis and behavioral responses to wage, price, and other economic factors.

The REMI model consists of thousands of simultaneous equations with a structure that is relatively straightforward. The exact number of equations used varies, depending on the extent of industry, demographic, demand, and other detail in the model. The overall structure of the model can be summarized in five major blocks: (1) Output and Demand, (2) Labor and Capital Demand, (3) Population and Labor Supply, (4) Compensation, Prices, and Costs, and (5) Market Shares. The blocks and their key interactions are shown in Figures C-1 and C-2.

The Output and Demand block includes output, demand, consumption, investment, government spending, import, product access, and export concepts. Output for each industry is determined by industry demand in a given region and its trade with the U.S. market, and international imports and exports. For each industry, demand is determined by the amount of output, consumption, investment, and capital demand on that industry. Consumption depends on real disposable income per capita, relative prices, differential income elasticities and population. Input productivity depends on access to inputs because the larger the choice set of inputs, the more likely that the input with the specific characteristics required for the job will be formed. In the capital stock adjustment process, investment occurs to fill the difference between optimal and actual capital stock for residential, non-residential, and equipment investment. Government spending changes are determined by changes in the population.

The Labor and Capital Demand block includes the determination of labor productivity, labor intensity and the optimal capital stocks. Industry-specific labor productivity depends on the availability of workers with differentiated skills for the occupations used in each industry. The occupational labor supply and commuting costs determine firms’ access to a specialized labor force.

Labor intensity is determined by the cost of labor relative to the other factor inputs, capital and fuel. Demand for capital is driven by the optimal capital stock equation for both non-residential capital and equipment. Optimal capital stock for each industry depends on the relative cost of labor and capital, and the employment weighted by capital use for each industry. Employment in private industries is determined by the value added and employment per unit of value added in each industry.

The Population and Labor Supply block includes detailed demographic information about the region. Population data are given for age and gender, with birth and survival rates for each group. The size and labor force participation rate of each group determine the labor supply. These participation rates respond to changes in employment relative to the potential labor force and to changes in the real after-tax compensation rate. Migration includes retirement, military, international and economic migration. Economic migration is determined by the relative real after-tax compensation rate, relative employment opportunity and consumer access to variety.

annex c

» Description of the REMI PI+ Model


Figure C-1. REMI Model Linkages (Excluding Economic Geography Linkages)

EmploymentOpportunity

HousingPrice

Compensation Rate

CompositeCompensation Rate

RealCompensation Rate

ProductionCosts

Composite Prices

ConsumerPrices

Investment

Migration Population Employment

LaborProductivity

OptimalCapital

Stock

OutputState and LocalGovernment Spending

ParticipationRate

DomesticMarket Share

InternationalMarket Share

LaborForce

Exports

Consumption

Real Disposable Income

1) Output and Demand

2) Labor and Capital Demand3) Population and Labor Supply

4) Compensation, Prices, and Costs

5) Market Shares

The Compensation, Prices, and Costs block includes delivered prices, production costs, equipment cost, the consumption deflator, consumer prices, the price of housing, and the wage equation. Economic geography concepts account for the productivity and price effects of access to specialized labor, goods and services.

These prices measure the value of the industry output, taking into account the access to production locations. This access is important due to the specialization of production that takes place within each industry, and because transportation and transaction costs associated with distance are significant. Composite prices for each industry are then calculated based on the production costs of supplying regions, the effective distance to these regions, and the index of access to the variety of output in the industry relative to the access by other uses of the product.

The cost of production for each industry is determined by cost of labor, capital, fuel and intermediate inputs. Labor costs reflect a productivity adjustment to account for access to specialized labor, as well as underlying compensation rates. Capital costs include costs of non-residential structures and equipment, while fuel costs incorporate electricity, natural gas and residual fuels.


Figure C-2. Economic Geography Linkages

Intermediate InputProductivity

Commodity Access Index

Output

LaborProductivity

Employment

Labor AccessIndex

Economic Migrants

CompositeCompensation

Rate

ProductionCosts

CompositePrices

IntermediateInputs

DomesticMarketShare

InternationalMarketShare

1) Output and Demand

2) Labor and Capital Demand

5) Market Shares

3) Population and Labor Supply

4) Compensation, Prices, and Costs

The consumption deflator converts industry prices to prices for consumption commodities. For potential migrants, the consumer price is additionally calculated to include housing prices. Housing price changes from their initial level depend on changes in income and population density. Regional employee compensation changes are due to changes in labor demand and supply conditions, and changes in the national compensation rate. Changes in employment opportunities relative to the labor force and occupational demand change determine compensation rates by industry.

The Market Shares equations measure the proportion of local and export markets that are captured by each industry. These depend on relative production costs, the estimated price elasticity of demand, and effective distance between the home region and each of the other regions. The change in share of a specific area in any region depends on changes in its delivered price and the quantity it produces compared with the same factors for competitors in that market. The share of local and external markets then drives the exports from and imports to the home economy.

As shown in Figure C-2, the Labor and Capital Demand block includes labor intensity and productivity, as well as demand for labor and capital. Labor force participation rate and migration equations are in the Population and Labor Supply block. The Compensation, Prices, and Costs block includes composite prices, determinants of production costs, the consumption price deflator, housing prices, and the wage equations. The proportion of local, interregional and international markets captured by each region is included in the Market Shares block.


This Annex summarizes the methodology used to scale up the costs and savings associated with the implementation of the Energy Supply (ES), Residential Commercial and Industrial (RCI), and Agriculture, Forestry and Waste (AFW) “super options” to the national level. The costs and savings of the Transportation and Land Use (TLU) options are estimated using the U.S. Department of Energy (USDOE) Voluntary Innovative Sector Initiatives (VISION) tool. These national-level costs and savings data are used as direct effect input data in the Regional Economic Models, Inc. (REMI) model to estimate the macroeconomic impacts of the greenhouse gas (GHG) mitigation supper options.

In order to perform macroeconomic impact analyses of GHG mitigation super options using the REMI model, information is needed on basic microeconomic considerations, such as the direct costs and direct savings of each GHG mitigation option, as well as on aspects that relate to macro linkages. This more detailed information is usually not available in the state climate action plan reports, which in most cases only provide results on GHG reductions in target years, net cost/savings in net present value (NPV) over the whole study period, and cost-effectiveness (per-ton cost/saving of GHG removed) in the target years. Moreover, the REMI analysis can be enhanced by disaggregated information on costs and savings. For example, for options related to clean and renewable electricity generation, efficiency improvement in the Power sector, combined heat and power, etc., the accuracy of the analysis is improved if both the cost of new energy or generation technology and the cost of avoided generation are disaggregated into capital cost, operation and maintenance (O&M) cost, and fuel cost. On the savings side, the energy savings should be disaggregated into different fuel types and for different economic sectors (such as the Residential sector, Commercial sector, and Industrial sector). In addition, in the REMI analysis, input data, such as capital cost, O&M cost, and annual savings from reduced energy use, for each individual year in the study period, are needed. All these detailed data can only be obtained from the original calculation workbooks the sectoral analysts used to quantify the costs and savings of the options recommended in the state climate action plans.

In recent years, the Center for Climate Strategies (CCS) has facilitated the development of climate action plans through a fact-finding and consensus building process for over 20 states. This study uses the action plan data of 16 states (“existing states”): Alaska, Arkansas, Arizona, Colorado, Florida, Iowa, Maryland, Michigan, Minnesota, Montana, North Carolina, New Mexico, Pennsylvania, South Carolina, Vermont, and Washington. The authors went through a systematic update process to re-evaluate the GHG reduction potentials, costs and savings of the mitigation options (corresponding to the 23 super options) recommended in the original action plans of these 16 states to reflect changes in fuel price projections in the USDOE Energy Information Administration (EIA) Annual Energy Outlook (AEO) 2009, the impacts of recent state or federal climate actions, and the impacts of the recession.

The updated results on GHG reductions and cost-effectiveness of the mitigation options in the 16 existing states have been utilized to extrapolate the results to the remaining states in the U.S. (see Annex A). Then the 50-state data are aggregated and utilized in the national cost curve development.

As for the macroeconomic analysis, the national REMI input data for the TLU super options are developed using the VISION tool. The national REMI input data for the ES, RCI, and AFW options are scaled up from the state-level data. Because of the limitation of time, in this study, we only extracted detailed

annex d

» Scale-up Approach for National REMI Inputs Preparation


information on costs and savings from sectoral quantification workbooks of seven states: Colorado, Florida, Iowa, Michigan, North Carolina, Pennsylvania, and Washington.1 The national-level REMI input data are estimated based on the data scaled up from these seven states’ data. These states are chosen because reliable policy measure data are available and they provide a reasonably good representation of national diversity. In addition, all of these seven state action plans were developed recently and have incorporated good coverage of the super options (option bundles) on which updates have been focused. In order to test how well the seven states represent the U.S., we compare the energy consumption by end-use sector and electricity generation by source between the seven states and the U.S. in Tables D-1 and Table D-2, respectively. The tables show that each of the seven states has its own characteristics of energy consumption and electricity generation mix. However, when aggregated, the seven states reflect similar features in terms of energy end use and electricity generation composition as the nation.

Table D-1. 2007 Energy Consumption by End User

States Residential Commercial Industrial Transportation

Colorado 23% 20% 27% 30%

Florida 29% 24% 12% 35%

Iowa 19% 16% 40% 26%

Michigan 26% 21% 27% 26%

North Carolina 27% 21% 24% 28%

Pennsylvania 24% 18% 32% 26%

Washington 24% 19% 25% 33%

State Total 26% 20% 25% 30%

U.S. Total 22% 18% 32% 28%

Source: EIA. 2009. State Energy Profiles.

Table D-2. 2007 Electricity Generation by Type

States Coal Petroleum Natural Gas Nuclear Renewables Other

Colorado 67% 0% 28% 3% 2% 0%

Florida 30% 9% 44% 13% 2% 1%

Iowa 76% 1% 6% 9% 8% 0%

Michigan 59% 1% 11% 26% 2% 0%

North Carolina 61% 0% 3% 31% 4% 0%

Pennsylvania 54% 1% 8% 34% 2% 1%

Washington 8% 0% 7% 8% 77% 0%

State Total 46% 3% 18% 21% 11% 1%

U.S. Total 49% 2% 22% 19% 8% 1%

Source: EIA. 2009. Electric Power Annual 2007.

I. Development of National REMI Input Data for the ES, RCI, and AFW Super Options

The input data of the ES, RCI, and AFW super options used in the national REMI macroeconomic analysis model are scaled up from the data of the seven states: Colorado, Florida, Iowa, Michigan, North Carolina, Pennsylvania, and Washington. The general steps used to extrapolate the costs and savings of the seven states to the national level are:

1. For each super option, identify the states that recommended the option in the state climate action plans (see Table D-3 for the list of options recommended in the seven states). Many options are not recommended in all the seven states. For example, if only five states (among the seven) recommended the option, the scale-up calculation is based on the data from the five states.

1. CCS previously performed state-level follow-up macroeconomic analyses for Florida, Michigan, North Carolina, and Pennsylvania.


2. Compute the U.S. over the state ratio of the option applicability for each super option. The variable that measures the applicability of the mitigation options varies from option to option. For the RCI options, sectoral energy consumptions are used; for the ES options, electricity generation is used; for the AFW options, applicability can be variables such as estimated cropland without no-till (for the crop production practices option), total non-forest non-urban land (for the reforestation/afforestation option), municipal solid waste (MSW) landfilled (for the MSW landfill gas management option), etc. Table D-4 lists the applicability variable used for each individual super option.

3. For both costs and savings, multiply the costs or the savings in each year of each state by the applicability ratio of U.S. over the corresponding state computed in Step 2. The results are the scaled-up costs or savings at the national level based on each individual state’s data. Please note because of the lack of projections on the applicability variables, the ratios of U.S. over the state option applicability are computed based on the most recently available year data (e.g., energy consumption by sector is based on year 2007 EIA data). Then this same ratio is applied to the state data for each year in the study period to scale up to the national level.

4. Compute the weighted average of the national-level costs or savings scaled up from each individual state’s data. The variables used to compute the weights are again the applicability

variables of individual options as indicated in Table D-4.

The general extrapolation formula used for each ES, RCI, and AFW super option in the scale-up calculation is:

COSTU.S. = ApplicabilityU.S.

Applicabilityi

Weighti n

i = 1COSTi∑ × ×

SAVINGSU.S. = ApplicabilityU.S.

Applicabilityi

Weighti n

i = 1Savingsi∑ × ×

WEIGHTi. = Applicabilityi.

