## Chapter 5: Economic Impacts and Benefits

5.1 Agricultural Impacts

5.1.1 Models Utilized

EPA used a suite of tools to model the poten tial domestic and international impacts of the RFS2 renewable fuel volumes on the agricultural sector. The Forest and Agricultural Sector Optimization Model (FASOM), developed by Professor Bruce McCarl of Texas A&M University and others, provide s detailed information on domestic agricultural and greenhouse gas impacts of renewable fuels. The Food and Agri cultural Policy Research Institute (FAPRI) at Iowa State University and the University of Missouri-Columbia maintains a number of econometric models that are capable of providing deta iled information on impacts on international agricultural markets from the wider use of renewable fuels in the U.S.

FASOM is a long-term economic model of the U.S. agriculture sector that maximizes total revenues for producers while meeting the demands of consumers. Using a number of inputs, FASOM determines which crops, livestock , and processed agricultural products would be produced in the U.S. In each model simulation, crops compete for price sensitive inputs such as land and labor at the regi onal level. The cost of these and ot her inputs are used to determine the price and level of production of primary commodities (e.g., field crops, livestock, and biofuel products). FASOM also estimates pr ices using costs associated with the processing of primary commodities into secondary products (e.g., conver ting livestock to meat and dairy, crushing soybeans to soybean meal and oil). FASOM doe s not capture short-term fluctuations (i.e., month-to-month, annual) in prices and production, however, as it is designed to identify longterm trends.

The FASOM model also contains a forestry component, which details forest acres across the U.S., as well as forestry products. Running both the forestry and agriculture components of the model would shows the interaction between these two sectors as they compete for land in various regions, as well as the effect on products and prices in each respective sector. In addition, FASOM also includes a representation of seven major land use categories, including cropland, cropland pasture, fore stland, forest pasture, rangel and, developed land, and acres enrolled in the Conservation Reserve Progra m (CRP). More information on these land categories can be found below in Chapter 5.1.2.

FASOM uses supply and demand curves for the 11 major U.S. domestic regions, A which are calibrated to historic pri ce and production data. FASOM al so includes detailed supply and

A

U.S. regions consist of the Pacific Northwest (West and East), Pacific Southwest, Rocky Mountains, Great Plains, Southwest, South Central, Corn Belt, Lake States, Southeast, and the Northeast.2 demand data for corn, wheat, soybeans, rice and sorghum across 37 foreign regions. B FASOM contains transportation costs to a ll regions and then uses all of this information to determine U.S. exports to the point where prices are then equated in all markets.

We chose to use FASOM to model the full potential impacts on the domestic agricultural and forestry sectors given higher renewable fuel volumes, in part because FASOM also provides detailed greenhouse gas information resulting from th ese changes (see Chapter 2 of this RIA for more information). FASOM is not able to agricu ltural sector changes internationally, however. Therefore, we are working with the FAPRI modeling system to better understand international agricultural impacts. Additional details on the FASOM model are included in the docket. C

The FAPRI models are a system of economet ric models covering many agricultural commodities. These models capture the biologic al, technical, and economic relationships among key variables within a particular commodity and across commodities. They are based on historical data analysis, current academic research, and a reliance on accepted economic, agronomic, and biological re lationships in agricultural production and markets. The international modeling system includes international grains, oilseeds, ethanol, sugar, and livestock models. In general, for each commodi ty sector, the economic relationship that supply equals demand is maintained by determining a market-clearing price for the commodity. In countries where domestic prices are not solved endogenously, th ese prices are modeled as a function of the world price using a price tran smission equation. Since econometric models for each sector can be linked, changes in one commodi ty sector will impact other sectors. Elasticity values for supply and demand responses are ba sed on econometric analysis and on consensus estimates. Additional details on the FAPRI models are included in the docket. D

Agricultural and trade policies for each commodity in a country are included in the models to the extent that they affect the s upply and demand decisions of the economic agents. These policies include taxes on exports and imports , tariffs, tariff rate quotas, export subsidies, intervention prices, and set-aside rates. The FAPRI models assume that existing agricultural and trade policy variables will remain unchanged in the outlook period.

We recognize that there are inherent challenges in reconciling the results from two different models, however using two models provi des a more complete and robust analysis than either model would be able to provide alone. As described in Chapter 5.1.3, we have attempted to align as many of the key assumptions as possi ble to get a consistent set of modeling results. However, there are structural differences in th e models that make account for some of the difference in the model results. For example, since FASOM is a long-term econometric model, short-term spikes are smoothed out over the fi ve year reporting period. In comparison, the

B FASOM Foreign Regions include: the European Economic Community, North Central Europe, Southwest Europe, Eastern Europe, Adriatic, Eastern Mediterranean, Fo rmer Soviet Union, North Africa, East Africa, West Africa, South Africa, Red Sea, Iran, India, Taiwan, Japan, South Korea, North Korea, China, Bangladesh, Indonesia, Myanmar, Pakistan, Philippines, Thailand, Vietnam, West Asia, Southeast Asia, Australia, Caribbean, Eastern Mexico, Eastern South America, Western South America, Argentina, Brazil, Canada, Other.

C Beach, Robert; McCarl, Bruce, Agricultural Impacts of the Energy Independence and Security Act: FASOM Results and Model Description , RTI International, October, 2008.

D Technical Report: An Analysis of EPA Biofuel Scenarios with the FAPRI International Models , CARD Staff, November, 2008.3 FAPRI model captures annual fluctuations that may include short-term supply and demand responses. Some of the discre pancies may be attributed to different underlying assumptions pertaining to elasticities of s upply and demand for different comm odities. These differences, in turn, affect projections of imports and exports , acreage shifting, and total consumption and production of various commodities. Some of the differences in results are described in more detail in the following sections.

5.1.2 Model Modifications Si nce the RFS2 Proposal

Since the analysis for the RFS2 proposal was completed, a number of updates have been made to the FASOM and FAPRI models to reflect comments received and the availability ofnew data. The major changes to the agricultural modeling framework include adding priceinduced yields, updating cellulosic yields, updating di stillers grains replacement rates of corn and soybean meal in animal feed, adding corn oil from extraction as a biodiesel fuel pathway, adding additional land categories in the FASOM model, and adding a detailed Brazil module to the FAPRI modeling system.

5.1.2.1 Price-Induced Yields

The FAPRI model includes elasticity factors fo r yields to respond to changes in prices over time both in the U.S. and internationally. As the price of corn increase, farmers, seed producers, and others involved in crop production have an additional incentive to improve yields. The price induced yield phenomenon is partially offset by the reduced yields that result from expanding on to new crop acres, which is often re ferred to as extensification. However, the price-induced yield impact is projected to be larger than the extensificati on effect. For example, in 2022 the price of corn increases by $0.10 (3.3 pe rcent) in the U.S. In response, the average corn yield in 2022 increases by 0.4 bushels per ac re (0.4 percent). In another example, in 2022, world corn prices increase by $0.12 per bushel (3.1 percent). As a result, corn yields in China increased from 101.9 bushels per acre in the Re ference Case to 102.3 bushels per acre in the Control Case in 2022, a 0.3 percent improvement. Additional details on the methodology behind the estimation of price-induced yields can be found in the FAPRI Technical Report in the docket. E Additional international results can be found below in this chapter.

5.1.2.2 Cellulosic Yields

Based on new research conducted by the National Renewable Energy Laboratory (NREL), we have updated the rate s of conversion for cellulosic f eedstocks into ethanol in the FASOM model. F As a result of these changes, the gallons per ton yields for switchgrass and several other feedstocks increased from the values used in the RFS2 proposal, while the yields for corn residue and several other feedstocks de creased slightly from the values used in the proposal. For additional detail, please see Ch apter XX. In addition, we also updated our switchgrass production yields based on new work conducted by the Pacific Northwest National Laboratory (PNNL). In the analysis for the RFS2 proposal, national av erage switchgrass yields

E Technical Report: An Analysis of EPA Biofuel Scenarios with the FAPRI International Models , CARD staff, November, 2008.

F Tao, Aden, Technoeconomic Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR) , Nov. 2008.4 were 6.3 wet tons per acre in the Control Case in 2022. For the final rulemaking analysis, national average switchgrass yields are 7.8 wet tons per acre in the Control Case in 2022. For more information on switchgrass yields, please refer to the FASOM technical documentation. G

5.1.2.3 Distillers Grains Replacement Rates

One of the byproduct of the dry mill ethanol processes is the creation of distillers grains with solubles (DGS). This byproduct is a co mmon source of animal feed, and can be used to feed beef cattle, dairy cows, swine, and poultry. When DGS are used in feed, they can replace other sources of feed that would otherwise be used, such as corn and soybean meal. Based on research conducted by Argonne National Laboratory, H one pound of DGS can be used to replace 1.196 pounds of total corn and soybean meal for va rious beef cattle and dairy cows due to the ability of this livestock to take advantage of the higher nutritional content of DGS per pound compared to corn and soybean meal. Curren t livestock production practices use 1 pound of DGS to replace 1 pound of a combination of corn and soybean meal. For our analysis, the replacement rates for corn and soybean meal increase steadily over time from a 1:1 replacement rate, to the maximum technological replacement rate of 1:1.196 in 2015 for beef cattle and dairy cows. A replacement rate of 1:1 is used for swine and poultry throughout the time period analyzed. Based on work by Shurson, I DGS produced in combination with the corn oil fractionation/extraction processes has different nutritional characteristics than traditional DGS containing higher levels of oil. According to this research, fractionated/ extracted DGS replaces a slightly higher proportion of soybean meal rather than corn compared to traditional DGS (although the total displacement rate for both type s of DGS is 1:1). We have therefore used these modified replacement rates of corn and soybean meal of fractionated/extracted DGS for swine and poultry than traditional DGS. The Shurson paper does not include changes in replacement rates of corn and soybean meal for beef cattle and dairy cows, so replacement rates are assumed to be the same for traditional DGS and fractionated/extracted DGS. Maximum inclusion rates for DGS in feed are based on the Argonne research.

5.1.2.4 Corn Oil Extraction as Biodiesel Pathway

For the RFS2 analysis, both FASOM and FA PRI explicitly model corn oil withdrawn from the extraction as a source for biodiesel. Based on engineering research (see Chapter 1.4) regarding expected technological adoption, it is estimated that 70 percent of dry mill ethanol plants will withdraw corn oil via extraction from di stillers grains, resulting in corn oil that is nonfood grade and can only be used as a biodiesel source; 20 percent will withdraw corn oil via fractionation (prior to the creation of distillers grains), resulting in corn oil that is food-grade; and 10 percent will do neither ex traction or fractionation.

G RTI Technical Report

H Salil Arora, May Wu, and Michael Wang, “Update of Distillers Grains Displacement Ratios for Corn Ethanol Life-Cycle Analysis,” September 2008. See http://www.transportation.anl.gov/pdfs/AF/527.pdf

I Shurson, The Value of High-Protein Distillers Coproducts in Swine Feeds , Distillers Grains Quarterly, First Quarter 2006.5 5.1.2.5 Detailed Land Use Categories

Since the proposal, the FASOM model has been updated to include several additional land use categories covering the majority of th e U.S. land base. These categories are based on the USDA National Agriculture Statistics Service (NASS) data. These land classifications enable the FASOM model to explicitly link th e interaction between livestock, pasture land, cropland, and forest land. For each of thes e categories, FASOM accounts for how much is actively used in production, and how much idled, in a particular time perio d. A brief description of these categories is described below. Add itional detail on the land categories are included in the technical report on the FASOM model included in the docket. J

• Cropland is actively managed cropland, used fo r both traditional crops (e.g., corn and soybeans) and dedicated energy crops (e.g., switchgrass).

• Cropland pasture is managed pasture land used fo r livestock production, but which can also be converted to cropland production.

• Forestland contains a number of sub-categories , tracking the number of acres both newly and continually harvested (reforested), the number of acres harvested and converted from other land uses (afforested), as well as th e amount of forest acres on public land.

• Forest pasture is unmanaged pasture land with varying amounts of tree cover that can be used for livestock production. A portion of this land may be used for timber harvest.

• Rangeland is unmanaged land that can be used for livestock grazing production. While the amount of rangeland idled or used fo r production may vary, rangeland may not be used for any other purpose than for animal grazing.

• Developed (urban) land is assumed to have an inhe rently higher value than land used for any other use, but the rate of urbanization does not change between the cases analyzed.

• Conservation Reserve Program (CRP) refers to land that is voluntarily taken out of crop production.

5.1.2.6 Brazil Module

In the FAPRI modeling system, all non-U.S. c ountries are analyzed at the national level, with the exception of Brazil. Due to the importance of Brazil in determining the international impacts of increased biofuel demand, including the increase in U.S. demand for imported ethanol, the FAPRI model was updated to include additional agricultural detail in Brazil. The FAPRI model now includes an integrated Br azil module that provides additional detail on agricultural land use in Brazil for six geographi c regions: the Amazon Biome, Northeast, NorthNortheast Cerrados, Central-We st Cerrados, Southeast, and the South. The Brazil module explicitly models the competition between cropland and pastureland used for livestock production in each region. In addition, the Brazil module allows for region -specific agriculture practices such as double cropping and livestock in tensification in response to higher commodity prices. This level of detail allows for a more refined analysis of land use change and economic

J RTI Technical Report6 impacts in Brazil than a national-level analysis . For more detail on the Brazil module and its development, please refer to the FAPRI model technical report in the docket. K

Figure 5.1-1. Map of Brazil by Geographic Region in FAPRI

5.1.3 Key Modeling Assumptions

To analyze the U.S. and international agri culture sectors impact of the RFS2 renewable fuel volumes, a number of key assumptions and input parameters were standardized in the FASOM and FAPRI models. These assumptions were developed with the input of other government agencies, such as USDA and DOE. As shown in Table 5.1-1, key assumptions include corn and soybean yields, L corn ethanol dry and wet mill plant processing energy use, corn ethanol yields, corn ethanol by-product us e, estimated corn stover yields, domestic energy

K

CARD Technical Report

L USDA Agricultural Projections to 2018 (OCE-2009-1), February, 2008.7 prices, M and others. Additiona l details on the assumptions included in FASOM N and FAPRI O
are included in the docket. For other estimate s of input parameters, we relied on external expertise, such as FAPRI; the Greenhouse gase s, Regulated Emissions and Energy use in Transportation (GREET) model; P the Assessment System for Population Exposure Nationwide (ASPEN) model; Q and the Agriculture Resource Management Survey (ARMS). R

M Energy prices in all cases are based on the April Release of the 2009 Annual Energy Outlook, published by the Energy Information Administration (EIA) within DOE. Prices used include Gasoline, Diesel, E85, Coal, and Electricity. See: http://www.eia.doe.gov/oiaf/servicerpt/stimulus/index.html

N Beach, Robert; McCarl, Bruce, Agricultural Impacts of the Energy Independence and Security Act: FASOM Results and Model Description , RTI International, October, 2008.

O Technical Report: An Analysis of EPA Biofue l Scenarios with the FAPRI International Models , CARD staff, November, 2008.

P The GREET model is run by Argonne National Laboratory at the Department of Energy. GREET can simulate more than 100 fuel production pathways and more than 80 vehicle/fuel systems. Q ASPEN is a computer simulation model used to estimate toxic air pollutant concentrations is called the Assessment System for Population Exposure Nationwide, or. This model is based on the EPA’s Industrial Source Complex Long Term model (ISCLT) which simulates the behavior of the pollutants after they are emitted into the atmosphere. ASPEN uses estimates of toxic air pollutant emissions and meteorological data from National Weather Service Stations to estimate air to xics concentrations nationwide.

R

ARMS is sponsored by the Economic Research Service (ERS) and National Agricultural Statistics Service (NASS) at USDA, and provides observations of field-level farm practices, the economics of the farm business, and the characteristics of the American farm household.8 Table 5.1-1. Agriculture Model Assumptions

Assumption Notes Feedstock Production Prices for Gasoline, Diesel, E85, Coal, and Electricity used in all volume cases AEO 2009, April Release
U.S. national average corn yields are approximately 170 bu/acre in 2017 and 180 bu/acre in 2022 (a 1.6% annual increase over the baseline year) U.S. national average soybean yields are approximately 50 bu/acre in 2022 (a 0.4% annual increase) Consistent with USDA projections (http://www.ers.usda.gov/publications/oce081/)

International corn yields increasing over time, for example: Argentina ~133 bu/acre in 2022 (a 0.8% annual increase) Brazil ~72 bu/acre in 2022 (a 1.1% annual increase) International soybean yields increasing over time, for example: Argentina ~46 bu/acre in 2022 (a 0.7% annual increase) Brazil ~46 bu/acre in 2022 (a 1.1% annual increase) Price-Induced Yields, for example: World Price of Corn increases by 3.1% from the Reference Case to the Control Case in 2022; Corn Yields in China increases by 0.3% from the Reference Case to the Control Case in 2022 FAPRI Models

High Yield sensitivity runs for corn and soybeans:

For example, U.S. High Yields in 2022: Corn: approximately 230 bu/acre (27% higher than Base Yield) Soybeans: approximately 60 bu/acre (30% higher than Base Yield)

Similar yield increases for top producers of corn (China, Mexico, EU, Argentina, Brazil) and soybeans (China, Argentina, Brazil) FASOM and FAPRI Models

Represented as increases in technological rates of progress, no additional inputs required

Corn residue removal rates of 50% are allowed for no till practices; 35% removal rate allowed for reduced till practices (no removal from conventional till) Derived from Graham et. al., Agronomy Journal, 99:1–11 (2007). “Current and Potential U.S. Corn Stover Supplies.” and Perlack, R. D., L. L. Wright, A. F. Turhollow, R. L. Graham, B. J. Stokes, and D. C. Erbach. 2005. Biomass as Feedstock for a Bioenergy and Bioproducts Industry: the Technical Feasibility of a Billion-ton Annual Supply. Report prepared for the U.S. Department of Energy and the U.S. Department of Agriculture.9 Updated Conversion Rates (gallons per dry ton) for all cellulosic ethanol feedstock sources.

For example: Proposal Analysis: Corn Reisude: 94.2 gallons/dry ton Switchgrass: 78.9 gallons/dry ton Final Rule Analysis: Corn Reisude: 92.3 gallons/dry ton Switchgrass: 92.3 gallons/dry ton Based on NREL Research

Tao, Aden, Technoeconomic Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR) , Nov. 2008.

Switchgrass Yields by Region in the U.S. Based on preliminary PNNL Research

Non-Food Grade (NFG) Corn Oil modeled in FASOM and FAPRI as a biodiesel feedstock. NFG Corn Oil is a byproduct of the extraction and fractionation processes of dry mill ethanol plants. Based on enginnering cost projections and expected rates of technology adoption. See Chapter XX of the RIA.

By 2022: 70% of dry mill ethanol plants will conduct extraction, 20% will conduct fractionation, and 10% will do neither The Conservation Reserve Program (CRP) has a maximum limit of 32 million acres enrolled in the program at any given time 2008 Farm Bill

USDA baseline assumptions Fertilizer Use U.S. nitrogen application rate in corn is approximately 136 lbs/acre in the corn belt in 2022

U.S. phosphorous application rate in corn is approximately 28 lbs/acre in the corn belt in 2022 Based on ARMS data, adjusted for differences in regions and irrigation practices

For U.S. assume higher yields require no increase in fertilizer use Based on USDA baseline assumptions

This holds for all farming rotations (e.g., corn / soybean and corn / corn) and land types (e.g., prime and marginal land); see below for stover removal impacts Nitrogen nutrient replacement application = 7 lbs/ton corn residue removed

Phosphorous nutrient replacement application = 3.6 lbs/ton corn residue removed These numbers come from the Argonne National Lab Report, Fuel Cycle Assessment of Selected Bioethanol Production Pathways in the United States. (November 7, 2006). (Used and cited by GREET) Processing 1 bushel of corn produces 17 lbs of dried distillers grains (dry tons).

1 pound of DGS substitutes 1.196 pounds of corn and soybean meal feed for beef cattle, dairy cows by 2015

1 pound of DGS substitutes 1 pound of corn and soybean meal feed for swine and poultry, with adjustments for fractionated/extracted DGS www.ethanol.org

Argonne National Laboratory: Update of Distillers Grains Displacement Ratios for Corn Ethanol Life-Cycle Analysis,” September 2008

Shurson: The Value of High-Protein Distillers Coproducts in Swine Feeds, Distillers Grains Quarterly, First Quarter 2006.

Projected crop yields, both domesti cally and internationally, are an important factor in the analysis of increased renewable fuel volumes. The U.S. yields presented in Table 5.1-1 are based on the USDA projections through 2018 (the last year of the USDA baseline projections report) and then extrapolated out to 2022. The U.S. yields in this table represented the national weighted average yields, which vary by region. Th ese yields are based on hi storical averages for10 the region, which increase over time in both the AEO 2007 Reference Case and the EISA Control Case. FASOM assumes the rate of increase for corn yields are 1.6 percent per year.

The international crop yields included in FA PRI are different for each country and for each crop. The FAPRI model bases on each coun try’s crop yield on historical trends and projects this technical rate of progress into the fu ture. As described in the previous section, the FAPRI model also incorporates yield responses to changes in price and for expansion onto marginal lands. Examples of how yields vary by region and crop are included in Table 5.1-1.

For the lifecycle analysis, sensitivity runs were conducted in both FASOM and FAPRI to observe the effects of higher yields for both corn and for soybeans. The assumption behind these high-yield runs is that the technological rate of progress over time is higher for corn and soybeans than compared to our “base yield” m odeling efforts used for the rulemaking impacts. This increase in the technological rate of prog ress begins in 2012 in the FASOM model, and in 2010 in the FAPRI model (the next future time period in each respective model). By 2012, Corn yields in the U.S. are 7.1 percent higher in the High Yield Control Case than in the Base Yield Control Case. By 2017, it is 18 percent higher, and by 2022 it is 30 percent higher. Similarly, soybean yields in the U.S. are 7.8 percent higher in 2012, 20 percent higher in 2017, and 31 percent higher in the High Yield Control Case th an in the Base Yield Control Case. In the FAPRI model, similar increases in technological ra tes of progress for corn were applied to other top corn producers (China, Mexico, the EU, Arge ntina, and Brazil), and likewise for other top soybean producers (China, Argentina, and Brazil). Results from these high yield sensitivity runs can be found in Chapter 2 of the RIA. Table 5.1-2 lists the yields for the top producers of corn and soybeans in the Control Case in 2022, both fo r the “base” results and for the “high-yield” results. For overall impacts of the “high-yield” sensitivity model runs, please refer to Chapter 2.

Table 5.1-2. Corn and Soybean Yields of Top Producers in the Control Case in 2022 with “Base Yield” and “High-Yield” Sensitivity Runs (bushels per acre)

Corn Country Base Yield High Yield % Difference U.S. 181.2 231.6 27.9% China 102.3 129.9 27.0% Mexico 55.8 70.9 27.0% EU 110.5 140.4 27.0% Argentina 133.9 170.1 27.0% Brazil * 72.2 81.8 13.3% Soybeans Country Base Yield High Yield % Difference U.S. 46.0 60.5 31.4% China 29.8 38.9 30.4% Argentina 46.1 60.1 30.4% Brazil * 46.3 57.8 25.0% * Note: Yields in individual regions in Brazil are 27% higher than in the Base yield, similar to other countries. However, sin ce some regions are more productive than others, the regional distribution of soybean production is different between the “Base” and “H ighYield” model runs. This results in the overall yields of corn and soybeans in Brazil in the “High Yield” sensitivity run to no t be the same percentage higher than the “Base” model run compared to other countries.11 For cellulosic biofuels from corn residues, the current assumptions in FASOM for residue removal rates are based on the Graham et. al. paper S and the Perlack et al study. T This approach uses a maximum percent removal of residues on acres based on tillage practices. U Although we requested comment on whether a be tter metric is the minimum amount of mass that must remain on an acre of land to prevent runoff and maintain soil carbon levels, we did not receive sufficient data to incorporate this approach into our modeling framework. V

FASOM assumes fertilizer appli cation rates do not increase ov er time in proportion to the increase in yields (i.e., delinks fertilizer application rates and crop yield changes through time). The principal reason for this is USDA data that shows fertilizer application rates per acre remaining relatively steady for the past 30 years, during which time corn yields have increased approximately 70 percent. W However, when residues are removed from the field, some of the nutrients that are contained in the residue must be replaced through additional fertilizer use. For the analysis, we assumed that 7 additiona l pounds of nitrogen and 3.6 pounds of phosphorous must be applied per ton of corn stover residue removed.

Lastly, there is a limit to how many acres crops can be planted on Conservation Reserve Program (CRP) land. X CRP is run by the Natural Resour ces Conservation Se rvice at USDA. This program is designed to maintain Federal, State, and tribal envir onmental laws by making payments to farmers equivalent to the income otherwise earned from developing the land enrolled in the program. In the 2008 Farm Bill, the number of acres enrolled in the CRP was given a maximum limit of 32 million acres. Ba sed on input from USDA, we assumed that USDA will increase payments to maintain 32 million acres in CRP through 2022.

5.1.4 Volumes

For the agricultural sector analysis, we modeled the AEO2007 Reference Case and the Control Case (i.e., EISA manda ted) volumes described in Chapter 1.2. Where possible, we modeled the same volumes in bot h FASOM and FAPRI. However, some of the projected future sources of renewable fuels are not explicitly in cluded in both models. For example, since FASOM is a domestic agriculture and forestry model, it cannot explicitl y model U.S. biofuel imports and its effect on worldwide trade and la nd use as the FAPRI model does. In addition, the FAPRI model does not currently model cellu losic renewable fuel feedstock production. Therefore, the cellulosic renewable fuel analysis relies on results from the FASOM model. Y
Neither of the two models used for this an alysis — FASOM or FAPRI — include biofuel produced from domestic municipal solid waste (MSW). Thus, for the RFS2 analysis, this biofuel

S See also http://www.cpnrd.org/Harvesting%20Stover.pdf

T Available at http://feedstockreview.ornl.gov/pdf/billion_ton_vision.pdf

. U Many site specific factors associated with the sustainable removal of residue (e.g., crop type, soil type, soil fertility, slope, and climate) affect which geographic regions are suitable for crop residue removal. Detailed modeling of these factors was beyond the scope of this analysis.

V Wilhelm et. al Corn Stover to Sustain Soil Organic Carbon Further Constrains Biomass Supply, Ag Journal (2007)

W Data from the National Agriculture Statistics Service (NASS) at USDA: http://www.nass.usda.gov/

X See: http://www.nrcs.usda.gov/programs/CRP/

Y The FAPRI model was used to estimate the indirect land use effects of additional switchgrass acres on other crops in the U.S., as shown by the FASOM model, and the resultant impacts on trade and land use worldwide. Please refer to Chapter XX of the RIA for more information.12 was modeled outside of the agriculture sector. For more information on how MSW was modeled elsewhere in the RFS2 analysis, please see Chapters XX and XX. We estimate that approximately 2.3 Bgal of cellu losic renewable fuels will be produced from municipal solid waste in 2022.

All the results presented in the following section are relative to the AEO 2007 Reference Case renewable fuel volumes, which include 12.3 Bgal of grain-based ethanol, 0.1 Bgal of biodiesel from soybean oil, 0.1 Bgal of biodiesel from waste oils and greases, 0.3 Bgal of cellulosic ethanol, and 0.6 Bgal of imported et hanol in 2022. The domestic figures are provided by FASOM and FAPRI, and all of the internati onal numbers are provided by FAPRI. For a more detailed set of results of the agricultural sector impacts of the RFS2 volumes, see the analytical reports submitted by FASOM and FAPRI in the docket of this rule.

