November 2014 J-1



# Appendix J: Anticipated Baselines: Background and Modeling Considerations

Table of Contents

1. Introduction ......................................

................................................... ................................................... ...................... J-2

2. Anticipated Baselines: Definitions and Applications

.................................................. ................................. J-3

2.1. Definitions and Comparison of Baseline Approaches .

................................................... .................... J-3

2.1.1. Additionality .....................................

................................................... ................................................... ... J-4

2.2. Review of Relevant Literature .....................

................................................... ............................................. J-5

2.3. Application of Future Anticipated Baseline Approach in this Report ................................... ...... J-6

2.4. Limitations and Implications of the Future Anticipa ted Baseline Approach ........................... J -7

3. Key Design Elements to Consider ...................

................................................... .................................................. J-7

3.1. Model Function Types and Model Dynamics ...........

................................................... ........................... J-8

3.1.1. Modeling Scope: Economic Optimization .............

................................................... ...................... J-8

3.1.2. Temporal Dynamics .................................

................................................... ............................................ J-9

3.2. Anticipated Future Conditions (Macroeconomic, Bioph ysical) ........................................... ........ J-10

3.3. GHG Emissions Representation ......................

................................................... ........................................ J-11

3.4. Forest Sector Representation.......................

................................................... ........................................... J-11

3.5. Agricultural Sector Representation ................

................................................... ...................................... J-12

3.6. Land Use Competition ..............................

................................................... .................................................. J-12

3.7. Energy Sector Representation ......................

................................................... .......................................... J-13

3.8. International Representation ......................

................................................... ............................................ J-13

3.9. Consideration of Scenario Development .............

................................................... ............................... J-14

4. Modeling Approach and Tools Chosen for Illustrative Framework Applications .......................... J-15

4.1. Brief Overview of Baseline Construction ...........

................................................... ................................ J-15

4.2. Brief Overview of Regional Feedstock Case Studies .

................................................... ..................... J-16

4.3. Use of FASOM-GHG ..................................

................................................... ................................................... .J_17

4.3.1. Overview of Key FASOM-GHG Attributes and Functions

................................................... ... J-18

5. References ........................................

................................................... ................................................... ...................... J-21



## List of Tables

Table 1. FASOM-GHC Attributes. ....................

................................................... ................................................... ........ J-18 Table 2. Definition of FASOM-GHG Production Regions and Market Regions. .............................. ........... J-19November 2014 J-2



## List of Figures
Figure 1. Conceptual Diagram Outlining the Basic An ticipated Future Baseline Modeling Approach Presented in this Appendix. .......................

................................................... .................................................. J-17 Figure 2. Map of the FASOM-GHG Regions (Source: Bea ch et al., 2010b ).

.................................................. J-20


## 1. Introduction

This appendix describes the anticipated baseline ap proach and the purpose of, and potential components needed when, applying an anticipated bas eline approach prospectively to estimate the land use and biogenic carbon-based emissions and se questration implications of U.S. biogenic feedstock consumption at stationary sources. Baseli ne specification can vary in terms of what entity/groups are being analyzed (e.g., industries, economic sectors), time scale, geographic resolution, and, depending on context, environmenta l issues/attributes (EPA, 2010a). 1 Determination of the most appropriate baseline appr oach and consequently appropriate modeling approaches and level of detail depends largely on t he goals of the assessment.

Establishing a baseline creates a point of comparis on necessary for evaluating changes to a system. 2 However, the choice of approach largely depends on the question being asked. Applications of the framework may require a baseline or baselines again st which changes of landscape carbon stocks can be measured. Other applications may necessitate a baseline against which the emissions and sequestration associated with the production and us e of additional biogenic feedstocks at stationary sources can be estimated and analyzed. A lternative baseline assumptions can yield different results and should be finalized after car eful consideration of the specific context in which the framework is applied.

This appendix first highlights what an anticipated baseline is in general and how it compares with other baseline approaches, such as the reference po int baseline. The appendix then presents the rationale for using this type of forward-looking ap proach, followed by a discussion on the rationale for using a prospective, or future, anticipated bas eline approach for assessing biogenic emissions at stationary sources. Next is a discussion of data ne eds, model constructs, and model attributes that should be considered when constructing a future ant icipated baseline analysis. The last section briefly describes the model chosen for constructing the baseline and alternative scenarios for the illustrative framework applications of the future a nticipated baseline used in this report—the U.S. Forest and Agricultural Sector Model with Greenhous e Gases (FASOM-GHG).

1 Guidelines for Preparing Economics Analyses (NCEE) , Chapter 5: http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-056 8-05.pdf/$file/EE-0568-05.pdf

2 Definitions for baseline vary, including “the refe rence for measurable quantities from which an alter native outcome can be measured” (IPCC AR4 WGIII) or “the baseline (or reference) is the state against which change is measured. It might be a ‘current baseline,’ in which case it represents observable, present-day conditions. It m ight also be a ‘future baseline,’ which is a projected future set of conditions excluding the driving factor of inter est. Alternative interpretations of the reference conditions can giv e rise to multiple baselines” (IPCC AR4 WGII).November 2014 J-3



## 2. Anticipated Baselines: Definitions and Applications

2.1. Definitions and Comparison of Baseline Approaches

Two potential baseline approaches for assessing the biological carbon cycle effects related to biogenic feedstock production and use at stationary sources are evaluated in this appendix. The first baseline approach assesses the estimated net change in carbon between two points in time (i.e., between reference points) (e.g., Fargione et al., 2008; UNFCCC, 2009). This approach allows for estimation of net carbon changes relative to partic ular points in time. It establishes as the baseline the aggregate carbon stock related to a specific fe edstock type on a given land base at a given point in time. It is against this measured reference poin t that aggregate stocks at another point in time will be compared, and then determines if those aggr egate stocks are rising or falling between the two points in time.

In contrast, the second approach, the anticipated b aseline, is a comparison between two distinct scenarios, not two points in time. While there is a time element to the anticipated baseline approach, the basis of comparison for evaluating em issions changes is the difference between two modeled scenarios (i.e., between a business-as-usua l [BAU] scenario and an alternative or counterfactual scenario with changes in environment al, economic, and/or policy conditions) (e.g., Searchinger et al., 2009; Sohngen and Sedjo, 2000). This approach uses information regarding carbon stocks and the carbon balance of biogenic fe edstock production systems at a certain point in time to determine either the landscape carbon profi le related to specific feedstock use at stationary sources or the marginal impact of specific feedstoc k use at a stationary source. Both of these baseline approaches, reference point and anticipate d, can be prospective (e.g., assessing the impact of a particular policy or change in biomass utiliza tion on future carbon stocks) or retrospective (e.g., finding what the impact of an existing polic y or biomass utilization has been on carbon stocks)

. A primary difference between the two baseline appro aches is the ability of the anticipated baseline to evaluate the additional emissions associated wit h biomass consumption at stationary sources. This “additionality” component is vital to determin ing the net contribution of additional biogenic feedstock consumption at stationary sources relativ e to a baseline scenario in which that consumption did not occur (additionality is discuss es further in the next section). Another major difference between the reference point and anticipa ted baseline is the latter’s ability to incorporate potential fundamental changes in a system to gain i nsights about potential outcomes. In an evolving bioenergy market, such an ability can be useful to test various market and policy conditions. Finally, while a reference point approach can assess what ha s been taking place on the landscape in terms of emissions fluxes, an anticipated baseline approa ch provides information about what level of “adjustment” may be appropriate given current and e xpected future market and production system changes.