Applicabilityi n

i = 1∑

COST program cost of the GHG mitigation super option

SAVINGS energy savings associated with the implementation of the super option

Applicability variables used as the scale-up bases

Weight weightings of the seven states used to compute the weighted-average values for the U.S.

n total number of states (among the seven states) that recommended the super option in the state climate action plan

i states with the super option recommended in the state action plan


Table D-3. ES, RCI, and AFW Super Options Recommended in the Seven States

Super Options CO FL IA MI NC PA WA

Energy Supply (ES)

ES-1: Renewable Portfolio Standard (RPS) √ √ √ √ √

ES-2: Nuclear √ √ √ √

ES-3: Carbon Capture, Sequestration and Reuse (CCSR) √ √

ES-4: Coal Plant Efficiency Improvements √ √ √

Residential, Commercial, and Industrial (RCI)

RCI-1: Demand Side Management (DSM) √ √ √ √ √ √ √

RCI-2: High Performance Buildings √ √ √ √ √ √ √

RCI-3: Appliance Standards √ √ √ √ √

RCI-4: Building Codes √ √ √ √ √

RCI-5: Combined Heat and Power (CHP) √ √ √ √ √ √ √

Agriculture, Forestry, and Waste Management (AFW)

AFW-1: Crop Production Practices √ √ √ √ √ √ √

AFW-2: Livestock Manure √ √ √ √ √ √ √

AFW-3: Forest Retention √ √ √ √ √

AFW-4: Reforestation/Afforestation √ √ √ √

AFW-5: Urban Forestry √ √ √ √ √ √

AFW-6: Municipal Solid Waste (MSW) Source Reduction √ √

AFW-7: Enhanced Recycling of MSW √ √ √ √ √ √ √

AFW-8: MSW Landfill Gas Management √ √ √ √ √ √

Table D-4. Applicability Variables Used for the ES, RCI, and AFW Super Options in the Scale-up Calculation

Super Options Applicability Variable

Energy Supply (ES)

ES-1: Renewable Portfolio Standard (RPS) Total Electricity Sales

ES-2: Nuclear Nuclear Electricity Generation

ES-3: Carbon Capture, Storage and Reuse Coal-fired Electricity Generation

ES-4: Coal Plant Efficiency Improvements Coal-fired Electricity Generation

Residential, Commercial, and Industrial (RCI)

RCI-1: Demand Side Management (DSM) Total RCI Consumption of Electricity for the Portion of Electricity

Total RCI Consumption of Natural Gas (NG) and Oil for the Portion of Other Fuels

RCI-2: High Performance Buildings

RCI-3: Appliance Standards Total RCI Consumption of Electricity for the Portion of Electricity

Total RCI Consumption of NG for the Portion of NGRCI-4: Building Codes

RCI-5: Combined Heat and Power (CHP)

Total NG Consumption of Commercial and Industrial Sector for NG-fired CHP

Total Biomass Consumption of Commercial and Industrial Sector for Biomass-fired CHP

Agriculture, Forestry, and Waste Management (AFW)

AFW-1: Crop Production Practices Estimated Cropland without No-Till

AFW-2: Livestock Manure Total Population of Dairy Cattle, Beef Cattle, and Swine

AFW-3: Forest Retention State-Level Acres of Forest

AFW-4: Reforestation/Afforestation Combination of Ag land and Other Non-forest, Non-urban Land

AFW-5: Urban Forestry Total Urban Land Area

AFW-6: MSW Source Reduction Municipal Solid Waste (MSW) Landfilled

AFW-7: Enhanced Recycling of MSW Waste in Place at Uncontrolled Landfills with Landfill Gas-to-Energy Potential

AFW-8: MSW Landfill Gas (LFG) Management MSW Landfilled


Additional assumptions adopted in the scale-up calculation are summarized below:

1. For the options of ES-1, ES-2, and ES-3, the capital cost, O&M cost, and fuel cost of the renewable electricity generation, nuclear electricity generation, and carbon capture and storage or reuse are scaled up separately from the state level data. The total avoided cost is assumed to be all coal-fired electricity cost. The total avoided cost is first scaled up to the national level based on the state level data and then is split between capital cost, O&M cost, and fuel cost using the percentages of 40% capital, 15% O&M, and 45% fuel.

2. For the option of ES-4, the capital cost and O&M cost of the coal plan efficiency improvements and the total avoided cost are scaled up separately from the state level data. The total avoided cost is assumed to be all fuel cost.

3. For the options of RCI-1, RCI-2, RCI-3, and RCI-4, the program costs and energy savings are scaled up from the state level to the national level for the components of electricity, natural gas (NG), and oil, separately. Then, for RCI-1 (Demand Side Management [DSM]), the costs and savings are split between the rResidential, Commercial, and Industrial sectors using the weights of the sectoral total electricity consumption, NG consumption, and oil consumption, respectively. For RCI-2, RCI-3, and RCI-4, when we split the costs and savings between the RCI sectors, the weighting of the Industrial sector is computed based on just 9.4% of the sectoral total energy consumption. This is because based on the EIA 2002 report on energy consumption by manufacturers, approximately 9.4% of industrial energy use in the U.S. is for heating, ventilating and air conditioning (HVAC), lighting, and other facilities — i.e., energy use reductions from high performance buildings, appliance standards, and building codes apply only to 9.4% of the total industrial energy use.2

Next, we take the electricity DSM component of super option RCI-1 as an example to illustrate how we scaled up the seven states’ data to the national level. Table D-5 presents the program costs of electricity DSM and the potential savings of electricity in the seven states. As indicated in Table D-4, the option applicability variable used for the scale-up of RCI-1 DSM is the total energy (electricity in this illustrative case) consumption of the Residential, Commercial, and Industrial sectors. The first numerical column in Table D-6 shows the total RCI electricity consumption in 2007 of the seven states and of the U.S. In the second numerical column, the ratios of electricity consumption of the U.S. over each individual state are computed. These ratios are used to scale up the state-level costs and savings to the national level based on each individual state’s data. The last column shows the weights of the seven states that are used to compute the weighted-average national-level costs and savings. The option applicability variable — i.e., the total RCI electricity consumption in each state — is used to compute the weights.

In Table D-7, the total costs and savings of RCI-1 DSM (electricity) are first scaled up to the national level using the data presented in Tables D-5 and D-6 and following the scale-up calculation steps illustrated at the beginning of this section. The total costs and savings are then split among the Residential, Commercial, and Industrial sectors, based on the percentage of electricity consumptions in these three sectors, as shown in Table D-8.

2. U.S. Department of Energy, Energy Information Administration. 2005. 2002 Energy Consumption by Manufacturers. http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html.


Table D-5. Total Program Costs and Electricity Savings of RCI-1 DSM (Electricity) (in millions of 2006 dollars)

State 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

CO Costs $9.6 $33.7 $60.4 $89.9 $119.5 $139.9 $165.4 $190.5 $214.7 $248.9 $269.6

Savings $78.0 $177.7 $277.0 $380.7 $487.7 $547.4 $613.7 $677.6 $753.1 $835.2 $976.7

FL Costs $17.7 $61.9 $131.8 $210.9 $299.1 $396.3 $493.8 $591.6 $689.9 $788.8 $888.1

Savings $44.9 $156.9 $334.2 $534.7 $758.6 $1,005.0 $1,252.2 $1,500.2 $1,749.4 $2,000.1 $2,251.9

IA Costs $5.0 $10.3 $17.0 $32.4 $47.6 $62.4 $85.5 $108.5 $131.4 $154.0 $176.5

Savings $8.8 $18.8 $31.1 $46.2 $74.3 $95.8 $142.9 $201.6 $254.9 $298.7 $342.3

MI Costs $32.8 $65.7 $98.5 $131.4 $164.2 $197.0 $229.9 $262.7 $295.5 $328.4 $361.2

Savings $65.7 $131.4 $197.0 $262.7 $328.4 $394.1 $459.7 $525.4 $591.1 $656.8 $722.4

NC Costs $111.8 $166.7 $228.3 $290.1 $352.6 $415.3 $479.0 $544.2 $610.5 $677.2 $745.2

Savings $251.3 $374.0 $511.4 $649.3 $789.0 $929.0 $1,071.0 $1,216.6 $1,364.5 $1,513.5 $1,665.2

PA Costs $0.0 $0.0 $12.9 $25.7 $38.8 $96.1 $154.1 $212.5 $271.6 $331.2 $391.5

Savings $0.0 $0.0 $29.5 $57.8 $87.2 $218.4 $365.2 $518.2 $686.2 $842.7 $1,026.9

WA Costs $0.2 $0.4 $0.6 $0.6 $0.6 $0.6 $0.6 $0.6 $0.6 $0.6 $0.6

Savings $11.7 $23.4 $35.2 $34.6 $34.6 $34.7 $34.9 $35.2 $35.5 $35.9 $36.2

Table D-6. Data Used in the Scale-up Calculation of RCI-1 DSM (Electricity)

State 2007 RCI Total Electricity Consumption (trillion Btu) Weights U.S. vs. State RCI Electricity Consumption Ratio

Colorado 552 6.4% 73.29

Florida 2,489 28.7% 16.26

Iowa 488 5.6% 82.96

Michigan 1,178 13.6% 34.37

North Carolina 1,421 16.4% 28.48

Pennsylvania 1,624 18.7% 24.92

Washington 924 10.6% 43.81

State Total 8,674 100.0%

U.S. Total 40,469

RCI = Residential, Commercial and Industrial; DSM = demand side management.

Table D-7. Scaled-up Costs and Saving of RCI-1 DSM (Electricity)

Sectors 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Costs $827 $1,580 $2,563 $3,643 $4,770 $6,101 $7,503 $8,914 $10,330 $11,799 $13,216

Res. $306 $586 $950 $1,350 $1,768 $2,261 $2,781 $3,304 $3,829 $4,373 $4,898

Com. $294 $562 $912 $1,296 $1,697 $2,171 $2,669 $3,171 $3,675 $4,198 $4,702

Ind. $226 $432 $701 $997 $1,305 $1,669 $2,053 $2,439 $2,826 $3,228 $3,616

Savings $2,148 $4,116 $6,603 $9,173 $11,942 $15,042 $18,380 $21,809 $25,354 $28,845 $32,759

Res. $796 $1,525 $2,447 $3,400 $4,426 $5,575 $6,812 $8,083 $9,397 $10,691 $12,141

Com. $764 $1,464 $2,349 $3,263 $4,248 $5,351 $6,539 $7,759 $9,020 $10,262 $11,654

Ind. $588 $1,126 $1,807 $2,510 $3,267 $4,116 $5,029 $5,967 $6,937 $7,892 $8,963

RCI = Residential, Commercial and Industrial; DSM = demand side management.


Table D-8. Electricity Consumption in the Residential, Commercial, and Industrial Sectors

Sectors Electricity Consumption (trillion Btu) Sectoral Percentage

Residential 14,999.0 37.1%

Commercial 14,397.0 35.6%

Industrial 11,073.0 27.4%

RCI Total 40,469.0 100.0%

RCI = Residential, Commercial, and Industrial; Btu = British thermal unit.

II. Development of National REMI Input Data for the TLU Super Options

The REMI input data for the six Transportation and Land Use (TLU) super options were developed in large part using USDOE’s VISION spreadsheet tool. Developed by the Argonne National Laboratory ([ANL] one of USDOE’s research institutions), VISION is an Excel-based model that forecasts the potential energy use, oil use, and carbon emission impacts for time periods through the year 2050 of advanced light duty vehicle

and heavy duty vehicle technologies and alternative fuels.

The model was designed as a simplified and easy-to-use tool that can be applied to assess the potential impact of new vehicle and fuel technologies on energy use and carbon emissions.

Use of the VISION tool has been recommended in a study conducted for the American Association of State Highway and Transportation Officials (AASHTO) and the Transportation Research Board (TRB) in 2006. The report for the National Cooperative Highway Research Program recommended adaptation and use of the national-level VISION tool. The report describes VISION as “a spreadsheet tool designed for quick analyses of the impacts of changes in vehicle technology shares, fuel prices, and VMT growth on carbon emissions at the national level.”

Vehicle Purchase Incentives

Vehicle purchase incentives are a category of incentives that encourage consumers to buy more fuel-efficient vehicles. As a tool for reducing emissions, an incentives policy is considered as a potential alternative to options such as fuel taxes or vehicle miles traveled (VMT) taxes. This analysis was also done using the most recent update of the VISION tool. Using VISION, the effects of a vehicle incentives program were modeled consistent with a recent study on the subject produced by authors at USDOE’s Oak Ridge National Laboratory (ORNL).

Effectiveness and Cost Analysis of the New Vehicle Purchase Incentives Policies

The application of the ORNL findings using the VISION tool finds that a set of incentive policies could have a significant effect on the fuel efficiency of new cars and light trucks. By 2020, the fuel efficiency of new cars (light duty automobiles) could be more than 5 miles per gallon (MPG), higher than in a business-as-usual scenario (reaching over 43 MPG, rather than 38 MPG). New light trucks would see a 7-MPG gain, averaging over 35 MPG, rather than the 28 MPG projected in the baseline scenario. On average, the light

duty vehicle fleet would average 39 MPG, rather than the baseline scenario 33 MPG.

As shown in Table D-9, a set of incentives policies that improves fuel efficiency to such a degree has the potential to reduce GHG emissions from cars and light trucks by over 6.2% (98.8 million metric tons of carbon dioxide equivalent [MMtCO2e]) in the year 2020. Over the 11-year period from 2010 to 2020, the cumulative potential emissions reduction totals 443.4 MMtCO2e. Fuel savings are also significant—annual savings of petroleum-based transportation fuels increase every year and are projected to exceed 3 billion gallons saved each of the last five years of the decade.


The data results indicate that the various impacts are expected to grow in magnitude over time, because incentives policies are modeled to apply at the time of new vehicle purchase. As a result, in the first year the policy will only affect those vehicles bought during that year. All other vehicles on the road (i.e., more than 90% of the light duty fleet) will be unaffected because they will have been purchased prior to the adoption of the incentive program. By the seventh year of the program, however, fully half of the vehicles on the road will have been affected by the policy. The analysis shows that while buyers of new vehicles would see an increase in vehicle costs as manufacturers incorporate new fuel-efficient technologies into vehicles, such a policy would also create consumer savings in the form of lower fuel costs. The savings from fuel expenditures are projected to significantly exceed the costs of the more efficient vehicles.

Table D-9. Summary of Projected Emission Reductions and Fuel Savings from Vehicle Incentives

Year Baseline Light-Duty Emissions (MMtCO2e)

Scenario Emissions Reduction (MMtCO2e)

Scenario Emissions Reduction (%)

Scenario Gasoline & Diesel Savings (billions of gallons)

2010 1,647.18 –0.96 –0.058% –73.69

2011 1,684.07 –4.03 –0.239% –309.62

2012 1,679.63 –9.06 –0.539% –695.54

2013 1,667.65 –15.93 –0.955% –1,221.99

2014 1,653.12 –24.40 –1.476% –1,869.61

2015 1,635.44 –34.16 –2.089% –2,603.42

2016 1,617.07 –45.05 –2.786% –3,413.25

2017 1,601.23 –57.06 –3.564% –4,310.36

2018 1,584.47 –69.94 –4.414% –5,255.43

2019 1,577.48 –84.00 –5.325% –6,284.84

2020 1,569.82 –98.787 –6.293% –7,339.06

Cumulative 17,917.16 –443.378 –33,376.806

MMtCO2e = million metric tons of carbon dioxide equivalent.

Vehicle costs were calculated by multiplying the number of new vehicles sold by the difference in the cost of a more fuel-efficient vehicle as compared to the cost of a conventional gasoline vehicle. Dollar values of changes in gasoline, diesel, ethanol, and other motor fuels sales are calculated by multiplying forecast gallons of fuel affected by the forecast VISION 2009 U.S. fuel prices for each category of fuel.

As with the projected effect on GHG emissions, the effect of a new vehicle purchase incentives policy on costs also changes significantly as the years progress. As shown in Table D-10, in the first year, most vehicles on the road were purchased before the policy is implemented, and so fuel cost savings for the fleet are relatively small. However, as each year goes by, a larger and larger share of the light duty fleet is consuming less fuel, and fuel savings increase and eventually overwhelm the additional vehicle cost.