For ethanol, we assumed 15 billion gallons (Bga l) of corn ethanol would be produced for use as transportation fuel in the U.S. by 2022 in the Control Case in both FASOM and FAPRI. FASOM modeled an increase of 13.5 Bgal of cellu losic renewable fuel from the Reference Case in 2022. Z To satisfy the cellulosic renewable fuel requirements, the FASOM model was allowed to choose how much cellulosic renewable fuel wa s produced from different feedstocks, taking account the various harvesting and processing cost s and the income the agriculture and forestry sectors derive from each feedstock. FASOM proj ects that 7.9 Bgal of cellulosic renewable fuel will be produced from switchgrass in 2022, 4.9 Bgal from corn residue, 0.4 Bgal from sugarcane bagasse, and 0.1 billion gallons fro m forestry logging and milling residues. FAPRI modeled an increase of 1.6 Bgal from ethanol imported to th e U.S. over the AEO2007 Reference Case level in 2022.

For biodiesel, both FASOM and FAPRI modeled an increase of 0.5 Bgal of biodiesel produced from soybean oil above the AEO2007 Reference Case in 2022. FASOM and FAPRI also modeled an increase of 0.1 Bgal of biodiesel produced from various animal fats, waste oils, and greases to 0.4 Bgal in the Control Case in 2022. Similarly, FASOM and FAPRI modeled an increase of 0.6 Bgal of biodiesel from non-food gr ade corn oil. This non-food grade corn oil used for biodiesel production is a byproduct of dry mill ethanol plants that undertake the extraction processes.

Z FASOM does not include renewable diesel or biomass to liquids as potential cellulosic pathways, therefore all cellulosic volumes were assumed to be ethanol.13 Table 5.1-3. Biofuel Volumes Modeled in 2022 (Billions of Gallons)

Biofuel AEO2007 Reference Case Control Case Change

Corn Ethanol 12.3 15.0 2.7 Switchgrass Cellulosic Ethanol 0 7.9 7.9 Corn Residue Cellulosic Ethanol 0 4.9 4.9 Sugarcane Bagasse Cellulosic Ethanol 0.2 0.6 0.4 Forest Residue Cellulosic Ethanol 0 0.1 0.1 Imported Ethanol 0.6 2.2 1.6 Total Ethanol (FASOM) 12.5 28.7 16.2 Total Ethanol (FAPRI) 13.2 17.5 4.3 Soybean Oil Biodiesel 0.1 0.6 0.5 Corn Oil NFG Biodiesel 0.0 0.6 0.6 Biodiesel from Other Fats, Oils, Greases 0.3 0.4 0.1 Total Biodiesel 0.4 1.7 1.3

5.1.5 Domestic Agricultural Impacts

For this economic analysis, the FASOM model is utilized for all domestic agriculture impacts. Although the FAPRI models do not provide the same amount of detail on GHG emissions for the domestic agriculture impacts as the FASOM model, FAPRI does estimate some of the same outputs, such as national crop acres, pr ices, and exports. In this chapter, we present both the FASOM and FAPRI results to demonstrate the range of potential agricultural impacts. Presenting both sets of results allows for a usef ul comparison between the two models, reinforces the accuracy of our domestic analysis, and ensures consistency when analyzing the impacts of the RFS2 fuel volume requirements on the domes tic and international agriculture markets.

5.1.5.1 Commodity Prices

To meet the RFS2 renewable fuel volumes, there are a number of price effects on the agricultural commodities. For instance, FASOM estimates that the Control Case renewable fuel volumes result in an increase in the U.S. corn price of $0.27 per bushel (8.2 percent) above the Reference Case price in 2022. By 2022, FASOM projects that U.S. soybean prices increase by $1.02 per bushel (10.3 percent) above the Reference Case price. FASOM also projects the price of soybean oil increases by $183 per ton (37.9 per cent) over the 2022 Reference Case price. In 2022, FAPRI projects that the price of corn increases by $0.10 per bushel (3.3 percent), the price of soybeans increases by $0.07 per bushel (0.9 percen t), and the price of soybean oil increases by $12.35 (1.6 percent) relative to the AEO2007 Reference Case.

FASOM projects that the price of switchgra ss increases by $20.12 per wet ton as a result of the Control Case renewable fuel volumes in 2022. Similarly, the price of corn residue increases by $29.48 per wet ton in 2022, relative to the AEO2007 reference case. FASOM also projects that the Control Case price of sugar cane bagasse increases by $23.27 per wet ton in14 2022. By 2022, FASOM projects that hardwood a nd softwood logging residues are used to produce cellulosic ethanol, and their prices are $23.22 per wet ton and $18.37 per wet ton, respectively, in 2022 in the Control Case. These prices do not include the storage, handling, or delivery costs. Since the FAPRI models do not explicitly model cellulosic ethanol production from agriculture residues or dedicated energy crops, comparable price impacts are not available. Additional details on the changes in commodity prices are included in Table 5.1.4.

The prices for byproducts of renewable fuel production are also affected by the increased demand for renewable fuels required by the RFS2 rule. Soybean meal, while not exclusively used for animal feed, is an important element of the feed market. In 2022, FASOM projects that the price of soybean meal decreases by $0.48 per ton (-0.1 percent) relative to the AEO2007 Reference Case. In 2022, FASOM projects the pri ce of fractionated/extracted DGS increases by $7.69 per ton (6.5 percent) relative to the AEO200 7 Reference Case. FASOM projects that the price of traditional DGS, produced by corn etha nol plants that do not conduct fractionation or extraction of corn oil, increases by $7.94 per ton (6.8 percent) relative to the AEO2007 Reference Case in 2022. In FAPRI, the price of soybean meal increases by $1.05 (0.5 percent), and the price of DGS increases by $3.52 per ton (3.9 percent) in 2022 relative to the AEO2007 Reference Case.15 Table 5.1-4. U.S. Commodity Prices in 2022 (2007$ per Unit)

Biofuel Feedstocks FASOM FAPRI Commodity (Unit) AEO 2007 Reference Case Control Case Change % Change AEO 2007 Reference Case Control Case Change % Change
Corn (bushel) $3.32 $3.60 $0.27 8.2% $2.96 $3.06 $0.10 3.3% Soybeans (bushel) $9.85 $10.87 $1.02 10.3% $8.12 $8.19 $0.07 0.9% Soybean Oil (ton) $483.10 $666.42 $183.32 38% $782.13 $794.48 $12.35 1.6% Switchgrass (wet ton) $20.73 $40.85 $20.12 97% N/A N/A N/A N/A Corn Residue (wet ton) $5.01 $34.49 $29.48 588% N/A N/A N/A N/A
Bagasse (wet ton) $6.43 $29.70 $23.27 362% N/A N/A N/A N/A Hardwood Logging Residue (wet ton) $5.37 $23.22 $17.85 332% N/A N/A N/A N/A
Softwood Logging Residue (wet ton) $9.37 $18.37 $8.99 96% N/A N/A N/A N/A
Byproducts FASOM FAPRI Commodity (Unit) AEO 2007 Reference Case Control Case Change % Change AEO 2007 Reference Case Control Case Change % Change
Soybean Meal (ton) $402.11 $401.63 -$0.48 -0.1% $206.81 $207.87 $1.05 0.5% DGS Traditional (ton) $116.75 $124.69 $7.94 6.8% N/A N/A N/A N/A DGS Fractionated/Extracted (ton) $118.88 $126.57 $7.69 6.5% N/A N/A N/A N/A

DGS (Overall, FAPRI) N/A N/A N/A N/A $90.60 $94.12 $3.52 3.9%

5.1.5.2 Commodity Use Changes

The increased demand for renewable fuels also a ffects the use of these feedstocks in other markets. This section will review the use of these commodities for biofuels, their levels of exports, and their use in the animal feed mark et. In 2022, FASOM projects an additional 1 billion bushels of corn will be used for corn ethanol production (22 perc ent) relative to the Reference Case. FASOM also projects that in 2022, an additional 98 million wet tons of switchgrass, 60 million wet tons of corn residue , 6.1 million tons of sugarcane bagasse, and 1.7 million tons of forestry residues will be used to produce cellulosic ethanol, relative to the AEO2007 Reference Case.

FASOM estimates that an add itional 2 million tons of soybean oil is used to produce soybean biodiesel in 2022, relative to the AE O2007 Reference Case. In addition, FASOM projects that an additional 17.5 million tons of non-food grade corn oil from the extraction process will be used for biodiesel production in 2022.16 The increase in renewable fuel volumes requi red by the RFS2 also impacts U.S. exports. For instance, FASOM estimates that the amount of corn exported from the U.S. decreases by 188 million bushels (-8.2 percent) in 2022, relative to the AEO2007 Reference Case. This change represents a decrease of $57 million (-0.8 percent) in the total value of corn exports in the FASOM model in 2022. In FAPRI, U.S. corn exports decrease by 407 million bushels (-15.7 percent) in 2022. This change translates into a decrease in the total value of corn exports of $991 million (-12.9 percent) in 2022.

Similarly, as more soybean oil is used fo r biodiesel production, the amount of soybeans and soybean oil exported from the U.S. can be expected to be affected. In FASOM, soybean exports decrease by 135 million bushels (-13.6 percent) in 2022 relative to the AEO2007 Reference Case. This change represents a decrease of $453 million (-4.6 percent) in the total value of U.S. soybean exports in 2022. In FAPRI, soybean exports decrease by 32 million bushels (-3 percent), relative to the AEO2007 Reference Case. This change represents a decrease in the total value of U.S. soybean exports of $185 million (-2.1 percent) in 2022. The FASOM model projects that U.S. soybean oil ex ports decrease by 1.2 million tons (-51 percent) in 2022 relative to the AEO2007 Reference Case. In the FAPRI model, soybean oil exports decrease by 0.3 million tons (-6.2 percent) in 2022 relative to the AEO2007 Reference Case.

Table 5.1-5. U.S. Exports in 2022

Exports (millions of units) FASOM FAPRI Commodity (Unit) AEO 2007 Reference Case Control Case Change % Change AEO 2007 Reference Case Control Case Change % Change
Corn (bushel) 2,281 2,093 -188 -8.2% 2,589 2,182 -407 -15.7% Soybeans (bushel) 993 858 -135 -13.6% 1,073 1,041 -32 -3.0%
Soybean Oil (ton) -1.2 -51.2% -0.3 -6.2% Total Value of Exports (millions of 2007$) FASOM FAPRI Commodity AEO 2007 Reference Case Control Case Change % Change AEO 2007 Reference Case Control Case Change % Change
Corn $7,585 $7,527 -$57 -0.8% $7,669 $6,679 -$991 -12.9% Soybeans $9,780 $9,327 -$453 -4.6% $8,709 $8,524 -$185 -2.1%

Higher U.S. demand for renewable fuels causes a decrease in the use of corn for U.S. livestock feed. The total amount of corn used for feed in FASOM decreases by 3.3 million tons (-2.5 percent) in 2022 relative to the AEO2007 Re ference Case. Substitutes are available for corn as a feedstock, and this market is price sensitive. Severa l ethanol processing byproducts can be used to replace a portion of the corn used as feed, depending on the type of animal. DGS are a byproduct of the dry milling process, whereas gluten meal and gluten feed are byproducts of wet milling corn ethanol producti on. FASOM estimates that total DGS used in feed increases by 5.2 million tons (15.2 percent), gluten meal us ed in animal feed decreases by 0.1 million tons (-4.5 percent), and gluten feed use increases by 0.3 million tons (6.4 percent) in 2022 relative to the AEO2007 Reference Case. As DGS are used mo re in the feed market in the Control Case17 than in the Reference Case, corn and soybean meal used in the feed market is replaced. Thus, soybean meal used in feed decreases by 0.04 million tons (-0.4 percent) in 2022 to 10.5 million tons in the Control Case. Overall, the total et hanol byproducts used in feed (DGS, gluten meal, and gluten feed) increase by 5.4 million tons (13.2 percent) in 2022 relative to the AEO2007 Reference Case.

Table 5.1-6. Animal Feed Sources in 2022 (millions of tons)

FASOM FAPRI Feed Source AEO 2007 Reference Case Control Case Change % Change AEO 2007 Reference Case Control Case Change % Change

Corn 134.4 131.1 -3.3 -2.5% 158.8 152.3 -6.5 -4.1% Soybean Meal 10.53 10.49 -0.04 -0.4% 39.4 38.7 -0.7 -1.7% DGS Total 34.1 39.3 5.2 15.2% 33.9 39.1 5.2 15.2% Gluten Meal 2.2 2.1 -0.1 -4.5% 1.0 1.1 0.1 7.5% Gluten Feed 4.5 4.8 0.3 6.4% 7.6 7.9 0.3 4.1% Total Ethanol Byproducts 40.7 46.1 5.4 13.2% 42.5 48.1 5.5 13.0%

5.1.5.3 Changes in Crop Acres

In order to meet the Control Case volumes of renewable fuels, FASOM estimates an increase of 3.6 million acres (4.6 percent) for harvested corn acres in 2022. AA Most of the new corn acres come from a reduction in existing crop acr es, such as rice, wheat, barley, rye and hay. FASOM projects that rice acres decrease by 788 thousand acres (-20.6 percent), wheat acres decrease by 2.9 million acres (-4.9 percent), and hay acres decrease by 752 thousand acres (-1.5 percent) in 2022 relative to the AEO2007 Refere nce Case. See Table 5.1-7 for additional changes in crop acres in the FASOM and FAPRI models.

Although the RFS2 Control Case includes more soybean biodiesel than the AEO2007 Reference Case, competing demands for land results in a decrease in U.S. harvested soybean acres. According to the FASOM model, harvested soybean acres decrease by approximately 1.4 million acres (-2.1 percent) in 2022 relative to the C ontrol Case. As described in the previous section, most of the additional soybeans needed for increased biodiesel production are diverted from exports. FAPRI also projects that the in creased demand for biodiesel from soybean oil results chiefly in a reduction in soybean oil exports, rather than an increase in acres harvested. FAPRI projects that harvested soybean acres decrease by 0.9 million acres (-1.1 percent) in 2022 relative to the AEO2007 Reference Case.

As the demand for cellulosic renewable fuels increases, FASOM projects that most of the cellulosic biofuels will be deri ved from switchgrass. In 2022, sw itchgrass acres increase by 12.5

AA

FASOM estimates that total planted corn acres increas e to 89.4 million acres in the Control Case from the Reference Case level of 84.6 million acres in 2017. Total planted acres increases to 87.1 million acres in the Control Case from the Reference Case level of 83.5 million acres in 2022.18 million acres, relative to the AEO2007 Reference Case . The remainder of the cellulosic biofuel is produced from corn residue, forestry residues, and sugarcane bagasse. The FAPRI models do not explicitly model the producti on of cellulosic ethanol, nor does it explicitly model the feedstocks for cellulosic ethanol. Table 5.1-7 and Figure 5.1-2 shows the change in acres for all crops in the U.S. in 2022.19 Table 5.1-7. U.S. Crop Acres in 2022 (millions of acres)

FASOM FAPRI Crop AEO 2007 Reference Case Control Case Change % Change AEO 2007 Reference Case Control Case Change % Change
Barley 9.7 8.5 -1.2 -12.4 2.96 3.0 0.04 1.2% Corn 77.9 81.5 3.6 4.6% 79.1 80.9 1.8 2.2% Cotton 11.3 11.1 -0.2 -1.7% 7.7 7.7 0.0 0.1% Wheat 59.0 56.0 -2.9 -4.9% 48.3 48.1 -0.2 -0.5% Hay 50.7 50.0 -0.8 -1.5% 60.8 60.8 0.0 -0.1% Oats 5.5 5.4 -0.2 -3.2% 1.1 1.1 0.0 -1.0% Rice 3.8 3.0 -0.8 -20.6% 2.6 2.6 0.0 0.0% Sorghum 8.7 8.5 -0.2 -1.8% 5.8 5.8 0.0 0.3% Soybeans 68.1 66.6 -1.4 -2.1% 78.3 77.4 -0.9 -1.1% Sugarbeet 1.3 1.2 -0.1 -7.8% 1.2 1.2 0.0 -0.3% Sugarcane 0.7 0.9 0.1 19.8% 0.8 0.8 0.0 0.2% Switchgrass 0.1 12.6 12.5 20,261% 0.0 0.0 0.0 N/A Other (FASOM)* 9.5 9.1 -0.4 -4.4% N/A N/A N/A N/A Other (FAPRI)** N/A N/A N/A N/A 4.7 4.7 0.0 0.1% Total 306.3 314.4 8.1 2.6% 288.7 289.4 0.7 0.2%20 Figure 5.1-2. Estimated Change in U.S. Crop Acres Relative to the AEO 2007 Reference Case in 2022

-4 -2 0 2 4 6 8 10 12 14BarleyCornCottonWheatHayOatsRiceSorghumSoybeansSugarbeetSugarcaneSwitchGrassOther (FASOM)Other (FAPRI)TotalMillions of Acres FASOM

FAPRI

As switchgrass and corn residue are the larg est feedstocks of cellulosic ethanol, it is important to know which regions in the U.S. thes e feedstocks are the most competitive. Corn residue removal takes place chiefly in the Corn Belt region of the U.S. Based on the residue removal rates outlined in Table 5.1-1, corn acres w ith residue removal in the Control Case takes place mostly in Iowa (9 million acres) and I llinois (7.4 million acres). Switchgrass production, on the other hand, takes place mostly in the Sout hwest region of the U.S. This includes 2.7 million acres in Kansas, 1.7 million acres each in Missouri and Texas, as well as 1.3 million acres in Oklahoma. To see the top ten producing states for corn residue and switchgrass, please refer to Figures 5.1-3 and 5.1-4 below.21 Figure 5.1-3. Top Ten Producing States of Corn Acres with Residue Removal in 2022 FASOM Control Case

0 1 2 3 4 5 6 7 8 9 10IowaIllinoisSouthDakotaNebraskaNorthDakotaMissouriMichiganIndianaKansasOhioMillions of Acres22 Figure 5.1-4. Top Ten Producing States of Switchgrass in 2022 FASOM Control Case

0.0 0.5 1.0 1.5 2.0 2.5 3.0KansasMissouriTexasOklahomaArkansasOhioKentuckyNorthCarolinaTennesseeLouisianaWisconsinFloridaMillions of Acres

5.1.5.4 Land Use Change

Changes in these land categories are summarized in Table 5.1-8 below. In 2022, FASOM projects that total cropland increases by 3.1 million acres (1.0 percent) relative to the Reference Case. The increase in cropland is de rived primarily from a combination of decreased idle cropland acres, a decrease in idled cropland pasture acres, a nd a decrease in forest acres. FAPRI does not explicitly model U.S. forest or pasture acres.23 Table 5.1-8. Change in U.S. Major Land Use Categories in 2022 Relative to the Reference Case (millions of acres)

Land Category AEO 2007 Reference Case Control Case Change % Change Cropland 311.7 314.8 3.1 1.0% Used for Production 306.3 314.4 8.1 2.6% Idled 5.4 0.4 -5.0 -93.0% Cropland Pasture 32.0 30.1 -1.9 -5.8% Used for Production 23.1 25.0 1.8 8.0% Idled 8.9 5.2 -3.7 -41.7% Forest Pasture 148.4 149.5 1.1 0.7% Used for Production 113.1 114.4 1.3 1.1% Idled 35.3 35.1 -0.2 -0.5% Forestland 344.5 343.3 -1.2 -0.3% Rangeland 578.8 578.8 0 0% Used for Production 522.6 516.8 -5.8 -1.1% Idled 56.3 62.0 5.7 10.2% CRP 32.0 32.0 0.0 0% Developed Land 35.0 35.0 0.0 0%

5.1.5.5 Fertilizer Use

As crop acres increase to meet the additional demand for corn and other crops for biofuel production, fertilizer use increases as a result. In 2022, FASOM estimates that nitrogen fertilizer use in the U.S. agricultural sector will incr ease by 1.5 billion pounds (5.7%) relative to the AEO2007 Reference Case levels . FASOM also projects that phosphorous fertilizer use will increase by 714 million pounds (12.7%) relative to the Reference Case level in 2022. The FAPRI model does not provide estimates for fertilizer use.

Table 5.1-9. Change in U.S. Fertilizer Use Relative to the Reference Case (millions of pounds)

Fertilizer AEO 2007 Reference Case Control Case Change %Change

Nitrogen 26,209 27,710 1,501 5.7% Phosphorous 5,614 6,328 714 12.7%

5.1.5.6 Impact on U.S. Farm Income

The increase in renewable fuel production pr ovides a significant increase in net farm income to the U.S. agricultural sector. FASOM pr edicts that net U.S. farm income will increase by $13 billion dollars in 2022 (36 percent).24 5.1.5.7 Impact on U.S. Food Prices

Higher corn and soybean prices also result in higher meat prices. For example, in 2022, the average price for all meat production in th e FASOM model increases by 0.1 percent. In FAPRI, the price of beef increases by $0.37 per hundredweight (0.4 percent) in 2022 to $95.84 per hundredweight in the Control Case, a nd the price of pork increases by $0.77 per hundredweight (1.6 percent) in 2022 to $49.19 per hundredweight in the Control Case.

Due to higher commodity prices, FA SOM estimates that U.S. food costs BB would increase by roughly $10 per person per year by 2022, relative to the Reference Case. CC Total effective farm gate food costs would in crease by $3.6 billion (0.2 percent) in 2022. DD To put these changes in perspective, average U.S. per capita food expenditures in 2007 were $3,778 or approximately 10 percent of pers onal disposable income. The total amount spent on food in the U.S. in 2007 was $1.14 trillion dollars. EE

5.1.6 International Impacts

The FAPRI models are utilized to assess the international impacts on trade, land use, and food consumption as a result of the RFS2 renewabl e fuel volume requirement in the U.S. In the FAPRI models, links between the U.S. and inte rnational models are made through commodity prices and net trade equations. In general, fo r each commodity sector, the economic relationship that quantity supplied equals qua ntity demanded is achieved through a market-clearing price for the commodity. In other countries domestic prices are modeled as a function of the world price using a price transmission equation. Since econo metric models for each sector can be linked, changes in one commodity sector will impact the other sectors.

The model for each commodity consists of a number of countries/regions, including a rest-of-the-world aggregate to cl ose the model. The models sp ecify behavioral equations for production, use, stocks, and trade between countries /regions. The models solve for representative world prices by equating excess supply and dema nd across countries. Using price transmission equations, the domestic price for each country is linked with the representative world price through exchange rates. It is through change s in world prices that change in worldwide commodity production and trade is determined.

5.1.6.1 Global Commodity Price Changes

BB FASOM does not calculate changes in price to the consumer directly. The proxy for aggregate food price change is an indexed value of all food prices at the farm gate. It should be noted, however, that according to USDA, approximately 80% of consumer food expenditures are a result of handling after it leaves the farm (e.g., processing, packaging, storage, marketing, and distribution). These costs consist of a complex set of variables, and do not necessarily change in proportion to an increase in farm gate costs. In fact, these intermediate steps can absorb price increases to some extent, suggesting that only a portion of farm gate price changes are typically reflected at the retail level. See http://www.ers.usda.gov/publications/foodreview/septdec00/FRsept00e.pdf.

CC These estimates are based on U.S. Census population projections of 331 million people in 2017 and 348 million people in 2022. See http://www.census.gov/population/www/projections/summarytables.html

DD Farm Gate food prices refer to the prices that farmers are paid for their commodities.

EE See www.ers.usda.gov/Briefing/CPIFoodAndExpenditures/Data/table15.htm

.25 As demand for renewable fuels in the U.S. in creases, the FAPRI model projects that U.S. and world commodity prices will generally increase. FF FAPRI projects that the world price of corn increases by $0.12/bu (3.1 percent) rela tive to the AEO2007 Reference Case in 2022. Similarly, FAPRI projects that world soybean pr ices increase by $0.08/bu (0.8 percent) and the world soybean oil price increases by $13.22 per ton (1.5 percent) in 2022 relative to the AEO2007 Reference Case.

Since increased biofuel demand in the U.S. also impacts the livestock market, in terms of land use (i.e., pasture) and the feed market, we exp ect prices to change as well. The world price for beef, which is based on U.S. prices, in creases $7.34 per ton (0.4 percent) in 2022 to $1,917 per ton in the Control Case.

Table 5.1-10. Global Commodity Price Changes from RFS2 in 2022 (2007$ per unit)

Commodity (Unit) AEO 2007 Reference Case Control Case Change % Change

Corn (bushel) $3.76 $3.88 $0.12 3.1% Soybeans (bushel) $9.55 $9.63 $0.08 0.8% Soybean Oil (ton) $854.45 $867.67 $13.22 1.5% Beef (ton) $1,909.42 $1,916.76 $7.34 0.4%

5.1.6.2 World Renewable Fuels Trade

As the U.S. increases its demand for renewable fuels, world trade markets for renewable fuels are also likely to be impacted. As desc ribed in Section 1.2, we estimate that in 2022, the U.S. will increase net imports of ethanol by 1.6 billion gallons (248%) relative to the AEO2007 Reference Case. In response, FAPRI projects that Brazil will increase net exports by 1.5 billion gallons (37.8 percent) in 2022 relative to the Refe rence Case. However, since the U.S. demand for ethanol imports exceeds the increase in Brazilian net exports, FAPRI projects that other countries will reduce their net imports of ethanol . In 2022, FAPRI projects that China decreases net imports of ethanol by 7.9 million gallons (-5.3 percent), the European Union decreases net imports by 16.8 million gallons (-2.3 percent), India decreases net imports by 15.2 million gallons (-7.4 percent), Japan decreases net im ports by 3.1 million gallons (-0.5 percent), and South Korea decreases net imports by 1.6 million gallons (-0.4 percent) relative to the AEO2007 Reference Case. The rest of the world decreas es net imports of ethanol by 0.7 million gallons (- 0.2 percent) in 2022 relative to the Reference Case.

FF FAPRI U.S. and world prices are different based on XXX.26 Figure 5.1-5. Ethanol Net Exports by Country in 2022

-3 -2 -1 0 1 2 3 4 5 6

Brazil China Canada EU India Japan South Korea US ROWBillions of Gallons Reference Case

Control Case

Since the world price of soybean oil increase s (1.5 percent), whereas the world price of biodiesel decreases (-1.3 percent) due to the RFS2 renewable fuel volume requirements, it becomes relatively more profitable to increase net exports of soybean oil for major producers. This results in less soybean oil being used in th e production of biodiesel and therefore a decrease in biodiesel net exports in some countries. Arge ntina decreases their biodiesel net exports by 1.3 million gallons (-0.8 percent) to 152.4 million gall ons, Brazil decreases their biodiesel net exports by 6.2 million gallons (-7.4 percent) to 77.4 million gallons in 2022. In response the EU increases their net exports of biodiesel by 8.6 million gallons (29.6 percent) to 37.8 million gallons in the Control Case. Additionally, Ja pan reduces its net imports of biodiesel by 0.5 million gallons (-5.9 percent).27 Figure 5.1-6. Biodiesel Net Exports by Country in 2022

-0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2

Argentina Brazil Indonesia Malaysia US EU Japan ROWBillions of Gallons

Reference Case

Control Case

5.1.6.3 International Crop Acre Changes

Changes to the global commodity trade markets and world commodity prices result in changes in international land use. The FAPRI m odel provides internationa l change in crop acres as a result of the RFS2 renewable fuel volumes. Internationally, Brazil has the largest increase in crop acres in 2022, followed by a subset of na tions in Africa, Japan, the EU, Mexico, and Indonesia. As the U.S. increases its net impor ts of ethanol by 1.5 billion gallons in 2022, the major supplier of this increase in ethanol is Br azil which produces ethanol from sugarcane. The FAPRI model estimates that Brazil crop acres in crease by 2.2 million acres (1.6 percent) relative to the AEO2007 Reference Case. The major crop contributing to this increase is sugarcane, which increases by 1.2 million acres (4.4 percent) in 2022. “Africa, Other” increases total crop acres by 0.20 million acres (0.25 percent), the large majority of which is corn, which increases by 0.19 million acres (0.32 percent). Japan incr eases its total crop acres by 0.14 million acres (3.3 percent), solely due to a 0.14 million acre increas e in rice acres. The EU increases its crop acres as well, by 0.12 million acres (0.1 percent). This is a result of a 0.06 million acre increase in wheat (0.1 percent), a 0.04 million acre increa se in barley (0.1 percent), and a 0.02 million acre increase in corn (0.1 percent). Mexico increases its crop acres by 0.11 million acres in 2022 (0.4 percent), which is primarily due to a 0.14 mill ion acre increase in corn (0.6 percent) and small decreases in other crops. Indonesia incr eases its total crop acres by 0.11 million acres in28 2022 (0.2 percent), including a 0.09 million acre increase in corn (0.9 percent) and a 0.02 million acre increase in rice (0.1 percent).