One purpose of applying an anticipated baseline is to provide information on potential impacts (positive or negative) of a policy, activity, or ot her decisions. An anticipated baseline describes th e expected “business-as-usual” (BAU) conditions absen t a project, policy or other “shock” to a system, against which the potential impacts of a policy or change in markets or other behavior can be described and used to understand the directionality and magnitude of possible impacts.November 2014 J-4

When anticipated baselines are applied prospectivel y, they can be used to evaluate scenarios of potential future outcomes relative to an estimated future BAU baseline (e.g., with and without a policy), given a set of assumptions about technolog ies, markets, and biophysical conditions. It is the difference between these two possible futures that provides insight regarding potential policy impacts. Prospective anticipated baselines are espe cially important when analysis of existing or recent historical trends to assess potential future conditions or impacts may not be appropriate. For example, it would be inappropriate to construct a retrospective baseline if there is limited or no historical data or experience from which to draw in ferences (e.g., national GHG reduction incentives, bioenergy production goals, the use of currently noncommercial technologies such as new agricultural production technologies, or feedst ocks not grown at the commercial scale). Use of an anticipated baseline prospectively allows for ev aluation of not only future market and policy impacts, but also provides insight into how those i mpacts deviate from, or are additional to, the BAU trajectory.

Also, an application of a future anticipated baseli ne approach provides a means to estimate the potential additional emissions and sequestration ch anges over time in response to changes in biogenic feedstock demand. An assessment over long future time frames is particularly important in the case of the production of long rotation feed stocks such as forest-derived feedstocks as well as in cases of land use or land use management practic es that may have long-term effects on landscape fluxes, including deforestation, afforestation to p rovide woody feedstocks, or fluxes from soil carbon pools.

2.1.1. Additionality
One of the primary purposes for applying an anticip ated baseline approach is to ascertain whether an activity or policy has or will have resulted in GHG emission reductions or removals in addition to what would have occurred in the absence of such an action. The difference in net atmospheric carbon dioxide (CO 2 ) emissions with and without changes in biogenic fe edstock use is known as additionality (Murray et al., 2007). Additionality can be determined by assessing the difference in potential net atmospheric CO 2 emissions of a specific level of biogenic feedstoc k use over a certain period of time (in many cases the BAU baseline) ver sus the net atmospheric CO 2 emissions that would have occurred over the same time period with a different level of biogenic feedstock use (counterfactual scenario), holding other factors an d assumptions consistent between scenarios. When an anticipated BAU baseline consists of no bio mass consumption at stationary sources, the counterfactual anticipated scenario allows evaluati on of the aggregate, or average, potential market and landscape-level effects of all biogenic feedsto ck consumption at stationary sources. Similarly, when applied prospectively and compared with a BAU of a specific level of biogenic feedstock usage at stationary sources, an anticipated baseline allo ws evaluation of the incremental, or marginal, future potential market- and landscape-level effect s.
This ability to assess potential additionality is p articularly useful in capturing the complex interactions between biogenic feedstock production and forest product markets, including: biogenic feedstock demand; market-driven changes in planting, management, harvest regimes; market substitution effects; and direct land use ch ange and related GHG implications. It also allows for consideration of alternate fates (i.e., what wo uld happen to the feedstock if not combusted forNovember 2014 J-5

energy), regional differences, and behavioral respo nses to market incentives. By estimating the impact of a change in policy (or other action) hold ing all other conditions and assumptions constant, the resulting complex interactions betwee n markets and land use management decisions (e.g., planting regimes, land use) can be attribute d to that change in policy (or other action).



### 2.2. Review of Relevant Literature

There is an extensive body of literature on the pot ential GHG implications of the expanded use of biogenic feedstocks for energy, many of which use s ome form of future anticipated baseline for analysis. Most studies have focused on annual, ofte n agricultural, crops as feedstock for bioenergy production, mostly in the context of liquid biofuel s (see reviews conducted in Gerber et al., 2008 and Pérez Domínguez and Müller, 2008). A seminal ar ticle by Searchinger et al. (2008) questioned the GHG benefits of corn ethanol given the potentia l for land use change and large increases in GHG emissions as an indirect market response to the bio fuel demand stimuli.

More recently, increased attention has been paid to the net GHG consequences of forest bioenergy—either for transportation fuels or for el ectricity generation (Searchinger et al., 2009; Sedjo and Sohngen, 2012). Researchers have also beg un modeling forest bioenergy pathways to better understand the GHG implications of forest bi oenergy expansion (Daigneault et al., 2012; Mosnier et al., 2013; Latta et al., 2013). Although this issue mirrors many of the land use and market change concerns of the annual feedstock lite rature, there are unique challenges in the treatment of feedstocks with long rotations. Evalua tion of these feedstocks, such as roundwood, would entail investment dynamics and interactions w ith traditional forest product markets.

A subset of the agricultural and forest bioenergy l iterature has applied an intertemporal optimization modeling approach—which explicitly ass umes perfect foresight of anticipated future market and policy conditions. Several published man uscripts have applied intertemporal optimization to evaluate the impacts of bioenergy e xpansion. The Regulatory Impact Analysis of the Renewable Fuel Standard 2 (EPA, 2010b) applied a fu ll suite of models to examine fuel pathway lifecycle analysis of U.S. biofuel expansion, inclu ding land use and GHG implications.

Daigneault et al. (2012) analyzed the impacts of fo rest biomass electricity generation in the United States using a global forest and land use model dev eloped in earlier work and updated to reflect demand for biomass-based energy. Several scenarios were developed to test the sensitivity of results to basic assumptions of land use competitio n. In cases where conversion of agricultural land to new forest biomass production was unconstrained, carbon stock immediately increased. When land use was constrained, this resulted in a net in crease in emissions (decrease in forest carbon stock).

Latta et al. (2013) applied an intertemporal partia l equilibrium model of the U.S. forest and agricultural sectors to assess the market, land use , and GHG implications of biomass electricity expansion. Results showed how intertemporal optimiz ation procedures can yield different biomass feedstock portfolios and GHG performance metrics at different points in time. They also evaluated the impacts of restricting feedstock eligibility, l and use change, and commodity substitution. TheNovember 2014 J-6

authors highlighted the importance of dynamic consi derations and forest and agricultural sector interactions on projecting the GHG effects of bioma ss electricity expansion in the United States.

Another prominent study is the U.S. Billion-Ton Upd ate (DOE, 2011), which relied on the generation of future biomass supply estimates derived from giv en price paths for biomass feedstocks. That study estimated forest and agriculture biomass supp ly (in physical units) and bioenergy supply (in gallons of biofuel and kilowatt-hours [kWh]) at $40 , $50, and $60 per dry ton from 2012 to 2030. At $40 and $50 per dry ton, the majority of the biomas s supply is derived from agricultural residues and wastes, while at $60 per dry ton bioenergy crop s dominate the supply in later years (2022 and 2030).

In the U.S. Energy Information Administration’s (EI A’s) Annual Energy Outlook (AEO) 2012 projections, biomass energy is one of the fastest g rowing renewable fuel sources, with expected growth of more than 3% per year until 2035 in the R eference case. The AEO reports both total production of energy from biomass (including wood a nd wood waste, biomass for liquid fuels, and nonelectric energy demand from wood) and generation in the electric generation and end-use

sectors. 3

There is a currently an expanding pool of research focused specifically on accounting for biogenic emissions, especially in the context of biogenic em issions from forest-related electricity and industrial sector stationary sources. As researcher s have not reached agreement regarding the appropriateness of a standard baseline approach, li terature addressing this topic will become progressively more available.