The net cost-effectiveness of the vehicle purchase incentives policy also changes significantly over time, beginning as a relatively more expensive way to reduce emissions (nearly $200 per ton in 2010). The net cost changes to net savings within three years, and eventually results in a net savings of nearly $190 per ton in 2020. Over the entire 11-year period, the net cost savings per ton is approximately $79.


Table D-10. Summary of Costs and Savings from New Vehicle Purchase Incentives

Year Additional Vehicle Costs (mil 2007$)

Additional Fuel Costs (mil 2007$)

Net Costs (mil 2007$)

Emissions Reduction (MMtCO2e) Cost/Ton (2007$)

2010 $363.02 –$174.99 $188.03 –0.96 $196.88

2011 $1,220.48 –$863.85 $356.63 –4.03 $88.46

2012 $2,201.21 –$2,097.01 $104.20 –9.06 $11.50

2013 $3,302.16 –$3,896.90 –$594.75 –15.93 –$37.34

2014 $4,462.35 –$6,300.31 –$1,837.96 –24.40 –$75.34

2015 $5,676.00 –$9,222.12 –$3,546.12 –34.16 –$103.81

2016 $6,942.11 –$12,606.84 –$5,664.73 –45.05 –$125.74

2017 $8,321.57 –$16,525.42 –$8,203.86 –57.06 –$143.76

2018 $9,703.59 –$20,848.52 –$11,144.93 –69.94 –$159.35

2019 $11,134.02 –$25,751.49 –$14,617.46 –84.00 –$174.01

2020 $12,545.91 –$30,947.54 –$18,401.63 –98.787 –$186.28

Cumulative –443.378

NPV $38,695.15 –$73,662.03 –$34,966.88 –$78.86

MMtCO2e = million metric tons of carbon dioxide equivalent; NPV = net present value.

Renewable Fuel Standard (RFS)

The policy scenario for the Renewable Fuel Standard (RFS) considers the impact of increased sales of biofuels as a percentage of conventional fuel sales by volume. These increases in biofuel sales are considered in a manner consistent with commensurate increases in flex-fuel vehicles that will use the biofuels. Over the time period analyzed, the percentage of biofuels from cellulosic fuel sources also increases in order to further reduce GHG emissions. The scenario analyzed is consistent with the 20% biofuel use by 2020 (20-by-20) performance goal that is included in several state energy and climate action plans around the country.

The scenario analyzed includes assumptions that the program starts in 2010, the first year of increased emission reductions. In order to achieve the 20-by-20 goal, transportation fuel providers would need to undertake changes in their production and distribution methods. The vehicle cost scenario includes the assumption that vehicle technologies will see reduced unit costs, consistent with USDOE EIA estimates. Increased production of vehicles that are capable of using biofuels is expected to make vehicle technologies less expensive as the years progress.

Effectiveness and Cost Analysis of the RFS Policy Scenario

The RFS was analyzed using the October 2009 version of the VISION tool, a state-of-the-art analytical tool that was developed and updated by the Transportation Technology Research and Development Center at ANL. The most recent version is VISION 2009, which ANL released in early October 2009. VISION 2009 incorporates the most current projections for fuel prices and vehicle fleet characteristics, consistent with USDOE’s AEO 2009.

As shown in Table D-11, the analysis finds that changing the nation’s on-road fuel supply to 20% biofuels by 2020 can reduce GHG emissions by over 3% in 2020, which provides for a savings of 66.8 MMtCO2e. Over the 11-year period from 2010 to 2020, the cumulative potential emission reduction reaches 262.1 MMtCO2e.


Table D-11. Summary of Emissions Savings from 20% Biofuels Scenario

YearBaseline Heavy Duty Emissions

(MMtCO2e)

Reduction Due to 20% Biofuels

(MMtCO2e)

Baseline Light Duty Emissions

(MMtCO2e)

Reduction Due to 20% Biofuels

(MMt CO2e)

Total Emissions Reductions (MMtCO2e)

Total Emissions Reductions (%)

2010 467.36 –0.03 1,647.18 –2.48 –2.51 –0.12%

2011 480.66 –0.01 1,684.07 –0.42 –0.43 –0.02%

2012 500.18 –2.01 1,679.63 –0.72 –2.73 –0.13%

2013 514.68 –7.68 1,667.65 –1.41 –9.08 –0.42%

2014 520.48 –11.01 1,653.12 –1.56 –12.58 –0.58%

2015 523.46 –16.80 1,635.44 –3.71 –20.51 –0.95%

2016 530.71 –16.71 1,617.07 –7.56 –24.27 –1.13%

2017 538.82 –19.14 1,601.23 –11.31 –30.45 –1.42%

2018 547.46 –18.61 1,584.47 –20.51 –39.13 –1.84%

2019 554.03 –18.51 1,577.48 –35.09 –53.60 –2.51%

2020 561.59 –15.87 1,569.82 –50.91 –66.78 –3.13%

Cumulative –262.07


The shift to higher-percentage shares of biofuels also has a cost impact. Flex-fuel cars and light trucks cost slightly more to produce, and biodiesel is expected to cost significantly more than diesel through the analysis period. On the other hand, the cost of ethanol is expected to be competitive with the cost of gasoline for light duty vehicles. The cost impact from both of these factors results in an overall additional cost of $4.76 billion3 by 2020. Comparing these cost impacts to the avoided emissions produces an estimated cost per ton of emissions avoided of approximately $71 in 2020 (Table D-12).

Table D-12. Summary of Vehicle and Fuel Costs for 20% Biofuels Scenario

Year LDV Costs (mil 2007$)

LDV Fuel Costs (mil 2007$)

HDV Fuel Costs (mil

2007$)

Total Costs (mil 2007$)

Change in Gasoline Use (bil gallons)

Change in Ethanol Use (bil gallons)

Amt. of Biodiesel Replacing Diesel (bil

gallons)

Cost/Ton of Avoided

Emissions (mil 2007$)

2010 $0 $120 $2 $122 0.00 0.00 0.00 $48.56

2011 $108 $15 $0 $123 –0.28 0.42 0.14 $286.19

2012 $225 $86 $937 $1,247 –0.82 1.20 0.65 $457.17

2013 $357 $240 $2,919 $3,517 –1.56 2.31 2.48 $387.15

2014 $538 $299 $3,659 $4,496 –2.61 3.84 3.57 $357.48

2015 $643 $364 $5,135 $6,142 –3.74 5.50 5.44 $299.46

2016 $698 $239 $4,719 $5,656 –4.85 7.16 5.41 $232.99

2017 $760 $145 $4,930 $5,835 –5.97 8.80 6.20 $191.59

2018 $823 –$353 $4,505 $4,975 –7.09 10.46 6.01 $127.14

2019 $758 $11 $4,449 $5,218 –8.04 11.84 5.96 $97.36

2020 $816 $0 $3,941 $4,757 –8.98 13.20 5.10 $71.24

Cumulative $5,728 $1,166 $35,195 $42,088 –43.95 64.74 40.81

2007 NPV $3,431 $823 $21,199 $25,453 $97.12

LDV = light duty vehicles; HDV = heavy duty vehicles; NPV = net present value.

The cost per ton of emissions rises in the first five years of the decade, and then falls in the second five years. There are two primary reasons for this. First, as is shown in Table D-13, most of the increase in biofuels use is projected to occur after 2016, while the change in the vehicle fleet shift is spread through the entire decade. As a consequence, the major source of costs (more expensive cars and trucks) occur for a few years before the biofuels supply ramps up. Second, as is also shown in Table D-14, the shift from

3. Dollar amounts referenced in this memo are expressed in 2007 dollars.


corn-based ethanol to ethanol made from lower-carbon-content feedstock occurs primarily in the last three years. While the transition to lower-carbon biofuels not projected to change costs significantly, the change does result in significantly higher emission reductions.

The scenario for achieving the 20% biofuels by 2020 goal largely relies on four assumptions. The first assumption is an increase in the share of the light duty (flex-fuel) vehicles that are capable of burning E85—fuel containing up to 85% ethanol. The second assumption is an increase in the ratio of ethanol to gasoline in the fuel these flex-fuel vehicles consume. The third assumption is an increase in the percentage of biodiesel as a share of total diesel for heavy duty vehicles. A fourth assumption, smaller in its impact, is that all gasoline will contain the statutory limit of 10% ethanol by volume. (The current actual share is around 9.5%, so this change is a minor adjustment.)

Table D-13. Summary of Vehicle Fleet Assumptions in 20% Biofuels by 2020 Scenario

Year Market Share of Flex-Fuel Cars (%)

Market Share of Flex-Fuel Light

Trucks (%)

E85 Share (by Volume) of Fuel in Flex-Fuel Vehicles (%)

Share of Biodiesel in Diesel (%)

Share of Biofuels in Fuel Mix (%)

2010 5.05% 15.23% 0.51% 2.24% 7.89%

2011 5.71% 22.41% 0.38% 1.79% 7.91%

2012 7.82% 27.39% 0.35% 4.35% 8.34%

2013 10.20% 31.68% 0.29% 10.04% 9.67%

2014 12.52% 38.18% 0.28% 13.26% 10.51%

2015 14.49% 40.43% 12.69% 18.83% 12.64%

2016 15.93% 40.83% 28.04% 18.52% 13.74%

2017 16.88% 41.58% 34.85% 20.54% 15.08%

2018 18.83% 41.19% 50.58% 19.73% 16.53%

2019 18.89% 36.37% 66.72% 19.38% 18.28%

2020 19.57% 37.09% 86.46% 16.73% 19.93%

The RFS analysis assumes a significant shift away from corn as the primary feedstock for ethanol production. In its place, cellulosic sources (primarily switchgrass but also corn stover) contribute a larger share. The extent to which ethanol is produced from one feedstock rather than another has a critical impact on ethanol’s capacity to reduce GHG emissions from the Transportation sector. Different fuel feedstocks result in fuels with very different carbon content.

A gallon of ethanol made from corn produces far more CO2 when burned than does a gallon of ethanol from switchgrass, and a gallon of ethanol made from corn stover produces less CO2 than either of the other two. Table D-14 shows the projected share of ethanol from each of three different sources: corn, corn stover and switchgrass. It also displays the carbon coefficients and emission reductions (compared to gasoline) of those sources. The row to the far right shows the change in overall carbon content of E85 as the feedstock shares change. That number is expressed in kilograms of CO2 per gasoline gallon equivalent.


Table D-14. Summary of Ethanol Feedstock Assumptions in 20% Biofuels by 2020 Scenario

YearProjected

Ethanol Share from Corn (%)

Projected Ethanol Share from Corn

Stover (%)

Projected Ethanol Share from

Switchgrass (%)

Projected E85 CO2 Coefficient

(kgCO2/gge)

Carbon Emissions Reduction

(vs. gasoline) (%)

2010 99.77% 0.05% 0.18% 10.10 18.0%

2011 99.30% 0.49% 0.21% 10.07 18.2%

2012 98.73% 0.95% 0.32% 10.04 18.5%

2013 97.29% 1.93% 0.78% 9.96 19.1%

2014 97.26% 2.05% 0.69% 9.95 19.2%

2015 96.94% 2.25% 0.81% 9.93 19.3%

2016 95.20% 2.99% 1.81% 9.84 20.1%

2017 92.31% 3.75% 3.94% 9.69 21.3%

2018 85.33% 4.97% 9.70% 9.33 24.2%

2019 75.13% 5.75% 19.12% 8.81 28.5%

2020 69.77% 9.07% 21.16% 8.51 30.9%

E85 CO2 coefficient (kgCO2/gge) 10.11 4.23 5.07

% Below Gasoline (12.32 gCO2/gge) 17.9% 65.6% 58.8%

E85 = ethanol 85; kgCO2/gge = kilograms carbon dioxide per gasoline gallon equivalent.

Truck Anti-Idling

To quantify the GHG emission reductions and cost-effectiveness of a transition to low-carbon methods of moving goods, two policies are analyzed related to reduced truck idling: (1) encouraging truck stop electrification and (2) promoting the use of plug-in trailer refrigeration units. The freight-related analyses were conducted using stand-alone spreadsheet modeling independent of the VISION analysis tool.

The anti-idling analysis scenario models the effects of two sets of energy-saving investments. The first set of investments is in truck stop electrification, which provides an electric alternative to trucks at rest stops that would otherwise idle their engines in order to provide heat or air conditioning and power to other electrical appliances in sleeper cabs overnight. The second set of investments provides for electricity at freight loading and unloading points. In particular, trucks carrying refrigerated cargo can plug into electricity in order to avoid engine idling to run refrigerated units. The anti-idling analysis does not assume any investment in upgrades to existing heavy duty vehicles, or any new technology in new heavy duty vehicles.

Effectiveness and Cost Analysis of Increased Truck Anti-Idling

As shown in Table D-15, the analysis of truck anti-idling finds that reducing idling by establishing electricity sources at truck stops and at loading and unloading points can reduce GHG emissions by over 20 MMtCO2e in 2020, and by over 70 MMtCO2e over the entire 2010–2020 period. In addition, the potential fuel savings reach over 1 billion gallons of fuel per year by the end of the decade, totaling nearly 6 billion gallons over the entire period.


Table D-15. Summary of Emissions Savings from Anti-Idling Scenario

Year

2010–2020 Results

Cost-Effectiveness ($/ton)

Change in Technology Cost

(mil 2007$)

Change in Fuel Cost

(mil 2007$)

Net Total Cost

(mil 2007$)

Gas and Diesel Savings

(bil gallons)

Emissions Reduction (MMtCO2e)

2010 $285 –$210 $74 0.13 1.44 $51.39

2011 $250 –$262 –$12 0.15 1.68 –$7.03

2012 $285 –$342 –$57 0.17 1.98 –$28.91

2013 $334 –$425 –$91 0.21 2.40 –$37.89

2014 $404 –$528 –$124 0.26 3.00 –$41.56

2015 $505 –$681 –$176 0.33 3.85 –$45.66

2016 $651 –$900 –$249 0.43 5.12 –$48.63

2017 $863 –$1,208 –$345 0.59 7.00 –$49.24

2018 $1,171 –$1,668 –$497 0.83 9.83 –$50.52

2019 $1,608 –$2,316 –$708 1.17 14.01 –$50.54

2020 $2,247 –$3,259 –$1,012 1.70 20.35 –$49.75

Cumulative $8,601 –$11,798 –$3,198 5.97 70.67 –$45.24


The truck anti-idling policy option is projected to result in increased net costs in the first year as investment outpaces fuel savings, but will return a net savings in the second year. The net savings results from the difference between the increased cost of equipment and infrastructure and the savings from reduced fuel use. Increasing fuel savings outpace the slower-growing costs of infrastructure. The net savings increase annually through 2020.