Figure 5.1-7. Change in World Crop Acres from the RFS2 Rule by Country in 2022

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Brazil US Africa,
Other Japan EU Mexico Indonesia Rest of
World WorldMillions of Acres

In response to the increased U.S. demand fo r imported ethanol, which is largely satisfied by increases in Brazil exports of sugarcane ethano l, sugarcane acres increase in various regions in Brazil. For instance, FAPRI projects 2022 sugarcane acres will increase by 0.01 million acres (2.7 percent) in the Amazon Biome region, 0.13 milli on acres (3.8 percent) in the Central-West Cerrados region, 0.15 million acres (3.6 percent) in the Northeast Coast region, 0.02 million acres (2.7 percent) in the North-Northeast Cerr ados region, 0.05 million acres (2.5 percent) in the South region, and 0.87 million acres (5.0 percent) in the Southeast region, relative to the AEO2007 Reference Case.

Area for other crops in Brazil, including corn and soybeans, are affected not only by the increase in sugarcane crop acres, but also by the changes in world price for each commodity. Overall, total crop area in Brazil increases by 2.2 million acres (1.6 percent) in 2022 relative to the AEO2007 Reference Case. This change is includes an increase of 0.2 million acres (1.8 percent) in the Amazon Biome, an increase of 0.6 million acres (1.9 percent) in the Central-West Cerrados, an increase of 0.2 million acr es (2.1 percent) in the Northeast Coast, an increase of 0.2 million acres (1.6 percent) in the North-Northeas t Cerrados, an increase of 0.2 million acres (0.429 percent) in the South, and an increase of 0.7 milli on acres (2.6 percent) in the Southeast, relative to the AEO2007 Reference Case in 2022.

Table 5.1-11. Change in Brazil Sugar cane Acres and Total Crop Area from RFS2 Rule by Region in 2022 (millions of acres)

Sugarcane Total Crops Region AEO 2007 Reference Case Control Case Change % Change AEO 2007 Reference Case Control Case Change % Change

Amazon Biome 0.45 0.46 0.01 2.7% 12.0 12.2 0.2 1.8% Central-West Cerrados 3.38 3.51 0.13 3.8% 31.8 32.4 0.6 1.9%

Northeast Coast 4.07 4.21 0.15 3.6% 11.1 11.3 0.2 2.1% North-Northeast Cerrados 0.64 0.66 0.02 2.7% 14.6 14.8 0.2 1.6%

South 2.02 2.07 0.05 2.5% 39.9 40.1 0.2 0.4% Southeast 17.47 18.34 0.87 5.0% 26.3 27.0 0.7 2.6% Brazil, Total 28.04 29.27 1.23 4.4% 135.7 137.9 2.2 1.6%

5.1.6.4 World Food Markets

The increase in renewable fuel volumes associated with the RFS2 will also impact world food consumption patterns. GG Since major agricultural commodity prices increase globally, FAPRI projects that world consumption of food decreases by 2.5 milli on metric tons (-0.1 percent) in 2022, relative to the AEO2007 Reference Ca se. This change is includes a decrease of consumption of dairy food produc ts of 0.1 million metric tons (- 0.03 percent), a decrease of 0.1 million metric tons (-0.05 percent) of livestock, a decrease of 0.3 million metric tons of sugar (- 0.15 percent), a decrease of 0.4 milli on metric tons of grains (-0.12 percent) of gr ains, a decrease of 1.7 million metric tons (-4.5 per cent) of vegetable oils, and an increase of 0.1 million metric tons of rice (0.03 percent), relative to th e AEO2007 Reference Case in 2022. Wheat consumption levels do not change between the Reference Case and the Control Case. While FAPRI provides estimates of changes in world food consumption, estimating effects on global nutrition is beyond the scope of this analysis.

GG The food commodities included in the FAPRI model include corn, wheat, sorghum, barley, soybeans, sugar, peanuts, oils, beef, pork, poultry, and dairy products.30 Table 5.1-12. Change in World Food Consumption Relative to the Reference Case (millions of metric tons)

Category AEO 2007 Reference Case Control Case Change % Change

Dairy 288.2 288.1 -0.1 -0.03% Livestock 301.4 301.3 -0.1 -0.05% Sugar 206.3 206.0 -0.3 -0.15% Wheat 605.9 605.9 0.0 0.00% Grains 314.5 314.1 -0.4 -0.12% Vegetable Oils 37.1 35.4 -1.7 -4.5% Rice 500.6 500.7 0.1 0.03% Total Food 2,258.4 2,256.0 -2.5 -0.1%



### 5.2 Petroleum, Biofuels and Energy Security Impacts

Increasing usage of renewable fuels helps to reduce U.S. petroleum imports. A reduction of U.S. petroleum consumption a nd imports reduces both financial and strategic risks associated with a potential disruption in supply or a spike in cost of a particular energy source. This reduction in risks is a measure of improved U.S. en ergy security. In this section, we detail an updated methodology for estimating the energy securi ty benefits of reduced U.S. oil imports which explicitly includes biofuels. Based upon this updated approach, we estimate the monetary value of the energy security benefits associated with the increased usage of renewable fuels in the U.S. required by the RFS2 rule.

5.2.1 Implications of Reduced Petroleum Use on U.S. Imports

In 2008, U.S. petroleum import expenditures repr esented 21 percent of total U.S. imports of all goods and services. HH In 2008, the U.S. imported 66 perc ent of the petroleum it consumed, and the transportation sector accounted for 70 per cent of total U.S. petroleum consumption. This compares to approximately 37 percent of petrol eum from imports and 55 percent consumption of petroleum in the transportation sector in 1975. II It is clear that petroleum imports have a significant impact on the U.S. economy. Requiring the wider use of renewable fuels in the U.S. is expected to lower U.S. petroleum imports.

For this rule, EPA estimated the reductions in U.S. petroleum imports using a modified version of the National Energy Modeling Syst em (EPA-NEMS). EPA-NEMS is an energyeconomy modeling system of U.S. energy ma rkets through the 2030 time period. EPA-NEMS projects U.S. production, imports, conversion, cons umption, and prices of energy subject to assumptions on world energy markets, resource availability and costs, behavioral and

HH Source: U.S. Bureau of Economic Analysis, U.S. International Transactions Accounts Data, as shown on June 24, 2009.

II Source: U.S. Department of Energy, Annual Energy Review 2008, Report No. DOE/EIA-0384(2008), Tables 5.1 and 5.13c, June 26, 2009.31 technological choice criteria, co st and performance characteristics of energy technologies, and demographics. For this analysis, the 2009 NE MS model was modified to use the 2007 (preEISA) Annual Energy Outlook (AEO) levels of biof uels in the Reference Case. These results were compared to our Control Case, which a ssumes the renewable fuel volumes required by EISA will be met by 2022. Details on how the EPA-NEMS model was adjusted to incorporate these volumes are included in the docket. JJ The reduction in U.S. oil imports projected by EPANEMS is roughly 0.9 million barrels per day (a 9. 5 per cent reduction in 2022). It is estimated that U.S. oil production in 2022 declines by mu ch less, just 0.01 million barrels per day.

Using the EPA-NEMS model, we also calculat ed the change in expenditures in both U.S. petroleum and biofuel imports with the RFS2 rule and compared these with the U.S. trade position measured as U.S. net exports of all goods and services economy-wide. Changes in fuel expenditures were estimated by multiplying the changes in petroleum and biofuel net imports by the respective imported petroleum prices and whol esale ethanol price forecasts. In Table 5.2.1-1, the net expenditures in reduced petroleum imports and increased biofuel im ports are compared to the total value of U.S. net exports of goods and services of the whole economy for 2022 as estimated by the EPA-NEMS model. We project that avoided expenditures on imported crude oil and petroleum products from the 2022 RFS2 vol umes of renewable fuels would be roughly $41.5 billion. Taking into consideration imports of renewable fuels, the total avoided expenditures on imported transpor tation fuels are projected to be $37.2 billion in the RFS2 control case.

Table 5.2.1-1. Selected U.S. Exports and Imports in 2022 (billions of 2007$)

Category AEO 2007 Reference Case RFS2 Control

Case Change Percent Change

Total U.S. Exports of Goods and Services 3,838 3831 7 +0.22%
Total U.S. Imports of Goods and Services 3,840 3833 7 +0.23%
Total U.S. Net Imports of Goods and Services 2 2 0 0%
Expenditures on Net Petroleum Imports 456 414 -41.5 -9.1% KK

Expenditures on Imported Ethanol 1 5.3 +4.3 +419% Total Expenditures on Transportation Fuel Imports 457 420 -37.2 -8.1%

JJ See Onlocation Technical Report, dated XXX, Docket Number XXX

. KK

Note: the 9.1 per cent reduction included in this table is a change in the monetary value of the oil reductions, whereas the 9.5 per cent reduction in oil imports cited in the previous paragraph refers to the volumetric change in imports.32 5.2.2 Background on U.S. Energy Security

U.S. energy security is broadly defined as protecting the U.S. economy against circumstances that threaten significant short- and long-term increases in energy costs. Most discussion of U.S. energy security revolves ar ound the topic of the economic costs of U.S. dependence on oil imports, although energy security is also a function of the stability of overall fuel supply and the flexibility of demand. An imp ortant part of the problem stems from U.S. reliance on imported oil, and the global oil mark et is strongly influenced by potentially unfriendly and unstable sources. In addition, oil ex porters have the ability to raise the price of oil by exerting monopoly power through the formati on of a cartel, the Organization of Petroleum Exporting Countries (OPEC). Finally, these fact ors contribute to the vulnerability of the U.S. economy to episodic oil supply shocks and price sp ikes. In 2008, U.S. imports of crude oil were roughly $391 billion (2007$, see Figure 5.2.2-1).

Figure 5.2.2-1. U.S. Expenditures on Crude Oil



### U.S. Expenditures on Crude Oil

0 100 200 300 400 500 600 700

1970 1975 1980 1985 1990 1995 2000 2005

Year(2007$ Billions) Domestic Imported

Source: Annual Energy Reviews and AEO 2009.

By requiring the wider use of renewable fuels, the RFS2 rule promotes diversification of transportation fuels in the U.S. and helps to imp rove the U.S.’s energy security. For the RFS2 proposal, an “oil import premium” approach was utilized to identify those energy securityrelated impacts which are not reflected in the mark et price of oil, and which are expected to change in response to an incremental change in the level of U.S. oil imports. For this analysis,33 the “oil import premium” approach was extended to explicitly consider the energy security implications of the expansion of renewa ble fuels required by the RFS2 rule.

5.2.3 Methodology Used to Estimate U.S. Energy Security Benefits

In order to understand the energy security implications of reducing U.S. oil imports, EPA has worked with Oak Ridge National Laboratory (ORNL), which has developed approaches for evaluating the social costs and energy security im plications of oil use. In a recent study entitled

" The Energy Security Benefits of Reduced Oil Use, 2006-2015 ," completed in 2007 for the final RFS1 rulemaking, ORNL updated and applie d the method used in the 1997 report " Oil Imports: An Assessment of Benefits and Costs ", by Leiby, Jones, Curlee and Lee. LL,MM The updated 2007 report was included as part of the record in th e final RFS1 rulemaking, and revisions were made based on external comment and peer review. NN,OO

Significant factors that drive energy security costs have been changing over the last decade, including: projected world oil prices, cu rrent and anticipated levels of OPEC production, U.S. oil import levels, the es timated responsiveness of regiona l oil supplies and demands to price, and the likelihood of oil supply disruptions. For this analysis, oil prices and supply and demand energy balances from the EIA's AEO 200 9 Reference Case were used. In many instances, the recent market trends and projections suggest reasons for greater concern about oil security costs, compared to the prior decade: higher oil prices; growing U.S. import levels; and a larger value-share of oil in GDP. To the ex tent that the U.S. economy has become more resilient, and less sensitive to o il shocks, or that improved macr oeconomic policie s have reduced the impact of oil shocks, there may be influences countervailing to the oil market trends. This possibility is considered in the security es timates, but recent macroe conomic disturbances indicate that greater future macroeconomic st ability cannot be assured. The degree to which sharply higher oil prices contributed to, or exacerbated, the recent global recession has not yet been resolved. PP

LL Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and Costs , ORNL-6851, Oak Ridge National Laboratory, November, 1997.

MM The 1997 ORNL paper was cited and its results used in DOT/NHTSA’s rules establishing CAFE standards for 2008 thro ugh 2011 model year light trucks. See DOT/NHTSA, Final Regulatory Impacts Analysis: Corporate Average Fuel Economy and CAFE Reform MY 2008-2011, March 2006.

NN Leiby, Paul N. “Estimating the Energy Security Benefits of Reduced U.S. Oil Imports: Final Report”, ORNL/TM-2007/028, Oak Ridge National Laboratory, March, 2008.

OO Updating the ORNL methodology to incorporate the comments from the Peer Reviewers, based on AEO2007, ORNL estimated that the total energy security benefits asso ciated with a reduction of imported oil is $12.38/barrel, with a range of $6.88 - $18.52/barrel of imported oil reduced ($2006). When the same methods and assumptions are applied to the AOE2009 Reference outlook, comparable estimates for 2025 are $19.21/barrel, with a range of $10.8 to $29.6/barrel.

PP See Hamilton, J. D. 2009, “Causes and Consequences of the Oil Shock of 2007-08 ”, Brookings Papers on Economic Activity, 2009, or the congressional testimony of Yergin, D. " The Long Aftershock: Oil: Oil and Energy Security After the Price Collapse " Testimony to U.S. Congress, Hearings, Joint Economic Committee, Oil and the Economy: The Impact of Rising Global Demand on the U.S. Recovery. May 20, 2009.34 In order to understand the energy security implications of this rule, EPA used the Oil Security Metrics Model QQ,RR (OSMM), developed a nd maintained by Oak Ridge National Laboratory. The OSMM estimates the U.S. energy security benefits from increased availability and use of renewable transportation fuels. The OSMM took as inputs the re newable fuel volumes that are required under EISA as well at the biofue l costs estimated in Chapter 4.4 of this RIA. In addition, it assumed EPA’s pr ojections of flexible fueled vehi cles and use of E85. In conducting this analysis, ORNL considered the full economic cost of importing petroleum into the U.S. The full economic cost of importing petroleum into the U.S. is defined for this analysis to include two components in addition to the purchase price of petr oleum itself. These are: (1) the higher costs for oil imports resulting from the effect of U.S. import demand on the world oil price and OPEC market power (i.e., the "import demand" or "monopson y" costs); and (2) the risk of reductions in U.S. economic output and disruption of the U.S. economy caused by sudden disruptions in the supply of imported oil to the U.S. (i.e., “mac roeconomic disruption/adjustment costs”). Analogously, this analysis for the RFS2 rule also considers the economic costs of importing biofuels to meet the RFS2 rule requirements, and the estimated disruption/adjustment costs to the economy of biofuel price volatilit y due to biofuel supply disruptions (e.g., droughts and floods, etc.).

This energy security analysis extends the prior “oil import premium” analysis by considering risk-shifting that might occur as th e U.S. reduces its dependency on petroleum by increasing its use of biofuels. The analysis accounts for the energy security implications associated with biofuels, such as possible s upply disruptions of ethanol made from corn or ethanol derived from cellulosic f eedstocks such as switchgrass. The use of OSMM broadens our energy security analysis to incorporate es timates of overall motor fuel supply and demand flexibility and reliability, and the impacts of possible agricultural sector market disruptions. For example, the use of biofuels can modestly alter short and long run demand elasticities (i.e., flexibility) in the motor fuel market, with implica tions for robustness of the fuel system in the face of diverse supply shocks.

As in the “oil import premium” analysis for the RFS2 proposal, U.S. military costs are excluded from the analysis performed by ORNL because their attribution to particular missions or activities is difficult. Most military forces serve a broad range of security and foreign policy objectives. Attempts to attribute some share of U.S. military costs to oil imports are further challenged by the need to estimate how those costs might vary with incremental variations in

QQ The OSMM methods are consistent with the recommended methodologies of the National Resource Council’s (NRC’s) (2005) Committee on Prospective Benefits of DOE’s Energy Efficiency and Fossil Energy R&D Programs. The OSMM defines and implements a method that makes use of the NRC’s typology of prospective benefits and methodological framework, satisfies the NRC’s criteria for prospective benefits evaluation, and permits measurement of prospective energy security benefits for policies and technologies related to oil. It has been used to estimate the prospective oil security benefits of Department of Energy’s Energy Efficiency and Renewable Energy R&D programs, and is also applicable to other strategies and policies aimed at changing the level and composition of U.S. petroleum demand. To evaluate the RFS2, the OSMM was modified to include supplies and demand of biofuels as well as petroleum.

RR

Greene D.L. and P.N. Leiby, 2006. The Oil Security Metrics Model: A Tool for Evaluating the Prospective Oil Security Benefits of DOE's Energy Efficiency and Renewable Energy R&D Programs , ORNL/TM-2006/505, Oak Ridge National Laboratory (ORNL), 2006.35 U.S. oil consumption and imports. Similarly, the Strategic Petroleum Reserve (SPR) size and policy is assumed unchanged by the RFS2 rule.

5.2.4 Effect of Oil Use on Long-Run Oil Price and U.S. Import Costs

The first component of the full economic costs of oil use in the U.S. follows from the effect of U.S. import demand on the world oil pr ice over the long-run. Because the U.S. is a sufficiently large purchaser of foreign oil supplies, its purchases can affect the world oil price. This monopsony power means that increases in U.S. petroleum demand can cause the world price of crude oil to rise, and conversely, that reductions in U.S. pe troleum demand can reduce the world price of crude oil. Thus, one benef it of decreasing U.S. oil purchases, due to the increased availability and use of other transportation fuels, is the potential decrease in the crude oil price paid for all crude oil purchased.

The demand or monopsony effect can be readily illustrated with an example. If the U.S. imports 10 million barrels per day at a world oil pri ce of $50 per barrel, its total daily bill for oil imports is $500 million. If a decrease in U.S. imports to 9 million barrels per day causes the world oil price to drop to $49 per barrel, the da ily U.S. oil import bill drops to $441 million (9 million barrels times $49 per barrel). While the world oil price only declines $1, the resulting decrease in oil purchase payments of $59 million per day ($500 million minus $441 million) is equivalent to an incremental be nefit of $59 per barrel of oil im ports reduced, or $10 more than the newly-decreased world price of $49 per barrel . This additional $10 pe r barrel “import cost premium” or “monopsony” benefit represents the in cremental external benefit to U.S. society as a whole for avoided import costs beyond the price pa id for oil purchases. This additional benefit arises only to the extent that the reduction in U.S. oil imports affects the world oil price.

A similar rationale can be a pplied to estimate the monopsony disbenefits of the increased use of biofuels from the RFS2 rule. In the sa me way, but working in the opposite direction of the oil market, increased use of biofuels in the U.S. is expected to increase demand for domestic and imported biofuels, and to increase the world pri ce of renewable fuels. This results in higher total costs of U.S. renewable fuel imports. While the total cost of renewable fuel imports under a policy like RFS2 rule will include the cost of the additional imports, the monopsony cost portion is the added amount paid for the imports that would have occurred without the RFS2 rule renewable fuel volumes. Thus, to look at the total monopsony impacts of the RFS2 rule renewable fuel volumes, two separate impacts ne ed to be assessed. First, U.S. oil import reductions result in paying lower prices for all barrels of U.S. imported oil, providing monopsony benefits. Second, increased use of renewa ble fuels results in higher prices of U.S. imported renewable fuels, yielding monopsony disbenefits for renewable fuels. The total monopsony benefit is the combined sum of these separate market impacts.

Table 5.2.4.1 shows the RFS2 Reference Case leve ls of U.S. oil and biofuel imports as well as the average change in oil prices and renewable fuel prices projected due to the RFS2 volumes in 2022. The Reference Case renewable fuel imports are relatively modest compared to oil (roughly 0.015 billions of barrels of biof uel are imported versus 3.283 billion barrels of oil). Projected U.S. renewable fuel imports in 2022 are ethanol, principally made from sugar cane harvested in Brazil. In 2022, the estimated change in ethanol price due to the RFS236 renewable fuel volumes is $0.61/barrel, and the estimated reduction in the world oil price is $1.05/barrel. The monopsony effect is the change in costs of the quantities of fuel imported without the RFS2 renewable fuel volumes (i.e., the Reference Ca se fuel volumes). Since the change in biofuel price applies to a much sma ller quantity of biofuels imports than U.S. oil imports, the monopsony disbenefit per barrel of in creased biofuels use is much smaller, only $0.02/barrel, compared to the oil monopsony benef it, $7.88/barrel. Thus, including the impact of expanded biofuels use on biofuels imports and price yields a slightly lower estimate of the total monoposony benefits.

Table 5.2.4-1. Determinates of Monopsony Benefits of the RFS2 Renewable Fuel Volumes vs AEO2007 Reference Case

Fuel Reference Case Import Quantity (billions of barrels in 2022) Change in Price ($ per barrel in 2022) Monopsony Benefit ($ billion in 2022) Monopsony Benefit ($/barrel of biofuel in 2022) Biofuels 0.015 0.61 -0.009 -0.02 Oil 3.282 -1.06 3.476 7.88 Total 7.86

This analysis of the import cost and monopson y effect is based on th e net import levels of petroleum as projected by the EPA-NEMS, and is not sensitive to the mix of crude and product imports. It is possible that in the future, while the U.S. will import most of its crude oil and some petroleum products, it may be a net expor ter of others, e.g. diesel fuel. SS However, oil security concerns stem from the total cons umption and net import of all petroleum fuels, whose prices are all directly dependent on the volatile (and non-co mpetitive) world crude oil market. For this analysis, the key issue is not the trade balance fo r particular petroleum pr oducts, but the net level of U.S. consumption and import of all petroleum, both crude and products. Reducing domestic gasoline or diesel fuel with renewable fuels use can reduce net imports of all petroleum, and net import costs, even if the U.S. remains a net expor ter of some petroleum pr oducts. Consider the case of U.S. diesel fuel. Replacing U.S. diesel fuel consumption with re newable biodiesel, whose root supply volatility is largely independent of that of petrol eum, can reduce the volatility of productive inputs to the macroeconomy, regardle ss of the trade balance in diesel fuel.

5.2.5 Macroeconomic Dislocation Costs Associat ed with Oil and Biofuel Price Variability

Fluctuations in oil and biof uel prices are estimated to cause macroeconomic losses due to dislocations and adjustment costs. Macroec onomic losses during price shocks reflect both aggregate output losses and so called “allocative” lo sses. The former are a reduction in the level

SS While at the time of the implementation of this rule, the U.S. exports some diesel fuel, it is not clear that this situation will long persist. Under EIA AEO2009, the U.S. is a net importer of refined products (as well as crude) throughout the forecast horizon. In particular, over the RFS2 horizon (2010-2022) diesel fuel consumption is expected to grow at 1.4 per cent per year (Table 11) while gasoline demand will decline at 1.0 per cent per year. Thus, under this outlook, crude refinery runs needed to meet gasoline supply will decline, while U.S. demand for the distillate/diesel cuts will grow.37 of output that the U.S. economy can produce full y using its available resources; and the latter stem from temporary dislocati on and underutilization of available resources due to the shock, such as labor unemployment and idle plant capaci ty. The aggregate output effect, a reduction in “potential” economic output, will last so long as the price is elevated. It depends on the extent and duration of any disruption in the world suppl y of oil, since these factors determine the magnitude of the resulting increase in prices fo r petroleum products, as well as whether and how rapidly these prices return to their pre-disruption levels.

In addition to the aggregate contraction, there are “allocative” or “adjustment” costs associated with dislocated energy markets. Because supply disruptions and resulting price increases occur suddenly, empi rical evidence shows they impose additional costs on businesses and households which must seek to adjust thei r use of petroleum and other productive factors more rapidly than if the same price increase had occurred gradually. Opportunities for short run adjustments of energy use and other productiv e factors of the economy are limited and costly. Dislocational effects include the unemployment of workers and other resources during the time needed for their intersectoral or interregional re allocation, and pauses in capital investment due to uncertainty. These adjustments temporarily reduce the level of economic output that can be achieved even below the “potential” output level that would ultimately be reached once the economy’s adaptation to higher petroleum prices was complete. The additional costs imposed on businesses and households for making these adju stments reflect their limited ability to adjust prices, output levels, and their use of energy, labor and other inputs quickly and smoothly in response to rapid changes in prices for petroleum products.

In the prior “oil import premium” analysis undertaken for the RFS2 proposal, oil price shocks were estimated to have macroeconomic loss es based on a single fixed elasticity of Gross Domestic Product (GDP) with respect to oil price. For this final RFS2 rule analysis in the OSMM, it is recognized that the dislocation por tion of disruption costs depends not only on the magnitude of the price change, but on the changing importance of both oil and renewable fuels in the economy, as well as the degree to which the price movement is novel and disturbing. TT Thus, when a shock causes fuel prices to jump up and stay up, initially the dislocation is larger, and over time the economy adjusts to higher prices and the macroe conomic dislocation dissipates. To account for this, OSMM tracks th e evolution of an “adjusted” oil price and renewable fuel price, which is based on a lagged pa rtial adjustment process, essentially yielding a weighted average of past prices with geomet rically declining weights. This weighted-lag adjusted price is constructed to represent th e average price level to which the economy has already had time to adjust. UU It is deviations from this level that are dislocational and costly. For both oil price and renewable fuel price fluctu ations, the macroeconomic dislocation cost is

TT This attention to the degree to which the observed price is unusual or “novel” follows the work of Lee et al. (Lee, K.; Ni, S. & Ratti, R. “ Oil shocks and the Macroeconomy: The Role of Price Variability ” Energy Journal , 1995, 16, 39-56, and Hamilton’s NOPI formulation (e.g. Hamilton, J. “ What is an Oil Shock ?”, Journal of Econometrics

, Elsevier, 2003, 113, 363-398). Several works of Brown, Huntington, and Gately, for example, also consider the role of an “adjusted price” in the determination of supply and demand responses.

UU The adjusted price is the geometric di stributed (i.e., Koyck) lag average of a ll prior prices, to represent that the economy only partially adjusts to changing prices each year. The annual adjustment rate of this price, which corresponds to the assumed annual accommodation of new prices by the macroeconomy, is taken as 33 per cent. This is consistent with empirical evidence that the dislo cational impact of energy price shocks extends more than one year, but is mostly complete after three years.38 calculated by applying a GDP loss elasticity to the ratio of the current price to the adjusted price. Furthermore, the applied GDP elasticity varies wi th the value share of expenditure on the fuel, both for oil and renewable fuels, in the ec onomy. The estimated GDP losses from renewable fuel price fluctuations are based on a GDP Adjustment Cost VV elasticity with respect to oil prices, but rescaled in accordance with the ratio of renewable fuel expenditures to total oil expenditures.

