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

102 Johns Hopkins University and Center for Climate Strategies

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

Impacts of Comprehensive Climate and Energy Policy Options on the U.S. Economy 103

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.

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

Documents