Truck to Rail Freight Mode Shift

To quantify the GHG emission reductions and cost-effectiveness of a transition to low-carbon methods of moving goods, one policy was analyzed to encouraging increased use of rail to move goods as an alternative to truck movements. The effects of encouraging increased use of freight rail diversion were estimated from a national-level estimate of the impacts of freight rail diversion.

Several recent reports assess the capacity of the nation’s freight transportation system, especially the freight-rail system, to keep pace with the expected growth of the economy over the next 20 years. The report finds that relatively small public investments in the nation’s freight railroads can produce significant economic benefits through shifting of goods movement from expect truck travel to rail movements.

Effectiveness and Cost Analysis of Truck to Rail Freight Mode Shift

As shown in Table D-16, the analysis finds that shifting freight from on-road transportation to rail carriers has the potential to reduce GHG emissions from cars and light trucks by over 39 MMtCO2e in 2020. Over the 11-year period from 2010 to 2020, the cumulative potential emissions reduction reaches over 320 MMtCO2e. Fuel savings are also significant—over 1 billion gallons in 2010, rising to 3 billion gallons by 2020.

$


Table D-16. Summary of Emission Reductions and Fuel Savings from Freight-to-Rail Mode Shift

Year

2010–2020 Results

Cost-Effectiveness ($/ton)

Change in Infrastructure Cost

(mil 2007$)

Change in Fuel Cost (mil

2007$)

Net Total Cost (mil 2007$)

Gas and Diesel Savings (bil

gallons)

Emissions Reduction (MMtCO2e)

2010 $2,900 $2,901 –$1 1.50 19.55 –$0.01

2011 $2,900 $3,737 –$837 1.65 21.51 –$38.9

2012 $2,900 $4,509 –$1,609 1.80 23.46 –$68.6

2013 $2,900 $5,432 –$2,532 1.95 25.42 –$99.6

2014 $2,900 $6,229 –$3,329 2.10 27.37 –$121.6

2015 $2,900 $6,967 –$4,067 2.25 29.33 –$138.7

2016 $2,900 $7,696 –$4,796 2.40 31.28 –$153.3

2017 $2,900 $8,483 –$5,583 2.56 33.24 –$168.0

2018 $2,900 $9,226 –$6,326 2.71 35.19 –$179.8

2019 $2,900 $9,910 –$7,010 2.86 37.15 –$188.7

2020 $2,900 $10,552 –$7,652 3.01 39.10 –$195.7

Cumulative $31,900 $7,679 –$43,741 24.80 322.59 –$135.6

Negative numbers indicate cost savings; MMtCO2e = million metric tons of carbon dioxide equivalent.

The truck to rail freight mode shift analysis assumes a constant rate of investment in infrastructure improvements (in 2007 dollars), and projects an increasing fuel savings as more and more rail capacity comes on line. As a consequence, savings grow while costs do not, and emission reductions grow commensurate with fuel savings.

Transit

Improvements to existing transit service and expansion of transit routes can shift passenger transportation from single-occupant vehicles to public transit, thereby reducing emissions. This mitigation policy involves a number of actions to be undertaken by state and local governments and transit agencies. Improvements and expansion of existing transit service and implementation of innovative transit services can shift more passenger transportation to public transit, thereby reducing VMT. Public transportation improvements are critical to support smart growth initiatives and are essential to an ongoing effort to reduce VMT.

In recent years, several U.S. states have established an official policy goal of doubling transit ridership. This goal has been included in numerous official state climate and energy action plans, including those for Florida,4 Iowa,5 Alaska,6 and New Jersey.7 The policy goal of doubling transit ridership seems to have a resonance and usefulness for consideration by more U.S. cities, urban regions, and states. The goal is flexible in that it takes into account the “starting point” of transit ridership for a given city or urban region, and attempts to build upon this starting point. In addition, it implicitly recognizes the need for additional expansion of transit service, since it is not possible to double ridership at the already existing supply level of transit capacity and service.

The estimates of potential VMT reduced shown in Table D-17 were considered relative to a baseline forecast of VMT estimated for the state in the absence of the application of new technologies and best practices. The estimates of potential for GHG emission reductions from the TLU sector included estimates of GHG reduction potential related to reduce travel activity off of a baseline forecast. The most commonly used measure of reduced travel activity is reduction in VMT, and VMT will be the main focus of analysis for these strategies and best practices.

4. See http://www.flclimatechange.us/documents.cfm.5. See http://www.iaclimatechange.us/capag.cfm. 6. See http://www.akclimatechange.us/Mitigation.cfm. 7. See http://www.state.nj.us/globalwarming/home/documents/pdf/final_report20081215.pdf.

http://www.flclimatechange.us/documents.cfm

http://www.iaclimatechange.us/capag.cfm

http://www.akclimatechange.us/Mitigation.cfm

http://www.state.nj.us/globalwarming/home/documents/pdf/final_report20081215.pdf


Since the amount and types of travel activity are important factors in the determination of fuel use, the VISION model was used in combination with option-specific spreadsheet analyses for Smart Growth/ Land Use and Transit. The option-specific spreadsheet analyses and parameters for analyses of smart growth and transit were taken from the spreadsheets and policy designs developed for the states with completed state plans.

Table D-17. Summary of Emission Reductions and Fuel Savings from Transit

2020 EstimatesDirect VMT Reduced from

Doubling Transit Passenger Miles

Fuel Savings (gallons)

GHG Reduction (tCO2e)

Dollar Savings from Gallons Fuel Saved

United States (50 States and DC) 57,143,115,409 2,451,442,102 28,571,558 $6,128,605,256

Fuel Price Assumption $2.50 per gallon

tCO2e = metric tons of carbon dioxide equivalent; DC = District of Columbia.

Smart Growth

Supporting state, regional, and municipal land-use planning and development practices aimed at reducing the number and length of VMT and expanding travel mode opportunities is a multifaceted undertaking. There is no single program or policy mechanism that reaches the goal. Instead, several taken together over the long term have the potential to make a significant difference. This suite of policies can reduce the state’s GHG emissions by reducing the growth in VMT.

The estimates of potential VMT reduced shown in Table D-18 were considered relative to a baseline forecast of VMT estimated for the state in the absence of the application of new technologies and best practices. The estimates of potential for GHG emission reductions from the TLU sector included estimates of GHG reduction potential related to reduce travel activity off of a baseline forecast. The most commonly used measure of reduced travel activity is reduction in VMT, and VMT will be the main focus of analysis for these strategies and best practices.

Analyses of the potential impacts of smart growth strategies on the Transportation sector took into account key factors and state (and territorial) characteristics that included population growth and density, energy consumption by the Transportation sector, shares of urban, suburban, and rural population, forecasted VMT levels and growth, shares of use of public transportation, and current and projected fleet mixes for passenger, heavy duty, and freight vehicles.

Since the amount and types of travel activity are important factors in the determination of fuel use, the VISION model was used as a complement to option-specific spreadsheet analyses for Smart Growth/Land Use and Transit. The option-specific spreadsheet analyses and parameters for analyses of smart growth and transit were taken from the spreadsheets and policy designs developed for the states with completed climate action plans. The option-specific spreadsheets and parameters for smart growth incorporate the state-specific factors in terms of the baseline conditions for auto use and public transportation use, and in terms of their baseline VMT that may be associated with land development patterns.

Table D-18. Summary of Emission Reductions and Fuel Savings from Smart Growth

2020 Estimates Direct VMT Reduced from Smart Growth

Fuel Savings (gallons)

GHG Reduction

(tCO2e)

Dollar Savings from Gallons of Fuel Saved

United States (50 States and DC) 162,438,178,023 6,968,604,806 81,219,089 $17,421,512,014

Fuel Price Assumption $2.50 per gallon

VMT = vehicle miles traveled; tCO2e = metric tons of carbon dioxide equivalent; DC = District of Columbia.


Table E-1 shows the detailed simulation results of the simultaneous simulation for the 23 super policy options, for each year from 2010 through 2020.

Table E-1. Detailed Results of the Simultaneous Simulation

Year

Total Employment (thousands

of jobs)

Gross Domestic Product (billions

of fixed 2007 dollars)

Output (billions of fixed 2007

dollars)

Population (thousands)

Real Disposable Personal Income (billions of fixed

2007 dollars)

PCE-Price Index (2000=100 [nation])

Differences from Baseline Levels with Simultaneous Implementation of the 23 Super Options

2010 147.80 –$0.35 –$0.23 0.00 $0.60 –0.10

2011 399.67 $5.32 $13.29 0.00 $10.09 –0.16

2012 572.83 $12.68 $26.63 0.00 $20.97 –0.24

2013 716.91 $18.83 $38.01 0.00 $25.41 –0.23

2014 914.50 $29.48 $56.56 0.00 $39.02 –0.32

2015 1,117.97 $41.34 $77.24 0.00 $52.79 –0.41

2016 1,334.19 $55.69 $101.99 0.00 $69.03 –0.54

2017 1,573.56 $73.72 $133.06 0.00 $88.56 –0.69

2018 1,842.19 $96.28 $171.47 0.03 $110.37 –0.87

2019 2,167.16 $125.43 $220.07 0.00 $136.87 –1.07

2020 2,523.95 $159.60 $276.65 0.03 $164.92 –1.29

Net Present Value n.a $406.74 $737.98 n.a $479.31 n.a.

Levels After Implementation of the 23 Super Options

2010 172,909.2 $13,747.9 $21,148.8 311,755.9 $10,610.0 123.9

2011 173,821.3 $14,080.2 $21,697.7 315,180.6 $10,776.2 127.1

2012 175,536.6 $14,408.9 $22,235.4 318,619.5 $10,979.8 130.5

2013 177,320.7 $14,752.9 $22,797.8 322,074.9 $11,179.2 134.4

2014 179,231.9 $15,118.0 $23,394.8 325,544.5 $11,410.9 138.2

2015 180,761.3 $15,490.5 $24,005.2 329,026.0 $11,635.6 142.2

2016 182,211.3 $15,885.2 $24,653.9 332,511.8 $11,869.2 146.5

2017 183,493.5 $16,245.0 $25,288.7 336,004.1 $12,075.6 150.9

2018 184,868.4 $16,608.5 $25,919.8 339,505.1 $12,281.3 155.4

2019 186,328.8 $16,981.0 $26,559.1 343,010.4 $12,487.7 160.1

2020 187,380.0 $17,332.8 $27,160.9 346,511.1 $12,669.5 165.0

Percentage Changes from Baseline Levels

2010 0.086% –0.003% –0.001% 0.000% 0.006% –0.077%

2011 0.231% 0.038% 0.061% 0.000% 0.094% –0.127%

2012 0.327% 0.088% 0.120% 0.000% 0.191% –0.181%

2013 0.406% 0.128% 0.167% 0.000% 0.228% –0.170%

2014 0.513% 0.195% 0.242% 0.000% 0.343% –0.233%

2015 0.622% 0.268% 0.323% 0.000% 0.456% –0.286%

2016 0.738% 0.352% 0.415% 0.000% 0.585% –0.365%

2017 0.865% 0.456% 0.529% 0.000% 0.739% –0.453%

2018 1.007% 0.583% 0.666% 0.000% 0.907% –0.554%

2019 1.177% 0.744% 0.836% 0.000% 1.108% –0.661%

2020 1.365% 0.929% 1.029% 0.000% 1.319% –0.774%

annex e

» Detailed REMI Model Simulation Results


Table E-2. Sectoral GDP Impacts of the 23 GHG Mitigation Policy Options—Simultaneous Simulation (billions of fixed 2007$)

Sector NAICS Code 2010 2012 2015 2020 NPV

Forestry; Fishing, hunting, trapping 1131, 1132, 114 $3.222 $3.398 $2.658 $0.752 $20.415

Logging 1133 $0.004 $0.011 $0.029 $0.106 $0.282

Support activities for agriculture and forestry 115 $1.034 $3.233 $4.570 $6.725 $34.701

Oil and gas extraction 211 –$1.193 –$2.545 –$4.598 –$7.236 –$34.356

Coal mining 2121 –$0.630 –$1.511 –$3.013 –$7.053 –$24.884

Metal ore mining 2122 $0.002 $0.008 $0.019 $0.067 $0.184

Nonmetallic mineral mining and quarrying 2123 –$0.048 –$0.122 –$0.238 –$0.463 –$1.841

Support activities for mining 213 –$0.287 –$0.769 –$1.420 –$2.229 –$10.391

Electric power generation, transmission, and distribution 2211 –$5.060 –$14.578 –$30.440 –$59.686 –$237.877

Natural gas distribution 2212 –$0.266 –$1.038 –$2.254 –$4.024 –$16.632

Water, sewage, and other systems 2213 –$0.002 –$0.005 $0.000 $0.108 $0.121

Construction 23 –$0.613 –$2.197 –$3.159 $0.615 –$16.296

Sawmills and wood preservation 3211 $0.005 $0.008 $0.020 $0.077 $0.207

Veneer, plywood, and engineered wood product manufacturing 3212 –$0.002 –$0.002 $0.011 $0.095 $0.172

Other wood product manufacturing 3219 –$0.002 $0.014 $0.063 $0.280 $0.668

Clay product and refractory manufacturing 3271 $0.002 $0.019 $0.042 $0.107 $0.350

Glass and glass product manufacturing 3272 $0.007 $0.044 $0.102 $0.282 $0.884

Cement and concrete product manufacturing 3273 –$0.030 –$0.087 –$0.129 –$0.011 –$0.719

Lime, gypsum product manufacturing; Other nonmetallic mineral product manufacturing 3274, 3279 –$0.005 –$0.006 $0.006 $0.115 $0.170

Iron and steel mills and ferroalloy manufacturing 3311 $0.011 $0.036 $0.070 $0.207 $0.640

Steel product manufacturing from purchased steel 3312 $0.001 $0.007 $0.017 $0.058 $0.163

Alumina and aluminum production and processing 3313 $0.007 $0.024 $0.043 $0.091 $0.350

Nonferrous metal (except aluminum) production and processing 3314 $0.006 $0.021 $0.044 $0.103 $0.365