One feature of the OSMM is its explicit treatment of renewable fuel volatility. Renewable fuel supply and hence, the price of renewable fu els, is also subject to disturbances, and the resulting production cost volatility is anticipated to impose some costs on the economy. While E85 and gasoline prices are often strongly correlated at the level of individual retail stations, this correlation reflects primarily the process of market substitution, and the expected phenomenon that ordinarily end-use prices for close substitutes will equilibrate and track one another. Further upstream (i.e., closer to the terminal and production plant gate), this price correlation diminishes. The historical prices of agricultural products that are likely to be used as feedstocks for biofuels are somewhat volatile, but almost completely uncorrelated with oil prices. For example, consider monthly price changes be tween crude oil and key agricultural crops that would be used as feedstocks for renewable fu els—sugar, corn, switchgrass, and softwood lumber—during the time period from January, 1990 to December, 2008 (T able 5.2.5-1 below). In the case considered here, wheat prices are used as a surrogate for switchgrass prices, since both crops are likely to be grown in similar agricultural areas in the U.S. (i.e., the Great Plains) and subject to similar weather patterns. These agricultural commodities have relatively low correlations with crude oil; 3 percent, 5 percent, -1 percent and 1 percent, for sugar, wheat, corn and softwood lumber, respectively.

Table 5.2.5-1. Cross Correlations of Mo nthly Price Changes of Crude Oil and Selected Key Renewable Fuel Fe edstocks, January, 1990 to December, 2008

Crude Oil Sugar Wheat Corn Softwood Crude Oil 100% 3% 5% -1% 1% Sugar 100% 20% 10% 2% Wheat 100% 46% 23% Corn 100% -10% Softwood 100% Source: Leiby 2009, based on IMF/IFS database, Comm odity Prices & Indices, M onthly, 1970 to December 2008.

From the standpoint of quantifying the macr oeconomic/disruption of the increased use of renewable fuels, two factors are important. The first factor is an estimate of the variability in the supply of biofuels. The second factor is the ch ange in biofuel production costs that stems from

VV GDP Adjustment Costs from biofuel price fluctuations are based on applying the adjustment cost elasticity to the ratio of the current year price to the adjusted price. See Huntington (2005, “ The Economic Consequences of Higher Crude Oil Prices ,” Final Report EMF SR 9, Energy Modeling Forum, Stanford University, October, p. 43) notes “Economic theory suggests strongly that, in the absence of major threshold effects, the direct response of the GDP and price levels to oil price changes should be proportional to oil’s value share in total output.” In the OSMM, for both oil and biofuels, the adjustment cost elasticity varies from year to year according to the expenditure share in GDP. For biofuels it is given by the 1983 reference value for oil times the ratio of current biofuel expenditure share to 1983 oil expenditure share.39 fundamental supply volatility at the feedstock leve l. This analysis represents renewable fuel supply volatility and risk based on historical vari ations in annual crop yields. Crop yields vary substantially from year to year based on growin g conditions, including droughts and floods, and a variety of other factors. WW The supply risk of lower volumes of feedstock production due to a host of factors is assumed to be independent of oil prices.

Data on renewable fuel feedstock yield vol atilities are presented in Table 5.2.5-2 below. Resulting estimates of renewable fuel cost vol atility, based upon standa rd deviations from historical trends in biofuel f eedstock yields, vary from 2.4 percent to 8.7 percent, with ethanol derived from sugar cane estimated to have the lowest volatility, and ethanol derived from corn estimated to have the highest volatility. These estimates of feedstock yield volatility are then used to estimate variations in feedstock cost XX and implied variations in renewable fuel production costs at the plant gate. YY As one would expect, renewable fuel cost volatility increases with the volatility of feedstock suppl y, but decreases in cases where feedstocks comprise a lower percentage of total production cost (as is the case with cellulosic renewable fuel). By way of comparison, the historical volatilit y of world oil prices ove r the last twenty-five years is 28 percent, considerably higher than the estimated volatility of renewable fuels. ZZ

Table 5.2.5-2. Selected Key Renewable Fuel Feedstocks Annual Yield Volatility

Biofuel Feedstock Corn Soybeans Wheat Sugar Cane

Historical Yield Volatility 8.69% 6.81% 13.07% 2.37%

Based on Years 1960-2008 1960-2008 1960-2008 1997-2008 Source: OSSM Supporting Data, Leiby/ORNL 2009. Volatilit y is measured as the standard deviation of annual percentage deviation from historical trend yields. Yield data and volatility are from Bruce Babcock, CARD, Iowa State, November, 2009.

The introduction of renewable fuels affects macroeconomic disruption costs from oil by reducing the oil-intensity of the economy, and by changing oil price movements slightly (since the addition of renewable fuels slightly alters the elasticity of demand for motor fuels). In addition, the introduction of renewable fuels af fects the macroeconomic disruption costs by

WW In applying historical yield and feedstock price vari ations to projected outcomes, it was recognized that two offsetting factors may cause future biofuel feedstock supply ri sk to differ from the past. These factors may offset each other: future drought risk may increase with climat e change; yet some crops are also becoming increasingly drought resistant.

XX Data on yield volatility were obtained from Bruce Babcock of CARD, Iowa State. Variations in yield were converted to estimated variations in feedstock cost based on elasticities from Thompson, W., Meyer, S. & Westhoff, P. “ How Does Petroleum Price and Corn Yield Volatility A ffect Ethanol Markets With and Without an Ethanol Use Mandate? ” Energy Policy, Elsevier, 2009, 37, Pages 745-749.

YY Biofuel production cost economics used in this analysis are based upon estimates from Tao L. and A. Aden 2009, " The Economics of Current and Future Biofuels ," In Vitro Cell. Dev.Biol. - Plant, 45:199-217.

ZZ Volatility is calculated as the standard deviation of annual percentage price changes in the real price of imported crude oil, 1984-2009, U.S. EIA data. This result is robu st over the choice of the starting year for the volatility calculation: 2000-2009: 30.0 percent; 29.2 percent; 1990-2009: 26.4 percent; 1980-2009: 27.2 percent; and 1970- 2009: 29.5 percent.40 adding separate disruption costs associated with the independent volatility of renewable fuels supply. The magnitude of GDP dislocation losse s for a given oil price change is calculated based on a summary parameter, “GDP elasticity.” That elasticity is adjusted from historical (i.e., early 1980’s) levels based on the oil expenditure share in GDP. In the Reference Case, from 2010 to 2022, oil expenditures as a cost share of the U.S. economy vary from 2.5 percent in 2010 to peak at 4.7 percent in 2017, while declining thereafter to 3.8 percent by 2020. By way of comparison, in 1983, the oil cost share was 4.6 percent (for which the assumed 1983 macroeconomic adjustment elasticity is -0.041). Under the RFS2 control case, renewable fuel cost shares in the economy are quite small, gr owing from under 0.1 percent to about 0.2 percent, an order of magnitude lower than the oil cost shares.

The reduction in GDP adjustment losses due to oil shocks is a result of both the reduction in oil price and the slight decrease in oil shar e (about 0.2%). As men tioned above, the estimated volatility of crude oil price is greater than biof uel production costs under the RFS2 control case. This is understandable for two reas ons: historical crude prices are more volatile than agricultural commodity prices that are project ed to be the biofuel feedstocks; and biofuel production costs are less volatile than feedstock costs. The combination of these effects—lower costs shares of oil because of the introduction of higher levels of biofuels and less vola tility in biofuel costs compared to oil—leads to the offsetting gains an d losses in terms of m acroeconomic dislocation costs, with the avoided losses from crude shocks being greater than the added losses from biofuel shocks. Estimates of the avoided macroeconomic dislocation benef its, in dollars per barrel of biofuel, are displayed in Table 5.2.5-3 below.

Table 5.2.5-3. Avoided Macroeconomic Dislocations Be nefits for the RFS2 Control Case vs. the AEO2007 Reference Case ($ per barrel of biofuel)

Reduction in Dislocation Cost from Oil Shocks $7.08 Reduction in Dislocation Costfrom Biofuel Shocks -$0.52 Avoided Macroeconomic Dislocation Benefits $6.56

This approach has implications for estimated GDP losses due to shock and price fluctuations. If the use of renewable fuels re duces the expenditure share of oil in the U.S. economy, then the sensitivity of the economy to oil shocks is also re duced, and estimated GDP adjustment losses from oil shocks are lower. These effects are all relatively modest, since the changes in the renewable fuel cost share are fairly modest. However, the overall importance of oil in the economy and the greater estimated volatility of oil prices leads to a significant gain per barrel of oil use avoided. The applicable GDP elas ticity for renewable fuel price shocks is much smaller than that for oil, in proportion to the expe nditure share of renewable fuels. It does, of course, grow over time with increased use renewable fuels.

Although retail renewable fuel prices will move closely with petroleum prices, and to a lesser extent the renewable fuel plant-gate market prices and feedstock prices, there is still a macroeconomic benefit from replacing oil with renewable fuels. This is true for three reasons. First, the nature of the oil price increase and renewable fuel price increases are quite distinct.41 When the price of oil increases from an oil s upply shock, the resulting increase in renewable fuels has a very different origin–it is due to substitution toward renewable fuels, and thus reflects a demand response rather than a supply shock. While price change is commonly used as the summary measure of the shock, the underlying quan tity changes cannot be forgotten as the root cause of the economic loss. Unlike oil, the qua ntity of renewable fuel supplied during an oil shock will increase, or at least remain little chan ged. Second, the short-run increase in the price of renewable fuel is no different from the induced price increase in all other substitutes for oil: energy or non-energy. Adding a new, inde pendent supply source like renewable fuel is equivalent to adding any other a lternative fuel, or even to adding a conservation alternative. The price of all of these alternatives will rise to some extent with oil prices, but this substitution effect is part of the solution to the disrupti on rather than part of the problem. Through substitution, biofuels can also dampen the oil price shock, a beneficial effect that is only partially represented in this analysis. Third, to the extent that renewable fuel prices rise with little change in production, the payments will be largely to dome stic producers, as windfall gains rather than losses.

5.2.6 Estimates of per Barrel and Total U.S. Energy Security Benefits

Table 5.2.6-1 below summarizes ORNL’s estima te of the energy security benefits associated with the RFS2 renewable fuel volum es. The components of the energy security benefit estimate are shown as well as the total U.S. energy security benefits associated with increased renewable fuel production in Table 5.2.6-2.

Table 5.2.6-1. Energy Security Benefits (2007$/barrel of renewable fuel) Effect Updated ORNL Study Monopsony (best estimate) (range) $7.86 ($5.37-$10.71) Macroeconomic Disruption (best estimate) (range) $6.56 ($0.94-$12.23) Total (best estimate) (range) $14.42 ($6.31-$22.95)

The literature on the energy security for the last two decades has routinely combined the monopsony and the macroeconomic disruption com ponents when calculating the total value of the energy security premium. However, in the context of using a global value for the Social Cost of Carbon (SCC) the question arises: how should the energy security premium be used when some benefits from the rule, such as the be nefits of reducing gree nhouse gas emissions, are calculated at a global level? Monopsony benefits represent avoided payments by the U.S. to oil producers in foreign countries that result from a decrease in the world oil price as the U.S. decreases its consumption of imported oil (net of increased imported biofuel payments by the U.S.) Although there is clearly a benefit to the U.S. when considered from the domestic perspective, the decrease in price due to decrease d demand in the U.S. also represents a loss to other countries. Given the redist ributive nature of this effect, do the negative effects on other countries “net out” the positive impacts to the U. S.? If this is the case, then, the monopsony42 portion of the energy security premium should be excluded from the net benefits calculation for the rule. Based on this reasoning, EPA's estimates of net benefits for this rule exclude the portion of energy security benefits stemming fr om the U.S. exercising its monopsony power in oil markets. Thus, EPA only includes the macroe conomic disruption/adjus tment cost portion of the energy security premium.

However, even when the global value for gr eenhouse gas reduction benefits is used, a strong argument can be made that the monopsony benefits should be in cluded in net benefits calculation for this rule. Maintaining the eart h’s climate is a global public good and as such requires that a global perspective be taken on the benefits of GHG mitigation by all nations, including the U.S. The global SCC is used in these calculations, not because the global net benefits of the rule are being computed (they ar e not), but rather because in the context of a global public good, the global marg inal benefit is the correct benefit against which domestic costs are to be compared. In other words, using the global SCC does not transform the calculation from a domestic (i.e., U.S.) to a globa l one. Rather, the domestic perspective is maintained while recognizing that the impacts from domestic GHG emissions are truly global in

nature.

Energy security, on the other hand, is broadly defined as protecting the U.S. economy against circumstances that threaten significant s hort- and long-term increases in energy costs. Energy security is inherently a domestic benef it. However, the use of the domestic monopsony benefit is not necessarily in conflict with the use of the global SCC, because the global SCC represents the benefits against which the costs associated with our (i.e., the U.S.’s) domestic mitigation efforts should be judged. In addition, the U.S. values both maintaining the earth’s climate and providing for its own energy security. If this reasoning holds, the two benefits—the global benefits of reducing gr eenhouse gas emissions and the full energy security premium, including the monopsony benefits—should be counted in the net benefits es timates of the rule. In the final analysis, the Agency determined that the first argument is more compelling and therefore has determined that using only the ma croeconomic disruption component of the energy security benefit is the appropriate metric for this rule.

The energy security benefits of increasing the total renewable fuel volumes from the AEO 2007 Reference Case volumes of 13.56 billi on gallons to the Primary Control Case volumes of 30.5 billion gallons are shown in Ta ble 5.2.4-2. Total annual energy security benefits are estimated by multiplying the chan ge in renewable fuel volumes (16.94 billion gallons or 403 million barrels) and the macroeco nomic disruption/adjustment portion of the energy security premium ($6.56/barrel of renewable fuels).

Table 5.2.4-2. Total Energy Security Benefits (billions of 2007$)
Year 2022 Benefits $2.643 

### 5.3. Benefits of Reducing GHG Emissions

5.3.1 Introduction

As discussed in Section 2, the increased vol umes of renewable fuels mandated by the RFS2 standards are projected to reduce greenhous e gas emissions (GHG). This section presents estimates of the economic benefits that could be monetized for the reductions in GHG emissions projected due to the RFS2 renewable fuel volumes. The total benefit estimates were calculated by multiplying a marginal dollar value (i.e., cost pe r ton) of carbon emissions, also referred to as “social cost of carbon” (SCC), by the anticipat ed level of emissions reductions in tons.

The SCC values underlying the benefits estimates for this rule represent U.S. government-wide interim values for SCC. As discussed below, federal agencies will use these interim values to asse ss some of the economic benefits of GHG reductions while an interagency workgroup develops SCC values for use in the long-term. The interim values should not be viewed as an expectation about the results of the longer-term process. Although these values were not used in the NPRM, some commenters raised issues with these values and the methodology used to develop them in response to their publication elsewhere. Many of these issues are being examined by the interagency workgroup.

The rest of this section provides the basis for the interim SCC values, and the estimates of the total climate-related benefits of the RFS2 renewable fuel volumes that follow from these interim values. As discussed below, the interim dollar estimates of the SCC represent a partial accounting of climate change impacts.

In addition to the partial quantitative account presented in this section, a qualitative appraisal of climate-related impacts that are not fully captured in these values is published in other recent climate change analyses. For example, EPA’s final Endangerment and Cause or Contribute Findings for Greenhouse Gases under S ection 202(a) of the Clean Air Act and the accompanying Technical Support Document (TSD) pr esents a summary of impacts and risks of climate change projected in the abse nce of actions to mitigate GHG emissions. AAA The TSD synthesizes major findings from the best available scientific assessments of the scientific literature that have gone through rigorous and transparent peer review, including the major assessment reports of both the Intergovernmental Panel on Climate Change (IPCC) and the U.S. Climate Change Science Program (CCSP).

5.3.2 Derivation of Interim Social Cost of Carbon Values

The “social cost of carbon” (SCC) is in tended to be a monetary measure of the incremental damage resulting from carbon dioxide (CO 2 ) emissions, including (but not limited to) net agricultural productivity loss, human health effects, property damages from sea level rise, and changes in ecosystem services. Any effort to quantify and to monetize the consequences associated with climate change will raise serious questions of science, economics, and ethics. But

AAA See Federal Register /Vol.74, No.2398/Wednesday, December 16, 2009/Rules and Regulations at http://frwebgate4.access.gpo.gov/cgi-bin/PDFgate.cgi?WAISdocID=969788398047+0+2+0&WAISaction=retrieve44 with full regard for the limits of both quantification and monetization of impacts, the SCC can be used to provide an estimate of the social benefits of reductions in GHG emissions.

For at least three reasons, any particular figure will be contestable. First, scientific and economic knowledge about the impact s of climate change conti nues to grow. With new and better information about releva nt questions, including the cost, burdens, and possibility of adaptation, current estimates will inevitably chan ge over time. Second, some of the likely and potential damages from climate change—for example, the loss of endangered species—are generally not included in current SCC estimates. These omissions may turn out to be significant in the sense that they may mean that the best current estimates are too low. As noted by the IPCC Fourth Assessment Report, “It is very likely that globally aggregated figures underestimate the damage costs because they cannot include many non-quantifiable impacts.” Third, when economic efficiency criteria, under specific assumptions, are juxtaposed with ethical considerations, the outcome may be controversial. These ethical consider ations, including those involving the treatment of future generations, shoul d and will also play a role in judgments about the SCC (see in particular the discu ssion of the discount rate, below).

To date, SCC estimates presented in recent regu latory documents have varied within and among agencies, including DOT, DOE, and EPA. For example, a regulation proposed by DOT in 2008 assumed a value of $7 per metric ton CO 2 BBB (2006$) for 2011 emission reductions (with a range of $0-14 for sensitivity analysis). One of the regulations proposed by DOE in 2009 used a range of $0-$20 (2007$). Both of these ranges were designed to reflect the value of damages to the United States resulting from carbon emissions , or the “domestic” SCC. In the final MY2011 CAFE EIS, DOT used both a domestic SCC value of $2/t-CO 2 and a global SCC value of $33/tCO 2 (with sensitivity analysis at $80/t-CO 2 ) (in 2006 dollars for 2007 emissions), increasing at 2.4% per year thereafter. The final MY2011 CAFE rule also presented a range from $2 to $80/tCO 2

.
In the May 2009 RFS2 Proposal leading to t oday’s final rule, EPA identified preliminary SCC estimates that spanned three orders of ma gnitude. EPA’s May 2009 proposal also presented preliminary global SCC estimates developed from a survey analysis of the peer reviewed literature (i.e., meta analysis). The global m ean values from the meta analysis were $68 and $40/t-CO 2 for discount rates of 2% and 3% re spectively (in 2006 real dollars for 2007 emissions). CCC

Since publication of the May 2009 RFS2 propos al, a federal interagency working group has established a methodology for selecting a range of interim SCC estimates for use in regulatory analyses. Today’s final rule presents the methodology and the resulting interim set of SCC estimates, which reflect the Administration’s current understanding of the relevant literature. Recent federal regulatory documents have also presented the interim SCC estimates,

BBB For the purposes of this discussion, we present all values of the SCC as the cost per metric ton of CO 2 emissions. Some discussions of the SCC in the literature use an alternative presentation of a dollar per metric ton of carbon. The standard adjustment factor is 3.67, which means, for example, that a SCC of $10 per ton of CO 2 would be equivalent to a cost of $36.70 for a ton of carbon emitted. Un less otherwise indicated, a “ton” refers to a metric ton.

CCC 74 FR 25094 (May 26, 2009).45 including a proposal to limit vehicle greenhouse ga s emissions that requests public comment on the estimates and underlying methodology. DDD

It should be emphasized that the analysis he re is preliminary. These interim estimates are being used for the short-term while an inte ragency group develops a more comprehensive characterization of the distribution of SCC values for future economic and regulatory analyses. The interim values should not be viewed as an expectation about the results of the longer-term

process.

This process will allow the workgroup to expl ore questions raised in the May 2009 RFS2 Proposal as they are relevant to the developmen t of SCC values for use in the long-term. The workgroup may evaluate factors not currently captured in today’s estimates due to time constraints, such as the quantification of a dditional impact categories where possible and an uncertainty analysis. The Administration will s eek comment on all of th e scientific, economic, and ethical issues before establishing improved estimates for use in future rulemakings.

The outcomes of the Administration’s process to develop interim values are judgments in favor of a) global rather than domestic values, b) an annual grow th rate of 3%, and c) interim global SCC estimates for 2007 (in 2007 dollars) of $56, $34, $20, $10, and $5 per metric ton of CO 2 . The interim set of values is based on the following judgments.

5.3.2.1 Global and Domestic Measures

Because of the distinctive nature of the climate change problem, we present both a global SCC and a fraction of that value that represents impacts that may occur within the borders of the U.S. alone, or a “domestic” SCC, but fix our attention on the global measure. This approach represents a departure from past practices, which relied, for the most part, on domestic measures. As a matter of law, both global and domestic values are permissi ble; the relevant statutory provisions are ambiguous and allo w selection of either measure. EEE

Under OMB guidance, analysis from the domestic perspective is required, while analysis from the international perspective is optional. The domestic decisions of one nation are not typically based on a judgment about the effect s of those decisions on other nations. But the climate change problem is highly unusual in the sense that it involves (a) a global public good in which (b) the emissions of one nation may inflic t significant damages on other nations and (c) the United States is actively engaged in promoting an international agreement to reduce worldwide emissions.

DDD Federal Register 40 CFR Parts 86 and 600, September 28, 2009 “Proposed Rulemaking To Establish LightDuty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Proposed Rule”

EEE It is true that federal statutes are presumed not to have ex traterritorial effect, in part to ensure that the laws of the United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give extraterritorial effect to federal law and hence does not intrude on such interests.46 In these circumstances, we believe the globa l measure is preferred. Use of a global measure reflects the reality of the problem and is consistent with the continuing efforts of the United States to ensure that emissions reductions occur in many nations.

Domestic SCC values are also presented. The development of a domestic SCC is greatly complicated by the relatively few region- or country-specific estimates of the SCC in the literature. One potential source of estimates comes from EPA’s ANPR Benefits TSD, using the FUND model. The resulting estimates suggest that th e ratio of domestic to global benefits varies with key parameter assumptions. W ith a 3% discount rate, for exampl e, the U.S. benefit is about 6 percent of the global benef it of GHG reductions for the “central” (mean) FUND results, while, for the corresponding “high” estimates associated with higher climate sensitivity and lower global economic growth, the U.S. be nefit is less than 4 percent of the global benefit. With a 2 percent discount rate, the U.S. share is about 2-5 percent of the global estimate.

Based on this available evidence, an interim domestic SCC value equal to 6 percent of the global damages is proposed. It is recognized that the 6 percent figure is approximate and highly speculative and alternative approaches will be explored before establishing improved values for future rulemakings. However, it should be noted that it is difficult to properly apportion global benefits to different regions, because not a ll the damages citizens of one country would be willing to pay to avoid will occur only within their own borders. For example, impacts outside U.S. border can have significant welfare implications for U.S. populations (e.g. tourism, disaster relief) and if not included, these omissions w ill lead to an underestimation of the “domestic” SCC.

5.3.2.2 Filtering existing analyses

There are numerous SCC estimates in the existing literature, and it is reasonable to make use of those estimates in order to produce a figure for current use. A starting point is provided by the meta-analysis in Richard Tol, 2008. FFF With that starting point, the Administration proposes to “filter” existing SCC estimates by using those that (1) are derived from peer-reviewed studies; (2) do not weight the monetized damages to one c ountry more than those in other countries; (3) use a “business as usual” climate scenario; and (4) are based on the most recent published version of each of the three major integrat ed assessment models (IAMs): FUND, PAGE, and DICE.

Proposal (1) is based on the view that those st udies that have been subject to peer review are more likely to be reliable than those that ha ve not. Proposal (2) avoids treating the citizens of one nation (or different citizens within the US) di fferently on the basis of income considerations, which some may find controversial and in any event would complicate that analysis. Further it is consistent with the potential co mpensation tests of Kaldor (1939) and Hicks (1940), which form the conceptual foundations of benefit-cost anal ysis and use unweighted sums of willingness to pay. Finally, this is the approach used in rulemakings across a variety of settings and consequently keeps USG policy consistent across contexts.

FFF Richard Tol, The Social Cost of Carbon: Trends, Outliers, and Catastrophes, Economics: The Open-Access, Open-Assessment E-Journal, Vol. 2, 2008-25. http://www.economics-ejournal.org/economics/journalarticles/2008- 25 (2008).47 Proposal (3) stems from the judgment that as a general rule, the proper way to assess a policy decision is by comparing the implementati on of the policy against a counterfactual state where the policy is not implemented. In addition, our expectation is that most policies to be evaluated using these interim S CC estimates will constitu te small enough cha nges to the larger economy to safely assume that the marginal be nefits of emissions reductions will not change between the baseline and policy scenarios.

Proposal (4) is based on four complementar y judgments. First, the FUND, PAGE, and DICE models now stand as the most comprehens ive and reliable efforts to measure the economic damages from climate change . Second, the latest versions of th e three IAMs are likely to reflect the most recent evidence and learning, and hence they are presumed to be superior to those that preceded them. However, it is acknowledged that the most recently published results do not necessarily repeat prior modeling exercises with an updated model, so va luable information may be lost, for instance, estimates of the SCC us ing specific climate se nsitivities or economic scenarios. In addition, although some older m odel versions were used to produce estimates between 1996 and 2001, there have been no significant modeling paradigm changes since 1996.

Third, any effort to choose among them, or to reject one in favor of the others, would be difficult to defend at the present time. In the ab sence of a clear reason to choose among them, it is reasonable to base the SCC on all of them. Four th, in light of the uncertainties associated with the SCC, a range of values is more represen tative and the additional information offered by different models is important.

5.3.2.3 Use a Model-weighted Average of th e Estimates at Each Discount Rate

At this time, a strong reason to prefer any of the three major IAMs (FUND, PAGE, and DICE) over the others has not been identified. Acco rdingly, to address the concern that certain models not be given unequal weig ht relative to the other models, the estimates are based on an equal weighting of the means of the estimates from each of the models. Among estimates that remain after applying the filter, we begin by taking the average of all estimates within a model. The estimated SCC is then calculated as the average of the three model-specific averages. This approach is used to ensure that models with a greater number of published results do not exert unequal weight on the interim SCC estimates. However, note that the resulting set of SCC estimates does not provide informa tion about variability among or within models except in so far as they have different discounting assumptions. In the future interagency process to generate a more comprehensive distribution of SCC, we expe ct to exercise the available SCC models in a systematic manner such that the resulting distributions of SCC values may incorporate a wider range of uncertainties including discount rates, growth rates, climate sensitiv ities, and other important parameters. This may lead to changes in the span of SCC estimates that are relevant for policy analyses.

5.3.2.4 Apply a 3 Percent Annual Growth Rate to the Chosen SCC Values

SCC is expected to increase over time, because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed as the magnitude of climate change increases. Indeed, an implied growth rate in the SCC can be48 produced by most of the models that estima te economic damages caused by increased GHG emissions in future years. But neither the rate itself nor the information necessary to derive its implied value is commonly reported. In light of the limited amount of debate thus far about the appropriate growth rate of the S CC, applying a rate of 3 percen t per year seems appropriate at this stage. This value is consistent with th e range recommended by IPCC (2007) and close to the latest published estima te (Hope 2008).