### 2.3. Application of Future Anticipated Baseline Approac h in this Report

The goal of prospectively applying an anticipated b aseline in this report is to assess the potential future net biogenic CO 2 contributions to the atmosphere from changes in bi ogenic feedstock consumption for energy generation at stationary sou rces. This future anticipated baseline approach addresses the question “Is more or less carbon stor ed in the system over time compared to what would have been stored in the absence of changes in biogenic feedstock use?” Thus, the future anticipated baseline approach requires a means to e stimate the potential incremental impact of changes in biogenic feedstock production and use at stationary sources under specific scenario assumptions into the future. The future anticipated baseline approach accomplishes this by first establishing a BAU baseline scenario (with establis hed levels of biogenic feedstock demand from stationary sources) and uses this as an emissions b enchmark of biogenic feedstock use based on a specific set of anticipated future environmental an d socioeconomic conditions. The BAU projection is then compared with a simulated alternative futur e scenario (or scenarios) that incorporates the same set of anticipated future environmental and so cioeconomic conditions as the BAU baseline scenario and only a single specific change (e.g., i ncrease or decrease) in biogenic feedstock demand.

3 AEO projections are used as part of the baseline a nd scenario construction in other appendices of thi s report. However, it is important to remember that estimates of future possible biomass supply results from AEO as well as other reports cited here cannot be compared directl y with the results produced using the proposed meth od in this appendix because of the evaluation of different fee dstocks, different end users, and other evaluation parameters.November 2014 J-7

The resulting difference between these scenarios in dicates possible impacts of biogenic feedstock

use.



### 2.4. Limitations and Implications of the Future Anticipa ted Baseline Approach

Although models are useful for gauging the responsi veness of complex economic systems and providing insights into potential responses to poli cy or actions, all models have associated uncertainties and limitations. This is especially t rue for intertemporal optimization models in which optimal economic decisions are influenced by expect ations of future market and policy conditions. Results from economic optimization models are sensi tive to the selection of model functions and parameters (e.g., biophysical yield parameters or d emand elasticities) as well as scenario assumptions. There are also inherent uncertainties regarding input data, parameters, and model structure (in the historical data as well as future expectations). In addition, model scenario results are not predictions of the future. Instead, they sh ould be viewed as providing insights as to what may happen under scenarios of plausible potential f utures.

Future anticipated baselines allow for consideratio n of potential intermediate and distant futures where economic drivers may fall outside of historic ranges of data or where there is no experience with specific policies. In the case of forestry, a longer-term analysis (i.e., 40 years or greater) is often necessary to capture biophysical consideratio ns (e.g., growth rates, rotation periods) and related investment behavior. Simulation models are often designed to consider long-run potential outcomes that tend to fall outside of historic expe rience.

Although longer time frames offer the advantage of capturing long-term investments for natural resource systems with long biological growth interv als, economic and physical uncertainties grow with longer time frames. Given the size and complex ity of many intertemporal optimization models, characterizing uncertainty through Monte Carlo anal ysis or stochastic dynamic programming creates computational difficulty. Thus, uncertainty is often evaluated through sensitivity analysis by adjusting key parameters across multiple scenarios (i.e., evaluating multiple future anticipated baselines or deviations from a common baseline). Se nsitivity analysis can be used to test the impacts of different assumptions employed in the mo del, including assumptions about future economic conditions or policies.



## 3. Key Design Elements to Consider

A future anticipated baseline approach application requires information about biomass production systems and associated CO 2 emissions as well as the demand system and related economic factors. The data inputs, model parameters, and assumptions about the BAU and counterfactual trajectories all play significant roles in determining results. The analysis in this section includes consideration of desired model framework/function types, future m acroeconomic conditions (i.e., population, gross domestic product), and relevant sector repres entation. It also considers capabilities for representing future potential biophysical condition s, the land use and energy sectors, land use and commodity competition, and GHG accounting. If all o f these components and model functions cannot be included in the assessment, there will be trade-offs and resulting implications.November 2014 J-8

The list below illustrates the key components to be considered in a modeling platform for simulating anticipated baselines. Each of these com ponents is discussed in the following subsections:

• Model function types and model dynamics (economic o ptimization, intertemporal or recursive dynamic);

• Anticipated future conditions (macroeconomic, bioph ysical);

• GHG emissions representation;

• Forest sector representation;

• Agricultural sector representation;

• Land use competition;

• Energy sector representation; and

• International representation.



### 3.1. Model Function Types and Model Dynamics

3.1.1. Modeling Scope: Economic Optimization

In the context of this framework, a model should ha ve a detailed biophysical component to evaluate biogenic emissions, be well grounded in economic th eory, and represent the benefits, costs, and opportunity costs associated with land management a nd biomass processing alternatives. A suitable model or set of models should allocate res ources and economic inputs to production and consumption processes that achieve the highest net economic return. This attribute is essential because it is consistent with rational economic beh avior, and allows for the inclusion of autonomous adaptation to changing conditions throug h incentives. By optimizing net returns to economic welfare-producing activities, a model allo cates resources efficiently to produce final economic goods and services. For models with a dire ct linkage to land use systems, optimization helps reflect the opportunity costs of different la nd use and/or management alternatives, with implications for future management decisions.

Different modeling frameworks are appropriate for d ifferent research questions. Generally speaking, if a broader look at the overall economy is necessary for an analysis, then an economywide computable general equilibrium (CGE) model may be used. If specific sectors should be evaluated in detail, such as forestry and/or agricu lture, then a sectoral partial equilibrium (PE) model may be chosen.

A number of studies evaluate the trade-offs and imp lications of employing either CGE or PE models to evaluate the impacts of biomass (liquid or solid ) production for energy. General trade-offs for using different modeling types for climate and land use policy evaluation are examined in Van der Werf and Peterson (2009). Kretschmer and Peterson ( 2008) present a thorough review of CGE models integrating bioenergy as well as some discus sion of PE models (mostly those focusing on the agriculture sector in the context of transporta tion fuels).

In the context of bioenergy, CGE models can evaluat e the direct and indirect impacts of activities or policy shocks across a variety of sectors and, in t he case of global models, across countries. ThisNovember 2014 J-9

modeling approach can be useful for analyzing overa ll impacts of bioenergy but may not have the ability to look at the level of geographic, biophys ical, or other sector-specific details that may be necessary. PE models can offer detailed information for the sector(s) being evaluated (in this context, agriculture and forestry sectors) but do n ot include all sectors of the economy and cannot provide a complete picture of macroeconomic impacts . There are variations between CGE and PE models as well to consider. For example, as Kretsch mer and Peterson (2008) pointed out, FASOMGHG differs from other PE models in that it covers both agriculture and forestry (most other PEs currently focus on one or the other) and includes u ses for biomass other than liquid biofuels. Latta et al. (2013) applied FASOM-GHG to assess the marke t, land use, and GHG implications of biomass electricity expansion. The authors evaluated differ ent CGE and PE models used to model forestderived biomass use for energy.

3.1.2. Temporal Dynamics
A future anticipated baseline simulation would idea lly be based on expectations of future market conditions. To evaluate future biogenic feedstock p roduction trends and potential impacts, a model will need to optimize resource allocation and provi de projections over long time horizons. This ability is especially important in order to capture the market and GHG effects of long rotation cycles of forest-derived feedstocks, in addition to other landscape GHG effects that can take decades to unfold (e.g., soil carbon pool impacts, decay rates ). The need for long time frames implies use of either intertemporal optimization or recursive dyna mic models. Static (single time period) models are not considered for use here, although in certai n contexts they could provide key insights.