Foundries 3315 $0.051 $0.103 $0.168 $0.293 $1.308

Forging and stamping 3321 $0.020 $0.046 $0.071 $0.125 $0.555

Cutlery and handtool manufacturing 3322 $0.004 $0.023 $0.044 $0.091 $0.348

Architectural and structural metals manufacturing 3323 –$0.027 –$0.069 –$0.087 $0.037 –$0.443

Boiler, tank, and shipping container manufacturing 3324 $0.001 $0.005 $0.012 $0.045 $0.116

Hardware manufacturing 3325 $0.001 $0.008 $0.019 $0.045 $0.154

Spring and wire product manufacturing 3326 $0.000 $0.005 $0.013 $0.044 $0.124

Machine shops; turned product; and screw, nut, and bolt manufacturing 3327 $0.004 $0.037 $0.086 $0.252 $0.759

Coating, engraving, heat treating, and allied activities 3328 –$0.010 –$0.005 $0.004 $0.065 $0.086

Other fabricated metal product mfg. 3329 $0.011 $0.048 $0.103 $0.287 $0.909

Agriculture, construction, and mining machinery manufacturing 3331 –$0.030 –$0.080 –$0.169 –$0.333 –$1.307

Industrial machinery manufacturing 3332 $0.138 $0.302 $0.457 $0.581 $3.300

Commercial and service industry machinery manufacturing 3333 –$0.002 $0.001 $0.013 $0.067 $0.145

Ventilation, heating, air–conditioning, and commercial refrigeration equipment manufacturing 3334 $0.101 $0.334 $0.719 $1.407 $5.509

Metalworking machinery manufacturing 3335 –$0.007 –$0.024 –$0.052 –$0.101 –$0.400

Engine, turbine, power transmission equipment manufacturing 3336 $0.384 $0.775 $1.361 $2.595 $10.750

Other general purpose machinery manufacturing 3339 –$0.005 –$0.019 –$0.036 $0.007 –$0.171



Computer and peripheral equipment manufacturing 9341 –$0.019 –$0.044 –$0.149 $0.115 –$0.632

Communications equipment manufacturing 3342 –$0.005 –$0.001 $0.031 $0.312 $0.548

Audio and video equipment manufacturing 3343 $0.005 $0.018 $0.040 $0.132 $0.383

Semiconductor and other electronic component manufacturing 3344 $0.033 $0.196 $0.416 $1.295 $3.818

Navigational, measuring, electromedical, and control instruments manufacturing 3345 –$0.018 –$0.034 –$0.078 –$0.043 –$0.473

Manufacturing and reproducing magnetic and optical media 3346 $0.002 $0.014 $0.031 $0.086 $0.267

Electric lighting equipment manufacturing 3351 $0.034 $0.118 $0.232 $0.407 $1.712

Household appliance manufacturing 3352 $0.068 $0.220 $0.422 $0.767 $3.165

Electrical equipment manufacturing 3353 $0.040 $0.125 $0.221 $0.342 $1.588

Other electrical equipment and component manufacturing 3359 $0.037 $0.129 $0.271 $0.562 $2.105

Motor vehicle manufacturing 3361 $0.068 $0.409 $1.078 $3.149 $9.552

Motor vehicle body and trailer manufacturing 3362 $0.001 $0.018 $0.048 $0.157 $0.442

Motor vehicle parts manufacturing 3363 $0.050 $0.236 $0.469 $0.930 $3.638

Aerospace product and parts manufacturing 3364 $0.006 $0.031 $0.068 $0.293 $0.741

Railroad rolling stock manufacturing 3365 $0.023 $0.019 $0.013 $0.008 $0.120

Ship and boat building 3366 $0.000 $0.008 $0.024 $0.090 $0.236

Other transportation equipment manufacturing 3369 $0.001 $0.012 $0.027 $0.081 $0.245

Household and institutional furniture and kitchen cabinet manufacturing 3371 $0.013 $0.089 $0.200 $0.491 $1.657

Office furniture (including fixtures) manufacturing 3372 –$0.014 –$0.044 –$0.086 –$0.149 –$0.647

Other furniture–related product manufacturing 3379 $0.004 $0.023 $0.055 $0.144 $0.463

Medical equipment and supplies manufacturing 3391 $0.012 $0.214 $0.668 $2.347 $6.362

Other miscellaneous manufacturing 3399 $0.029 $0.125 $0.254 $0.759 $2.334

Animal food manufacturing 3111 $0.002 $0.012 $0.026 $0.061 $0.210

Grain and oilseed milling 3112 $0.001 $0.011 $0.026 $0.088 $0.244

Sugar and confectionery product mfg. 3113 $0.002 $0.017 $0.042 $0.134 $0.385

Fruit and vegetable preserving and specialty food manufacturing 3114 $0.005 $0.033 $0.087 $0.277 $0.800

Dairy product manufacturing 3115 $0.002 $0.020 $0.056 $0.172 $0.500

Animal slaughtering and processing 3116 $0.007 $0.042 $0.105 $0.315 $0.939

Seafood product preparation and packaging 3117 $0.000 $0.004 $0.011 $0.036 $0.102

Bakeries and tortilla manufacturing 3118 $0.006 $0.042 $0.106 $0.309 $0.933

Other food manufacturing 3119 $0.006 $0.043 $0.108 $0.331 $0.980

Beverage manufacturing 3121 $0.011 $0.065 $0.165 $0.516 $1.512

Tobacco manufacturing 3122 $0.002 $0.018 $0.037 $0.075 $0.285

Fiber, yarn, and thread mills 3131 $0.001 $0.008 $0.017 $0.036 $0.133

Fabric mills 3132 $0.005 $0.023 $0.040 $0.074 $0.309

Textile and fabric finishing and fabric coating mills 3133 $0.002 $0.011 $0.020 $0.036 $0.152

Textile furnishings mills 3141 $0.006 $0.036 $0.086 $0.251 $0.764

Other textile product mills 3149 $0.006 $0.021 $0.036 $0.068 $0.284

Apparel knitting mills 3151 $0.002 $0.010 $0.020 $0.049 $0.170

Cut and sew apparel manufacturing 3152 $0.017 $0.082 $0.182 $0.418 $1.490

Apparel accessories and other apparel manufacturing 3159 $0.001 $0.005 $0.010 $0.023 $0.080

Leather, hide tanning, finishing; Other leather, allied product manufacturing 3161, 3169 $0.001 $0.007 $0.011 $0.027 $0.095

Footwear manufacturing 3162 $0.002 $0.013 $0.031 $0.089 $0.276



Pulp, paper, and paperboard mills 3221 $0.012 $0.081 $0.198 $0.607 $1.790

Converted paper product manufacturing 3222 $0.007 $0.061 $0.146 $0.413 $1.270

Printing and related support activities 323 $0.007 $0.074 $0.178 $0.448 $1.467

Petroleum and coal products manufacturing 324 –$0.397 –$0.824 –$1.650 –$3.632 –$13.639

Basic chemical manufacturing 3251 $0.011 $0.139 $0.385 $2.299 $4.499

Resin, synthetic rubber, and artificial synthetic fibers and filaments manufacturing 3252 $0.010 $0.065 $0.163 $0.537 $1.522

Pesticide, fertilizer, and other agricultural chemical manufacturing 3253 $0.042 $0.083 –$0.008 –$0.159 –$0.070

Pharmaceutical and medicine manufacturing 3254 $0.065 $0.430 $1.009 $2.403 $8.257

Paint, coating, and adhesive manufacturing 3255 $0.000 $0.010 $0.033 $0.127 $0.327

Soap, cleaning compound, and toilet preparation manufacturing 3256 $0.015 $0.096 $0.244 $0.767 $2.242

Other chemical product and preparation manufacturing 3259 –$0.007 $0.010 $0.052 $0.229 $0.538

Plastics product manufacturing 3261 $0.010 $0.144 $0.396 $1.335 $3.698

Rubber product manufacturing 3262 $0.004 $0.039 $0.090 $0.233 $0.762

Wholesale trade 42 $0.203 $1.649 $3.732 $10.725 $32.715

Retail trade 44–45 $0.096 $2.093 $5.403 $17.546 $49.675

Air transportation 481 $0.031 $0.244 $0.623 $1.669 $5.275

Rail transportation 482 $1.461 $1.303 $1.087 $0.765 $9.411

Water transportation 483 –$0.001 $0.001 $0.005 $0.024 $0.055

Truck transportation 484 –$1.799 –$1.578 –$1.240 $0.159 –$9.603

Couriers and messengers 492 $0.011 $0.112 $0.301 $0.959 $2.745

Transit and ground passenger transportation 485 $1.367 $4.126 $8.295 $15.304 $63.393

Pipeline transportation 486 –$0.419 –$0.890 –$1.591 –$4.021 –$13.857

Scenic and sightseeing transportation and support activities for transportation 487, 488 $0.018 $0.052 $0.107 $0.291 $0.942

Warehousing and storage 493 –$0.039 $0.030 $0.145 $0.532 $1.323

Newspaper, periodical, book, and directory publishers 5111 $0.031 $0.200 $0.399 $0.757 $3.008

Software publishers 5112 –$0.082 –$0.271 –$0.706 –$1.408 –$5.483

Motion picture and sound recording industries 512 $0.023 $0.169 $0.449 $1.347 $4.007

Internet and other information services 516, 518, 519 $0.029 $0.309 $1.010 $3.410 $9.414

Broadcasting (except internet) 515 $0.011 $0.107 $0.293 $0.878 $2.597

Telecommunications 517 $0.029 $0.709 $2.186 $7.077 $19.886

Monetary authorities, credit intermediation 521, 522 $1.446 $4.689 $10.693 $24.969 $87.938

Funds, trusts, and other financial vehicles 525 $0.011 $0.087 $0.232 $0.658 $2.007

Securities, commodity contracts, and other financial investments and related activities 523 $0.252 $1.875 $5.653 $17.502 $51.110

Insurance carriers 5241 $0.020 $0.334 $0.845 $2.087 $6.880

Agencies, brokerages, and other insurance related activities 5242 $0.014 $0.242 $0.677 $1.944 $5.862

Real estate 531 $0.466 $3.562 $9.622 $27.742 $84.112

Automotive equipment rental and leasing 5321 $0.002 $0.033 $0.091 $0.252 $0.777

Consumer goods rental and general rental centers 5322, 5323 $0.010 $0.053 $0.137 $0.410 $1.225

Commercial and industrial machinery and equipment rental and leasing 5324 $0.032 $0.033 $0.050 $0.179 $0.549

Lessors of nonfinancial intangible assets 533 –$0.405 –$0.696 –$1.053 –$0.625 –$6.779

Legal services 5411 –$0.007 $0.227 $0.671 $1.954 $5.799



Accounting, tax preparation, bookkeeping, and payroll services 5412 $0.056 $0.248 $0.576 $1.433 $4.794

Architectural, engineering, and related services 5413 –$0.049 –$0.081 –$0.157 $0.417 –$0.254

Specialized design services 5414 $0.013 $0.063 $0.152 $0.440 $1.353

Computer systems design and related services 5415 –$0.097 –$0.286 –$0.604 –$1.225 –4.762

Management, scientific, and technical consulting services 5416 $0.274 $0.665 $1.380 $3.098 $11.312

Scientific research and development services; Other professional, scientific, and technical services 5417, 5419 –$0.004 $0.284 $0.852 $2.933 $7.944

Advertising and related services 5418 $0.007 $0.100 $0.255 $0.708 $2.185

Management of companies and enterprises 55 –$0.039 $0.446 $1.314 4.411 $12.120

Office administrative services; Facilities support services 5611, 5612 –$0.011 $0.046 $0.220 $0.929 $2.241

Employment services 5613 –$0.015 $0.103 $0.378 $1.334 $3.527

Business support services; Investigation and security services; Other support services 5614, 5616, 5619 $0.027 $0.230 $0.636 $1.961 $5.719

Travel arrangement & reservation services 5615 $0.024 $0.074 $0.149 $0.321 $1.193

Services to buildings and dwellings 5617 $0.004 $0.107 $0.337 $1.152 $3.158

Waste collection; Waste treatment and disposal and waste management services 562 $0.701 $2.170 $5.074 $9.188 $37.396

Elementary and secondary schools; Junior colleges, colleges, universities, and professional schools; Other educational services

61 $0.008 $0.158 $0.486 $1.414 $4.197

Offices of health practitioners 6211–6213 $0.284 $2.517 $6.526 $17.506 $55.221

Outpatient, laboratory, and other ambulatory care services 6214–6216 $0.024 $0.232 $0.637 $1.778 $5.456

Home health care services 6219 $0.008 $0.088 $0.266 $0.766 $2.298

Hospitals 622 $0.038 $0.447 $1.301 $3.512 $10.896

Nursing care facilities 6231 $0.020 $0.118 $0.307 $0.809 $2.579

Residential care facilities 6232, 6233, 6239 $0.015 $0.107 $0.301 $0.848 $2.589

Individual, family, community, and vocational rehabilitation services 6241–6243 $0.015 $0.125 $0.375 $1.073 $3.242

Child day care services 6244 $0.007 $0.063 $0.188 $0.542 $1.626

Performing arts companies; Promoters of events, and agents and managers 7111, 7113, 7114 $0.006 $0.042 $0.110 $0.297 $0.935

Spectator sports 7112 $0.006 $0.059 $0.159 $0.451 $1.379

Independent artists, writers, and performers 7115 $0.011 $0.040 $0.096 $0.247 $0.810

Museums, historical sites, and similar institutions 712 $0.001 $0.012 $0.036 $0.107 $0.316

Amusement, gambling, and recreation industries 713 $0.095 $0.413 $1.042 $3.046 $9.257

Accommodation 721 $0.061 $0.321 $0.816 $2.197 $6.958

Food services and drinking places 722 $0.114 $0.584 $1.460 $3.760 $12.248

Automotive repair and maintenance 8111 $0.018 $0.168 $0.453 $1.194 $3.787

Electronic and precision equipment repair and maintenance 8112 $0.007 $0.036 $0.094 $0.244 $0.781

Commercial and industrial equipment (except automotive and electronic) repair and maintenance 8113 $0.032 $0.120 $0.315 $0.791 $2.593

Personal and household goods repair and maintenance 8114 $0.014 $0.070 $0.171 $0.486 $1.508

Personal care services 8121 $0.057 $0.321 $0.816 $2.229 $7.013

Death care services 8122 $0.001 $0.012 $0.033 $0.091 $0.284

Drycleaning and laundry services 8123 $0.019 $0.097 $0.228 $0.620 $1.964

Other personal services 8129 $0.050 $0.292 $0.778 $2.258 $6.844



Religious organizations; Grantmaking and giving services, and social advocacy organizations 8131–8133 $0.017 $0.120 $0.336 $0.961 $2.909

Civic, social, professional, and similar organizations 8134, 8139 $0.008 $0.069 $0.191 $0.548 $1.655

Private households 814 $0.004 $0.078 $0.201 $0.492 $1.628

Total* $1.673 $17.941 $48.274 $164.317 $453.227

* The total represents the sum of all the sectoral effects. The totals shown in this table differ from the simultaneous solutions shown in the last row of Table 3-5. The gap between the two is farm value added and government compensation, as well as rounding error. NAICS = North American Industry Classification System.