5.3.2.5 Discount Rates

For estimation of the benefits associated with the mitigation of climate change, one of the most complex issues involves the appropriate di scount rate. OMB’s current guidance offers a detailed discussion of the re levant issues and calls for discount ra tes of 3 percent and 7 percent. It also permits a sensitivity analysis with low rates (1 – 3 percent) for intergenerational problems: “If your rule will have important intergenerational benefits or costs you might consider a further sensitivity analysis using a lower but positive discount rate in addition to calculating net benefits using discount rates of 3 and 7 percent.” GGG

The choice of a discount rate , especially over long periods of time, raises highly contested and exceedingly difficult questions of science, economics, philosophy, and law. See, e.g., William Nordhaus, The Challenge of Gl obal Warming (2008); Nicholas Stern, The Economics of Climate Ch ange (2008); Discounting and Intergen erational Equity (Paul Portney and John Weyant eds. 1999). It is not clear that future generations would be willing to trade environmental quality for consumption at the sa me rate as the current generations. Under imaginable assumptions, decisions based on cost-ben efit analysis with high discount rates might harm future generations – at least if inves tments are not made for the benefit of those generations. See Robert Lind, Analysis for Inte rgenerational Discounting, id. at 173, 176-177. It is also possible that the use of low discount rates for particul ar projects might itself harm future generations, by diverting resources from private or public sector investments with higher rates of return for future generations. In the context of climate change, questions of intergenerational equity are especially important.

Because of the substantial length of time in which CO 2 and other GHG emissions reside in the atmosphere, choosing a discount rate which is higher than the actual discount rate could result in irreversible changes in CO 2 concentrations, and possibly irreversible climate changes (unless substantial reductions in short-lived climate forcing emissions are achieved). Even if these changes are reversible, delaying mitigation efforts could result in substantially higher costs of stabilizing CO 2 concentrations. On the other hand, usin g too low a discount rate in benefitcost analysis may recommend some potentially economically unwarranted investments in mitigation. In many cases these investments could be discontinued with littl e long term economic disruptions. However, it is also possible that the use of low discount rates for particular projects might itself harm future generations, by ensuring that resources are not used in a way that would greatly benefit them.

GGG See OMB Circular A-4, pp. 35-36, citing Portney and Weyant, eds. (1999), Discounting and Intergenerational Equity, Resources for the Future, Washington, DC.49 Reasonable arguments support the use of a 3 pe rcent discount rate. Fi rst, that rate is among the two figures suggested by OMB guidance, and hence it fits with existing national policy. Second, it is standard to base the disc ount rate on the compensation that people receive for delaying consumption, and the 3 percent is clos e to the risk-free rate of return, proxied by the return on long term inflation-adjusted U.S. Tr easury Bonds, as of this writing. Although these rates are currently closer to 2.5 percent, the us e of 3 percent provides an adjustment for the liquidity premium that is reflected in these bonds’ returns. However, this approach does not adjust for the significantly longer time horizon associated with climate change impacts. It also could be argued that the appropriate interest rate should be lower th an 3 percent if the benefits of climate mitigation policies tend to be higher than expected in time periods when the returns to investments in rest of the economy are lower than normal.

At the same time, others would argue that a 5 percent discount rate can be supported. The argument relies on several assumptions. First, this rate can be justified by reference to the level of compensation for delaying consumption, because it fits with market beha vior with respect to individuals’ willingness to trade-off consumption across periods as measured by the estimated post-tax average real returns to risky private investments (e.g., the S&P 500). In the climate setting, the 5 percent discount rate may be preferab le to the riskless rate because the benefits to mitigation are not known with certainty. In principa l, the correct discount rate would reflect the variance in payoff from climate mitigation policy and the correlation between the payoffs of the policy and the broader economy. HHH

Second, 5 percent, and not 3 percent, is ro ughly consistent with estimates implied by inputs to the theoretically derived Ramsey equa tion presented below, which specifies the optimal time path for consumption. That equation specifies the optimal discount rate as the sum of two components. The first term (the product of the el asticity of the marginal utility of consumption and the growth rate of consumption) reflects the fact that consumption in the future is likely to be higher than consumption today, so diminishing marginal utility implies that the same monetary damage will cause a smaller reduction of utility in the future. Standard estimates of this term from the economics literature are in the range of 3 percent-5 percent. III The second component reflects the possibility that a lower weight should be placed on utility in the future, to account for social impatience or extinction risk, which is specified by a pure rate of time preference (PRTP). A common estimate of the PRTP is 2 percent, thoug h some observers believe that a principle of intergenerational equity suggests that the PRTP should be close to zero. It follows that discount

HHH Specifically, if the benefits of the policy are highly co rrelated with the returns from the broader economy, then the market rate should be used to discount the benefits. If the benefits are uncorrelated with the broader economy the long term government bond rate should be applied. Furthermore, if the benefits are negatively correlated with the broader economy, a rate less than that on long term government bonds should be used (Lind, 1982 pp. 89-90).

III For example, see: Arrow KJ, Cline WR, Maler K–G, Munasinghe M, Squitieri R, Stiglitz JE. 1996. Intertemporal equity, discounting, and economic efficiency. Chapter 4 in Economic and Social Dimensions of Climate Change: Contribution of Working Group III to the Second Assessment Report, Summary for Policy Makers. Cambridge: Cambridge University Press; Dasgupta P. 2008. Discounting climate change. Journal of Risk and Uncertainty 37:141–169; Hoel M, Sterner T. 2007. Discounting and relative prices. Climatic Change 84:265–280; Nordhaus WD. 2008. A Question of Balance: Weighing the Options on Global Warming Policies. New Haven, CT: Yale University Press; Stern N. 2008. The economics of climate change. The American Economic Review 98(2):1–37..50 rate of 5 percent is near the middle of the range of values that are able to be derived from the Ramsey equation. JJJ

It is recognized that the arguments above – for use of market behavior and the Ramsey equation – face objections in the context of climate change, and of course there are alternative approaches. In light of climate change, it is possible that consumption in the future will not be higher than consumption today, and if so, the Ramsey equation will suggest a lower figure. The historical evidence is consistent with rising consumption over time. KKK

Some critics note that using observed interest rates for inter-generational decisions imposes current preferences on future genera tions, which some economists say may not be appropriate. For generati onal equity, they argue that the di scount rate should be below market rates to correct for market distortions and inefficiencies in intergenerational transfers of wealth (which are presumed to compensate future gene rations for damage), and to treat generations equitably based on ethical principles (see Broome 2008). LLL

Additionally, some analyses attempt to deal with uncertainty with respect to interest rates over time. We explore below how this might be done. MMM

5.3.2.6 Interim Social Cost of Carbon Estimates

The application of the methodology outlined above yields interim estimates of the SCC that are reported in Table 5.3.2.6-1. These estimates are reported separately using 3 er cent and 5 percent discount rates. The cells are empty in rows 10 and 11, because these studies did not report estimates of the SCC at a 3 percent discount rate. The model-weighted means are reported in the final or summary row; they are $34 per t-CO 2 at a 3% discount rate and $5 per t-CO 2 with a 5 percent discount rate.

JJJ Sterner and Persson (2008) note that a consistent treatment of the marginal utility of consumption would require that if higher discount rates are justified by the diminishing marginal utility of consumption, e.g., a dollar of damages is worth less to future generations because they ha ve greater income, then so-called equity weights should be used to account for the higher value that countries with lower income would place on a dollar of damages relative to the U.S. This is a consistent and logical outcome of application of the Ramsey framework. Because the distribution of climate change related damages is expected to be skewed towards developing nations with lower incomes, this can have significant implications for estimates of total global SCC if the Ramsey framework is used to derive discount rates.

KKK However, because climate change impacts may be outsid e the bounds of historical evidence, predictions about future growth in consumption based on past experience may be inaccurate.

LLL See Arrow, K.J., W.R. Cline, K-G Maler, M. Munasinghe, R. Squiteri, J.E.Stiglitz, 1996. "Intertemporal equity, discounting and economic efficiency," in Climate Change 1995: Economic and Social Dimensions of Climate Change, Contribution of Working Group III to the Second Assessment Report of the Intergovernmental Panel on Climate Change. See also Weitzman, M.L., 1999, in Portney P.R. and Weyant J.P. (eds.), Discounting and Intergenerational Equity, Resources for the Future, Washington, D.C.

MMM Richard Newell and William Pizer, Discounting the dist ant future: how much do uncertain rates increase valuations? J. Environ. Econ. Manage. 46 ( 2003 ) 52-71.51 Table 5.3.2.6-1. Global Social Cost of Carb on (SCC) Estimates ($/t-CO 2 in 2007 (2007$)), Based on 3 Percent and 5 Percent Discount Rates *

Model Study Climate Scenario 3% 5% 1 FUND Anthoff et al. 2009 FUND default 6 -1 2 FUND Anthoff et al. 2009 SRES A1b 1 -1 3 FUND Anthoff et al. 2009 SRES A2 9 -1 4 FUND Link and Tol 2004 No THC 12 3 5 FUND Link and Tol 2004 THC continues 12 2 6 FUND Guo et al. 2006 Constant PRTP 5 -1 7 FUND Guo et al. 2006 Gollier discount 1 14 0 8 FUND Guo et al. 2006 Gollier discount 2 7 -1 FUND Mean 8.47 0 9 PAGE Wahba & Hope 2006 A2-scen 59 7 10 PAGE Hope 2006 7 11 DICE Nordhaus 2008 8 Summary Model-weighted Mean 34 5 *The sample includes all peer reviewed, non-equity-weighted estimates included in Tol (2008), Nordhaus (2008), Hope (2008), and Anthoff et al. (2009), that are based on the most recent published version of FUND, PAGE, or DICE and use business-as-usual climate scenarios. NNNOOO All values are based on the best available information from the underlying studies about the base year and year dollars, rather than the Tol (2008) assumption that all estimates included in his review are 1995 values in 1995$. All values were updated to 2007 using a 3% annual growth rate in the SCC, and adjusted for inflation using GDP deflator.

In today’s rule, benefits of reducing GHG emissions have been estimated using global SCC values of $34 and $5 as these represent the estimates associated with the 3 percent and 5 percent discount rates, respectively. PPP The 3 percent and 5 percent estimates have independent

NNN Most of the estimates in Table 1 rely on climate scenarios developed by the Intergovernmental Panel on Climate Change (IPCC). The IPCC published a new set of scenarios in 2000 for use in the Third Assessment Report (Special Report on Emissions Scenarios - SRES). The SRES scenario s define four narrative storylines: A1, A2, B1 and B2, describing the relationships between the forces driving greenhouse gas and aerosol emissions and their evolution during the 21st century for large world regions and globally. Each storyline represents different demographic, social, economic, technological, and environmental developments that diverge in increasingly irreversible ways. The storylines are summarized in Nakicenovic et al., 2000 (see also http://sedac.ciesin.columbia.edu/ddc/sres/ ). Although they were intended to represent BAU scenarios, at this point in time the B1 and B2 storylines are widely viewed as representing policy cases rather than business-as -usual projections, estimates derived from these scenarios to be less appropriate for use in benefit-cost analysis. They are therefore excluded.

OOO Guo et al. (2006) report estimates based on two Gollier discounting schemes. The Gollier discounting assumes complex specifications about individual utility functions and risk preferences. After various conditions are satisfied, declining social discount rates emerge. Gollier Discounting Scheme 1 employs a certainty-equivalent social rate of time preference (SRTP) derived by assuming the regional growth rate is equally likely to be 1% above or below the original forecast growth rate. Gollier Discounting Scheme 2 calculates a certainty-equivalent social rate of time preference (SRTP) using five possible growth rates, and applies the new SRTP instead of the original. Hope (2008) conducts Monte Carlo analysis on the PRTP component of the discount rate. The PRTP is modeled as a triangular distribution with a min value of 1%/yr, a most likely value of 2 %/yr, and a max value of 3 %/yr.

PPP It should be noted that reported discount rates may not be consistently derived across models or specific applications of models: while the discount rate may be identical, it may reflect different assumptions about the individual components of the Ramsey equation identified earlier.52 appeal and at this time a clear preference for one over the other is not warranted. Thus, we have also included – and centered our current attention on – the average of the estimates associated with these discount rates, which is $20. (Bas ed on the $20 global value, the approximate domestic fraction of these benefits would be $1.20 per metric ton of CO 2 assuming that domestic benefits are 6% of the global benefits.

The distinctions between sets of estimates generated using di fferent discount rates are due only in part to discount rate differences, because the models and parameters used to generate the estimates in the sets associated with different discount rates also vary.

It is true that there is uncertainty a bout interest rates ove r long time horizons. Recognizing that point, Newell and Pizer (2003) have made a careful effort to adjust for that uncertainty. The Newell-Pizer approach models discount rate uncertainty as something that evolves over time. QQQ This is a different way to model discount rate uncertainty than the approach outlined above, which assu mes there is a single discount rate with equal probability of 3 percent and 5 percent.

Table 5.3.2.6-2 reports on the application of the Newell-Pizer adjustments. The precise numbers depend on the assumptions a bout the data generating process that governs interest rates. Columns (1a) and (1b) assume that “random wa lk” model best describe s the data and uses 3 percent and 5 percent discount rate s, respectively. Columns (2a) and (2b) repeat this, except thatit assumes a “mean-reverting” process. While the empirical evidence does not rule out a meanreverting model, Newell and Pizer find stronger empirical s upport for the random walk model.

QQQ In contrast, an alternative approach based on Weitzman (2001) would assume that there is a constant discount rate that is uncertain and represented by a probability distribution. The Newell and Pizer, and Weitzman approaches are relatively recent contributions to the literature. .53 Table 5.3.2.6-2. Global Social Cost of Carbon (S CC) Estimates ($ per metric ton CO 2 in 2007 (2007$))*, Using Newell & Pizer (2003) Adjustment for Future Discount Rate Uncertainty**Randomwalk model Mean-reverting model Model Study Climate Scenario 3% 5% 3% 5% (1a) (1b) (2a) (2b) 1 FUND Anthoff et al. 2009 FUND default 10 0 7 -1
2 FUND Anthoff et al. 2009 SRES A1b 2 0 1 -1 3 FUND Anthoff et al. 2009 SRES A2 15 0 10 -1
4 FUND Link and Tol 2004 No THC 21 6 13 4 5 FUND Link and Tol 2004 THC continues 21 4 13 2 6 FUND Guo et al. 2006 Constant PRTP 9 0 6 -1 7 FUND Guo et al. 2006 Gollier discount 1 14 0 14 0 8 FUND Guo et al. 2006 Gollier discount 2 7 -1 7 -1 FUND Mean 12 1 9 0

9 PAGE Wahba & Hope 2006 A2-scen 100 13 65 8 10 PAGE Hope 2006 13 8 11 DICE Nordhaus 2008 15 9

Summary Model-weighted Mean 56 10 37 6 *The sample includes all peer reviewed, non-equity-weighted estimates included in Tol (2008), Nordhaus (2008), Hope (2008), and Anthoff et al. (2009), that are based on the most recent published version of FUND, PAGE, or DICE and use business-as-usual climate scenarios. All values are based on the best available information from the underlying studies about the base year and year dollars, rather than the Tol (2008) assumption that all estimates included in his review are 1995 values in 1995$. All values were updated to 2007 using a 3% annual growth rate in the SCC, and adjusted for inflation using GDP deflator. See the Notes to Table 1 for further details. **Assumes a starting discount rate of 3% or 5%. Newell and Pizer (2003) based adjustment factors are not applied to estimates from Guo et al. (2006) that use a different a pproach to account for discount rate uncertainty (rows 7-8). Note that the correction factor from Newell and Pizer is based on the DICE model. The proper adjustment may differ for other integrated assessment models that produce different time schedules of marginal damages. We would expect this difference to be minor.

The resulting estimates of the social cost of carbon are necessarily greater. When the adjustments from the random walk model are app lied, the estimates of the social cost of carbon are $10 and $56 per ton of CO 2 , with the 5 percent and 3 percen t discount rates, respectively. The application of the mean-reverting adjustme nt yields estimates of $6 and $37. Relying on the random walk model, analyses are also conducte d with the value of the SCC set at $10 and $56.

5.3.2.7 Caveats

There are at least four caveats to the approach outlined above.

First, the impacts of climate change are expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these impacts is uncertain, because of the inherent randomness in the Earth’ s atmospheric processes, the U.S. and global economies, and54 the behaviors of current and future populations . The existing IAMs do not currently individually account for and assign value to a ll of the important physical and other impacts of climate change that are recognized in the climate change literature. RRR Therefore, as noted by the IPCC, SCC estimates are “very likely” underestimated. SSS In addition, the SCC approach also likely underestimates the value of GHG reductions because the marginal values apply only to CO 2 emissions, which have different impacts than non-CO 2 emissions because of variances in atmospheric lifetimes and radiative forcing. TTT Although it is likely that our capability to quantify and monetize impacts will improve with time, it is also likely that even in future applications, a number of potentially significant benefits categories will remain unmonetized. In order to more fully characterize of benefits of mitigation these non-monetiz ed benefits should be discussed along with monetized benefits based on the SCC.

Second, in the opposite direction, it is unlikel y that the damage estimates adequately account for the directed technolog ical change that climate change will cause. In particular, climate change will increase the return on i nvestment to develop te chnologies that allow individuals to better cope with climate change. For example, it is likely that scientists will develop crops that are better able to withstand high temperatures. In this respect, the current estimates may overstate the likely quantified damages, though the costs associated with the investments in adaptive technologies must also be considered (technologies must also be included in the calculations, as the benefits s hould reflect net welfare changes to society).

Third, there has been considerable recent discussion of the risk of catastrophic impacts and of how best to account for worst-case scenarios. Recent research by Weitzman (2009) specifies some conditions under which the possibi lity of catastrophe would undermine the use of IAMs and conventional cost-benefit analysis. This re search requires further exploration before its generality is known and the proper way to incorpor ate it into regulatory reviews is understood.

Fourth, it is also worth noting that the SCC estimates are only relevant for incremental policies relative to the projected baselines, wh ich capture business-as-usual scenarios. To evaluate non-marginal changes, such as might occur if the U.S. acts in tandem with other nations, then it might be necessary to go beyond th e simple expedient of using the SCC along the BAU path. This would require explicitly calculating the total benefits in a move from the BAU scenario to the policy scenario, without imposing th e restriction that the marginal benefit remains constant over this range.

5.3.2.8 Other Options

RRR Examples of impacts that are difficult to monetize, and have generally not been included in SCC estimates, include risks from extreme weather (death, disease, agricu ltural damage, and other economic damage from droughts, floods and wildfires) and possible long-term catastrophic events, such as collapse of the West Antarctic ice sheet or the release of large amounts of methane from melting permafrost.

SSS IPCC WGII. 2007. Climate Change 2007 - Impacts , Adaptation and Vulnerability Contribution of Working Group II to the Fourth Assessment Report of the IPCC

. TTT Radiative forcing is the change in the balance between solar radiation entering the atmosphere and the Earth's radiation going out. On average, a positive radiative forcing tends to warm the surface of the Earth while negative forcing tends to cool the surface. Greenhouse gases have a positive radiative forcing because they absorb and emit heat. See http://www.epa.gov/climatechange/science/recentac.html for more general information about GHGs and climate science.55 The Administration considered other interim SCC options in addition to the approach described above. Similar to May 2009 RFS2 Propoal, one alte rnative option was to bring in SCC estimates in studies published after 1995, rather than limiting the estimates to those in studies relying on the most recent published version of ea ch of the three major integrated assessment models: PAGE, FUND, and DICE. Although some ol der model versions (and old versions of other models) were used to produce estima tes between 1996 and 2001, there have been no significant modeling paradigm changes since 1996. Rather, improvements to PAGE, FUND, and DICE since 1996 have reflected incr emental technical enhancements.

Another option was to select a range of S CC values for separate discount rates. For example, sensitivity analysis could be conducted at the lowest and high est SCC values reported in the filtered set of estimates for each discount rate cons idered. If considering SCC estimates from studies published after 1995 and a discount rate of 2 percent, this option would result in a range of SCC values of $5/t-CO 2 to $260/t-CO 2 (2007 emissions in 2007 dollars); at a 3 percent discount rate, the range would be $0 to $58/ t-CO 2

.
Finally, we considered that the use of certain key assumptions under the Ramsey framework, such as placing approximately equal weight on the welfare of current and future generations, would imply use of a 2% disc ount rate. The Newell and Pizer (2003) method applied to recent long-term risk free rates would likewise be approx imately consistent with a rate of 2 percent. UUU

5.3.2.9 Ongoing SCC Development

As noted, this is an emphatically interim SCC value. The judgments herein will be subject to further scrutiny and exploration.

5.3.3 Application of Interim SCC Estimates to GHG Emissions Reductions from this Final Rule

While no single rule or action can independ ently achieve the deep worldwide emissions reductions necessary to halt and reverse the growth of GHGs, the combined effects of multiple strategies to reduce GHG emissions domestically and abroad could make a major difference in the climate change impacts experienced by future generations. VVV The projected net GHG emissions reductions associated with this final ru le reflect an incremental change to projected total global emissions. Given that the climate response is projected to be a marginal change relative to the baseline climate, we estimate the marginal value of changes in climate change impacts over time and use this value to measure the monetized marginal benefits of the GHG emissions reductions projected for this rule.

UUU Specifically, Newell and Pizer (2003) found that modeling of uncertainty in economic growth causes the effective discount rate to decline over time. When starting at a 4% discount rate, the effective discount rate is 2% at 100 years and 1% at 200 years.

VVV The Supreme Court recognized in Massachusetts v. EPA that a single action will not on its own achieve all needed GHG reductions, noting that “[a]gencies, like legislatures, do not generally resolve massive problems in one fell regulatory swoop.’’ See Massachusetts v. EPA, 549 U.S. at 524 (2007).56 Accordingly, EPA has used the set of inter im, global SCC values described above to estimate the benefits resulting from the renewable fuel volumes mandated by EISA. The interim SCC values, which reflect the Administration’s interim interpretation of the current literature, are $5, $10, $20, $34, and $56, in 2007 dollars, and are based on a CO 2 emissions change of 1 metric ton in 2007. Table 5.3.3-1 presents the interim SCC values for the years 2007 and 2022 in 2007 dollars.

Table 5.3.3-1. Interim SCC Schedul e (2007$ per metric tonne of CO 2 )

Year 5% 5% (NewellPizer)* Average SCC from 3% and 5% 3% 3% (NewellPizer)* 2007 $5 $10 $20 $34 $56 2022 $8 $16 $30 $53 $88 Note: The SCC values are dollar-year and emissions-year specific. These values are presented in 2007$, for individual year of emissions. To determine values for years not presented in the table, use a 3% growth rate. SCC values represent only a partial accounting for climate impacts. *SCC values are adjusted based on Newell and Pizer (2003) to account to future uncertainty in discount rates.

Tables 5.3.3-2 through 5.3.3-4 provide, for th e high, base, and low cases, the average annual GHG emissions reductions in 2022. The a nnualized emissions re ductions are multiplied by the SCC estimates for 2022 from Table 5.3.3-1 to produce the average annual monetized benefit from the emissions reductions from the rule for CO 2 -equivalent GHGs.

Table 5.3.3-2. Average Annual Emissions Reduction (Million Metric Tonnes CO 2 -e) and Monetized Benefits (Million 2007$) of RFS- 2 Volumes in 2022, High Case

CO 2 Non-CO 2 GHG Total GHG Emissions Reductions 148.525 -8.234 140.291

5% $1,188 -$66 $1,122 5% (Newell-Pizer) $2,376 -$132 $2,245 Average SCC from 3% and 5% $4,515 -$250 $4,265 3% $7,842 -$435 $7,407 3% (Newell-Pizer) $13,069 -$725 $12,34457 Table 5.3.3-3. Average Annual Emissions Reduction (Million Metric Tonnes CO 2 -e) and Monetized Benefits (Million 2007$) of RFS- 2 Volumes in 2022, Base Case

CO 2 Non-CO 2 GHG Total GHG Emissions Reductions 146.645 -8.234 138.411

5% $1,173 -$66 $1,107 5% (Newell-Pizer) $2,346 -$132 $2,215 Average SCC from 3% and 5% $4,458 -$250 $4,208 3% $7,743 -$435 $7,308 3% (Newell-Pizer) $12,903 -$725 $12,179

Table 5.3.3-4. Average Annual Emissions Reduction (Million Metric Tonnes CO 2 -e) and Monetized Benefits (Million 2007$) of RFS- 2 Volumes in 2022, Low Case

CO 2 Non-CO 2 GHG Total GHG Emissions Reductions 144.338 -8.234 136.104

5% $1,155 -$66 $1,089 5% (Newell-Pizer) $2,309 -$132 $2,178 Average SCC from 3% and 5% $4,388 -$250 $4,138 3% $7,621 -$435 $7,186 3% (Newell-Pizer) $12,700 -$725 $11,976



### 5.4 Quantified and Monetized Co-pol lutant Health and Environmental Impacts

5.4.1 Overview

This section describes EPA’s analysis of the co-pollutant health and environmental impacts that can be expected to occur as a result of the increase in renewable fuel use throughout the period from initial implementati on through 2022. Although the purpose of this final rule is to implement the renewable fuel requirements es tablished by the Energy Independence and Security Act (EISA) of 2007, the increased use of renewable fuels will also impact emissions of criteria and air toxic pollutants and their resultant ambient concentrations. The fuels changes detailed in Section 3.1 of the RIA will influence emissions of VOCs, PM, NO X , and SO X and air toxics and affect exhaust and evaporative e missions of these pollutants from vehicles and equipment. They will also affect emissions from upstream sources such as fuel production, storage, distribution and agricultural emissions . Any decrease or increase in ambient ozone,
PM 2.5 , and air toxics associated with the increased use of renewable fuels will impact human58 health in the form of a decrease or increase in the risk of incurring premature death and other serious human health effects, as well as other important public health and welfare effects.

This analysis reflects the impact of th e 2022 volumes compared with two different reference scenarios that include the use of re newable fuels: a 2022 baseline projection based on the RFS1-mandated volume of 7.5 billion gallons of renewable fuels, and a 2022 baseline projection based on the AEO 2007 volume of roughl y 13.6 billion gallons of renewable fuels. Thus, the results represent the impact of an incr emental increase in etha nol and other renewable fuels. We note that the air quality modeling results presented in this final rule do not constitute the “anti-backsliding” analysis required by Clean Air Act section 211(v). EPA will be analyzing air quality and health impacts of increased renewable fuel use through that study and will promulgate appropriate mitigation measures under section 211(v) , separate from this final action.

As can be seen in Section 3.4 of this RI A, there are both increased and decreased concentrations of ambient criteria pollutants and air toxics. Overall, we estimate that the final rule will lead to net criteria po llutant-related disbenefits. By 2022, the final RFS2 rule volumes relative to both reference case scenarios (RFS1 and AEO2007), are projected to adversely impact
PM 2.5 air quality over parts of the U.S., while some areas will experience decreases in ambient
PM 2.5 . As described in Section 3.4, ambient PM 2.5 is likely to increase as a result of emissions at biofuel production plants and from biofuel transport, both of which are more prevalent in the Midwest. PM concentrations are also likely to decrease in some areas. While the PM-related air quality impacts are relatively small, the impact of the change in national average PM 2.5 exposure on human health results in net disbenefits (the increase in national population weighted annual average PM 2.5 is 0.006 μ g/m3 and 0.002 μ g/m3 relative to the RFS1 and AEO2007 reference cases, respectively).