Intertemporal optimization models , or models that optimize over a dynamic interval, can be useful for an anticipated future baseline evaluatio n. Intertemporal models incorporate expectations of future market conditions, and the model solves o ver a user-defined planning horizon. Economic agents in intertemporal models are forward looking, and management decisions in the present are based on expectations of current and future market conditions. Intertemporal PE or CGE models require that all markets clear simultaneously for a ll years in the simulation horizon. For instance, a dynamic forestry sector model would choose rotation s, product supply, land management intensity, and equilibrium market conditions in order to maxim ize economic welfare over the full time horizon.

Recursive dynamic models produce projections 1 year at a time, building on the conditions established in the previous year. For example, land deforested in time period t-1 would be reflected in the initial forestland endowment in period t . Anticipated changes in demand and/or prices can be incorporated such that a model continuously face s new biophysical, technological, or economic environmental parameters in each time step. However , decisions in current time periods are not made with expectations of future conditions in mind , so the decision variables in each time period are static in nature. Given the importance of expec tations over long planning horizons for forest investment and management decisions, many existing forest models incorporate intertemporal optimization. However, recursive dynamic models cou ld also potentially be used for analyses of forestry, ideally with key anticipated baseline con ditions introduced in the model, and forest management in time t+1 tied to management decisions in time period t (e.g., through theNovember 2014 J-10

incorporation of additional equations based on expe cted landowner behavior or linkages to other models).

Both intertemporal optimization and recursive dynam ic approaches use idealized scenarios of the future. These scenarios are built to reflect perfec t competition and information about future markets and conditions as well as optimized markets (where supply equals demand). It is important to recognize that simulations of future s cenarios are meant to provide insights about the potential directionality and, in some cases, magnit ude of market responses to an activity, target, or other policy shock. Such approaches are best suited for comparing base optimized solutions (i.e., the BAU) with scenario optimal solutions with a sho ck (alternative scenarios) in order to look at the market impacts between the model solutions.



### 3.2. Anticipated Future Conditions (Macroeconomic, Biophy sical)

Anticipated baseline and alternative scenario analy sis include expectations of anticipated macroeconomic and, in the context of accounting of GHG emissions, biophysical conditions. BAU and scenario projections should use published and r eputable economic data forecasts, extrapolating only when necessary. Economic project ions of population and income exogenously influence demand over time. For example, it makes s ense that timber demand is represented not only as a function of price, but also as a function of population and income, and these factors can vary by region. Gross domestic product (GDP) and po pulation estimates can also be introduced to a model to shift the demand curve over time, thus req uiring additional timber from the system. 4

There are several well-known and widely used projec tions of socioeconomic and physical data to choose from, including those from U.S. EIA and Bure au of Economic Analysis (BEA). Alternatively, one could adopt any number of country-specific proj ections of key economic variables. At a minimum, simulating an anticipated baseline require s the following economic data:

• GDP projections;

• Population growth projections;

• Demand functions tied to population/income;

• Technological progress assumptions, especially if a pplicable to stationary sources where efficiency improvements are possible and anticipate d;

• Energy market forecasts, especially if energy marke t data are exogenous to the forestry/land use model; and

• Representation of current and anticipated energy, e nvironmental, or other policies that can constrain BAU trajectories such as renewable portfo lio standards (RPS) or the Regulatory Impact Analysis of the Renewable Fuels Standard (RF S2) legislation.

Because some of these data sources are typically on ly projected over medium time horizons (2035 in the case of the AEO), a consistent method for ex trapolating to long-term horizons is required. Defining the relevant time horizon is a determinati on that will affect the choice of modeling

4 Note that this could also reduce total demand for timber if regional timber demand is negatively corr elated with per capita income for a particular country/region.November 2014 J-11

procedures to estimate the impacts of using biogeni c feedstocks for energy. For woody biomass, a future anticipated baseline methodology would requi re a long time horizon (40 years or greater) that fully captures forest rotation time frames. Fo r agriculture, a shorter time frame might be sufficient, although a longer time frame would allo w one to examine perennial biomass or short rotation woody crops that take several years to rea ch maturity.

Future anticipated baseline and alternative scenari o analysis also requires information about future expected biophysical conditions, such as forestry a nd agricultural productivity, and landscape and carbon cycle dynamics. Specifically, this informati on includes parameterization of forest growth curves by class and region as well as biophysical l imits to productivity, which can include climate change or environmental conditions (e.g., anticipat ed shifts in temperature, atmospheric CO 2 , and water availability). Changing environmental conditi ons could be reflected in yield curves reflective of natural and anthropogenic disturbance risks, due to climate change, increased urbanization, land use changes, or ecological encroachment. Although c limate change projections or natural disturbance risks may not be applicable to all acco unting applications, it may be necessary to include them in some instances.



### 3.3. GHG Emissions Representation

Future anticipated baseline modeling requires an as sessment of landscape-level emissions changes from an increase or decrease in biogenic feedstock consumption relative to the anticipated baseline. Thus, a modeling framework should directly account for emissions from land management activities across a variety of biogenic feedstock a nd landscape carbon pools. The choice of modeling framework in large part dictates the complexity ava ilable for GHG accounting. The degree of detail in GHG pools as well as the emissions associated wi th land use and land use change activities depends on what sectors of the economy are included in the model and what degree of detail is available in those sectors. Generally speaking, if a broader economy-wide CGE is used, the GHG accounting will be coarse yet comprehensive across the full macro-economy. If a PE model focusing on specific sectors, such as forestry and/or agricu lture, should be employed, the level of detail in GHG pools and emissions consequences of specific la nd use and land use change actions can be included, but at the expense of comprehensive econo my-wide coverage.



### 3.4. Forest Sector Representation

Anticipated future baseline analyses offer the abil ity to construct and evaluate long-run projections where structural changes are more likely. In the ca se of forestry, longer-term analysis is often necessary to capture biophysical considerations (e. g., growth rates, rotation periods) as well as the related investment behavior. In the forest sector, decisions about planting and harvest schedules depend in part on expectations about future markets (Sedjo and Tian, 2012).

Forest sector representation should, to the extent possible, be based on observed demand rates for forest products. Future supply and demand can then be determined endogenously through intertemporal optimization under anticipated future macroeconomic conditions. Underlying datasets include historic price and quantity demand ed data for forestry products such as timber, pulp/paper, biomass, pellets, etc., as well as dema nd projections for calibration. Note that, in someNovember 2014 J-12

instances, it might be preferable to model specific biomass demand assumptions as given in the baseline. The modeling framework should also have c omprehensive wood product market representation (including raw timber, pulp/paper, a nd processed wood products). Ideally, product demand would vary by production region. In addition , the modeling platform should depict growth rates and harvest schedules that vary by region, sp ecies, and management regime. Management regime (or intensity) is especially important becau se intensification of forest stands can alter the carbon intensity of the biomass. Depicting the cost s and utilization potential of forest logging and milling residues is another important modeling attr ibute for future anticipated baseline modeling in this context. Forest residues are an important s ource of biogenic feedstock supply, and the emissions profile of residues post-harvest may need to be tracked (as discussed in Appendices H and K).



### 3.5. Agricultural Sector Representation

Similar to the forestry sector, the modeling framew ork should represent agricultural sector production possibilities, markets, and land managem ent options. Output prices modeled endogenously would allow for supply-side responses and reallocation of resources in response to demand shocks. The livestock sector should also be represented, with explicit linkages to the crop sector through the market for animal feed sourced f rom agricultural crops and by-products. In addition to conventional commodity production possi bilities, models should be representative of dedicated energy crop possibilities and crop residu als as potential biomass feedstock sources. This would include representation of the land requiremen t, costs of harvesting, transporting, and storing the energy biomass prior to combustion.