Table E-3. Sectoral Employment Impacts of the 23 GHG Mitigation Policy Options—Simultaneous Simulation (in thousands)

Sector NAICS Code 2010 2012 2015 2020

Forestry; Fishing, hunting, trapping 1131, 1132, 114 48.844 51.537 40.358 10.892

Logging 1133 0.032 0.106 0.261 0.867

Support activities for agriculture and forestry 115 103.533 316.416 435.074 601.263

Oil and gas extraction 211 –7.038 –14.791 –26.331 –40.016

Coal mining 2121 –3.630 –8.594 –16.946 –38.598

Metal ore mining 2122 0.009 0.037 0.061 0.175

Nonmetallic mineral mining and quarrying 2123 –0.562 –1.447 –2.835 –5.468

Support activities for mining 213 –3.223 –8.296 –14.620 –21.144

Electric power generation, transmission, and distribution 2211 –9.707 –26.161 –49.773 –84.145

Natural gas distribution 2212 –1.195 –4.628 –10.006 –17.545

Water, sewage, and other systems 2213 –0.026 –0.057 –0.050 0.679

Construction 23 –12.612 –45.175 –67.702 –8.885

Sawmills and wood preservation 3211 0.062 0.089 0.209 0.779

Veneer, plywood, and engineered wood product manufacturing 3212 –0.037 –0.051 0.064 0.969

Other wood product manufacturing 3219 –0.033 0.175 0.753 2.963

Clay product and refractory manufacturing 3271 0.026 0.176 0.355 0.801

Glass and glass product manufacturing 3272 0.040 0.232 0.474 1.070

Cement and concrete product manufacturing 3273 –0.367 –1.045 –1.563 –0.567

Lime, gypsum product manufacturing; Other nonmetallic mineral product manufacturing 3274, 3279 –0.042 –0.065 –0.014 0.554

Iron and steel mills and ferroalloy manufacturing 3311 0.042 0.116 0.171 0.373

Steel product manufacturing from purchased steel 3312 0.008 0.051 0.113 0.365

Alumina and aluminum production and processing 3313 0.052 0.162 0.266 0.504

Nonferrous metal (except aluminum) production and processing 3314 0.055 0.176 0.281 0.475

Foundries 3315 0.453 0.832 1.189 1.678

Forging and stamping 3321 0.172 0.366 0.535 0.821

Cutlery and handtool manufacturing 3322 0.033 0.184 0.332 0.591

Architectural and structural metals manufacturing 3323 –0.345 –0.896 –1.189 0.115

Boiler, tank, and shipping container manufacturing 3324 0.007 0.042 0.082 0.325

Hardware manufacturing 3325 0.004 0.040 0.088 0.197

Spring and wire product manufacturing 3326 –0.006 0.044 0.114 0.319

Machine shops; turned product; and screw, nut, and bolt manufacturing 3327 0.036 0.355 0.706 1.623

Coating, engraving, heat treating, & allied activities 3328 –0.107 –0.078 –0.031 0.280

Other fabricated metal product manufacturing 3329 0.090 0.404 0.846 2.204

Agriculture, construction, & mining machinery mfg. 3331 –0.229 –0.552 –1.024 –1.625

Industrial machinery manufacturing 3332 0.961 2.014 2.871 3.306



Commercial and service industry machinery manufacturing 3333 –0.023 –0.001 0.095 0.474

Ventilation, heating, air–conditioning, and commercial refrigeration equipment manufacturing

3334 0.836 2.546 4.854 7.690

Metalworking machinery manufacturing 3335 –0.086 –0.274 –0.560 –0.986

Engine, turbine, power transmission equipment manufacturing 3336 1.975 3.520 5.199 7.353

Other general purpose machinery manufacturing 3339 –0.040 –0.150 –0.268 –0.095

Computer and peripheral equipment manufacturing 3341 –0.022 –0.037 –0.091 0.003

Communications equipment manufacturing 3342 –0.019 –0.013 0.070 0.601

Audio and video equipment manufacturing 3343 0.049 0.152 0.275 0.601

Semiconductor and other electronic component manufacturing 3344 0.064 0.317 0.497 1.111

Navigational, measuring, electromedical, and control instruments manufacturing 3345 –0.109 –0.215 –0.463 –0.387

Manufacturing and reproducing magnetic and optical media 3346 0.011 0.079 0.160 0.391

Electric lighting equipment manufacturing 3351 0.325 0.989 1.695 2.320

Household appliance manufacturing 3352 0.296 0.849 1.354 1.802

Electrical equipment manufacturing 3353 0.361 1.035 1.669 2.177

Other electrical equipment and component manufacturing 3359 0.244 0.810 1.540 2.714

Motor vehicle manufacturing 3361 0.124 0.637 1.343 2.918

Motor vehicle body and trailer manufacturing 3362 0.007 0.248 0.608 1.651

Motor vehicle parts manufacturing 3363 0.376 1.712 3.209 5.713

Aerospace product and parts manufacturing 3364 0.030 0.166 0.292 1.182

Railroad rolling stock manufacturing 3365 0.315 0.214 0.103 0.006

Ship and boat building 3366 0.001 0.089 0.211 0.598

Other transportation equipment manufacturing 3369 0.015 0.095 0.187 0.405

Household and institutional furniture and kitchen cabinet manufacturing 3371 0.181 1.248 2.589 5.619

Office furniture (including fixtures) manufacturing 3372 –0.126 –0.367 –0.676 –1.074

Other furniture related product manufacturing 3379 0.039 0.254 0.535 1.169

Medical equipment and supplies manufacturing 3391 0.058 1.091 3.011 8.623

Other miscellaneous manufacturing 3399 0.232 0.855 1.405 2.984

Animal food manufacturing 3111 0.042 0.142 0.233 0.364

Grain and oilseed milling 3112 0.016 0.096 0.188 0.434

Sugar and confectionery product manufacturing 3113 0.015 0.098 0.200 0.453

Fruit and vegetable preserving and specialty food manufacturing 3114 0.030 0.204 0.429 0.998

Dairy product manufacturing 3115 0.021 0.170 0.407 1.050

Animal slaughtering and processing 3116 0.122 0.695 1.627 4.336

Seafood product preparation and packaging 3117 0.008 0.059 0.140 0.347

Bakeries and tortilla manufacturing 3118 0.048 0.394 0.926 2.423

Other food manufacturing 3119 0.036 0.238 0.537 1.392

Beverage manufacturing 3121 0.046 0.257 0.545 1.325

Tobacco manufacturing 3122 0.004 0.028 0.056 0.110

Fiber, yarn, and thread mills 3131 0.025 0.121 0.230 0.481

Fabric mills 3132 0.053 0.269 0.502 0.938

Textile and fabric finishing and fabric coating mills 3133 0.031 0.152 0.250 0.344

Textile furnishings mills 3141 0.040 0.248 0.531 1.268



Other textile product mills 3149 0.128 0.413 0.645 1.107

Apparel knitting mills 3151 0.024 0.111 0.201 0.411

Cut and sew apparel manufacturing 3152 0.220 1.004 1.947 3.618

Apparel accessories and other apparel manufacturing 3159 0.020 0.091 0.173 0.357

Leather, hide tanning, finishing; Other leather, allied product manufacturing 3161, 3169 0.033 0.132 0.184 0.388

Footwear manufacturing 3162 0.065 0.311 0.662 1.683

Pulp, paper, and paperboard mills 3221 0.026 0.183 0.375 0.899

Converted paper product manufacturing 3222 0.065 0.533 1.169 2.777

Printing and related support activities 323 0.098 1.005 2.339 5.432

Petroleum and coal products manufacturing 324 –0.864 –1.570 –2.612 –4.164

Basic chemical manufacturing 3251 0.008 0.282 0.669 3.778

Resin, synthetic rubber, and artificial synthetic fibers and filaments manufacturing 3252 0.032 0.200 0.421 1.102

Pesticide, fertilizer, and other agricultural chemical manufacturing 3253 0.189 0.349 –0.054 –0.632

Pharmaceutical and medicine manufacturing 3254 0.268 1.706 3.787 8.202

Paint, coating, and adhesive manufacturing 3255 –0.007 0.036 0.126 0.470

Soap, cleaning compound, and toilet preparation manufacturing 3256 0.036 0.221 0.477 1.131

Other chemical product and preparation manufacturing 3259 –0.042 0.020 0.146 0.560

Plastics product manufacturing 3261 0.050 0.909 2.214 6.172

Rubber product manufacturing 3262 0.032 0.327 0.659 1.358

Wholesale trade 42 1.267 9.915 19.832 46.729

Retail trade 44–45 1.107 30.990 70.260 185.051

Air transportation 481 0.144 1.047 2.344 5.027

Rail transportation 482 10.444 9.279 7.877 5.958

Water transportation 483 –0.010 0.003 0.016 0.086

Truck transportation 484 –28.009 –25.778 –19.990 1.055

Couriers and messengers 492 0.116 1.149 2.798 7.857

Transit and ground passenger transportation 485 39.012 115.254 226.145 395.979

Pipeline transportation 486 –1.575 –3.198 –5.402 –12.283

Scenic and sightseeing transportation and support activities for transportation 487, 488 0.383 1.093 2.288 6.170

Warehousing and storage 493 –0.857 0.169 1.315 4.321

Newspaper, periodical, book, and directory publishers 5111 0.336 2.479 5.702 12.412

Software publishers 5112 –0.178 –0.517 –1.136 –1.822

Motion picture and sound recording industries 512 0.200 1.412 3.319 8.034

Internet and other information services 516, 518, 519 0.117 1.157 3.109 7.649

Broadcasting (except internet) 515 0.111 1.054 2.639 6.804

Telecommunications 517 0.053 1.801 4.810 12.107

Monetary authorities, credit intermediation 521, 522 9.017 27.975 60.321 126.874

Funds, trusts, and other financial vehicles 525 0.064 0.536 1.432 3.954

Securities, commodity contracts, and other financial investments and related activities 523 1.622 10.920 28.397 69.044

Insurance carriers 5241 0.165 2.880 7.338 18.045

Agencies, brokerages, and other insurance related activities 5242 0.124 2.278 5.858 14.636

Real estate 531 1.730 14.216 36.757 97.851



Automotive equipment rental and leasing 5321 0.011 0.403 1.003 2.275

Consumer goods rental and general rental centers 5322, 5323 0.370 1.876 4.106 10.026

Commercial and industrial machinery and equipment rental and leasing 5324 0.274 0.234 0.245 0.811

Lessors of nonfinancial intangible assets 533 –0.075 –0.123 –0.176 –0.114

Legal services 5411 –0.115 2.420 7.150 20.291

Accounting, tax preparation, bookkeeping, and payroll services 5412 1.015 4.535 10.529 25.577

Architectural, engineering, and related services 5413 –0.692 –1.280 –2.775 2.927

Specialized design services 5414 0.249 1.198 2.666 6.888

Computer systems design and related services 5415 –1.406 –4.201 –9.144 –19.639

Management, scientific, and technical consulting services 5416 3.301 8.216 17.811 41.730

Scientific research and development services; Other professional, scientific, and technical services 5417, 5419 –0.073 2.113 5.732 16.968

Advertising and related services 5418 0.073 1.162 2.928 7.856

Management of companies and enterprises 55 –0.307 2.066 5.571 16.619

Office administrative services; Facilities support services 5611, 5612 –0.125 0.353 1.657 6.229

Employment services 5613 –0.400 2.404 8.569 28.440

Business support services; Investigation and security services; Other support services 5614, 5616, 5619 0.487 4.536 11.254 29.645

Travel arrangement and reservation services 5615 0.484 1.440 2.675 4.983

Services to buildings and dwellings 5617 –0.044 3.812 11.072 33.634

Waste collection; Waste treatment and disposal and waste management services 562 6.626 19.217 44.338 75.011

Elementary and secondary schools; Junior colleges, colleges, universities, and professional schools; Other educational services

61 0.168 5.117 15.343 42.179

Offices of health practitioners 6211–6213 3.079 26.568 65.442 159.590

Outpatient, laboratory, and other ambulatory care services 6214–6216 0.301 3.105 8.028 20.044

Home health care services 6219 0.176 2.021 6.066 16.760

Hospitals 622 0.495 6.666 18.079 42.505

Nursing care facilities 6231 0.650 3.687 8.830 20.271

Residential care facilities 6232, 6233, 6239 0.492 3.412 9.611 26.517

Individual, family, community, and vocational rehabilitation services 6241–6243 0.557 4.756 13.633 34.211

Child day care services 6244 0.295 2.980 8.555 21.978

Performing arts companies; Promoters of events, and agents and managers 7111, 7113, 7114 0.192 1.403 3.652 9.392

Spectator sports 7112 0.078 0.926 2.563 7.091

Independent artists, writers, and performers 7115 0.665 2.487 5.507 13.251

Museums, historical sites, and similar institutions 712 0.044 0.350 0.953 2.512

Amusement, gambling, and recreation industries 713 1.817 7.452 16.945 41.949

Accommodation 721 1.017 5.359 12.875 31.474

Food services and drinking places 722 4.224 21.169 47.680 105.287

Automotive repair and maintenance 8111 0.362 3.845 9.813 23.276

Electronic and precision equipment repair and maintenance 8112 0.084 0.397 0.976 2.255

Commercial and industrial equipment (except automotive and electronic) repair and maintenance 8113 0.391 1.336 3.086 6.158



Personal and household goods repair and maintenance 8114 0.225 1.024 2.210 5.198

Personal care services 8121 2.126 11.693 28.139 69.494

Death care services 8122 0.009 0.209 0.536 1.233

Drycleaning and laundry services 8123 0.490 2.520 5.469 13.341

Other personal services 8129 0.443 2.441 5.885 14.524

Religious organizations; Grantmaking and giving services, and social advocacy organizations 8131–8133 0.696 5.174 14.424 40.254

Civic, social, professional, and similar organizations 8134, 8139 0.184 1.704 4.364 11.060

Private households 814 0.533 11.463 29.222 70.125

Total 186.161 682.760 1,281.417 2,706.294

* The total represents the sum of all the sectoral effects. The totals shown in this table differ from the simultaneous solutions shown in the last row of Table 3-6. The gap between the two is public employment, as well as rounding error.NAICS = North American Industry Classification System.