The required renewable fuel volumes, relative to both reference scenarios, are also projected to adversely impact ozone air quality over much of the U.S., especially in the Midwest, Northeast and Southeast. These adverse impacts are likely due to increased upstream emissions of NOx in many areas that are NOx-limited (acting as a precursor to ozone formation). There are, however, ozone air quality improvements in so me highly-populated areas that currently have poor air quality. This is likely due to VOC emissi on reductions at the tailpipe in urban areas that are VOC-limited (reducing VOC’s role as a precursor to ozone formation). Relative to the RFS1 mandate reference case, the RFS2 volumes resu lt in a small ozone-relate d disbenefit (population weighted maximum 8-hour average ozone incr eases by 0.177 ppb). Relative to the AEO2007 reference case, the RFS2 volumes result in sma ll ozone-related disbenefits (population weighted maximum 8-hour average ozone increases by 0.116 ppb).

The analysis of national-level PM 2.5 - and ozone-related health and environmental impacts associated with the final rule is based on peer-reviewed studies of air quality and human health effects (see US EPA, 2006 and US EPA, 2008). 1,2 We are also consistent with the benefits analysis methods that supported the recently proposed Portland Ceme nt National Emissions Standards for Hazardous Air Polluta nts (NESHAP) RIA (U.S. EPA, 2009a), 3 the proposed NO 2 primary NAAQS RIA (U.S. EPA, 2009b), 4 and the proposed Category 3 Marine Diesel Engines RIA (U.S. EPA, 2009c). 5 To model the ozone and PM air quality impacts of the 2022 renewable fuel volumes, we used the Community Multiscale Air Quality (CMAQ) model (see Chapter 3.4).59 The modeled ambient air quality data serves as an input to the Environmental Benefits Mapping and Analysis Program (BenMAP). WWW BenMAP is a computer program developed by the U.S. EPA that integrates a number of the modeling elements used in previous analyses (e.g., interpolation functions , population projections, health impact functions, valu ation functions, analysis and pooling methods) to translate modeled air concentration estimate s into health effects incidence estimates and monetized benefits estimates.

Emissions and air quality modeling decisions we re made early in the analytical process and as a result, there are a number of important limitations and uncertainties associated with the air quality modeling analysis that must be kept in mind when considering the results. A key limitation of the analysis is that it employed interim emission inventories, which were enhanced compared to what was described in the propos al, but did not include some of the later enhancements and corrections of the final e mission inventories presented in this FRM (see Section 3.3 of this RIA). Most significantly, our modeling of the air quality impacts of RFS2 relied upon interim inventor ies that assumed that ethanol w ill make up 34 of the 36 billion gallon renewable fuel mandate, that approximately 20 billion gallons of this ethanol will be in the form of E85, and that the use of E85 results in fewer emissions of direct PM 2.5 from vehicles. The emission impacts, air quality results and benefits an alysis would be different if, instead of E85, more non-ethanol biofuels are used or mid-level ethanol blends are a pproved and utilized. In fact, as explained in Chapter 1, our more recent analyses indicate that ethanol and E85 volumes are likely to be significantly lower than what we assumed in the interim inventories. Furthermore, the final emission inventories do not include vehicle- related PM reductions associated with E85 use, as discussed in Chap ters 3.1-3.3 of this RIA. There are additional, important limitations and uncertainties associated with the interim inventories that must be kept in mind when considering the results, which are de scribed in more detail in Chapter 3.4. While it is difficult to describe the overall impact of these limitations and uncertainties on the quantified and monetized health impacts of the increased renewable fuel volumes without updating the airquality modeling analysis, we believe the results are still useful for describing potential nationallevel health impacts.

Additionally, after the air quality modeling was completed, we discovered an error in the way that PM 2.5 emissions from locomotive engines were a llocated to counties in the inventory. The mismatched allocations between the reference and control scenarios resulted in PM 2.5 emission changes that were too hi gh in some counties and too low in others, by varying degrees. As a result, we did not present the modeling results for specific localized PM 2.5 impacts in Section 3.4. However, because the error was ra ndom and offsetting, th ere was very little impact on national-level PM 2.5 emissions. An analysis of the error's impact on the national emission inventories found that direct PM 2.5 emissions were inflated by 8% relative to the AEO reference case and by 0.6% relative to the RFS1 reference case, leading to a small overestimation of national PM-related disbenefits. Note that th is error did not impact other PM precursor inventories such as NOx and SO2. As a result, we have concluded that PM 2.5 modeling results are still informative for national-level benefits assessment, particularly given that other uncertainties in the PM 2.5 inventory (such as E85 usage , discussed below) have a more important (and offsetting) effect.

WWW Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ benmodels.html.60 Table 5.4-1. Estimated 2022 Monetized PM-and Oz one-Related Health Impacts from the Mandated Renewable Fuel Volumes a 2022 Total Ozone and PM Benefits, RFS2 Compared to RFS1 Reference Case a
Premature Ozone Mortality Function Reference Total Benefits (Billions, 2007$, 3% Discount Rate) b,c Total Benefits (Billions, 2007$, 7% Discount Rate) b,c Bell et al., 2004 Total: -$1.4 to -$2.8 PM: -$0.92 to -$2.3 Ozone: -$0.52 Total: -$1.4 to -$2.6 PM: -$0.84 to -$2.0 Ozone: -$0.52 Huang et al., 2005 Total: -$1.8 to -$3.1 PM: -$0.92 to -$2.3 Ozone: -$0.83 Total: -$1.7 to -$2.9 PM: -$0.84 to -$2.0 Ozone: -$0.83 Multi-city analyses

Schwartz, 2005 Total: -$1.7 to -$3.0 PM: -$0.92 to -$2.3 Ozone: -$0.77 Total: -$1.6 to -$2.8 PM: -$0.84 to -$2.0 Ozone: -$0.77 Bell et al., 2005 Total: -$2.5 to -$3.8 PM: -$0.92 to -$2.3 Ozone: -$1.6 Total: -$2.4 to -$3.6 PM: -$0.84 to -$2.0 Ozone: -$1.6 Ito et al., 2005 Total: -$3.1 to -$4.5 PM: -$0.92 to -$2.3 Ozone: -$2.2 Total: -$3.0 to -$4.2 PM: -$0.84 to -$2.0 Ozone: -$2.2 Meta-analyses

Levy et al., 2005 Total: -$3.1 to -$4.5 PM: -$0.92 to -$2.3 Ozone: -$2.2 Total: -$3.1 to -$4.3 PM: -$0.84 to -$2.0 Ozone: -$2.2 2022 Total Ozone and PM Benefits, RFS2 Compared to AEO Reference Case a
Premature Ozone Mortality Function Reference Total Benefits (Millions, 2007$, 3% Discount Rate) b,c Total Benefits (Millions, 2007$, 7% Discount Rate) b,c Bell et al., 2004 Total: -$0.63 to -$1.0 PM: -$0.29 to -$0.70 Ozone: -$0.34 Total: -$0.60 to -$0.98 PM: -$0.26 to -$0.63 Ozone: -$0.34 Huang et al., 2005 Total: -$0.84 to -$1.3 PM: -$0.29 to -$0.70 Ozone: -$0.55 Total: -$0.81 to -$1.2 PM: -$0.26 to -$0.63 Ozone: -$0.55 Multi-city analyses

Schwartz, 2005 Total: -$0.80 to -$1.2 PM: -$0.29 to -$0.70 Ozone: -$0.51 Total: -$0.77 to -$1.1 PM: -$0.26 to -$0.63 Ozone: -$0.51 Bell et al., 2005 Total: -$1.3 to -$1.8 PM: -$0.29 to -$0.70 Ozone: -$1.0 Total: -$1.3 to -$1.7 PM: -$0.26 to -$0.63 Ozone: -$1.0 Ito et al., 2005 Total: -$1.7 to -$2.2 PM: -$0.29 to -$0.70 Ozone: -$1.5 Total: -$1.7 to -$2.1 PM: -$0.26 to -$0.63 Ozone: -$1.5 Meta-analyses

Levy et al., 2005 Total: -$1.8 to -$2.2 PM: -$0.29 to -$0.70 Ozone: -$1.5 Total: -$1.7 to -$2.1 PM: -$0.26 to -$0.63 Ozone: -$1.5

a Total includes premature mortality-related and morbidity-related ozone and PM 2.5 benefits. Range was developed by adding the estimate from the ozone premature mortality function to the estimate of PM 2.5 -related premature mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006).

b Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed listing of unquantified health and welfare effects is provided in Table 5.4-2.61 c Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines for Preparing Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease of presentation and computation.

The monetized estimates in Table 5.4-1 include all of the human health impacts we are able to quantify and monetize at this time. However, the full complement of human health and welfare effects associated with PM and ozone remain unquantified becaus e of current limitations in methods or available data. We have not quantified a number of known or suspected health effects linked with ozone and PM for which approp riate health impact functions are not available or which do not provide easily interpretable outcomes (i.e., changes in heart rate variability). Additionally, we are unable to quantify a numbe r of known welfare effects, including acid and particulate deposition dama ge to cultural monuments and othe r materials, and environmental impacts of eutrophication in coastal areas. These are listed in Table 5.4-2.62 Table 5.4-2. Unquantified and Non-Monetized Potential Effects from the Mandated Renewable Fuel Volumes

POLLUTANT/EFFECTS EFFECTS NOT INCL UDED IN ANALYSIS - CHANGES IN:
Ozone Health a Chronic respiratory damage b Premature aging of the lungs b Non-asthma respiratory emergency room visits Exposure to UVb (+/-) e

Ozone Welfare Yields for -commercial forests -some fruits and vegetables -non-commercial crops Damage to urban ornamental plants Impacts on recreational demand from damaged forest aesthetics Ecosystem functions Exposure to UVb (+/-) e

PM Health c Premature mortality - short term exposures d
Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non-asthma respiratory emergency room visits Exposure to UVb (+/-) e
PM Welfare Residential and recreatio nal visibility in non-Class I areas Soiling and materials damage Damage to ecosystem functions Exposure to UVb (+/-) e Nitrogen and Sulfate Deposition Welfare Commercial forests due to acidic sulfate and nitrate deposition Commercial freshwater fishing due to acidic deposition Recreation in terrestrial ecosyst ems due to acidic deposition Existence values for currently healthy ecosystems Commercial fishing, agriculture, and forests due to nitrogen deposition Recreation in estuarine ecosystems due to nitrogen deposition Ecosystem functions Passive fertilization CO Health Behavioral effects
HC/Toxics Health f Cancer (benzene, 1,3-butadien e, formaldehyde, acetaldehyde) Anemia (benzene) Disruption of production of blood components (benzene) Reduction in the number of blood platelets (benzene) Excessive bone marrow formation (benzene) Depression of lymphocyte counts (benzene) Reproductive and developmental effects (1,3-butadiene) Irritation of eyes and mucus membranes (formaldehyde) Respiratory irritation (formaldehyde) Asthma attacks in asthmatics (formaldehyde) Asthma-like symptoms in non-asthmatics (formaldehyde) Irritation of the eyes, skin, and respiratory tract (acetaldehyde) Upper respiratory tract irritation and congestion (acrolein) HC/Toxics Welfare Direct toxic effects to animals Bioaccumulation in the food chain Damage to ecosystem function Odor63 a The public health impact of biological responses such as increased airway responsiveness to stimuli, inflammation in the lung, acute inflammation and respiratory cell damage, and increased susceptibility to respiratory infection are likely partially represented by our quantified endpoints.

b The public health impact of effects such as chronic respiratory damage and premature aging of the lungs may be partially represented by quantified endpoints such as hospital admissions or premature mortality, but a number of other related health impacts, such as doctor visits and decreased athletic performance, remain unquantified.

c In addition to primary economic endpoints, there are a numbe r of biological responses th at have been associated with PM health effects including morphological changes and altered host defense mechanisms. The public health impact of these biological responses may be partly represented by our quantified endpoints. d While some of the effects of short-term exposures are likely to be captured in the estimates, there may be premature mortality due to short-term exposure to PM not captured in the cohort studies used in this analysis. However, the PM mortality results derived from the expert elicitation do take into account premature mortality effects of short term exposures.

e May result in benefits or disbenefits. f Many of the key hydrocarbons related to this rule are also hazardous air pollutants listed in the Clean Air Act.

While there will be impacts associated with air toxic pollutant emission changes that result from the increased use of renewable fuels, we do not attempt to monetize those impacts. This is primarily because currently available t ools and methods to assess air toxics risk from mobile sources at the national scale are not adequate for extrapolation to incidence estimations or benefits assessment. The best suite of tools and methods current ly available for assessment at the national scale are those used in the National-Scale Air Toxics Assessment (NATA). The EPA Science Advisory Board specifically commented in their review of the 1996 NATA that these tools were not yet ready for use in a national- scale benefits analysis, because they did not consider the full distribution of exposure and risk, or address sub-chronic health effects. XXX While EPA has since improved the tools, there remain critical limitations for estimating incidence and assessing benefits of reducing mobile source air toxics. EPA continues to work to address these limitations; however, we did not have the methods a nd tools available for nationalscale application in time for the analysis of the final rule. YYY

5.4.2 Quantified Human Health Impacts

Tables 5.4-3 and 5.4-4 present the annual PM 2.5 and ozone health impacts in the 48 contiguous U.S. states associated with the RF S2 volumes relative to both the RFS1 and AEO reference cases for 2022. For each endpoint presented in Tables 5.4- 3 and 5.4-4, we provide both the mean estimate and the 90% confidence interval.

Using EPA’s preferred estimates, based on the ACS and Six-Cities studies and no threshold assumption in the model of mortality, we estimate that the RFS2 volumes will result in

XXX Science Advisory Board. 2001. NATA – Evaluating the National-Scale Air Toxics Assessment for 1996 – an SAB Advisory. http://www.epa.gov/ttn/atw/sab/sabrev.html.

YYY In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This workshop built upon the work accomplished in the June 2000 Science Advisory Board/EPA Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on approaches to estimating human health benefits from re ductions in air toxics exposure, but no consensus was reached on methods that could be implemented in the near term for a broad selection of air toxics. Please visit http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials.64 between 110 and 270 cases of PM 2.5 -related premature deaths annually in 2022 when compared to the RFS1 reference case. When compared to the AEO reference scenario, we estimate that the RFS2 volumes will result in between 33 and 85 cases of avoided PM 2.5 -related premature deaths annually in 2022. As a sensitivity analysis, when th e range of expert opinion is used we estimate that in 2022 the RFS2 volumes will result in between 34 and 360 PM-related premature mortalities when compared to the RFS1 re ference case and between 11 and 110 PM-related premature mortalities when compared to the AEO reference case.

The range of ozone impacts associated with the RFS2 volumes is based on changes in risk estimated using several sources of ozone-related mortality effect estimates. This analysis presents six alternative estimates for the associ ation based upon different functions reported in the scientific literature, derived from both multi-city studies, such as the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) (Bell et al., 2004, Huang et al., 2005, and Schwartz et al., 2005) and from a series of recen t meta-analyses (Bell et al., 2005, Ito et al., 2005, and Levy et al., 2005). This approach is not inconsistent with recommendations provided by the NRC in their recent report (NRC, 2008) on the estimation of ozone-re lated mortality risk, “The committee recommends that the greate st emphasis be placed on estimates from new systematic multicity analyses that use national databases of air pollution and mortality, such as in the NMMAPS, without excluding consideration of meta-analyses of previously published studies.” For ozone-related premature mortality, we estimate that national changes in ambient ozone will contribute to between 54 to 250 additi onal premature mortalities in 2022 as a result of the RFS2 volumes relative to the RFS1 scenario. When compared to the AEO reference scenario, we estimate that the RFS2 volumes will contribute to betw een 36 to 160 additional ozone-related prematur e mortalities in 2022.

Following these tables, we also provide a more comprehensive presentation of the distributions of mortality-rela ted incidence generated using the available information from empirical studies and expert elicitation associated with the RFS2 volumes compared to each reference scenario. Tables 5.4-5 and 5.4-6 present the distributions of PM 2.5 -related premature mortality based on the C-R distributions provided byeach expert, as well as that from the dataderived health impact functions, based on the stat istical error associated with the ACS study (Pope et al., 2002) and the Six-cities study (Laden et al., 2006). The 90% confidence interval for each separate estimate of PM-relate d mortality is also provided.

When comparing the RFS2 fuel volume scenario to the RFS1 reference case, the effect estimates of nine of the twelve experts included in the elicitation panel fall within the empirically-derived range provided by the ACS and Six-Cities studies. Only one expert falls below this range, while two of the experts are ab ove this range. This same relationship occurs when comparing the RFS2 fuel volume scenario to the AEO reference case. Although the overall range across experts is summarized in these tables, the full uncertainty in the estimates is reflected by the results for the full set of 12 expe rts. The twelve experts’ judgments as to the likely mean effect estimate are not evenly distributed across the range illustrated by arraying the highest and lowest expert means.65 Table 5.4-3. Estimated PM 2.5 -Related Health Impacts Associated with the Mandated Renewable Fuel Volumes a

a Note that negative incidence expressed in this table reflects disbenefits; in other words, an increase in total aggregated national-level ozone-related health impacts. Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United States.

b PM-related adult mortality based upon the American Canc er Society (ACS) Cohort Stud y (Pope et al., 2002) and the Six-Cities Study (Laden et al., 2006). Note that these are two alternative estimates of adult mortality and should not be summed. PM-related infant mortality based upon a study by Woodruff, Grillo, and Schoendorf, (1997). ZZZ

c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD), pneumonia and asthma.

d Cardiovascular hospital admissions for PM include tota l cardiovascular and subcategories for ischemic heart disease, dysrhythmias, and heart failure.

ZZZ Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health Perspectives 105(6):608-612.

Health Effect 2022 RFS2 Compared to RFS1 Reference Case (5 th % - 95 th %ile) 2022 RFS2 Compared to AEO Referenc e Case (5 th % - 95 th %ile) Premature Mortality – Derived from Epidemiology Literature b Adult, age 30+, ACS Cohort Study (Pope et al., 2002)

Adult, age 25+, Six-Cities Study (Laden et al., 2006)

Infant, age <1 year (Woodruff et al., 1997) -110 (-42 - -170) -270 (-150 - -400) 0 (0 - -1) -33 (-13 - -53) -85 (-46 - -120) 0 (0 - -1) Chronic bronchitis (adult, age 26 and over) -65 (-26 - -110) -19 (-4 - -18) Non-fatal myocardial infarction (adult, age 18 and ove r -180 (-65 - -290) -51 (-19 - -84) Hospital admissions - respiratory (all ages) c -26 (-25 - -26) -7 (-5 - -8) Hospital admissions - cardiovascular (adults, age >18) d -55 (-44 - -70) -12 (-9 - -16) Emergency room visits for asthma (age 18 years and younger) -180 (-110 - -260) -99 (-58 - -140) Acute bronchitis, (children, age 8-12) -160 (0 - -330) -50 (0 - -100) Lower respiratory symptoms (children, age 7-14) -1,900 (-910 - -2,900) -600 (-290 - -910) Upper respiratory symptoms (asthmatic children, age 9

- 18) -1,400 (-450 - -2,400) -450 (-140 - -750) Asthma exacerbation (asthmatic children, age 6-18) -1,700 (-190 - -4,800) -540 (-60 - -1,500) Work loss days -11,000 (-10,000 - -13,000) -3,200 (-2,800 - -3,700) Minor restricted activity days (adults age 18-65) -68,000 (-57,000 - -78,000) -19,000 (-16,000 - -22,000)66 Table 5.4-4. Estimated Ozone-Related Health Impacts Associated with the Mandated Renewable Fuel Volumes a

a Note that negative incidence expressed in this table reflects disbenefits; in other words, an increase in total aggregated national-level ozone-related health impacts. Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United States. Note that negative incidence estimates represent additional cases of an endpoint related to po llution increases associated with the rule.

b Estimates of ozone-related premature mortality are based upon incidence estimates derived from several alternative studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. (2005). The estimates of ozone-related premat ure mortality should therefore not be summed.

c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for COPD and pneumonia. Health Effect 2022 RFS2 Compared to RFS1 Reference Case (5 th % - 95 th %ile) 2022 RFS2 Compared to AEO Reference Case (5 th % - 95 th %ile) Premature Mortality, All ages b Multi-City Analyses Bell et al. (2004) – Non-accidental

Huang et al. (2005) – Cardiopulmonary

Schwartz (2005) – Non-accidental

Meta-analyses: Bell et al. (2005) – All cause

Ito et al. (2005) – Non-accidental

Levy et al. (2005) – All cause -54 (-17 - -92) -90 (-31 - -149) -83 (-24 - -140)

-180 (-80 - -270) -240 (-140 - -350) -250 (-170 - -330) -36 (-10 - -62) -59 (-18 - -100) -55 (-13 - -97)

-120 (-49 - -180) -160 (-90 - -230) -160 (-110 - -220) Hospital admissions- respiratory causes (adult, 6 and older) c -470 (-20 - -860) -310 (-5 - -580) Hospital admissions -respiratory causes (childre n under 2) -83 (-24 - -140) -190 (-52 - -330) Emergency room visit for asthma (all ages) -260 (0 - -740) -180 (0 - -510) Minor restricted activity days (adults, age 18-65 ) -300,000 (-110,000 - -500,000) -200,000 (-59,000 - -340,000) School absence days -110,000 (-35,000 - -180,000) -75,000 (-19,000 - -120,000)67 Table 5.4-5. Results of Applicat ion of Expert Elicitation: An nual Reductions in Premature Mortality in 2022 Associated with th e Mandated Renewable Fuel Volumes
2022 RFS2 compared to the RFS1 reference case Source of Mortality Estimate
5th Percentile Mean 95th Percentile

Pope et al. (2002) -42 -110 -170

Laden et al. (2006) -150 -270 -400

Expert A -53 -290 -530

Expert B -11 -210 -480
Expert C -39 -220 -470
Expert D -32 -150 -250
Expert E -180 -360 -550 Expert F -130 -200 -260 Expert G 0 -130 -240 Expert H -1 -160 -380 Expert I -34 -220 -390 Expert J -52 -180 -390 Expert K 9 -34 -180 Expert L 4 -130 -31068 Table 5.4-6. Results of Application of Expert Elicit ation: Annual Reductions in Premature Mortality in 2022 Associated with th e Mandated Renewable Fuel Volumes
2022 RFS2 compared to the AEO reference case Source of Mortality Estimate
5th Percentile Mean 95th Percentile

5.4.3 Monetized Benefits

Monetized values for each quantified health endpoint are presented in Table 5.4-7. For each endpoint presented in Table 5.4-7, we pr ovide both the mean estimate and the 90% confidence interval. Total aggregate monetized benefits are presented in Tables 5.4-8 and 5.4-9 using either a 3 percent or 7 perc ent discount rate, respectively. A ll of the monetary benefits are in constant-year 2007 dollars.

In addition to omitted benefits categories such as air toxics and various welfare effects, not all known PM 2.5 - and ozone-related health and welfar e effects could be quantified or monetized. The estimate of total monetized health impacts from the renewable fuel volumes are thus equal to the subset of monetized PM 2.5 - and ozone-related health impacts we are able to quantify plus the sum of the nonmonetiz ed health and welfare impacts.

Our estimate of total monetized disbenefits in 2022 for the RFS2 fuel volume scenario compared to the RFS1 reference case, using the ACS and Six-Cities PM mortality studies and the range of ozone mortality assumptions, is between $1.4 billion and $4.5 billion, assuming a 3 percent discount rate, or be tween $1.4 billion and $4.3 billion, assuming a 7 percent discount rate. When compared to the AEO reference case, we estimate the monetized disbenefits to be between $0.63 billion and $2.2 billion, assuming a 3 percent discount rate , or between $0.60 billion and $2.1 billion, assuming a 7 percent discount rate. The monetized impacts associated Pope et al. (2002) -13 -33 -53

Laden et al. (2006) -46 -85 -120

Expert A -17 -91 -170

Expert B 7 -65 -160
Expert C -12 -68 -150
Expert D -10 -48 -79
Expert E -57 -110 -170 Expert F -35 -59 -69 Expert G 0 -40 -74 Expert H 0 -51 -120 Expert I -11 -68 -121 Expert J -16 -55 -122 Expert K 0 -11 -54 Expert L 8 -38 -10169 with an increase in the risk of both ozone- and PM 2.5 -related premature mortality ranges between 90 to 98 percent of total monetized health impacts, in part because we are unable to quantify a number of health and environmental impact cat egories (see Table 5.4-2). These unquantified impacts may be substantial, although thei r magnitude is highly uncertain.

The next largest disbenefit is for increased incidence of PM-related chronic illness (chronic bronchitis and nonfatal he art attacks), although this valu e is more than an order of magnitude lower than for PM-related premature mortality. Hospital admissions for respiratory and cardiovascular causes, minor restricted activ ity days, and work loss days account for the majority of the remaining disbenefits. The remaining categories each account for a small percentage of total disbenefit. A comparison of the incidence table to the monetary disbenefits table reveals that there is not always a close correspondence betw een the number of incidences for a given endpoint and the monetary value associ ated with that endpoint . For example, there are over 100 times more work loss days than PM-related premature mortalities (based on the ACS study), yet work loss days account for onl y a very small fracti on of total monetized disbenefits. This reflects the fact that many of the less severe health effects, while more common, are valued at a lower level than the more severe health effects. Also, some effects,such as hospital admissions, are valued using a proxy measure of willingness-to-pay (e.g., costof-illness). As such, the true value of these effects may be different than that reported here.70 Table 5.4-7. Estimated Monetary Value of Health and We lfare Effect Incidence (in millions of 2007$) a,b

Adult, age 25+ - Six-cities study (Laden et al., 2006) 3% discount rate

7% discount rate -$2,200 (-$29 - -$5,500) -$2,000 (-$26 - -$5,000) -$680 (-$90 - -$1,700) -$620 (-$81 - -$1,600) Infant Mortality, <1 year – (Woodruff et al. 1997) -$4.0 (-$3.0 - -$15) -$1.7 (-$1.3 - -$6.7) Chronic bronchitis (adults, 26 and over) -$32 (-$2.5 - -$110) -$9.4 (-$0.72 - -$33) 2022 RFS2 Compared to RFS1

Reference Case 2022 RFS2 Compared to AEO Reference Case

PM 2.5 -Related Health Effect Estimated Mean Value of Reductions (5 th and 95 th %ile)

Premature Mortality – Derived from Epidemiology S di c d Adult, age 30+ - ACS study (Pope et al., 2002) 3% discount rate

7% discount rate -$860 (-$100 - -$2,300) -$770 (-$91 - -$2,000) -$270 (-$32 - -$700) -$240 (-$28 - -$630)71 a Negatives indicate a disbenefit, or an increase in health effect incidence. Monetary impacts are rounded to two significant digits for ease of presentation and computation. PM and ozone impacts are nationwide. b Monetary impacts adjusted to account for growth in real GDP per capita between 1990 and the analysis year (2022) Non-fatal acute myocardial infarctions 3% discount rate

7% discount rate -$23 (-$4.1 - -$58) -$23 (-$3.8 - -$58) -$6.6 (-$1.0 - -$17) -$6.4 (-$0.95 - -$16) Hospital admissions for respiratory causes -$0.39 (-$0.19 - -$0.57 -$0.11 (-$0.06 - -$0.17) Hospital admissions for cardiovascular causes -$1.5 (-$0.96 - -$2.1) -$0.33 (-$0.20 - -$0.45) Emergency room visits for asthma -$0.07 (-$0.04 - -$0.10) -$0.04 (-$0.02 - -$0.06) Acute bronchitis (children, age 8–12) -$0.01 ($0 - -$0.03) -$0.004 ($0 - -$0.01) Lower respiratory symptoms (children, 7–14) -$0.04 (-$0.01 - -$0.07) -$0.01 (-$0.004 - -$0.02) Upper respiratory symptoms (asthma, 9–11) -$0.04 (-$0.01 - -$0.10) -$0.01 (-$0.004 - -$0.03) Asthma exacerbations -$0.09 (-$0.009 - -$0.28) -$0.03 (-$0.003 - -$0.09) Work loss days -$1.7 (-$1.5 - -$1.9) -$0.49 (-$0.42 - -$0.55) Minor restricted-activity days (MRADs) -$4.3 (-$2.5 - -$6.2) -$1.2 (-$0.69 - -$1.7)
Ozone-related He alth Effect
Bell et al., 2004 -$480 (-$51 - -$1,300) -$320 (-$32 - -$880) Huang et al., 2005 -$800 (-$90 - -$2,200) -$530 (-$56 - -$1,400) Premature Mortality, All ages – Derived from Multi-city analyses

Schwartz, 2005 -$740 (-$76 - -$2,000) -$490 (-$48 - -$1,300) Bell et al., 2005 -$1,600 (-$200 - -$4,000) -$1,000 (-$130 - -$,700) Ito et al., 2005 -$2,200 (-$290 - -$5,400) -$1,400 (-$190 - -$3,600) Premature Mortality, All ages – Derived from Meta-analyses

Levy et al., 2005 -$2,200 (-$300 - -$5,300) -$1,400 (-$200 - -$3,500) Hospital admissions- respiratory causes (adult, 65 and older) -$11 (-$0.49 - -$20) -$7.4 (-$0.13 - -$14) Hospital admissions- respiratory causes (children, under 2) -$3.0 (-$1.0 - -$4,9) -$1.9 (-$0.52 - -$3.3) Emergency room visit for asthma (all ages) -$0.10 (-$0.009 - -$0.26) -$0.07 (-$0.008 - -$0.18) Minor restricted activity days (adults, age 18-65) -$19 (-$6.4 - -$35) -$13 (-$3.6 - -$24) School absence days -$10 (-$3.1 - -$16) -$6.7 (-$1.7 - -$11)72 c Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results reflect the use of 3 percent and 7 percent discount rates consiste nt with EPA and OMB guidelines for preparing economic analyses.