### 3.6. Land Use Competition

Additionally, the modeling framework should depict land use competition among alternative uses (timberland, cropland, grazing land), allowing for endogenous land use shifts in response to changing market and policy conditions. Furthermore, a model should be able to simulate regional or global supply-side responses to changes in bioma ss demand, feedstock prices, or renewable energy prices from baseline levels. For instance, t he modeling framework would allow for projections to simulate how requiring X tons of biomass energy per year in the Southeaster n United States might affect forest land use decisions local ly, in the Pacific Northwest (regionally), and/or globally. For an economic model, supply-side respon se potential should be reasonably constrained according to the biophysical nature of the system. That is, although supply might be particularly responsive to price/demand changes, physical constr aints such as land availability, biophysical growth capacities, or infrastructure limitations co uld constrain regional responses to a market change. Developing land constraints that reflect cu rrent activities can help account for these factors

, though such constraints limit flexibility when proj ecting into the future. Such functionality allows for evaluation of overall potential landscape land use changes (direct and/or indirect land use change) and related GHG impacts. Latta et al. (2013 ) highlighted the importance of land use competition between the forestry and agricultural s ectors for projecting the potential GHG effects of biomass electricity expansion in the United Stat es.November 2014 J-13



### 3.7. Energy Sector Representation

Forestry and/or land use models typically do not en dogenously capture energy market impacts of different policy scenarios that may affect biomass demand. Forestry and land use models can be excellent tools for estimating biomass supply (and subsequent environmental impacts) under exogenously defined policy scenarios (volumetric ma ndate or price incentive). However, such models currently do not capture the spillover effec ts of increased biomass energy demand/consumption in other sectors of the economy (including energy sectors). Without explicitly accounting for these sectors, one cannot reflect the true demand for woody biomass and the competition between biomass and other renewable energy sources under policy-induced shifts from an anticipated baseline. That is, although exi sting forestry and land use models can simulate biomass production pathways, this may not accuratel y reflect the fuel mix that will result from a change in policy or energy market conditions. There fore, it is important to calibrate land use models to projected energy market conditions or exi sting energy market models.

Also, if a policy or other exogenous change favors investment in a particular non-biomass generation technology, and costs begin to decline b ecause of this investment, the demand for biomass energy would presumably fall. The recent ri se in natural gas electricity generation is an example of this scenario and illustrates why modeli ng deviations from an anticipated baseline in the electricity system are important. If one assume s that biomass demand is tied to the price of renewable energy, then competition among alternativ e sources matters. This is an important factor that can ultimately affect the estimation procedure for future anticipated baselines in several ways, including those highlighted below.

The rebound effect is an unanticipated policy conse quence that has been discussed in the energy economics literature (Greening et al., 2000). Once demand initially declines, the price of fossil energy dips, leading consumers to increase consumpt ion, which reduces the net benefit of the original efficiency improvement. In the case of ren ewable energy, a policy mechanism that increases the demand for renewable energy can elici t a rebound effect if fossil fuel replacement is high enough to decrease the price of fossil energy.

Another shortcoming of using forestry and land use models w ith limited energy market interactions is that they likely do not capture competition betw een competing sources of renewable energy, including different biomass feedstock types. This c apability is important because estimation of values for future landscape GHG implications could vary depending on the total amount, and source, of biomass demanded from the system. For in stance, if net emissions increase nonlinearly with consumption of a particular feedstock source, it is important to know the total projected demand of biomass energy and the potential emission s impacts. Similar logic flows for systems in which biogenic emissions fall with the level (and p rice) of bioenergy demanded because of increased terrestrial carbon storage.



### 3.8. International Representation

An international scope allows for the interaction o f U.S. forestry and agricultural markets with the rest of the globe, acknowledging international comm odity production, demand, prices, and theNovember 2014 J-14

associated GHG emissions of that production and con sumption. Because many forestry and agricultural markets are globally traded, internati onal commodity prices can affect U.S. land use decisions. Conversely, in the case of some commodit ies where the United States is a large contributor to the global market, U.S. land use dec isions affecting commodity supply can impact global prices and thus international land use decis ions and related GHG fluxes. Therefore, a global modeling framework would allow for evaluation of in ternational impacts of changes in U.S. biogenic feedstock demand, including emissions leakage effec ts. Appendix E provides a detailed discussion of leakage and previous literature that has applied international models to project indirect land use emissions from domestic (U.S.) policies.



### 3.9. Consideration of Scenario Development

In addition to depicting a future anticipated BAU b aseline, the modeling framework should be able to easily calibrate to alternative future scenarios for simulation analysis. That is, if an assessment needs to evaluate the GHG emissions effects of a de viation from anticipated baseline woody biomass harvest regime, what is the appropriate pro jected baseline and how should an alternative scenario be designed such that a comparison between the two will provide the necessary insights? Numerous examples for how to model future anticipat ed baselines and alternative scenarios exist, including:

• Define an anticipated baseline with a projected amo unt of biomass energy demand and compare this against an alternative scenario where total projected biomass energy demand is different (higher, lower, or no biomass consumpt ion) relative to the baseline, holding everything else constant.

• Similarly, define an anticipated baseline with a pr ojected amount of biomass energy demand, but compare that amount against alternative scenarios that exhibit changes (higher or lower) in projected demand for a single feedstock (e.g., roundwood) relative to the level of consumption for that feedstock in the anticipated baseline.

• Model a major new policy and compare it to the base line. This shift could include an aggressive GHG reduction strategy, such as a carbon tax, renewable energy portfolio standard (RPS), or clean energy standard (CES).

Regardless of the scenario implemented, calculation of results will focus on the difference between the future anticipated baseline and the alternative scenario. Furthermore, the chosen modeling approach could vary depending on the alternative sc enario evaluated.

For example, one scenario could require an RPS-type policy framework in which a minimum percentage of electricity must be met through renew ables. In this example, an energy model could project the proportion of biomass used to help meet the policy-mandated renewable energy demand. Forestry sector models would use this infor mation to determine the final feedstock mix resulting from this demand from the energy sector. Alternatively, one could develop policy scenarios that mandate specific amounts of individu al bioenergy feedstocks, and then focus on the GHG implications of each, one at a time. The first approach is somewhat consistent with recently published modeling efforts (Daigneault et al., 2012 ; Galik and Abt, 2012; Mosnier et al., 2012; Latta et al., 2013), while the latter is consistent with RFS2 legislation. For the purposes of this framewor k,November 2014 J-15

there is not a specific policy being modeled, nor i s there a specific price path (e.g., for energy or CO 2 emissions allowance) or renewable energy target to be met. Therefore, the approach described below differs somewhat from most previous studies i n its construction of baselines and alternative
scenarios.



## 4. Modeling Approach and Tools Chosen for Illustrative Framework Applications

The most appropriate modeling approaches and level of detail depend largely on the goals of the assessment. It is important to assess candidate mod els against considerations appropriate for that application. Ideally, the modeling approach should handle all of the complex interactions necessary to address this complicated issue (many of which ha ve been discussed in previous sections). However, the reality is that a single ideal model d oes not currently exist. All existing models have various advantages as well as shortcomings that mus t be taken into account, and the use of one model over another implies trade-offs.

The remainder of this appendix explains the basic m ethodology developed for this report, focusing on the model attributes used to evaluate biogenic e missions, and the choice of a land use optimization model calibrated to existing energy ma rket projections for this assessment.