I. Introduction

The U.S. Congress and the Obama Administration are actively working on the design of legislation to address the problem of climate change. While the proposed Senate and House bills differ in their particulars, they all basically establish a target reduction for greenhouse gases (GHGs) and specify policies and measures to achieve these goals over the course of a planning horizon. A key policy instrument in these bills is “cap and trade,” or emissions allowance trading. However, because many emission sources are not responsive to price signals, other policy and measures, most notably direct regulation, are included in the policy mix.

Actions by emitters to reduce GHGs can achieve direct cost savings, as in the case of energy efficiency improvements, or can be cost-incurring, as in the case of shifting from coal-fired electricity generation to some more expensive types of renewable sources in the short run. However, any direct actions ripple through the economy, generating what are often referred to as indirect, multiplier, general equilibrium, or macroeconomic impacts. The last category is the broadest and takes into account: impacts from stimuli to successive rounds of suppliers and customers through a combination of material input needs and price changes in employment and income and successive rounds of re-spending effects, changes in investment, and changes in government revenues and expenditures. It is impossible to trace all of these linkages through the economy by direct observation, so instead various types of economic models are used.

The purpose of this study is to estimate the macroeconomic impacts of the major features of recent climate change legislation in the form of a U.S. Senate bill sponsored by Senators Kerry and Lieberman and its combination with sector-based policies and measures. The impacts are expressed in terms of major macroeconomic indicators – output, employment, and income – for the economy as a whole and for each of 169 sectors of the economy in the year 2020.

We identify the major features of the Kerry-Lieberman (K-L) bill relating to the emission cap, sectors covered by cap-and-trade programs and other major policy instruments, the allocation of allowances, the potential to use offsets from domestic and international sources, and the government spending (“recycling”) of allowance auction revenue. These design parameters, along with an extensive database built from stakeholder deliberations in nearly 20 states, facilitated by the Center for Climate Strategies (CCS), are fed into a macroeconomic model known as the Regional Economic Models, Inc., Policy Insight Plus (REMI PI+) to generate the macroeconomic impact estimates.

II. Overview of Policy Instruments for GHG Reduction

A. Cap and Trade

Cap-and-trade programs limit emissions by placing a “cap” on the emissions of pollutants that can be released from regulated, or “covered,” sources within a specified geographic area and interval of time. The cap is implemented by the issuance of permits (often freely granted or “grandfathered”), or “allowances,” for each ton of GHG emissions, which must be surrendered by each covered source in an amount equal to its emissions. Over time, the number of allowances issued can be decreased, thereby further reducing total emissions.

annex f

» Methodology for Analyzing Cap-and-Trade and Other Policies and Measures Using the REMI Model


Since the government regulates only the total emissions, how the reductions are achieved is left to each covered source. Creating a market in which allowances can be traded gives these allowances a financial value, which encourages the covered sources, individually and collectively, to implement the least-cost measures to achieve the capped emission reductions. Participants with costs of compliance lower than the market price of allowances will take on additional mitigation (“overcomply”) and sell their additional reductions to participants for whom compliance costs are higher than the allowance price. In an auction-based system, sometimes referred to as “cap and fee,” all emitters must purchase allowances to meet the caps; those with lower costs of compliance will need to purchase fewer allowances at auction.

It should be noted that the most cost-effective or highest-value (including co-benefits) approach for some sectors or sources may not be cap and trade; it may instead be technology-forcing or incentive policies that address specific market barriers (often referred to as ‘‘policies and measures’’ or ‘‘non-price instruments’’). A cap-and-trade program will not necessarily remove market barriers or lead to the fastest or broadest adoption of new technologies and practices. For instance, split incentives exist between the suppliers and the consumers of energy or products. Suppliers may not be able to participate in the benefits of lower-carbon goods or services provided to consumers at a higher production cost and lack an incentive to shift production, even though the net benefit of such action to society is positive. For example, electric utilities may not see it in their self-interest to provide energy-efficient technology options that reduce sales to consumers, and automobile companies may not see it in their self-interest to supply low-emitting vehicles that save consumers energy costs.

Cap and trade has a solid foundation in theory and practice. It is based on the property rights approach to eliminating externalities, the most vivid example of which is environmental pollution. The seminal work was done by Nobel laureate Ronald Coase (1960), and its refinement for application to pollution problems was done by many others (see, e.g., Tietenberg, 1985, 2007; Rose, 2009).

The practice of cap and trade was given a major boost in the 1990 Clean Air Act Amendments and is the basis for the U.S. sulfur allowance trading program for electric utilities (Ellerman et al., 2000). With respect to GHG reduction, the major experience has been the European Union Trading System, which, after a rocky start due to some design flaws, is proving successful as well (Ellerman, 2008).

B. Carbon Tax

A typical carbon tax operates on the same principle as cap and trade, that is, it imposes a cost on regulated entities for the purpose of affecting behavior and investments through a price signal. In this form, a carbon tax may generate revenue or it may be ‘‘revenue neutral’’ by allowing dollar-for-dollar reductions in other taxes and government fees.1

At first blush, the similarities between cap and trade and the carbon tax are striking; both represent a fee imposed on the release of GHGs designed to create an incentive for investments in reduced emissions and other beneficial behavioral changes. With cap and trade, the government sets a limit on the total emissions and the market, through allowance trading or auctions, establishes the price. With the carbon tax, the government sets the price, or tax rate, and the market response to that price determines the total resulting emissions.

The carbon tax has some distinct advantages over cap and trade, notably its administrative simplicity for both the government and the regulated community and the wide familiarity with taxation in general. The wide familiarity and broad unpopularity of taxation, however, work against the carbon tax, at least in the political realm (although such taxes currently exist as surcharges on electricity bills, gasoline prices, etc.).2 Regulated industries often favor the carbon tax over cap and trade because of the stability of the cost imposed by the tax, as opposed to the cap-and-trade allowance price, whose cost fluctuates as set

1. Another approach to carbon taxation is as a conventional tax imposed for the purpose of generating government revenue. The discussion here considers only the ‘ ‘price signal’’ form of carbon tax.2. British Columbia began administration of a nearly economy-wide carbon tax without having to add a single new position to its taxation ministry.


by the market. Most environmentalists and some political leaders tend to favor cap and trade due to the programmatic integrity offered by the cap and the fear that emission reduction targets will not be met due to the uncertainties associated with predicting industry and public response to the carbon tax price signal. The inclusion of cost containment mechanisms may, however, reduce environmental certainty and reduce the relative advantage of cap and trade to other approaches in this regard. Another advantage of cap and trade is that it is often institutionally easier to adjust the cap than to adjust the tax rate.

C. Other Policies and Measures

The traditional, and by far most common, approach to limiting emissions of pollutants is through sector-based policies and measures, including direct regulation (or the “command-and-control” approach), as well as other incentive systems in which emissions are limited at the source by enactment of codes and standards, funding and technical assistance, various forms of limited permitting, and other incentives or disincentives. Source-based standards, for instance, are set by rule and enforced by some combination of permit-based source monitoring, reporting, and inspection or verification. These systems can, and often do, include substantial flexibility and tailoring to local circumstance. They also can be constructed to allow “extra credit” for surplus environmental achievement that can be transferred to or purchased by entities that need additional help in meeting standards. In fact, this type of performance-based system of credits for overachievement largely defined early concepts of cap and trade. Financial incentives or assistance are often provided in conjunction with regulation to reduce cost, compensate losses, and/or accelerate responses.

Direct regulation can carry a heavy administrative burden and lack the flexibility to allow sources to seek and fund least-cost mitigation opportunities (depending on the design of the program). However, through rulemaking, permit writing, and review, this approach has the advantage of flagging specific concerns with the proposed limits. Barriers to compliance are often identified and addressed through the close interaction between the regulator and the regulated community. These barriers may take the form of contradictory government regulation, such as when an excessive occupational health and safety requirement for workplace air exchanges prevent an employer from effectively reducing heating or cooling loads. Barriers may also take the form of market failures where the entity responsible for the capital investment to improve efficiency cannot reap the benefits of lower energy use, for example, rental housing where the tenants are responsible for heat, electricity, or water heating.

Direct regulation offers the greatest opportunity to identify and address such barriers. Absent resolution of these barriers, a cap-and-trade or carbon tax policy may not have access to the lowest-cost mitigation opportunities. A price signal without concurrent policies and measures to reduce barriers could be relatively more expensive.

III. Methodology for Analyzing Environmental Policy Instruments

This section summarizes the methodology to simulate the macroeconomic impacts of various policies and measures to implement climate action plans at the national, regional, and state levels. In effect, our work in Florida, Pennsylvania, and Michigan and U.S. simulations considered the implementation of all recommended mitigation/sequestration options (Rose and Wei, 2009; Rose and Wei, 2010; Miller et al., 2010). Most real-world policies would likely involve a more targeted approach. One prime example would be applying a cap-and-trade policy to those options that respond to a price signal, and applying regulation to those that do not. Other policy instruments would include subsidies and information campaigns. For now, we model those two additional instruments in the same manner we model regulation (i.e., assuming the subsidy and information campaigns are successful without further examining the subtleties of individual emitter behavior or responsiveness).

For several of the policy instrument designs, it is not necessary to perform any additional simulations to ascertain the macroeconomic impacts of any individual options. We can simply use our previous individual option results for those options brought forth by these policy instruments and then add


them up for the “simple summation” of cases as noted below. Exceptions are the case where permits are auctioned, and expenditures on them must be added to the cost of production. The major new simulations will be for the new groupings of options (actually sub-groups) of the totality of options that we will need to run together for each of the cases to obtain our “simultaneous” totals.

The calculation steps are as follows:

A. Divide Mitigation Options into Three Categories: »» Fully price-responsive options (no market failures).

»» Options that are generally not price-responsive (due to market failures or other barriers) and that require regulation.

»» Other options for which the price responsiveness might improve with subsidies, information campaigns, etc., without formal regulation. This group might be modeled differently in the future.

B. Perform the Following Reference Case Simulations:

1. Cap and trade in the U.S. only (applying either all mitigation options or a subset of options).

a. Simulate a specific allowance price, such as the reserve price of the auction allowances implied by the K-L bill ($12/CO2e in 2013, and increases at the rate of inflation plus 3% for each year after), to determine what level of GHG reduction that will bring forth. Only those options whose mitigation cost per ton is at or below the allowance price would be included in the macro simulations.

b. Simulate a GHG cap and infer a permit price by using our U.S. marginal cost curve. Again, all the options whose mitigation cost is at or below the allowance price would be included.

2. Cap-and-trade with the possibility of the U.S. purchasing allowances from other nations, or offsets at home or abroad. This would require an estimate of a supply curve or price for these allowances/offsets. The mitigation options included in the response would only be those with a cost equal to or lower than the international allowance and offset prices. a. Simulate a specific allowance price , as in 1a. In this case, the amount of foreign allowances/offsets would be the difference between the reduction brought about by the K-L bill reserve allowance price and a predetermined overall cap on emissions. The issue would be how to add the allowance purchase price to individual emitters (sectors). b. Simulate a GHG cap and infer a permit price by using both our U.S. marginal cost curve and the foreign offset price, as well as any constraints on the use of offsets specified in the K-L bill. The purchases of foreign allowances/offsets would be determined by this equilibrium.

3. Mixed case of cap-and-trade and regulation.

a. In addition to the carbon cap-and-trade policy, several of the regulatory (mandated) options relating to given sectors covered by a given policy would automatically be included in our macro analysis regardless of their cost (we would treat subsidies and information campaigns as regulated options). The price-responsive options would be included in the same manner as 1a. Three Residential, Commercial, and Industrial (RCI) options are partially price-responsive: demand side management (DSM), high performance buildings, and combined heat and power (CHP). We decided that 30% of the emission reductions of these options is responsive to price signals, while 70% of the desired results can only be brought forth through regulation. In the simulations, we will split each of these options into two sub-options, price-responsive sub-option and non-price-responsive sub-option. Technically, we will treat the two sub-options as two separate options in the computation. The GHG reductions will be split using the ratio of 30:70, with 30% emission reductions assigned to the price-responsive sub-option. The cost-effectiveness will be assumed to be same for the two sub-options (equal to the original cost-effectiveness of the option).


b. This would be a combination of 1b and 3a (3b corresponds to 1b, in terms of setting the cap first. Otherwise the bifurcation is like 3a).

4. Mixed case with the possibility of buying international allowances or offsets.

a. Simulating a specific allowance price, having the possibility of buying allowances overseas, and having some of the options required by regulation is quite complicated. We can examine this after performing other cases.

b. Simulating a specific reduction target under this heading might be easy because a low public price like the K-L bill reserve allowance price would probably bring forth enough U.S. mitigation options to make up the gap to achieve the target. A higher permit price, such as an upper bound of $100 per ton, would be even more likely to do so.

C. Mimic Workings of Policy Instruments:

1. Free allocation equal to equilibrium sector emission requirements. The basic application of the REMI model to cap and trade without further adjustment beyond the stipulation of an allowance price directly, or indirectly via specification of a GHG emissions cap, essentially mimics the following institutional arrangement: free allocation of allowances, such that each covered sector gets exactly the number of allowances it needs for its remaining emissions, and the overall emissions cap is met. Each sector implements mitigation to avoid paying for allowances up to the point where its marginal cost of mitigation is less than or equal to the allowance price. This is a stylized case, often used in the economics literature, to derive the least-cost solution, as does a cap-and-trade system, but with no auction revenues actually generated (sectors mitigate to avoid having to buy allowances) and no allowance trading complications. Again, it is a reference point for other cases. In the absence of various distortions — pre-existing taxes, capital constraints, etc. — free allocation and auction scenarios should be identical except in the distributional effects.

This can be considered a lower bound on the cost of compliance for emitters, and hence an upper bound on the macro impacts.

2. Auctioning all allowances. This requires application of the allowance price to all non-mitigated emissions. These auction payments are then added to the production cost of each emitting sector. The cost can be distributed across the entirety of the REMI 169 sectors or a subset of sectors on the basis of sector emission weights from the emission inventories. Because the sector designations of the emissions inventory are coarser than the REMI classification, sectoral components may have to be based on fossil fuel use (in CO2 equivalents) or on the basis of output weights. Note that one other step is needed: re-injection of the option revenues back into the economy. There are at least two possibilities:

a. Add to government expenditures.

b. Decrease taxes by an equivalent amount. Increasing government expenditures has some expansionary offsetting effect to the effect of the cost increase. It should be noted that many studies of the “double-dividend” have found the second option to be expansionary as well if it is used to offset the most price-distorting taxes, usually considered to be sales, excise, or wage taxes. Note that this category represents an upper bound on the cost of implementation and hence a lower bound on the macro impacts.


3. Allowance trading following a grandfathering. This case is problematic in two regards. First, is the issue of the initial allocation of permits, i.e., which allocation criterion will be involved (often these criteria are related to equity, or justice, considerations). Alternatives include distributing permits in equal proportions of emissions to each sector, or favoring certain sectors because they are anticipated to be most adversely affected, or are weak sectors in the first place. The second is the actual trading. Sectors with marginal mitigation costs higher than the allowance price will buy allowances from sectors with marginal mitigation costs lower than the allowance price. This is an issue in the modeling due to the difficulty of connecting individual mitigation options with sectors, especially where several of the options pertain to each of the 169 sectors plus the Residential sector. Conceptually, marginal costs curves can be specified and trading can be simulated with a separate Rose-Wei non-linear programming allowance trading model. In this study, we will simulate allowance trading among sectors at a relatively aggregated sectoral scheme. Then the resulting sectoral purchase cost or sales revenue will be disaggregated and distributed among the REMI 169 sectors based on the sectoral emissions before feeding them into the REMI model.

The cost of compliance for this arrangement would be similar to that of Case 1. In effect, the expenditures and revenues from allowance trading cancel each other out to a zero net cost. Compliance costs are still just the actual mitigation costs.

4. Split case of auctioning and free granting (grandfathering) of allowances. This is actually the approach used by the Regional Greenhouse Gas Initiative. For methodological purposes, this is not much different from Case 2. Of course, the macro impacts will differ.

IV. Calculation Steps and Assumptions

Mitigation options are aggregated into four sectors: Electricity Supply (ES), Residential, Commercial, and Industrial (RCI), Transportation and Land Use (TLU), and Agriculture, Forestry, and Waste Management (AFW). The following are major elements in the further design of the simulations and an indication of how the analysis proceeds:

1. How much mitigation will each sector undertake? In the Stakeholder/Senate (Full Stakeholder Implementation) scenario, it is assumed that the 23 super options will be used to the maximum. In the Senate scenario, the reduction potentials of the super options are scaled-back separately for the cap-and-trade sector and the non-cap-and-trade sector to the level that the aggregate efforts of the cap-and-trade sector super options match with the K-L bill reduction target for the cap-and-trade sector and the non-cap-and-trade sector super options match with the K-L bill reduction goal for the non-cap-and-trade sector.

2. Which sectors would be covered under the cap? The ES and TLU sectors would be covered by the cap starting in 2013. The Industry sector and the Residential and Commercial sectors’ use of natural gas will be covered by the cap three years later (i.e., starting in 2016). The Agriculture and Forestry sectors are often the sectors that provide carbon offsets.

3. What is the allocation (or effective cap) for each cap-and-trade covered sector? In the Full Stakeholder scenario, we assume all the 23 super options will be implemented to their maximum reduction potentials. According to Figure 2-4 (the U.S. cost curve), this is equivalent to a total of 42.09% reduction of the 2020 baseline emissions. In the Senate scenario, we will apply the K-L bill target. The 2020 reduction target of the cap-and-trade sector specified in the K-L bill is 17% below the 2005 emissions level. The allowance allocation scheme specified in the K-L bill is presented in Table 3-14.

4. How do we determine the allowance price? In both simulation scenarios, we use the auction reserve price to compute the auction payments/revenues.

5. What is the total cost/saving for each option? Data from CCS workbooks are used.

6. How many allowances are purchased by each sector in the auction case? Sectors covered by the cap


need to purchase allowances from the auction market when the emission reductions from autarkic mitigations fall short of compliance.

7. How do allowance purchases affect the macro impacts? Once we determine the allowance purchases and the allowance price, we can compute the expenditure for buying allowances at auction by each sector. In the REMI simulation, we deem the appropriate variables to reflect these expenditures as a “Production Cost” increase.

8. How many allowances are purchased or sold by each sector in sectoral trading? Covered sectors with excess allowances can sell those allowances in the inter-sectoral trading market. We simulate the direct effect of revenue gains from allowance sales as a “Production Cost” decrease of the selling sectors, and simulate the direct effect of allowance payments as a “Production Cost” increase of the purchasing sectors.

9. How many offsets are purchased? In our analysis, we assume that the cap-and-trade covered sectors can also purchase offsets from non-cap-and-trade sectors (mainly AFW sectors) if they still fall short of allowances needed for compliance after purchases of allowances from the auction market and from the inter-sectoral trading market. For international offsets, the expenditures are added to purchasing sector production costs. No domestic sectors experience revenue gains though they do avoid incurring higher mitigation costs. The macro impacts of offset purchases of the cap-and-trade sector are simulated in a similar manner as the allowance purchases, i.e., the “Production Cost” variable is used to capture the direct impact of offset payments.

10. How many allowances are banked? We assume that a sector can bank any excess allowances for future compliance use, which are neither used for its own compliance purposes nor sold to other sectors in the inter-sectoral trading market.

11. How are the revenues recycled? The auction revenues are recycled in the following three ways in 2020 (please see details in Section V, Government Revenue Recycling):

a. Consumer Relief.

b. Highway Trust Fund.

c. Deficit Reduction Fund.

12. Since the allowance allocation rule varies among the Residential, Commercial, and Industrial sectors (see Table F-1), we disaggregate each RCI option in Table F-2 into sub-components of Residential sector, Commercial sector, Energy-Intensive Industrial sector (Ind-EIS), and Other Industrial sector (Ind-Other), respectively. The disaggregation methods for the RCI options include: a. The reduction potentials of RCI-1 (DSM) are split among the Residential, Commercial, and Industrial sectors using the weights of the sectoral total energy consumption of electricity, natural gas, and oil. b. For RCI-2 (High Performance Buildings), RCI-3 (Appliance Standards), and RCI-4 (Building Codes), when we split emission reduction potentials, the weighting of the Industrial sector is computed based on just 9.4% of the sectoral total energy consumption. This is because, based on the U.S. Department of Energy’s Energy Information Administration 2002 report on energy consumption by manufacturers, which indicates approximately 9.4% of industrial energy use in the U.S. is for heating, ventilating, and air conditioning, lighting, and other facilities, i.e., energy use reductions from high performance buildings, appliance standards, and building codes apply only to 9.4% of the total industrial energy use. c. For RCI-5 (CHP), the emission reduction potentials are split 50/50 between the Commercial sector and the Industrial sector. d. The determination of the Energy-Intensive Industrial sector is based on the U.S. Environmental Protection Agency (EPA) preliminary assessment of six-digit North American Industry


Classification System (NAICS) industries that are “presumptively eligible” for preferable allowance allocations under H.R.2454 (EPA, 2009b). The reduction potentials of the mitigation options of the Industrial sector are further disaggregated between the Energy-Intensive Industrial sector and Other Industrial sector based on their respective 2006 GHG baseline emissions. e. The cost-effectiveness (i.e., the per-ton costs/savings) of the RCI options remains the same after the disaggregation.

Table F-1. Percentages for RCI Options

Policy Option Residential Commercial Industrial–EIS Industrial–Other

RCI–1: Demand Side Management 34% 30% 15% 21%

RCI–2: High Performance Buildings 51% 45% 2% 3%

RCI–3: Appliance Standards 50% 46% 2% 2%

RCI–4: Building Codes 52% 43% 2% 3%

RCI–5: Combined Heat and Power 0% 50% 20% 30%

EIS = Energy-Intensive Industrial Sector; RCI = Residential, Commercial, and Industrial.

V. Government Revenue Recycling

The K-L bill specifies that the proceeds of the allowance auction will be devoted to “Consumer Relief,” “Universal Trust Fund,” “Highway Trust Fund,” and “Deficit Reduction Fund.” For the “Highway Trust Fund,” the increased government spending will be simulated as a “Production Cost” decrease of the “Transit and Ground Passenger Transportation” sector in REMI. Auction proceeds used to reduce the deficit cannot be simulated in the REMI model, because it does not contain the necessary linkages between government budgets and key variables, such as the interest rate.

“Consumer Relief” and the “Universal Trust Fund” relate to the government recycling auction revenues to households. For the Universal Trust Fund, all households are likely to be eligible. However, this fund will not be established until 2026. Since our analysis is focused on the year 2020, we will not simulate the impacts of revenue recycling to this fund.

The Consumer Relief Program includes the Working Families Refundable Credit Program and the Energy Refund Program. For the Working Families Refundable Credit Program, an eligible taxpayer is defined as an individual whose household income is less than 150% of the poverty line minus $1,000. For the Energy Refund Program, there are many criteria to define an eligible household, such as a household with an income less than 150% of the poverty line that is participating in the Supplemental Nutrition Assistant Program, Food Distribution Program, etc. In our simulation, we use the 150% federal poverty level to define the household income group that will be covered by the Energy Refund Program. We also assume that these government transfers to the low-income households are not subject to income taxes.

Table F-2 shows the 2009/2010 U.S. Department of Health and Human Services Poverty Guidelines for all states (except Alaska and Hawaii) and for the District of Columbia. According to the U.S. Census Bureau data, the 2006–2008 average family size is 3.2 people (U.S. Census Bureau, 2009). Therefore, the 150% poverty level of the average family size is computed as $27,465 + 20% × ($33,075 – $27,465) = $28,587. This income level is used to identify eligible household income groups for Energy Refund Program. The income level used for the Working Families Refundable Credit Program is computed as $28,587 – $1,000 = $27,587.

When we simulate revenue recycling favoring low-income groups in the REMI model, we cannot use the REMI “Transfer Payments (amount)” variable, since we cannot specify transfer payments for any specific income group in the model. An alternative way to do this simulation is to work with the consumption columns of the low-income brackets in IMPLAN3 and translate the transfer payment into “Exogenous Final Demand” changes for REMI sectors.

3. Minnesota IMPLAN Group, Inc. (MIG, Inc.) developed the IMPLAN® economic impact modeling system. IMPLAN® is used to create complete, extremely detailed Social Accounting Matrices and Multiplier Models of local economies.


Table F-2. 2009/2010 HHS Poverty Guidelines

Size of Family Unit

100% of Poverty Level







1 $10,830 $11,913 $13,538 $16,245 $18,953 $20,036 $21,660

2 $14,570 $16,027 $18,213 $21,855 $25,498 $26,955 $29,140

3 $18,310 $20,141 $22,888 $27,465 $32,043 $33,874 $36,620

4 $22,050 $24,255 $27,563 $33,075 $38,588 $40,793 $44,100

5 $25,790 $28,369 $32,238 $38,685 $45,133 $47,712 $51,580

6 $29,530 $32,483 $36,913 $44,295 $51,678 $54,631 $59,060

7 $33,270 $36,597 $41,588 $49,905 $58,223 $61,550 $66,540

8 $37,010 $40,711 $46,263 $55,515 $64,768 $68,469 $74,020

Source: U.S. Department of Health and Human Services.

Table F-3 shows the household income brackets used in IMPLAN. The first three income brackets and part of the fourth income bracket fall into the low-income household categories specified for the Energy Refund Program and the Working Families Refundable Credit Program in the K-L bill.

Table F-4 shows how the government transfer to the Energy Refund Program and to the Working Families Refundable Credit Program will be distributed among the first four income brackets based on total consumptions plus savings of respective income bracket.

The following steps show how to translate the government transfers to the low-income households into final demand changes by REMI sector:

»» The 440 IMPLAN sectors are first aggregated to the 169 REMI sectors.

»» The household consumption columns of the first four household income brackets are extracted from IMPLAN.

»» Consumption coefficients are computed for each of the first four income brackets.

»» The transfers distributed to each income bracket are computed based on the proportions shown in Table F-4.

»» For each income bracket, the transfers from the two consumer relief programs are translated to increases in sectoral goods consumption by multiplying the total transfer to this income bracket by its consumption coefficient of each individual REMI sector.

»» The total “Exogenous Final Demand” change to each REMI sector is the sum of consumption changes of the four income groups.

Table F-3. IMPLAN Household Income Brackets (thousand 2008$)

Income Brackets

1 2 3 4 5 6 7 8 9

< $10 $10–15 $15–25 $25–35 $35–50 $50–75 $75–100 $100–150 $150+

Table F-4. Income Transfer for the Energy Refund Program and Working Families Refundable Credit Program

Income BracketsEnergy Refund Program Working Families Refundable Credit Program

Income Transfer (billion 2008$) Percentage Income Transfer (billion 2008$) Percentage

< $10 $1.97 23.4% $0.42 24.7%

$10–15 $1.37 16.4% $0.29 17.3%

$15–25 $3.47 41.3% $0.73 43.6%

$25–28.6 $1.59 18.9% $0.24 14.4%

Total $8.40 100% $1.68 100%


References

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Rose, A., and Wei, D. 2009b. “Macroeconomic Assessment,” Chapter 11 in Pennsylvania Climate Action Plan. http://www.depweb.state.pa.us/energy/cwp/view.asp?q=539829.

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Tietenberg, T. 1985. Emissions Trading: An Exercise in Reforming Pollution Policy. Washington, DC: Resources for the Future).

Tietenberg, T. 2007. “Tradable Permits in Principle and Practice,” in J. Freemand and C. Kolstad (eds.), Moving to Markets: Lessons from Twenty Years of Experience. New York: Oxford University Press.

U.S. Department of Energy, Energy Information Administration. 2005. 2002 Energy Consumption by Manufacturers. http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html.

U.S. Department of Health and Human Services. Poverty Guidelines, Research, and Measurement. http://aspe.hhs.gov/poverty.

http://www.depweb.state.pa.us/energy/cwp/view.asp?q=539829

annexes: impacts of comprehensive climate and energy policy options on the u.s. economy

Documents

states new states

individual states

washington existing

states action plans

weightedaverage method

analogous state method

energy policy options

set of options