Table 5.4-8. Total Monetized Impacts Associ ated with the Mandated Renewable Fuel Volumes – 3% Discount Rate

Total Ozone and PM Monetized Impacts (billions, 2007$) – PM Mortality Derived from the ACS and Six Cities Studies 2022 RFS2 Compared to RFS1 Reference Case 2022 RFS2 Compared to AEO Reference Case
Ozone Mortality Function Reference Mean Total Benefits Ozone Mortality Function Reference Mean Total Benefits

Bell et al., 2004 -$1.4 to -$2.8 Bell et al., 2004 -$1.3 to -$1.8

Huang et al., 2005 -$1.8 to -$3.1 Huang et al., 2005 -$0.84 to -$1.3 Multi-city

Schwartz, 2005 -$1.7 to -$3.0 Multi-city

Schwartz, 2005 -$0.80 to -$1.2

Bell et al., 2005 -$2.5 to -$3.8 Bell et al., 2005 -$1.3 to -$1.8

Ito et al., 2005 -$3.1 to -$4.5 Ito et al., 2005 -$1.7 to -$2.2 Meta-analysis Levy et al., 2005 -$3.1 to -$4.5 Meta-analysis Levy et al., 2005 -$1.8 to -$2.2

Total Ozone and PM Monetized Impacts (millions, 2007$) – PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 2022 RFS2 Compared to RFS1 Reference Case 2022 RFS2 Compared to AEO Reference Case
Ozone Mortality Function Reference Mean Total Benefits Ozone Mortality Function Reference Mean Total Benefits

Bell et al., 2004 -$0.86 to -$3.5 Bell et al., 2004 -$0.45 to -$1.3

Huang et al., 2005 -$1.2 to -$3.8 Huang et al., 2005 -$0.66 to -$1.5 Multi-city

Schwartz, 2005 -$1.1 to -$3.8 Multi-city

Schwartz, 2005 -$0.62 to -$1.4

Bell et al., 2005 -$1.9 to -$4.6 Bell et al., 2005 -$1.2 to -$2.0

Ito et al., 2005 -$2.5 to -$5.2 Ito et al., 2005 -$1.6 to -$2.4 Meta-analysis Levy et al., 2005 -$2.6 to -$5.2 Meta-analysis Levy et al., 2005 -$1.6 to -$2.473 Table 5.4-9. Total Monetized Impacts Associ ated with the Mandated Renewable Fuel Volumes – 7% Discount Rate

Total Ozone and PM Monetized Impacts (billions, 2007$) – PM Mortality Derived from the ACS and Six Cities Studies 2022 RFS2 Compared to RFS1 Reference Case 2022 RFS2 Compared to AEO Reference Case
Ozone Mortality Function Reference Mean Total Benefits Ozone Mortality Function Reference Mean Total Benefits

Bell et al., 2004 -$1.4 to -$2.6 Bell et al., 2004 -$0.60 to -$0.98

Huang et al., 2005 -$1.7 to -$2.9 Huang et al., 2005 -$0.81 to -$1.2 Multi-city

Schwartz, 2005 -$1.6 to -$2.8 Multi-city

Schwartz, 2005 -$0.77 to -$1.1

Bell et al., 2005 -$2.4 to -$3.6 Bell et al., 2005 -$1.3 to -$1.7

Ito et al., 2005 -$3.0 to -$4.2 Ito et al., 2005 -$1.7 to -$2.1 Meta-analysis Levy et al., 2005 -$3.1 to -$4.3 Meta-analysis Levy et al., 2005 -$1.7 to -$2.1

Total Ozone and PM Monetized Impacts (millions, 2007$) – PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 2022 RFS2 Compared to RFS1 Reference Case 2022 RFS2 Compared to AEO Reference Case
Ozone Mortality Function Reference Mean Total Benefits Ozone Mortality Function Reference Mean Total Benefits

Bell et al., 2004 -$0.83 to -$3.5 Bell et al., 2004 -$0.44 to -$1.2

Huang et al., 2005 -$1.1 to -$3.5 Huang et al., 2005 -$0.65 to -$1.4 Multi-city

Schwartz, 2005 -$1.1 to -$3.5 Multi-city

Schwartz, 2005 -$0.61 to -$1.4

Bell et al., 2005 -$1.9 to -$4.3 Bell et al., 2005 -$1.1 to -$1.9

Ito et al., 2005 -$2.5 to -$4.9 Ito et al., 2005 -$1.6 to -$2.3 Meta-analysis Levy et al., 2005 -$2.5 to -$4.9 Meta-analysis Levy et al., 2005 -$1.6 to -$2.3

5.4.4 Methodology

Human Health Impact Functions

Health impact functions measure the change in a health endpoint of interest, such as hospital admissions, for a given change in ambien t ozone or PM concentr ation. Health impact functions are derived from primary epidemiology studies, meta-analyses of multiple epidemiology studies, or expert elicitations. A standard h ealth impact function has four components: 1) an effect estimate from a particul ar study; 2) a baseline incidence rate for the health effect (obtained from either the epidemiology study or a source of public health statistics such as the Centers for Disease C ontrol); 3) the size of the poten tially affected population; and 4) the estimated change in the relevant ozone or PM summary measures.74 A typical health impact function might look like: 

# ( ) 1 0 −

⋅ = Δ Δ

⋅ x e y y β

,

where y 0 is the baseline incidence (the product of the baseline incidence rate times the potentially affected population), β is the effect estimate, and Δ x is the estimated change in the summary pollutant measure. There are other functional fo rms, but the basic elements remain the same. The following subsections describe the sources for each of the first three elements: size of the potentially affected populations; PM 2.5 and ozone effect estimates; and baseline incidence rates. Chapter 5.4.6 describes the ozone and PM air qualit y inputs to the health impact functions.

Potentially Affected Populations

The starting point for estimating the size of potentially affected populations is the 2000 U.S. Census bloc k level dataset. 6 The Benefits Modeling and Analysis Program (BenMAP) incorporates 250 age/gender/race categories to ma tch specific populations potentially affected by ozone and other air pollutants. The software constructs specific populations matching the populations in each epidemiological study by ac cessing the appropriate age-specific populations from the overall population database. BenMAP projects populations to 2022 using growth factors based on economic projections. 7

Effect Estimate Sources

The most significant quantifiable impacts of exposure to ambient concentrations of ozone and PM are attributable to human health ri sks. EPA’s Ozone and PM Criteria Documents 8,9 and the World Health Organization’s 2003 and 2004 10,11 reports outline numerous human health effects known or suspected to be linked to expos ure to ambient ozone and PM. US EPA recently evaluated the ozone and PM literature for use in the benefits analysis for the final 2008 Ozone NAAQS and final 2006 PM NAAQS analyses. We use the same literature in this analysis; for more information on the studies that underlie th e health impacts quantified in this RIA, please refer to those documents.

It is important to note that we are unable to separately quantify all of the possible PM and ozone health effects that have been reported in the literature for three reasons: (1) the possibility of double counting (such as hospital admissions for specific respiratory diseases versus hospital admissions for all or a sub-set of respiratory di seases); (2) uncertainties in applying effect relationships that are based on c linical studies to the potentially affected population; or (3) the lack of an established concentration-response (C R) relationship. Table 5.4-10 lists the health endpoints included in this analysis.75 Table 5.4-10. Ozone- and PM-Related Health Endpoints

ENDPOINT POLLUTANT STUDY STUDY POPULATION Premature Mortality

Premature mortality – daily time series O 3 Multi-city Bell et al (2004) (NMMAPS study) 12 – Nonaccidental Huang et al (2005) 13 - Cardiopulmonary Schwartz (2005) 14 – Non-accidental Meta-analyses: Bell et al (2005) 15 – All cause Ito et al (2005) 16 – Non-accidental Levy et al (2005) 17 – All cause All ages

Premature mortality—cohort study, allcause PM 2.5 Pope et al. (2002) 18 Laden et al. (2006) 19 >29 years >25 years

Premature mortality, total exposures PM 2.5 Expert Elicitation (IEc, 2006) 20 >24 years

Premature mortality — all-cause PM 2.5 Woodruff et al. (1997) 21 Infant (<1 year)

Chronic Illness

Chronic bronchitis PM 2.5 Abbey et al. (1995) 22 >26 years

Nonfatal heart attacks PM 2.5 Peters et al. (2001) 23 Adults (>18 years) Hospital Admissions

Pooled estimate: Schwartz (1995) - ICD 460-519 (all resp) 24 Schwartz (1994a; 1994b) - ICD 480-486 (pneumonia) 25,26 Moolgavkar et al. (1997) - ICD 480-487 (pneumonia) 27 Schwartz (1994b) - ICD 491-492, 494-496 (COPD) Moolgavkar et al. (1997) – ICD 490-496 (COPD) >64 years
O 3

Burnett et al. (2001) 28 <2 years Respiratory

PM 2.5 Pooled estimate: Moolgavkar (2003)—ICD 490-496 (COPD) 29 >64 years767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165 Notes:

a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we extended the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical Planfor EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SABCOUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The National Academies Press.

b Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school absences for all school-aged children based on the biological similarity between children aged 5 to 17. In selecting epidemiological studies as sources of effect estimates, we applied several criteria to develop a set of studies that is likely to provide the best estimates of impacts in the U.S. To account for the potential impacts of different health care systems or underlying health status of populations, we give preference to U.S. studies over non-U.S. studies. In addition, due to the potential for confounding by co-pollutants, we gi ve preference to effect estimates from models including both ozone and PM over effect estimates from single-pollutant models. 46,47

Treatment of Potential Thresholds in PM2.5-Related Health Impact Functions

In recent analyses, OTAQ has estimated PM 2.5 -related benefits assu ming that a threshold exists in the PM-related concen tration-response functions (at 10 μ g/m 3 ) below which there are no associations between exposure to PM 2.5 and health impacts. For the analysis of the final rule, however, we have revised this assumption. As explained in the recently proposed Portland Cement MACT RIA, EPA’s pref erred benefits estimation appr oach assumes a no-threshold model that calculates incremental benefits down to the lowest modeled PM 2.5 air quality levels.

EPA strives to use the best available scienc e to support our benefits analyses, and we recognize that interpretation of the science re garding air pollution and health is dynamic and evolving. Based on our review of the body of scientific literature, EPA applied the no-threshold model in this analysis. EPA's draft Integrated Science Assessment, 48,49 which was recently reviewed by EPA’s Clean Air Scientific Advisory Committee, 50,51 concluded that the scientific literature consistently finds that a no-threshold log-linear model most adequately portrays the PM-mortality concentration-respons e relationship while recognizin g potential uncertainty about the exact shape of the con centration-response function. AAAA Although this document does not represent final agency policy that has undergone the full agency scientific review process, it provides a basis for reconsidering th e application of thresholds in PM 2.5 concentration-response functions used in EPA’s RIAs. BBBB It is important to note th at while CASAC provides advice regarding the science associated with setting the National Ambient Air Quality Standards, typically other scientific advisory bodies provide specific advice regarding benefits analysis. CCCC,52

AAAA It is important to note that uncertainty regarding the shape of the concentration-response function is conceptually distinct from an assumed threshold. An assu med threshold (below which there are no health effects) is a discontinuity, which is a specific example of non-linearity.

BBBB The final PM ISA, which will have undergone the full agency scientific review process, is scheduled to be completed in late December 2009.

CCCC In the Portland Cement RIA, EPA solicited comment on the use of the no-threshold model for benefits analysis within the preamble of that proposed rule. The comment period for the Portland Cement proposed NESHAP closed166 As can be seen in Table 5.4-11, we conduct ed a sensitivity analysis for premature mortality, with alternative thresholds at 3 μ g/m 3 (the “background,” or no-threshold, assumption), 7.5 μ g/m 3 , 10 μ g/m 3

, 12 μ g/m 3 , and 14 μ g/m 3

. By replacing the no-threshold assumption in the ACS premature mortality function with a 10 μ g/m 3 threshold model, the number of avoided incidences of premature mortality would change dramatically.

on September 4, 2009 (Docket ID No. EPA–HQ–OAR–2002–0051 available at http://www.regulations.gov ). EPA is currently reviewing those comments.167 Table 5.4-11. PM-Related Mortality Impacts Associated with the Mandated Renewable Fuel Volumes: Threshold Sensitivity Anal ysis Using the ACS Study (Pope et al., 2002) a168169170171172173174175176 Notes:

a Note that this table only presents the effects of a threshold on PMrelated mortality incidence based on the ACS study. Negative values indicate a disbenefit, or additional mortality incurred.

b Alternative annual PM NAAQS.

c Previous threshold assumption

d SAB-HES (2004) 86

e NAS (2002) 87

5.4.5 Economic Values fo r Health Outcomes

Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects for a large population, while th e reverse is also generally true. Therefore, the appropriate economic measure is willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health effect th at would occur with certainty (Freeman, 1993). Epidemiological studies generally provide estimates of the relative risks of a particular health effect that is avoided because of a reduction in air pollution. We converted those to units of avoided statistical incidence for ease of presentation. We calculated the value of avoided statistical incidences by divi ding individual WTP for a risk reduction by the related observed change in risk. For example, suppose a pollution- reduction regulation is able to reduce the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statisti cal premature death is $1 million ($100/0.0001 change in risk).

WTP estimates generally are not available for some health effects, such as hospital admissions. In these cases, we used the cost of treating or mitigating the effect as a primary estimate. These cost-of-illness (COI) estimates ge nerally understate the tr ue value of reducing the risk of a health effect, because they reflect the direct expenditures related to treatment, but not the value of avoided pain and suffering (H arrington and Portney, 1987; Berger, 1987). We provide unit values for health endpoints (along with information on the distribution of the unit value) in Table 5.4-12. All values are in constant year 2006 dollars, adjusted for growth in real income out to 2022 using projections provided by Standard and Poor’s. Economic theory argues that WTP for most goods (such as environmenta l protection) will increa se if real income increases. Many of the valuation studies used in this analysis were conducted in the late 1980s and early 1990s. Because real income has gr own since the studies were conducted, people’s willingness to pay for reductions in the risk of premature death and disease likely has grown as well. We did not adjust cost of illness-based values because th ey are based on current costs. Similarly, we did not adjust the value of school absences, because that value is based on current wage rates. For details on valuation es timates for PM-related e ndpoints, see the 2006 PM NAAQS RIA. For details on valuation estima tes for ozone-related endpoints, see the 2008 Ozone NAAQS RIA.177Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a178Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a(continued)179180Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a(continued)181182Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a(continued)183aAll monetized annual benefit estimates associated with the coordinated strategy have been inflated to reflect values in year 2006 dollars. We use the ConsumerPrice Indexes to adjust both WTP- and COI-based benefits estimates to 2007 dollars from 2000 dollars.64For WTP-based estimates, we use an inflation factor of1.20 based on the CPI-U for “all items.” For COI-based estimates, we use an inflation factor of 1.35 based on the CPI-U for medical care.bOur analysis accounts for expected growth in real income over time. Economic theory argues that WTP for most goods (such as environmental protection) willincrease if real incomes increase. Benefits are therefore adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor to account forincome growth over time. For a complete discussion of how these adjustment factors were derived, we refer the reader to the PMNAAQS regulatory impactanalysis. Note that similar adjustments do not exist for cost-of-illness-based unit values. For these, we apply the same unitvalue regardless of the future year ofanalysis.184 5.4.6 Manipulating Air Quality Modeling Data for Health Impacts Analysis

In Chapter 3.4, we summarized the methods for and results of estimating air quality impacts for the mandated renewable fuel volumes. These air quality results are in turn associated with human populations to estimate changes in health effects. For the purposes of this analysis, we focus on the health effects that have b een linked to ambient changes in ozone and PM 2.5 related to emissions associated with the RFS2 mandated fuel volumes. We estimate ambient
PM 2.5 and ozone concentrations using the Community Multiscale Air Quality model (CMAQ). This section describes how we converted the CMAQ modeling out put into full-season profiles suitable for the health impacts analysis.

General Methodology

First, we extracted hourly, surface-layer PM and ozone concentrations for each grid cell from the standard CMAQ output files. For ozone, these model predictions are used in conjunction with the observed concentrations obtained from the Aerometric Information Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season. 82,83 The predicted changes in ozone concentrations from th e future-year base case to future-year control scenario serve as inputs to the health and welfare impact functions of the benefits analysis (i.e., BenMAP).

To estimate ozone-related hea lth effects for the contiguous United States, full-season ozone data are required for every BenMAP grid-ce ll. Given available ozone monitoring data, we generated full-season ozone profiles for each locati on in two steps: (1) we combined monitored observations and modeled ozone pred ictions to interpolate hourly oz one concentrations to a grid of 12-km by 12-km population grid cells for the contiguous 48 states, and (2) we converted these full-season hourly ozone profiles to an ozone meas ure of interest, such as the daily maximum 8- hour average. 84,85

For PM 2.5 , we also use the model predictions in conjunction with observed monitor data. CMAQ generates predictions of hourly PM species concentrations for every grid. The species include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a primary fine fraction (corresponding to PM le ss than 2.5 microns in diameter), and several secondary particles (e.g., sulfates , nitrates, and organics). PM 2.5 is calculated as the sum of the primary fine fraction and all of the secondarily formed particles. Future-year estimates of PM 2.5 were calculated using relative reduction f actors (RRFs) applied to 2005 ambient PM 2.5 and PM 2.5 species concentrations. A gridded field of PM 2.5 concentrations was created by interpolating Federal Reference Monitor ambient data and IM PROVE ambient data. Gridded fields of PM 2.5 species concentrations were created by in terpolating US EPA speciation network (ESPN)

82 The ozone season for this analysis is defined as the 5-month period from May to September.

83 Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at least nine hourly observations per day [8 am to 8 pm] during the ozone season).

84 The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP.

85 This approach is a generalization of planar interpola tion that is technically refe rred to as enhanced Voronoi Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for download at http://www.epa.gov/air/benmap.185 ambient data and IMPROVE data. The ambient data were interpolated to the CMAQ 12 km grid.

The procedures for determining the RRFs are s imilar to those in US EPA’s draft guidance for modeling the PM 2.5 standard (EPA, 1999). The guidan ce recommends that model predictions be used in a relative sense to estimate changes expected to occur in each major PM 2.5 species. The procedure for calculating future-year PM 2.5 design values is called the “Speciated Modeled Attainment Test (SMAT).” EPA used this procedure to estimate the ambient impacts of the coordinated strategy to c ontrol ship emissions.

Table 5.4-13 provides those ozone and PM 2.5 metrics for grid cells in the modeled domain that enter the health impact functions for health benefits endpoints. The populationweighted average reflects the baseline levels a nd predicted changes for more populated areas of the nation. This measure better reflects the potenti al benefits through exposure changes to these populations.186 Table 5.4-13. Summary of CMAQ-Derived Popul ation-Weighted Ozone and PM 2.5 Air Quality Metrics

for Health Impact Endpoints Associated with the Mandated Renewable Fuel Volumes187188189190191192193194195196197198199200201202203204205206207208209210211212213 Notes:

a Ozone and PM 2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects estimates. Ozone metrics are calculated over relevant time periods during the daylight hours of the “ozone season” (i.e., May through September).

b The change is defined as the reference case value minus the final rule value. A negative change means that the population-weighted average has increased from the reference scenario to the final rule scenario.

c Calculated by summing the product of the projected CMAQ grid-cell population and the estimated CMAQ grid cell seasonal ozone concentration and then dividing by the total population.

5.4.7 Methods for Describing Uncertainty

The National Research Council (NRC) lxv highlighted the need for EPA to conduct rigorous quantitative analysis of uncertainty in its benefits estimates and to present these estimates to decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. In response to these comments, EPA’s Office of Air and Radiation (OAR) is developing a comprehensive strategy for characterizing the aggregate impact of uncertainty in key modeling elements on both health inciden ce and benefits estimates. Components of that process include emissions modelin g, air quality modeling, health effects incidence estimation, and valuation.

In benefit analyses of air pollution regulations conducted to date, the estimated impact of reductions in premature mortality has accounted fo r 85% to 95% of total benefits. Therefore, it is particularly important to char acterize the uncertainties associat ed with reductions in premature mortality. The health impact functions used to estimate avoided premat ure deaths associated with reductions in ozone have a ssociated standard errors that represent the statistical errors around the effect estimates in the underlying epidemiological studies. 86 In our results, we report credible intervals based on these standard errors, reflecting the uncertainty in the estimated change in incidence of avoided premature deaths. We also provide multiple estimates, to reflect model uncertainty between a lternative study designs.

For premature mortality associated with ex posure to PM, we follow the same approach that has been used in several recent RIAs. lxvi,lxvii,lxviii First, we use Monte Carlo methods for estimating random sampling error associated w ith the concentration response functions from epidemiological studies and ec onomic valuation functions. Mont e Carlo simulation uses random sampling from distributions of parameters to ch aracterize the effects of uncertainty on output variables, such as incidence of premature mortality. Specifical ly, we used Monte Carlo methods to generate confidence intervals around the es timated health impact a nd dollar benefits. Distributions for individual effect estimates are based on the reported standard errors in the epidemiological studies. Distributions fo r unit values are described in Table 6-11.

Second, as a sensitivity analysis, we use th e results of our expert elicitation of the concentration response function describing the relationship between pr emature mortality and ambient PM 2.5 concentration. 87, lxix Incorporating only the uncertainty from random sampling

86 Health impact functions measure the change in a health endpoint of interest, such as hospital admissions, for a given change in ambient ozone or PM concentration.

87 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually of multiple experts, are obtained (Ayyb, 2002).214 error omits important sources of uncertainty (e.g., in the functional form of the model; whether or not a threshold may exist). This second approa ch attempts to incorporate these other sources of uncertainty.

Use of the expert elicitation and incorporation of the standa rd errors approaches provide insights into the likelihood of different outcomes and about the state of knowledge regarding the benefits estimates. Both approaches have different strengths and weaknesses, which are fully described in Chapter 5 of the PM NAAQS RIA. lxx

These multiple characterizations, including confidence intervals, omit the contribution to overall uncertainty of un certainty in air quality changes, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. Furthermore, the approach presented here does not yet include methods for addressing correlation be tween input parameters and the identification of reasonable upper and lo wer bounds for input distri butions characterizing uncertainty in additional model elements. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncerta inty in the estimates. This information should be interpreted within the c ontext of the larger uncertainty surrounding the entire analysis.215 

### 5.5 Economy-Wide Economic Analysis

It is anticipated that the increase in renewable fuels associated with this RFS2 rulemaking will have impacts on the U.S. economy that extend beyond the U.S. transportation sector. By requiring 36 billion gallons of renewable fuels in the U.S. transportation sector, it is anticipated that the price of motor vehicle fuels will decrease modestly by 2022. This is due to two reasons. First, the costs of renewable fuels are estimated to decrease over time, while the price of petroleum is projected to increase over time. Ther efore, by 2022, the cost of renewable fuels is expected to be lower than the cost of gasoline and diesel fuels. Sec ondly, the decreased demand for petroleum products as a result of using more renewable fuels will modestly lower the world price of oil. The resulting overall decrease in the price of motor vehicle fuels will affect all sectors of the economy that use these fuels as intermediate inputs to production. For example, firms that transport products (i.e., shipping companies) will see a decline in their costs of production. Also, households will also be beneficial ly affected as consumers of these goods, and directly as consumers of motor vehicle fuels.

In the U.S. agriculture sector, it is anticipat ed that the production of renewable fuels will increase the demand for farm products, and incr ease farm incomes. U.S. food prices are anticipated to increase modestly as a resu lt of higher agricultural commodity prices. For example, corn and soybean prices are anticipated to increase with the RFS2 rule since there is greater demand for these commodities for use as feedstocks to produce ethanol and biodiesel fuels. Increased agricultural commodity prices will have ripple effects on sectors of the economy that supply inputs to the farm sector (e.g., tractors), and sect ors that demand outputs from the farm sector. The sum of all of these market impacts, both fuel market and agricultural sector impacts, will affect the total levels of output (i.e., Gross Domestic Product) and consumption of the U.S. economy.

A further issue that arises in assessing th e economy-wide impacts of the RFS2 rule is renewable fuel subsidies. For example, for corn ethanol, th e current subsidy is $0.45/gallon. Alternatively, for biodiesel, the current subsid y is $1.00/gallon. Cellulosic biofuel qualifies for a subsidy of $1.01/gallon. By the year 2022, the incr ease in renewable fuel s volumes associated with the RFS2 rule will result in an increase in renewable fuel subsidy expenditures of approximately $14.2 billion annually. To acc ount for the economy-wide impacts of the renewable fuel subsidies associated with the RFS2 renewable fuel volumes, we look at two cases. Case one looks at the economy-wide imp acts of the increased renewable fuel volumes required by the RFS2 rule without accounting for th e increase in renewable fuel subsidies. Case two examines the impacts of the RFS2 renewable fuel volumes with renewable fuel subsidies included. For case two, we assume we assume that the biofuel subsidies will be offset by raising taxes to maintain the “revenue neutrality” of the U.S. federal government’s overall budget. Since it is not clear what taxes would be raised, we have assumed that the marginal tax rate on labor income will be raised to offset the impact of the increase in renewable fuel subsidies on the federal government’s budget. Any remaining adju stments required to rest ore real government expenditures to their reference case levels are accomplished through changes in zero bracket exemptions (i.e., lump-sum transfers).216 To estimate the impacts of this rule on U.S. Gross Domestic Produc t (GDP) and personal consumption, EPA has used an economy-wide, com putable general equilibrium (CGE) model. The economy-wide model used in this analysis is the Intertemporal General Equilibrium Model (IGEM). 88 IGEM is a model of the U.S. economy with an emphasis on the economy’s energy and environmental aspects. IGEM is a dynamic model which depicts growth of the economy due to capital accumulation, technical change and popul ation change. The model utilizes a unified accounting framework consistent with the Na tional Income and Pr oduct Accounts and the Industry Classification System.