This study first uses the most recent biomass consu mption rates for energy generation at electricity sector and some industrial sector stationary source s available through the EIA and Department of Energy (from the EIA-923 database, in short dry ton s [DOE, 2011]). This analysis then applies the FASOM-GHG model, because it includes most of the ke y functions and components described in the above section (these functions are described in Sec tion 4.3).

Although FASOM-GHG is not explicitly tied to an ene rgy sector model, biomass projections and energy market assumptions are calibrated to the AEO 2012 forecasts, as described in brief below and in detail in Appendix K that discusses the futu re anticipated baseline construction methods.

The next subsections briefly describe the anticipat ed future baseline scenarios and the alternative biogenic feedstock production scenarios to provide context for the application of FASOM-GHG. Details on the baseline scenario development can be found in Appendix K and on the alternative scenario case study applications in Appendix L. The following section describes the key components and capabilities of the FASOM-GHG model, followed b y a subsection focusing on specific datasets and functions within FASOM-GHG that are pertinent t o this report. Full FASOM-GHG documentation is available online. 5



### 4.1. Brief Overview of Baseline Construction

Although there are numerous AEO model scenarios to choose from, the alternative baselines in this study were developed using four AEO 2012 projection s and two additional cases:

5 http://www.cof.orst.edu/cof/fr/research/tamm/fores t_and_agriculture_sector_op.htmNovember 2014 J-16

• Reference case: The Reference case is the baseline AEO 2012 model, which assumes real GDP grows at a 2.4% average annual rate from 2008 t o 2035, buoyed by a 1.5% per year growth in productivity in nonfarm businesses and a 0.6% growth in nonfarm employment. All the other scenarios pivot off of the Reference case scenario, changing specific assumptions.

• High GDP Growth case: The AEO 2012 High Economic Growth case assumes that real GDP grows by 3%, supported by productivity growth of 2. 4% and employment growth of 1.2%.

• Low GDP Growth case: The AEO 2012 Low Economic Growth case assumes that real GDP grows by 1.8%, supported by productivity growth of 1.5% and employment growth of 0.5%.

• Low Renewable Technology Cost case: The AEO 2012 Low Renewable Technology Cost case assumes annual levelized cost for non-hydropow er renewables is 10% lower than the Reference case in 2010 and is 35% lower by 2035 com pared with Reference case values.

• Zero Biomass case: A constraint is imposed that restricts all biomass consumption for energy generation in each region.

• Constant Biomass case: The Constant Biomass scenario begins with total bio mass demand constraints set to 2009 consumption levels.

Using these scenarios to derive biogenic feedstock demand projections for stationary sources, regional constraints are imposed requiring supply-s ide utilization of biomass for energy generation that matches these projections exactly. No restrict ions are imposed on regional feedstock mixes used to meet these overall biomass requirements. Th e model minimizes the costs of providing the requisite biomass. It is important to acknowledge t hat this study uses the 2009 EIA AEO information to be consistent with the other databas es used (the most recent Emissions and Generation Resource Integrated Database [eGRID] at the time of this work was from 2009).

Most dynamic models, particularly energy sector mod els, are calibrated to existing economic forecasts. This analysis adopts key forecasts from the AEO. Thus, anticipated market and policy conditions are consistent with assumptions underlyi ng the AEO and the National Energy Modeling System (NEMS). Energy price projections are calibra ted to the AEO 2012 to reflect anticipated energy market conditions. Biogenic feedstock consum ption projections are also calibrated to growth rates in renewable energy demand in the indu strial and electricity sectors. An advantage of calibrating scenarios to the AEO is that existing s tate policies (RPS, CES, etc.) are already accounte d for to the extent possible. Thus, growth parameters for renewable energy demand used in this analysis assume that state-level policies encouragi ng growth in renewable electricity will hold. Additional discussion on the specific scenarios cho sen for this report can be found in Appendix K.



### 4.2. Brief Overview of Regional Feedstock Case Studies

The following illustrative case studies are develop ed to focus on the net biogenic CO 2 effects of an increase in biogenic feedstock consumption for a sp ecific feedstock within a particular region, relative to the future anticipated future baseline. These case studies, representing different feedstock types and different regions in the United States in order to illustrate regional and feedstock differences, offer insight into the poten tial landscape emissions impact of increased consumption of a single biogenic feedstock:November 2014 J-17

• Southeast roundwood;

• Corn Belt corn stover; and

• Pacific Northwest logging residues.

In each region, the demand for the specified feedst ock is increased 1 million tons per year above biomass demand in the AEO Reference case level by 2 030. For example, roundwood demand in the Southeast increases by 1 million tons in relation t o the AEO-based baseline. All feedstock scenario results are calculated relative to the AEO Referenc e case and Zero Biomass case. Additional information on how these case study scenarios were developed and executed in FASOM-GHG, as well as variations on these case study evaluations, can be found in Appendices K and L. Some of the feedstock-specific case study sensitivities were ch osen to maintain consistency with the reference point case studies evaluated in this report and are reported in Appendix L.

Figure 1. Conceptual Diagram Outlining the Basic An ticipated Future Baseline Modeling Approach Presented in this Appendix.



### 4.3. Use of FASOM-GHG

FASOM-GHG has the basic necessary capabilities and characteristics to satisfy the requirements of this study. Table 1 highlights these abilities, whi ch are discussed in detail in the following section s.November 2014 J-18

Table 1. FASOM-GHC Attributes.

Attribute FASOM-GHG

Model framework/function type Intertemporal partia l equilibrium

Anticipated future conditions AEO 2012 energy mark et conditions, USDA baseline (2010), and 2005 RPA Assessment

GHG representation CO 2 , CH 4 , and N 2 O accounting across forest, crop, and livestock management activities; CO 2 accounting includes biogenic feedstock (forest carbon) and non-biogenic feedstock (soil) p ools

Forest sector representation Logs from timber harv est and secondary wood products, forest residues

Agricultural sector representation 40 primary crop commodities; 25 primary livestock products; 32 domestic and imported forest logs; 12 categories of forest and agricultural residues; 17 secondary crop products; 17 secondary livestock products; 10 processing by-products; 40 p rocessed forest products

Land use competition Endogenous competition between cropland, forestland, and grazing lands

Energy sector representation Ethanol (first and se cond generation), biodiesel, and biopower (100% biomass generation or co-firing levels of 5%, 10%, 15%, and 20%) with exogenous price or quantity constraint

International representation 18 regions for seven a gricultural traded commodities; forest sector includes endogenous activities for trade with Canad a as well as other significant trade flows (e.g., softwood lumber trad e with nonCanadian regions)

4.3.1. Overview of Key FASOM-GHG Attributes and Functions
This analysis applies an updated and enhanced versi on of FASOM-GHG. FASOM-GHG is a dynamic partial equilibrium economic model of the U.S. agri cultural and forestry sectors and has been applied in a wide range of policy settings. FASOM-G HG explicitly models GHG mitigation strategies, including many bioenergy processing options (Murray et al., 2005; Schneider and McCarl, 2003).

FASOM-GHG uses a price-endogenous mathematical prog ramming approach developed by Judge and Takayama (1973) and McCarl and Spreen (1980). T he model maximizes total intertemporal welfare across the U.S. agricultural and forestry s ectors, or the sum of producer surplus (area below the equilibrium price) and consumer surplus (area a bove the equilibrium price). Commodity and most factor prices are endogenous, determined by th e supply and demand relationships in all markets included within the model. The framework ac counts for market adjustments over time to systematic policy shocks by depicting changes in eq uilibrium prices and quantities supplied of all primary and secondary commodities. Because commodit y markets within agriculture and forestryNovember 2014 J-19

are highly interdependent, a systematic shock that disrupts the optimal production portfolio of one commodity (e.g., corn) can cycle through other prim ary or secondary commodity markets (such as ethanol and livestock, which use corn as a critical factor input, or corn substitutes such as alternative feed grains), through competition for p roduction inputs, consumers, trading partners, and land.

FASOM-GHG accounts for a comprehensive range of lan d use categories consistent with land classifications from multiple resources including t he Natural Resources Inventory (NRCS, 2003), Major Land Use Database (USDA-ERS, 2010), and agric ultural census (USDA-NASS, 2010). The model allows for explicit land use competition betw een cropland, grazing lands, and conservation lands (CRP) and forestland based on expected return s to alternative uses. This allows us to simulate potential land use change impacts of policy drivers that increase the relative value of land holdings in a particular use over time (Alig et al., 1998, A lig et al., 2010) and is a departure from the stati c CGE modeling approach that assumes conversion costs or elasticities of substitution between alternative land uses.

FASOM-GHG is disaggregated into 63 minor production regions in the lower 48 states and 11 main agro-forestry regions. Table 2 displays all major r egions with accompanying production units. All major regions include crop and forestry production opportunities except for the Great Plains and Southern Plains (which includes most of Texas and O klahoma). Land use change between forestry and agriculture is restricted to lands that fall wi thin a certain land suitability class, thus ensurin g that land transfers remain within realistic bounds (Alig et al., 2010).

Table 2. Definition of FASOM-GHG Production Regions a nd Market Regions.

Key Market Region Production Region (States/Subregions) NE Northeast Connecticut, Delaware, Maine, Maryland

, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, West Virginia LS Lake States Michigan, Minnesota, Wisconsin CB Corn Belt All regions in Illinois, Indiana, Iowa, Missouri, O hio (IllinoisN, IllinoisS, IndianaN, IndianaS, IowaW, IowaNE, IowaS, OhioNW, OhioS, OhioNE) SE Southeast Virginia, North Carolina, South Caroli na, Georgia, Florida SC South Central Alabama, Arkansas, Kentucky, Louis iana, Mississippi, Tennessee, Eastern Texas SW Southwest (agriculture only) Oklahoma, all of Te xas but the eastern part (Texas High Plains, Texas Rolling Plains, Texas Central Blacklands, Texas Edwards Plateau, Texas Coastal Bend, Texas South, Texas Trans Pecos) RM Rocky Mountains Arizona, Colorado, Idaho, Montan a, Nevada, New Mexico, Utah, Wyoming PSW Pacific Southwest All regions in California (Ca liforniaN, CaliforniaS) PNWE Pacific Northwest-East side Oregon and Washing ton, east of the Cascade mountain rangeNovember 2014 J-20

PNWW Pacific Northwest- West side

(forestry only) Oregon and Washington, west of the Cascade mountain range

Figure 2. Map of the FASOM-GHG Regions (Source: Beac h et al., 2010b ).

Land to development transfers are modeled on a regi onal basis by land type and drawn from data prepared for the 2010 Resources Planning Act (RPA) Assessment (Alig et al., 2009). These parameters help depict an anticipated future land b ase that is decreasing in the baseline due to development pressures. Accounting for land to devel opment pressures in an agriculture and forestry sectoral modeling framework is important b ecause varying levels of development pressures can affect land use competition between a griculture and forestry, GHG mitigation potential, and commodity prices (see Alig et al., 2 010 for additional discussion).

FASOM-GHG encompasses a suite of GHG mitigation opt ions, including biological sequestration of carbon in agricultural soils and forest stands, alt ernative crop and livestock production practices to reduce emissions, and bioenergy feedstock substitut es for fossil fuels. The gases represented are carbon dioxide, methane, and nitrous oxide. Forest carbon balances are tracked using a methodology consistent with the Forest Carbon accou nting system, FORCARB (Birdsey et al., 2000).November 2014 J-21

Forest carbon is tracked in trees, soils, understor y, and end products. Forest management offset opportunities are endogenously modeled in FASOM-GHG and include avoided deforestation, rotation extensions, altered species mix, partial t hinning, and reforestation. For a discussion of GHG accounting and mitigation options, as well as fores try and agricultural management options in FASOM-GHG, see Beach et al. (2010b).

The model allows for intensive and extensive margin shifts for both crop and forestry production activities (as discussed in Baker et al., 2013). Fu rthermore, land use competition and product substitution between the two sectors is a key model component, missing from other partial equilibrium models of the agriculture and forestry sectors. The inclusion of such a function has been found to have a dramatic impact on GHG emissio ns trajectories relative to less inclusive modeling approaches (Latta et al., 2013). Additiona l information on how FASOM-GHG depicts intensive and extensive margin production opportuni ties in forestry and agriculture can be found in Beach et al. (2010a) and Adams et al. (2008).

FASOM-GHG incorporates endogenous international tra de effects, such as international supply regions (18 regions) for seven agricultural traded commodities with import supply functions (Adams et al., 2008). The forest sector includes en dogenous activities for virtually all forms of trad e with Canada as well as other significant trade flow s to offshore regions (e.g., softwood lumber trade with non-Canadian regions). Details on FASOM-GHG’s international components are discussed in the supplemental online documentation (Beach et al. , 2010b; Adams et al., 2008). FASOM-GHG cannot conduct detailed analysis of global GHG impa cts of changes in U.S. biogenic feedstock production and consumption. However, the model coul d be linked with a global model, including forestry and agricultural trade components with rel ated land use and GHG accounting components.



## 5. References

Adams, M.B., L.H. Loughry, and L.L Plaugher. 2008. Experimental Forests and Ranges of the USDA Forest Service, revised. US Forest Service Gen Tech Rep NE-GTR-321; p. 183. Alig, R., D. Adams, B. McCarl. 1998. Impacts of inc orporating land exchanges between forestry and agriculture in sector models. Journal of Agricultural and Applied Economics 30 (2):389–401. Alig, R., A. Plantinga, D. Haim, and M. Todd. 2009. Land-use Projections for the 2010 RPA Assessment. Gen. Tech. Rep. PNW-GTR-in press. Portl and, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Alig, R., G. Latta, D. Adams, and B. McCarl. 2010. Mitigating Greenhouse Gases: The Importance of Land Base Interactions between Forests, Agriculture , and Residential Development in the Face of Changes in Bioenergy and Carbon Prices. Forest Policy and Economics 12:67-75. Baker, J.S., B.C. Murray, B. McCarl, S. Feng, and R . Johannson. 2013. Implications of Alternative Agricultural Productivity Growth Assumptions on Lan d Management, Greenhouse Gas Emissions, and Mitigation Potential. American Journal of Agricultural Economics , 95:435–441. Beach, R.H., E.O. Sills, T.M. Liu, and S.K. Pattana yak. 2010a. The Influence of Forest Management on Vulnerability to Severe Weather. In J. M. Pye, H. M . Rauscher, Y. Sands, D. C. Lee, and J. S. Beatty (Eds.), A dvances in threat assessment and their application to forest and rangeland management

.November 2014 J-22

General Technical Report PNW. Portland, OR: U.S. De partment of Agriculture, Forest Service, Pacific Northwest Station & Southern Research Stati on. Beach, R.H., D.A. Adams, R. Alig, J. Baker, G.S. La tta, B.A. McCarl, B.C. Murray, S.K. Rose, and E. Wh ite. 2010b. Model Documentation for the Forest and Agric ulture Sector Optimization Model with Greenhouse Gases (FASOMGHG). p. 188. http://www.cof.orst.edu/cof/fr/research/tamm/FASOMG HG_Model_Documentation_Aug2010. pdf. Birdsey, R.A., R. Alig, and D. Adams. 2000. Mitigat ion activities in the forest sector to reduce emissions and enhance sinks of greedouse gases. p. 112-131. In L.A. Joyce and R.A. Birdsey (ed.) The Impact of Climate Change on America’s Forests: A Technical Document Supporting the
2000 USDA Forest Service RPA assessment. RAIURS-GTR -59. USDA For. Serv., Rocky Mountain Res. Stn., Fort Collins, CO. Daigneault, A., B. Sohngen, R. Sedjo. 2012. Economi c approach to assess the forest carbon implications of biomass energy. Environ. Sci. Technol . 46, 5664–5671. Fargione, J., J. Hill, D.d Tilman, S. Polasky, P. H awthorne. 2008. Land Clearing and the Biofuel Carbo n Debt. Science, 319 (5867), p1235-1238.Galik, C.S. and R.C. Abt. 2 012. The effect of assessment scale and metric selection on the greenhouse gas be nefits of biomass. Biomass & Bioenergy 44:
1-7. Galik, C.S., and R. Abt. 2012. The Effect of Assess ment Scale and Metric Selection on the Greenhouse Gas Benefits of Woody Biomass

. Biomass and Bioenergy 44:1-7. Gerber, N., M. Van Eckert, and T. Breuer. 2008. The impacts of biofuel production on food prices: A review, ZEF discussion papers on development policy , No. 127. Haynes, R.W., Adams, D.M., Alig, R.J., Ince, P. J., John R.; Zhou, X. 2007. The 2005 RPA timber assessment update.Gen. Tech. Rep. PNW-GTR-699. Port land, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Rese arch Station. Judge, G.G. and T. Takayama (eds.) 1973. Studies in Economic Planning over Space and Time, NorthHolland, Amsterdam. Kretschmer, B., and S. Peterson. 2008. Integrating bioenergy into computable general equilibrium models: A survey. Kiel working paper, No. 1473, In: Kiel Inst. for the World Economy, Kiel (Ed.) Latta, G.S., J.S. Baker, R.H. Beach, S.K. Rose, and B.A. McCarl. 2013. A multi-sector intertemporal optimization approach to assess the GHG implication s of U.S. forest and agricultural biomass electricity expansion. Journal of Forest Economics 19(4):361-383. McCarl B.A. and T.H. Spreen. 1980. Price endogenous mathematical programming as a tool for sector analysis. American Journal of Agricultiral Economics 62:87-102. Metz, B., Davidson, O.R., Bosch, P.R., Dave, R., Me yer, L.A.(eds). Contribution of Working Group III t o the Fourth Assessment Report of the Intergovernment al Panel on Climate Change, 2007. Cambridge University Press, Cambridge, United Kingd om and New York, NY, USA. http://www.ipcc.ch/publications_and_data/ar4/wg3/en /contents.html. Mosnier A., P. Havlık, and H. Valin. 2012. The Net Global Effects of Alternative U.S. Biofuel Mandates : Fossil Fuel Displacement, Indirect Land Use Change and the Role of Agricultural Productivity Growth. Nicholas Institute for Environmental Policy Solutions, Duke University.November 2014 J-23

Murray B.C., B. Sohngen, A.J. Sommer, B. Depro, K. Jones, B. McCarl, D. Gillig, B. DeAngelo, and K. Andrasko. 2005. Greenhouse Gas Mitigation Potential in U.S. Forestry and Agriculture. Environmental Protection Agency. EPA 430-R-05-006. Murray, B.C., B. Sohngen, and M.T. Ross. 2007. Econ omic consequences of consideration of permanence, leakage and additionality for soil carb on sequestration projects. Climatic Change

, vol. 80, no. 1, pp. 127-143. Parry, M.L., O.F. Canziani, J.P. Palutikof, P.J. va n der Linden, and C.E. Hanson (eds.). Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007. Cambridge University Press, Cambridge , United Kingdom and New York, NY, USA. Pérez Domínguez, L., and M. Müller. (eds.). 2008. M odelling of Energy Crops in Agricultural Sector Models. A Review of Existing Methodologies. Seville , Joint Research Centre/Institute for Prospective Technological Studies. Schneider, U.A., and B.A. McCarl. 2003. Measuring A batement Potentials When Multiple Change is Present: The Case of Greenhouse Gas Mitigation in U .S. Agriculture and Forestry. Working Papers FNU-23, Research unit Sustainability and Glo bal Change, Hamburg University. Searchinger, T.D., R. Heimlich, R.A. Houghton, F. D ong, A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, T

. Yu, 2008. Use of U.S. croplands for biofuels increa ses greenhouse gases through emissions from land use change. Science 319:1238. Searchinger, T.D., S.P. Hamburg, J. Melillo, W. Cha meides, P. Havlik, D.M. Kammen, G.E. Likens, R.N. Lubowski, M. Obersteiner, M. Oppenheimer, G.P. Robe rtson, W.H. Schlesinger, and G.D. Tilman. 2009. Climate change. Fixing a critical climate acc ounting error. Science 23;326(5952):527-8. doi: 10.1126/science.1178797. Sedjo, R. and B. Sohngen. 2012. Wood as a major fee dstock for biofuel production in the United States: Impacts on forests and international trade. Journal of Sustainable Forestry 32(1-2). Special Issue: Sustainable Wood Bioenergy in the Un ited States Sedjo, R. and X. Tian. 2012. Does wood bioenergy in crease carbon stocks in forests? Journal of Forestry 110(6):304-311. Sohngen, B. and R. Sedjo. 2000. Potential carbon fl ux from timber harvests and management in the context of a global timber market. Climatic Change 44:151-172.

UNFCCC. 2009. Kyoto Protocol. Article 3.3. : United Nations Framework Convention on Climate Change,. http://www.unfccc.int. U.S. Department of Agriculture, National Agricultur al Statistics Service. 2010. 2010 Agricultural Statistics Annual. http://www.nass.usda.gov/Publica tions/Ag_Statistics/2010/. U.S. Department of Agriculture, Natural Resources C onservation Service. 2003. Natural Resources Inventory. U.S. Department of Agriculture, Economic Research S ervice (ERS). 2010. Major Uses of Land in the United States, 2010. U.S. Department of Energy. 2011. U.S. Billion-Ton U pdate. Biomass Supply for a Bioenergy and Bioproducts Industry. U.S. Energy Information Adminstration. 2009. Annual Energy Outlook 2009.November 2014 J-24

U.S. Energy Information Adminstration. 2012. Annual Energy Outlook 2012. U.S. Environmental Protection Agency. 2009. The Emi ssions & Generation Resource Integrated Database (eGRID). 8th ed. with year 2009 data. http ://www.epa.gov/cleanenergy/energyresources/egrid/. U.S. Environmental Protection Agency. 2010a

. Guidelines for Preparing Economics Analyses (NCEE), Chapter 5: http://yosemite.epa.gov/ee/epa/e erm.nsf/vwAN/EE-0568- 05.pdf/$file/EE-0568-05.pdf. U.S. Environmental Protection Agency. 2010b. Regula tory Impact Analysis: Renewable Fuel Standard Program. Van der Werf, E. and S. Peterson. 2009. Modeling li nkages between climate policy and land use: An
overview. Agricultural Economics 40(5):507-517, 09.