IGEM is a detailed multi-sector model covering thirty-five industries, consisting of five energy and thirty non-energy sectors. The mode l ensures market balan ces (i.e., supply equals demand) in value and quantity terms, including limits placed on private investment dictated by domestic and foreign saving behavior and by th e fiscal policies of federal, state and local governments. Capital accumulation in the model arises from the saving and investment behavior of households and businesses a nd provides an essential input to production and consumption. Households in the model make choices regarding present and fu ture consumption (i.e., saving) and make choices regarding the allocation of time between labor and leisure. The model covers all aspects of long-run growth including the s upply of capital, labor, im ported and intermediate inputs to production; the rates a nd directions of exogenous and endogenous technical change for each producing sector; and the degrees of s ubstitutability among inputs and commodities in production and final demand (i.e., consumption, investment, govern ments and foreign trade). The substitution possibilities for both producers an d consumers in IGEM are driven by model parameters that are based on observed market behavior revealed over th e past forty to fifty years.

5.5.1 Impacts on U.S. GDP

In order to estimate the real U.S. GDP impacts of the renewable fuel volumes required by the RFS2 rule, the CGE model uses outputs of th e transportation and agri cultural sector models as inputs to determine the economy-wide impact s of this rule. Specifically, IGEM uses information from other models on the change in fuel costs and renewable fuel subsidies, the change in agricultural sector prices, and the change in the world oil price. The change in fuel costs were calculated as described in Chapter 4.4 of this RIA. The value of the renewable fuel subsidies through time were calculated by multiplying the per gallon value of the renewable fuel subsidies by the volumes of renewable fuels requi red by the RFS2 rule. The data on changes in agricultural sector prices were taken from the FA SOM model, as described in Chapter 5.1 of the RIA. Finally, the estimates of the changes in world oil price were estimated using the Oil Security Metrics Model maintained by Oak Ridge National Laboratory, wh ich is discussed in more detail in Chapter 5.2 of the RIA. It is important to note that the economy-wide analysis does not include a representation of the bene fits associated with reducing greenhouse gas emissions, and thus the GDP impacts presented here do not represent the results of a complete cost benefit analysis. Figure 5.5-1 and Table 5.5- 1 show the Reference Case GDP and GDP with the two cases, without and with renewable fuel subsidies.

88 The model is developed and run by Dale W. Jorgenson Associates for EPA. Model Homepage: http://www.dja.insightworks.com/217 F igure 5.5-1. Annual GDP Impacts for the RFS2 Control Case vs. the AEO2007 Reference Case (Billions of 2007 dollars)

GDP

$0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000

2017 2022 2030Billion 2007$ Reference Case

Control Case with Subisidies

Control Case Without Subsidies

Table 5.5-1. Annual GDP Impacts for the RFS2 Contro l Case vs. the AEO2007 Reference Case (Billions of 2007 dollars)218219220221222223224225226227228229230231232233234235236237238 In the reference case, GDP rises fro m approximately $17.6 trillion in 2017 to $19.9 trillion in 2022, and $24.3 trillion in 2030. In case one (without renewable fuel subsidies), GDP rises from approximately $17.6 trillion in 2017 to $19.9 trillion in 2022 and $24.4 trillion in 2030. In case two (with renewable fuel subsid ies), GDP rises from a pproximately $17.6 trillion in 2017 to $19.9 trillion in 2022 and $24.4 trillion in 2030.

Figure 5.5.-2 and Table 5.5-2 show the per centage change in GDP with the RFS2 renewable fuel volumes. The co mbined impact of higher agricultural commodity prices and higher transportation fuel prices decrease GDP in the early years of the program. However, the costs of renewable fuels are projected to decrea se over time, while the cost of petroleum products is projected to increase over time. By 2016, renewa ble fuels are projected to be cheaper than the gasoline and diesel that they replace. By 2030, the impacts of lower transportation fuel costs and lower world oil prices outweigh the impacts of higher agricultural commodities, resulting in an increase in GDP in the two RFS cases relative to the AEO2007 reference case. Lower prices permit additional purchases of i nvestment goods from an unchang ed saving-investment balance which, in turn, leads to a larger capital stock. Th ese price reductions also allow higher levels of real government spending while improving U.S. competitiveness thus promoting increased exports relative to the growth driven increase in imports. These combined impacts have a favorable impact on GDP over time.

Figure 5.5-2. Percentage Change in Gr oss Domestic Product for the RFS2 Control Case vs. the AEO2007 Reference Case

Percentage Change in GDP

-0.15% -0.10% -0.05% 0.00% 0.05% 0.10% 0.15% 0.20% 0.25%

2017 2022 2030 Control Case with Subsidies

Control Case Without Subsidies239 Table 5.5-2. Percentage Change in Gross Domestic Product for the RFS2 Control Case vs. the AEO2007 Reference Case240241242243244245246247248249250251252253254255 5.5.2 Impacts on U.S. Consumption

In addition to showing the impacts of the RF S2 renewable fuel volumes on U.S. GDP, we also show their impacts on real personal consum ption expenditures. The following Figure 5.5-3 and Table 5.5-3 show estimates of the Reference Case Consump tion and Consumption after the imposition of the RFS2 renewable fu el volumes for Case One and Two.

Figure 5.5-3. Annual Consumption Impacts for the Control Case vs. the AEO 2007 Reference Case (Billions of 2007 dollars)

Consumption

$0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000 $16,000

2017 2022 2030Billion 2007$ Reference Case

Control Case with Subsidies

Control Case Without Subsidies256 Table 5.5-3. Annual Consumption Impacts for the RFS2 Co ntrol Case vs. the AEO2007 Reference Case (Billions of 2007 dollars)257258259260261262263264265266267268269270271272273274275276277 In the AEO2007 reference case, consumpti on rises from approxima tely $10.9 trillion in 2017 to $12.3 trillion in 2022, and $14.9 trillion in 2030. In case one (without renewable fuel subsidies), consumption rises from approx imately $10.9 trillion in 2017 to $12.3 trillion in 2022 and $15.0 trillion in 2030. In case two (the case with renewable fuel subsidies), consumption rises from approximately $10.9 trillion in 2017 to $12.3 trillion in 2022 and $14.9 trillion in 2030.

Figure 5.5-4 and Table 5.5-4 show the estimate d percentage change in consumption with the RFS2 rule volumes. The combined impact of higher agricultural commodity prices and higher transportation fuel costs decrease consump tion in the early years of the RFS2 program. However, as the cost of renewable fuels decreases over time, this impact gradually outweighs the impact of higher agricultural commodity prices. As a result of the lower renewable fuels costs through time, consumption increases relative to th e reference case in both RFS2 cases by 2030. The increase in labor taxes needed to offset the renewable fuel subsid ies partially erodes the benefits observed in case one. In case two, the increase in labor taxes reduces the opportunity cost of choosing leisure instead of labor. Th erefore, households work less when faced with higher labor taxes, thereby reducing overall house hold consumption slightly compared to case

one.

Figure 5.5-4. Percentage Change in Consumptio n for the RFS2 Control Case vs. the AEO2007 Re ference Case

Percentage Change in Consumption

-0.15% -0.10% -0.05% 0.00% 0.05% 0.10%

2017 2022 2030 Control Case with Subsidies

Control Case Without Subsidies278 Table 5.5-4. Percentage Change in Consumptio n for the RFS2 Control Case vs. the AEO2007 Re ference Case279280281282283284285286287288289290291292293294 In addition to showing the impacts of the rule on U.S. GDP and consumption, the impacts of the RFS2 renewable fuel volumes on pers onal consumption expenditures per household are estimated. The following Figure 5.5-5 and Tabl e 5.5-5 show estimates of the reference case annual consumption per househol d and consumption per househol d after the imposition of the RFS2 renewable fuel volumes for both case one and case two.

In the reference case, annual consump tion per household rises from $83,041 in 2017 to $88,298 in 2022, and $98,139 in 2030. In case one (wit hout renewable fuel subsidies), annual consumption per household rises from appr oximately $82,941 in 2017 to $88,243 in 2022 and $98,203 in 2030. In case two (with renewable fu el subsidies), annua l consumption per household rises from approximately $82,930 in 2017 to $88,203 in 2022 and $98,167 in 2030. Thus, consumption per household is roughly $100 lower in case one (without renewable fuel subsidies) in 2017 compared to the reference case, and consumption per household is roughly $55 lower than the reference case in 2022. By 2030, consumption per household is roughly $64 higher in case one compared to the reference case. For case two (with renewable fuel subsidies), consumption per household is roughly $111 lower than the reference case in 2017 and consumption per household is roughly $95 lower than the reference case in 2022. By 2030, however, consumption per household is higher th an the reference case in case two, by $28.

Figure 5.5-5. Consumption Expenditures Per Household for the RFS2 Control Case vs. the AEO2007 Reference Case (2007 dollars)

Consumption Per Household

$75,000 $80,000 $85,000 $90,000 $95,000 $100,000

2017 2022 20302007$ Control Case with Subsidies

Control Case Without Subsidies

Reference Case

Table 5.5-5. Consumption Expenditures Per Household for the295 RFS2 Control Case vs. the AEO2007 Reference Case (2007 dollars)296297298299300301302303304305306307308309310311312313314315316 

### 5.6 Impacts of Increasing Volume Requirements in the RFS2 Program

The displacement of gasoline and diesel with renewable fuels has a wide range of environmental and economic impacts. As we describe in Secti ons IV-IX of the preamble, we have assessed many of these impacts for the final rule. It is difficult to ascertain how much of these impacts might be due to the natural growth in renewable fuel use due to market forces as crude oil prices rise versus what might be for ced by the RFS2 standards. Regardless, these assessments provide impor tant information on the wider public policy considerations related to renewable fuel production and us e, climate change, and national energy security. Where possible, we have tried to provi de two perspectives on the impacts of the renewable fuel volumes mandated in EISA – both relative to the RFS1 mandated volumes, and relative to a projection from EIA (AEO 2007) of renewable fuel volumes that would have been expected without EISA.

Based on the results of our analyses, when fully phased in by 2022, the increased volume of renewable fuel required by this final rule in comparison to the AEO 2007 forecast would result in 138 million metric tons fewer CO2-equivalent GHG emi ssions (annual average over 30 years), the equivalent of removing 27 million vehicles from the road today.

At the same time, increases in emissions of hydrocarbons, nitrogen oxides, particulate matter, and other pollutants are projected to lead to increases in population-weighted annual average ambient PM and ozone concentrations, whic h in turn are anticipated to lead to up to 245 cases of adult premature mortality. The air quality impacts, however, are highly variable from region to region. Ambient PM 2.5 is likely to increase in areas associated with biofuel production and transport and decrease in other areas; for ozone, many areas of the country will experience increases and a few areas will see decreases. Ethanol concentrations will increase substantially; for the other modeled air toxics there are some localized impacts, but relatively little impact on national average concentrations. It is important to note that these air quality results represent the impact of an incremental increase in ethanol and other renewable fuels and do not estimate the total air quality impact of the RFS2 volumes of rene wable fuels as compared to near-zero levels. EPA will conduct that type of analysis as part of the “anti-backsliding” study required by Clean Air Act section 211(v), separate from this fina l action. The “anti-backsliding” study will use improved emissions data and consider different ethanol ble nd levels. Clean Air Act section 211(v) requires EPA to issue regulations that mitigat e, to the greatest extent achievable, adverse impacts on air quality, considering the results of the “anti-backsliding” study.

In addition to air quality, there are also expected to be adverse impacts on both water quality and quantity as the production of biof uels and their feedstocks increase.

In addition to environmental impacts, the in creased volumes of renewable fuels required by this final rule are also projected to have a number of other energy and economic impacts. The increased renewable fuel use is estimated to reduce dependence on foreign sources of crude oil, increase domestic sources of energy, and diversif y our energy portfolio to help in moving beyond a petroleum-based economy. The increased use of renewable fuels is also expected to have the added benefit of providing an expanded market for agricultural products such as corn and soybeans and open new markets for the deve lopment of cellulosic feedstock industries and317 conversion technologies. Overall, however, we es timate that the renewa ble fuel standards will result in significant net bene fits, ranging between $16 and $29 billion in 2022. Table I.B-1 summarizes the results of our impacts analyses of the RFS2 standards relative to the AEO2007 reference case and identifies the section where yo u can find further explanation of it. As we work to implement the requirements of EISA, we will continue to assess these impacts.318 Table 5.6-1 Impact Summary of the RFS2 Standards in 2022 Relative to the AEO2007 Reference Case (2007 Dollars)319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465 a The models used to estimate SCC values have not been exercised in a systematic manner that would allow researchers to assess the probability of different values. Th erefore, the interim SCC values should not be considered to form a range or distribution of possible or likely values. See Section VIII.D for a complete summary of the interim SCC values.

b Sum of Overall Fuel Costs, Energy Security, Monetized Health Impacts, and GHG Impacts (SCC).

1 U.S. Environmental Protection Agency. (2006). Final Regulatory Impact Analysis (RIA) for the Proposed National Ambient Air Quality Standards for Particulate Matter . Prepared by: Office of Air and Radiation. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html

2 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact Analysis . Prepared by: Office of Air and Radia tion, Office of Air Quality Planning and Standards. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html

3 U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Office of Air Quality Pla nning and Standards, Re search Triangle Park, NC. April. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/R IAs/portlandcementria_4-20-09.pdf>.

4 U.S. Environmental Protection Agen cy (U.S. EPA). 2009b. Proposed NO2 NAAQS Regulatory Impact Analysis (RIA). Office of Air Quality Planning a nd Standards, Research Triangle Park, NC. April. Available on the Internet at http://www.epa.gov/ttn/ecas/regdata/RIAs/proposedno2ria.pdf . Note: The revised NO2 NAAQS may be final by the publication of this action.

5 U.S. Environmnetal Protection Agency (U.S. EP A). 2009c. Draft Regulatory Impact Analysis: Control of Emissions of Air Pollution from Cate gory 3 Marine Diesel Engines. Office of Transportation and Air Quality, June . Available on the Internet at http://www.epa.gov/otaq/regs/nonroad/420d09002.htm. Note: The C3 rule may be final by the publication of this action.

6 GeoLytics Inc. (2002). Geolytics CensusCD ® 2000 Short Form Blocks. CD-ROM Release 1.0. GeoLytics, Inc. East Brunswick, NJ. Av ailable: http://www.geolytics.com/ [accessed 29 September 2004]

7 Woods & Poole Economics Inc. 2008. Populati on by Single Year of Age CD. CD-ROM. Woods & Poole Economics, Inc. Washington, D.C.

8 U.S. Environmental Protection Agency. (2006). Air quality criteria for ozone and related photochemical oxidants (second external review draft). Research Triangle Park, NC: National Center for Environmental Assessment; repor t no. EPA/600R-05/004aB-cB, 3v. Available: http://cfpub.epa.gov/ncea/cfm/record isplay.cfm?deid=137307[March 2006]

9 U.S. Environmental Protection Agency, 2004. Air Quality Criteria for Particulate Matter Volume II of II. National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Ag ency, Research Triangle Park, NC EPA/600/P99/002bF.466 10 World Health Organization (WHO). (2003). Health Aspects of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxid e: Report on a WHO Working Group. World Health Organization. Bonn, Germany. EUR/03/5042688.

11 Anderson HR, Atkinson RW, Peacock JL, Marston L, Konstantinou K. (2004). Meta-analysis of time-series studies and panel studies of Par ticulate Matter (PM) and Ozone (O3): Report of a WHO task group. Copenhagen, Denmark: World Health Organization.

12 Bell, M.L., et al. (2004). Ozone and short- term mortality in 95 US urban communities, 1987- 2000. JAMA , 2004. 292(19): p. 2372-8.

13 Huang, Y.; Dominici, F.; Bell, M. L. (2005) Ba yesian hierarchical dist ributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics . 16: 547-562.

14 Schwartz, J. (2005) How sensitive is the associa tion between ozone and daily deaths to control for temperature? Am. J. Respir. Crit. Care Med. 171: 627-631.

15 Bell, M.L., F. Dominici, and J.M. Samet. ( 2005). A meta-analysis of time-series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study. Epidemiology . 16(4): p. 436-45.

16 Ito, K., S.F. De Leon, and M. Lippmann (2005 ). Associations between ozone and daily mortality: analysis and meta-analysis. Epidemiology . 16(4): p. 446-57.

17 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiric bayes metaregression analysis. Epidemiology . 16(4): p. 458-68.

18 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. (2002). “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141.

19 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine Particulate Air Pollution and Mortality. American Journal of Respiratory and Critical Care Medicine . 173: 667-672.

20 Industrial Economics, Incorporated (IEc). (2006). Expanded Expert Judgme nt Assessment of the Concentration-Response Relationship Betw een PM2.5 Exposure and Mortality. Peer Review Draft. Prepared for: Office of Air Quality Pl anning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. August.

21 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. (1997). The Relationship Between Selected Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United States. Environmental Health Perspectives. 105(6):608-612.

22 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. M ills. (1995). Estimated Long-Term Ambient Concentrations of PM(10) a nd Development of Respiratory Symptoms in a Nonsmoking Population. Archives of Environmental Health. 50(2): 139-152.

23 Peters, A., D.W. Dockery, J.E. Muller, and M. A. Mittleman. (2001). In creased Particulate Air Pollution and the Triggering of Myocardial Infarction. Circulation. 103:2810-2815.

24 Schwartz J. (1995). Short te rm fluctuations in air pollution and hospital admissions of the elderly for respiratory disease. Thorax. 50(5):531-538.467 25 Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis St Paul, Minnesota. Arch Environ Health. 49(5):366-374.

26 Schwartz J. (1994b). Air Pollution and Hosp ital Admissions For the Elderly in Detroit, Michigan. Am J Respir Crit Care Med. 150(3):648-655.

27 Moolgavkar SH, Luebeck EG, Anderson EL. (1 997). Air pollution and hospital admissions for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology . 8(4):364-370.

28 Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR , Dales RE, et al. (2001). Association between ozone and hospitalization for acu te respiratory diseases in children less than 2 years of age. Am J Epidemiol . 153(5):444-452.

29 Moolgavkar, S.H. (2003). “Air Pollution a nd Daily Deaths and Hospital Admissions in Los Angeles and Cook Counties.” In Revised Analyses of Time-Ser ies Studies of Air Pollution and Health . Special Report. Boston, MA: Health Effects Institute.

30 Ito, K. (2003). “Associations of Particulat e Matter Components with Daily Mortality and Morbidity in Detroit, Michigan.” In Revised Analyses of Time-Series Studies of Air Pollution and Health . Special Report. Health Effects Institute, Boston, MA.

31 Moolgavkar, S.H. (2000). Air Pollution and Hospital Admissions for Diseases of the Circulatory System in Three U.S. Metropolitan Areas. Journal of the Air and Waste Management Association 50:1199-1206.

32 Sheppard, L. (2003). Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle, Washington, 1987-1994. In Revised Analyses of Time-Ser ies Studies of Air Pollution and Health . Special Report. Boston, MA: Health Effects Institute.

33 Jaffe DH, Singer ME, Rimm AA. (2003). Air po llution and emergency department visits for asthma among Ohio Medicaid recipients, 1991-1996. Environ Res 91(1):21-28.

34 Peel, J. L., P. E. Tolbert, M. Klein, et al. (2005). Ambient air pollution and respiratory emergency department visits. Epidemiology . Vol. 16 (2): 164-74.

35 Wilson, A. M., C. P. Wake, T. Kelly, et al . (2005). Air pollution, weather, and respiratory emergency room visits in two northern New England cities: an ecological time-series study.
Environ Res . Vol. 97 (3): 312-21.

36 Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, a nd J.W. Stout. (1999). An Association between Fine Particles and Asthma Emergency Department Visits for Children in Seattle. Environmental Health Perspectives 107(6):489-493.

37 Dockery, D.W., J. Cunningham, A.I. Damokosh, L. M. Neas, J.D. Spengler, P. Koutrakis, J.H. Ware, M. Raizenne, and F.E. Sp eizer. (1996). Health Effects of Acid Aerosols On North American Children-Respiratory Symptoms. Environmental Health Perspectives 104(5):500-505.

38 Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. (1991) . Respiratory Health and PM 10 Pollution: A Daily Time Series Analysis. American Review of Respiratory Diseases 144:668-674.468 39 Schwartz, J., and L.M. Neas. (2000). Fine Particles are More Strongly Associated than Coarse Particles with Ac ute Respiratory Health Effects in Schoolchildren. Epidemiology 11:6- 10.

40 Ostro, B., M. Lipsett, J. Mann, H. Braxton- Owens, and M. White. (2001). Air Pollution and Exacerbation of Asthma in African-American Children in Los Angeles. Epidemiology 12(2):200-208.

41 Vedal, S., J. Petkau, R. White , and J. Blair. (1998). Acute Effects of Ambient Inhalable Particles in Asthmatic and Nonasthmatic Children. American Journal of Respiratory and Critical Care Medicine 157(4):1034-1043.

42 Ostro, B.D. (1987). Air Pollution and Mo rbidity Revisited: A Specification Test. Journal of Environmental Economics Management 14:87-98.

43 Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. (2001). The effects of ambient air pollution on school absenteeism due to respiratory illnesses. Epidemiology 12(1):43-54.

44 Chen L, Jennison BL, Yang W, Omaye ST. ( 2000). Elementary school absenteeism and air pollution. Inhal Toxicol 12(11):997-1016.

45 Ostro, B.D. and S. Rothschild. (1989). Air Pollution and Acute Respiratory Morbidity: An Observational Study of Multiple Pollutants. Environmental Research 50:238-247.

46 U.S. Science Advisory Board. (2004). Advisory Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analys is –Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SAB-COUNCIL-ADV-04-004.

47 National Research C ouncil (NRC). (2002). Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.

48 U.S. Environmental Protection Agency (U.S. EPA). 2008. Integrated Science Assessment for Particulate Matter (External Review Draft). National Center for Envi ronmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139. December. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201805>.
49 U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter (Second External Review Draft). National Cent er for Environmental Assessment, Research Triangle Park, NC. EPA/ 600/R-08/139B. July. Available on the Internet at <http://cfint.rtpnc.epa.gov/ncea/ prod/recordisplay.cfm?deid=210586>.

50 U.S. Environmental Protection Agency - Scie nce Advisory Board (U.S. EPA-SAB). 2009. Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008). EPA-COUNCIL-09-008. May. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUC T.NSF/81e39f4c09954fcb85256ead006be86e/73AC CA834AB44A10852575BD0064346B/$File/EPA -CASAC-09-008-unsigned.pdf>.

51 U.S. Environmental Protection Agency - Scie nce Advisory Board (U.S. EPA-SAB). 2009. Consultation on EPA’s Particulat e Matter National Ambient Air Qu ality Standard s: Scope and Methods Plan for Health Risk and Exposur e Assessment. EPA-COUNCIL-09-009. May. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUC T.NSF/81e39f4c09954fcb85256ead006be86e/723F E644C5D758DF852575BD00763A32/$File/EPA -CASAC-09-009-unsigned.pdf>.469 52 U.S. Environmental Protection Agency. (2009). Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutant s from the Portland Cement Manufacturing Industry . Office of Air and Radia tion. Retrieved on May 4, 2009, from http://www.epa.gov/ttn/ecas/regdata /RIAs/portlandcementria_4-20-09.pdf

53 Viscusi, W.K., W.A. Magat, and J. Huber. (1991). Pricing Envir onmental Health Risks: Survey Assessments of Risk-Risk and Risk-Dollar Trade-Offs for Chronic Bronchitis. Journal of Environmental Economics and Management 21:32-51.

54 Cropper, M.L., and A.J. Krupnick. (1990). The Social Costs of Chronic Heart and Lung
Disease. Resources for the Future. Washingt on, DC. Discussion Paper QE 89-16-REV.

55 Russell, M.W., D.M. Huse, S. Drowns, E.C. Hame l, and S.C. Hartz. (1998). Direct Medical Costs of Coronary Artery Disease in the United States. American Journal of Cardiology 81(9):1110-1115.

56 Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. (1990). Medical Costs of Coronary Artery Disease in the United States. American Journal of Cardiol ogy 65(7):432-440.

57 Agency for Healthcare Research and Quality (AHRQ). (2000). HCUPnet, Healthcare Cost and Utilization Project. Roc kville, MD. Accessed April 10, 2009, from http://hcupnet.ahrq.gov/

58 Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okamoto, C. Battista, and W.B. Saunders. (1997). A National Esti mate of the Economic Costs of Asthma. American Journal of Respiratory and Critical Care Medicine 156(3 Pt 1):787-793.

59 Stanford, R., T. McLaughlin, and L.J. Okamoto. (1999). The Cost of Asthma in the Emergency Department and Hospital. American Journal of Respiratory and Critical Care Medicine 160(1):211-215.

60 Industrial Economics, Incorporated (IEc). ( 1994). Memorandum to Jim DeMocker, Office of Air and Radiation, Office of Policy Analysis and Review, U.S. E nvironmental Protection Agency. March 31.

61 Rowe, R.D., and L.G. Chestnut. (1986). Oxidants and Asthmatics in Los Angeles: A Benefits Analysis—Executive Summary. Prepared by Energy and Resource Consultants, Inc. Report to the U.S. Environmental Protection Agency, O ffice of Policy Analysis. EPA-230-09-86-018. Washington, DC.

62 Neumann, J.E., M.T. Dickie, and R.E. Unsworth. (1994). Linkage Between Health Effects Estimation and Morbidity Valuation in the S ection 812 Analysis—Draft Valuation Document. Industrial Economics Incorporated (IEc) Memora ndum to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and Radiati on, Office of Policy Analysis and Review. March 31.

63 Tolley, G.S. et al. January (1986). Valuation of Reductions in Human Health Symptoms and Risks. University of Chicago . Final Report for the U.S. E nvironmental Protection Agency.

64 Council of Economic Advisors. (2005). The Annual Report of the Council of Economic Advisors. In: Economic Report of the President. Table B-60. U.S. Gove rnment Printing Office: Washington, DC.470 lxv National Research Council (NRC). (2002). Estimating the Public Health Benefits of Proposed Air Pollution Regulations. The National Academies Press: Washington, D.C.

lxvi U.S. Environmental Protection Agency, (2004a). Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Prepared by Office of Air and Radiation. Retrieve d on April 10, 2009, from http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf

lxvii U.S. Environmental Protection Agency, (2005). Regulatory Impact Analysis for the Clean Air Interstate Rule. EPA 452/-03-001. Prepared by Office of Air and Radiation. Retrieved on April 10, 2009, from http://www.epa.gov/interstateairquality/tsd0175.pdf

lxviii U.S. Environmental Protection Agency, (2006). Regulatory Impact Analysis for the PM NAAQS. EPA Prepared by Office of Air and Radiation. Retrieved on April 10, 2009, from http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf

lxix Industrial Economics, Inc. (2006). Expanded Expert Judgment Assessment of the Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for EPA Office of Air Quality Planning and Standa rds, September. Re trieved on April 10, 2009, from http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf

lxx U.S. Environmental Protection Agency, (2006). Regulatory Impact Analysis for the PM NAAQS. EPA Prepared by Office of Air and Radiation. Retrieved on April 10, 2009, from http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf