November 2014 C-1



# Appendix C: Spatial Scale

Table of Contents

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

................................................... ................................................... ..................... C-1

2. Considerations and Implications of Spatial Scale Ch oice .............................................. ............................ C-2

2.1. Location and Land Base Characteristics ............

................................................... .................................. C-3

2.2. Data Availability and Accuracy ....................

................................................... ............................................ C-3

2.2.1. Cross-boundary Flows ..............................

................................................... ......................................... C-4

2.3. Range of Potential Spatial Scales and Related Impli cations and Tradeoffs ............................. C-5

2.3.1. Stand or Fuelshed .................................

................................................... ............................................... C-5

2.3.2. State .............................................

................................................... ................................................... ........... C-6

2.3.3. Regional ..........................................

................................................... ................................................... ...... C-6

2.3.4. National ..........................................

................................................... ................................................... ...... C-8

2.4. Spatial Scale in the Framework ....................

................................................... ........................................... C-9

2.4.1. Assessment at the Regional Scale ..................

................................................... ............................ C-10

2.4.1.1. RPA Regions .......................................

................................................... ................................................. C -10

2.4.1.2. FASOM-GHG Regions .................................

................................................... ...................................... C-11

3. Conclusion ........................................

................................................... ................................................... .................... C-12

4. References ........................................

................................................... ................................................... .................... C-13



## List of Figures Figure C-1. RPA Regions (USDA Forest Service, 2012)

. ................................................. ................................... C-10 Figure C-2. FASOM-GHG Regions. ....................

................................................... ................................................... .... C-12



## 1. Introduction

This appendix discusses the importance of spatial s cale choice when applying the assessment framework. Spatial scale selection can affect the r esults of any analysis evaluating GHG emissions and sequestration, regardless of whether that analy sis applies a retrospective reference point baseline approach or a future anticipated baseline approach (Galik and Abt, 2012). A range of options for choosing an appropriate spatial scale i s explored, along with a discussion of the advantages and disadvantages of each option. This a ppendix then lays out the technical underpinnings for the use of specific regions for c alculating illustrative biogenic assessment factor ( BAF ) equation term values using the retrospective refe rence point and future anticipated baselineNovember 2014 C-2

approaches. These regional constructs include: the Resources Planning Act 1 regions (8 regions) are used for the retrospective reference point forest-d erived feedstock examples, and the agro-forestry regions used in the U.S. Forest and Agriculture Sec tor Optimization Model with Greenhouse Gases (FASOM-GHG) (11 regions) are applied for retrospect ive reference point agricultural feedstock examples and future anticipated baseline agricultur e and forestry feedstock examples. Additional information on reference point baseline and future anticipated baseline methods can be found in Appendices H and K. Spatial scale considerations in the composition and management of wastederived feedstocks are briefly discussed here in Se ction 2.3.4 and in detail in Appendix N.



## 2. Considerations and Implications of Spatial Scale Ch oice

Different spatial scales offer different levels of precision in terms of estimates, can affect depth a nd breadth of measurement, dictate the availability an d verification of data, and limit modeling options. The size of an assessment area can also de termine the ability of an assessment system to reflect carbon dynamics in the biogenic feedstock s ource area and any inter-regional trade of biogenic feedstocks. The choice of spatial scale ca n allow for broad aggregation of or, conversely, evaluation of differences between, characteristics of the land base (e.g., ownership type, management regimes, soil types), biophysical charac teristics of the biogenic feedstock (e.g., species and growth and harvest rates), and feedstock produc tion and market dynamics. 2

Therefore, it is crucial that any application of th e assessment framework consider these trade-offs and implications on results when identifying the mo st appropriate spatial scale (or scales if more than one scale is appropriate) for use in a particu lar program or policy. In general, there is no sing le scientifically correct option or specific method fo r determining the “appropriate” spatial scale for all analyses: the appropriate spatial scale differs depending on the specific goals and parameters of a specific policy or program application of the fra mework. The issues related to spatial scale can differ with feedstock type, biophysical and economi c factors, and the circumstances for each program that needs to assess biogenic feedstock pro duction and use. Thus, the choice of spatial scale is primarily a function of the stated objecti ve of the specific program or project being developed.

The source of the biogenic feedstock is an importan t consideration because the biophysical attributes of the biogenic feedstock and land on wh ich it is produced are used to derive input values for use in the framework (reflected as values withi n the BAF equation in the main report Part 2). The biophysical attributes of different biogenic fe edstocks can vary between geographic locations because of a number of environmental factors and ne t primary productivity of the landscape (Beringer et al., 2011). Therefore, unless the glob al landscape were entirely homogenous, it would be inaccurate to assume the biophysical as well as feedstock production dynamics in one part of the country or world are the same as those in another w ithout evaluation.

1 For more information on the USDA Forest Service Re sources Planning Act (RPA) Assessment, see www.fs.fed.us/research/rpa/.

2 Leakage, represented by the LEAK term (see Appendix F), can also be influenced by t he spatial scale chosen in a specific policy analysis. For additional informatio n on leakage, refer to Appendix E.November 2014 C-3

Before discussing the range of different spatial sc ales and related tradeoffs and implications of each

, the subsections below first discuss how land base c haracteristics and geographic location, data availability, and data accuracy can affect the choi ce of spatial scale.



### 2.1. Location and Land Base Characteristics

Location of feedstock production dictates important biophysical factors such as temperature, soil type, precipitation, elevation, species type and mi x, and growth rates (Eagle et al., 2010). As such, the geographic location of the landscape not only d etermines, in part, what type of feedstocks can be produced but also the various biogenic CO 2 fluxes associated with feedstock production.

Ownership type also plays a role in determining wha t feedstocks are grown, how often they are harvested, and related GHG emissions fluxes. For ex ample, although the ratio of forest growth to removals (of woody biomass) at the national scale i s roughly 1.72 currently (Smith et al., 2009), it varies substantially with geographic region, specie s, and ownership. For example, the ratio of forest growth to harvest for private forests in the conter minous United States is 1.3, while the same ratio on public lands is 5.3 (DOE, 2011). An assessment a rea that includes a large proportion of publicly owned land would therefore be more likely to have l ower levels of harvest (and higher levels of growth) than a similar area with more private land ownership (DOE, 2005, 2011). A more detailed discussion of working lands is covered in Appendix H.

Given the transaction data collected and processed when a biogenic feedstock enters a stationary source, it is often possible to determine the preci se location of the feedstock harvest site, though i t may only be possible to know the broad geographic o rigin. For example, entities operating primarily on long-term procurement contracts will l ikely use the same feedstock production sites year after year, and the geographic location of tho se sites can be known. In such cases, measurement and analysis of production-related biog enic fluxes at a localized scale are possible. On the other hand, for stationary sources operating us ing aggregated feedstocks (e.g., agricultural residues from multiple landowners piled together at centralized site) or feedstocks that require storage and may become mixed (e.g., forest logging and milling residues), it may be difficult to know the precise origin of the feedstock, so only t he broad geographic region could be identified. Also, for some feedstocks, production sites may var y from year to year (e.g., logging residues from harvests that may not return to the same location f or decades or crop rotations and fallow cycles).



### 2.2. Data Availability and Accuracy

The choice of spatial scale can be greatly influenc ed by the availability and accuracy of data and the precision with which one can model feedstock produc tion and market dynamics. When a stationary source purchases biogenic material for energy produ ction, it is possible to measure every ton of material that is purchased or brought into the stat ionary source, or subsequently used in a particular process at the stationary source (e.g., using measurement equipment such as scales and monitors). However, when estimating the biogenic re source in a production source area, it is necessary to use sampling approaches, which are inh erently less precise than complete measurements due to sampling and measurement errors . For example, to estimate woody biomass in the forests of a region, trees on inventory plot s (samples) are measured periodically (FAO, 1997;November 2014 C-4

USDA, 2014). Tree measurements (e.g., species, diam eter, height) are used in conjunction with mathematical models to estimate biomass per tree an d then statistically expanded to obtain estimates of biomass per unit area of forest (FAO, 1997; USDA, 2014 ). Remote sensing approaches (e.g., satellite imagery, aerial photography) are a lso used to estimate the area of forest cover withi n a region (FAO, 1997; USDA, 2014).

The level of data accuracy varies with choice of sp atial scale. When carbon stocks are estimated at a larger spatial scale (e.g., national, regional) thr ough statistical sampling, the increase in sample s ize provides more precision (i.e., smaller sampling err ors). For smaller land areas, the estimates will be less reliable due to a lack of statistical power as sociated with small sample size (Westfall et al., 2013). Estimates at these smaller scales must then be derived from other sources such as special inventories or surveys (i.e., thorough inventories conducted as part of a forest management plan).

In addition to primary data collection for retrospe ctive analysis of landscape emissions, georeferenced land cover and forest inventory data oft en serve as a primary input to economic models that can be used to project landscape biogenic emis sions relative to an anticipated baseline. Thus, models that aggregate land use data to a larger reg ion will reduce the uncertainty associated with those primary model inputs.

2.2.1. Cross-boundary Flows
Another difficulty introduced by defining geographi c boundaries for analysis is assessment for transfers across political boundaries (e.g., cross- state or international trade). For example, it is common for wood-using mills in one state to purchas e wood from across state or regional boundaries (Teeter et al., 2006). As a result, the emissions from biogenic feedstock consumption for energy production may occur in a different region t han the sequestration in the forest-derived feedstock production area. In an assessment framewo rk, transportation across accounting boundaries introduces complexity in that feedstocks of the same type (e.g., trees) acquired from different areas or regions may be accounted for sep arately as they may have different biophysical attributes (e.g., species, growth rates). Thus, ent ities using biogenic feedstocks, or another party designated with this responsibility by a program/po licy, would need to anticipate and/or monitor the source region for all feedstocks a facility use s to account for regional differences. The data collection and modeling complexities will increase with the number of regions defined in a geographically divided assessment framework.

Further, it may not be possible to determine the sp ecific origin of all biogenic feedstocks. In the context of forest-derived biomass, even if the spec ific site is known, source locations would change annually because of the long-term nature of forest harvesting cycles. For agricultural feedstocks, it may be possible to know the specific locations that supply biomass to a procuring entity. In other cases, aggregators or suppliers may purchase materi al from a variety of sources, and knowledge of specific origins of feedstock may be lost. When the biogenic feedstock production location is known, it is possible to collect very detailed site-specif ic data, although this may be costly to collect and verify.November 2014 C-5

In addition to inter-regional considerations, inter national feedstock trade flows are important to acknowledge as well. International feedstock produc tion and the imports of those feedstocks can significantly affect overall U.S. biogenic feedstoc k resource availability and demand pressures on those resources. The pricing and flow of feedstocks and related commodities have the potential to influence domestic supply chains and land use activ ities. The report acknowledges the significance, but does not include assessment, of international b iologically based feedstock production and the role of imports and exports (i.e., the impacts of U .S. feedstock production on international trade flows and resource allocation). Deciding whether to include and therefore craft a means to account for imported and exported biogenic feedstocks would be a decision specific to application of this framework in the context of a particular policy or program requirements and objectives.



### 2.3. Range of Potential Spatial Scales and Related Impli cations and Tradeoffs

For purposes of this framework, several spatial sca le options were considered: stand/field, fuelshed, state, regional, and national scales. The ordering here is generally in the direction of increasing size; however, there could be instances in which a fuelshed (or woodshed) area may be larger than, for example, an individual state (e.g. , a small state such as Rhode Island). Furthermore, some scales may approximate an aggregate of other s cales, such as multiple states combining to form one region.

2.3.1. Stand or Fuelshed
The finest spatial scale would be at the specific s ite of the biogenic feedstock origin (agricultural field, forest stand, etc.). The linkage between fee dstock source area carbon dynamics and the net biogenic emissions from an entity using biomass is most direct at finer spatial scales. Accounting at the stand or field level directly links emissions a nd sequestration on the landscape producing a biogenic feedstock, and the impact of each entity’s biogenic feedstock use on the biogenic production site carbon fluxes could be determined. However, an assessment using the reference point baseline approach at these small scales can b e challenging because data would need to be collected for every site from which a stationary so urce procures feedstocks (e.g., feedstock tracking, record keeping), and these data must accompany the movement of the feedstocks around the country. An assessment that uses an anticipated bas eline approach would also be difficult, but one could model production systems rather than tracking each production plot.

Next may be an aggregate of areas from which feedst ock may be procured for use at a specific entity: the fuelshed. 3 When the location of feedstock production sites is known, the fuelshed can also be known because it would be the aggregate of sites from which feedstocks originate. In the case of unknown source locations, one might be able to generalize a fuelshed into a region encompassing local and likely sources. For example, several analyses have used a circular fuelshed with either a straight-line or road-distance radius to model the impact of increased forest-related feedstocks relative to business-as-usual conditions (50 miles straight-line: Galik and Abt [2012]; 30

3 Fuelshed is defined as an aggregate of areas from which feedstock may be drawn for a specific facilit y.November 2014 C-6

miles road distance: Brinkman and Munsell, [2012]). Thus, fuelsheds are specific to stationary sources procuring biogenic feedstocks, but fuelshed areas for multiple facilities could overlap, and this could change over time as supply and market dy namics change, capital depreciates, and new facilities are built.

An approach at a comparable scale to fuelsheds migh t be a fixed geographic region that approximates the area of a fuelshed. For example, G alik and Abt (2012) note that 50-mile radius fuelsheds approximate the area of USDA Forest Servi ce Forest Inventory and Analysis (FIA) 4 survey units, which are fixed regions (aggregates of count ies) defined to provide forest inventory information at specified precision (USDA Forest Ser vice, 2014). A 50-mile radius circle encompasses about 7,850 square miles (slightly over 5 million acres). This is approximately equal in size to each of the five FIA units within the st ate of Virginia (Rose, 2009). It is also approximat ely equal to the area of New Jersey or Massachusetts, o r the total area of the three smallest states (Delaware, Connecticut, and Rhode Island) combined. Specification of predefined fuelshed-sized regions enables consistent estimation of biomass pr oduction and harvest within a region, but also means that some entities may need to acquire feedst ocks from multiple regions.

Again, assessment at small scales like the fuelshed level can directly link landscape emissions and sequestration to the use of biogenic feedstock of a specific stationary source, but also necessitates feedstock tracking and other documentation, especia lly for the retrospective reference point approach.

2.3.2. State
An advantage of using a state-level approach is tha t they often coincide with other administrative or reporting units. Forest harvests and agricultura l yield data can be tracked by state (for tax reporting purposes, for example). State boundaries might be logical when states may implement different policies and regulations pertaining to fe edstock production as well as commercial trade. However, certain small states (e.g., Rhode Island) may not be large enough to offer adequate or accurate data on biogenic carbon stocks (i.e., fore st growth and removals), thus rendering retrospective and future anticipated modeling unrel iable because the associated sampling errors are likely too large or model inputs would not be r eliable (Crocker et al., 2011). Furthermore, state lines are political boundaries and do not take into account similar landscape types from one state to the next. State lines can divide landscapes that sh ould be considered as a whole. As discussed earlier in this appendix, another potential difficu lty with defining spatial scale with a political boundary is assessment for biogenic feedstock trans fers across such boundaries because states may have different laws and regulations.

2.3.3. Regional
Establishing a regional spatial scale could aggrega te multiple states into one primary region of assessment. Here, the regional scale of assessment is large enough that accurate data are available (i.e., adequate statistical power), but still small enough to capture important differences in land

4 For more information on FIA, consult www.fia.fs.fed.us

.November 2014 C-7

base and therefore feedstock characteristics, such as growth and removal rates, decomposition rates, and species mix (Westfall et al., 2013). In other words, regions achieve a balance between preferred statistical precision of larger scale ass essments and ability to capture important land base (biogeochemical and ownership types) and market dri vers of smaller spatial scales. Regional assessment allows for important distinctions betwee n drivers of changes in land-based biogenic carbon sequestration and resource supply and demand that, using a reference point baseline, could potentially be masked at the national level. Howeve r, regional assessment potentially ignores stateor site-level impacts as well as indirect impacts i n other regions (which would be inherently captured by a national approach). Also, determining regional boundaries might be related to market characteristics, with multistate regions for ming coherent markets for biogenic feedstocks. 5

Galik and Abt (2012) provide a thorough evaluation of the impact of spatial scale on the GHG balance of biomass energy production from forest so urces. They considered assessment scales from individual sites to fuelsheds to the state level (f or the state of Virginia) and projected carbon dynamics for a 25-year time frame relative to a bas eline scenario. Their conclusion was that “those assessment scales that do not include possible mark et effects attributable to increased biomass demand, including changes in forest area, forest ma nagement intensity, and traditional industry production, generally produce less favorable GHG ba lances than those that do.” They further concluded that the larger spatial assessment scales (in this context, states and regions) “most closely approximate the actual GHG emission implica tions” for the scenarios and locations they modeled. However, it is important to note that in s ome cases the regional scale, like the national scale, can also mask important fluxes in landscape emissions. 6

Regions could be defined on the basis of homogeneit y of biophysical characteristics such as, in the case of forest-derived feedstocks, species types, g rowth rates, and climate. Regional boundaries must be drawn carefully to ensure the region is lar ge enough to offer adequate data accuracy and availability, yet small enough to better reflect la ndscape biogenic carbon dynamics. One difficulty with choosing this spatial scale is that each regio n can encompass multiple states with different laws and regulations. For example, states with stro ng renewable energy incentives (including renewable portfolio standards or state incentives) and high relative biomass use could drive

5 An example of a fixed regional framework is the EP A Emissions & Generation Resource Integrated Databa se (eGRID) region structure. EGRID is used for calcula ting GHG emissions related to electricity generatio n. Subregions nest within regions defined by North Ame rican Electric Reliability Corporation (NERC). Regi ons vary widely in size from small portions of an individual state to areas encompassing portions of seven larg e states. For more information on NERC and eGRID regions, consult http://www.epa.gov/egrid.
6 Depending on the spatial scale considered, changes in forest carbon stock can be dramatically differe nt, as illustrated by the impact of hurricane Hugo on Sout h Carolina’s (SC) forest resources. In 1989, Hugo h it SC and caused extensive damage to the state’s forests. The hurricane reduced the inventory of softwood (e.g., pine) growing stock by 21% or 1 billion cubic feet (Sheffield and Thompson, 1992), which is equivalent to more than 2 years of the previous average forest harvest across the enti re state (Tansey, 1986). After the hurricane, the r emovals of softwood timber in the state exceeded the net growt h by 43% (Conner, 1993), whereas before the hurrica ne net growth exceeded removals by 2% (Tansey, 1986). Howe ver, in the subsequent assessment of forest resourc es (Haynes et al., 1995), southern softwood net growth exceeded harvests. Thus, the deficit situation in SC resulting from the hurricane impact was not observed in the l arger region of the south and applying regional sou thern assumptions regarding balance between growth and re movals to SC could have led to additional pressure on the

resource.November 2014 C-8

landscape biogenic feedstock removals and associate d emissions fluxes for an aggregated region. Landscape emissions impacts in neighboring states i n the same region could be modest, but a regional assessment could reveal large landscape em issions changes due to policy actions in one

state.

2.3.4. National
The next largest spatial scale possible for estimat ion and reporting would be national. Although a global assessment scale is certainly possible, the highest level of spatial aggregation evaluated in this report is national. The key advantages of a na tional-level assessment are that it captures market interactions, including domestic leakage eff ects, and offers high-level insights concerning general emissions fluxes from U.S. carbon stocks in forests and agricultural landscapes. The market interactions component is especially critical, espe cially for the anticipated baseline approach. A regional assessment of a biogenic feedstock demand shock may not capture emissions changes outside of the assessment region as markets adjust to the shock and production expands or contracts elsewhere. A national assessment using an anticipated baseline modeling approach would capture these interactions and indirect emissions i mpacts. Furthermore, evaluating landscape emissions in response to a national policy could ju stify a national assessment scale (Latta et al., 2013).

At the national scale, observing or projecting emis sions fluxes from managed terrestrial systems (i.e., from U.S. forests and agricultural lands) ca n be accomplished using published datasets such as the U.S. GHG Inventory and/or models designed to pr oject emissions from land management activities. At this assessment scale, however, quan tifying the relationship between the actions of an entity using biogenic feedstocks (or a group of suc h entities) (i.e., biogenic feedstock demand) and the carbon dynamics of the feedstock production sit e (which is defined nationally) (i.e., biogenic feedstock supply) could be difficult, especially fo r certain feedstocks. Assessing such causality at this scale is difficult as it is hard to differenti ate between this driver (biogenic feedstock demand) and other influences on the national landscape (e.g ., urbanization, natural disturbances). Also, reporting changes in biogenic CO 2 fluxes at the national scale could mask important regional differences in landscape and feedstock characterist ics such as growth rates, species composition, and other environmental conditions, especially when applying a reference point baseline approach.

For example, if one is interested in carbon stock c hanges associated with a particular forest harvest, reporting and considering the effects of the harves t at a national scale would likely reveal little or no measurable impact on overall carbon stocks at th e national level. However, by normalizing the impacts (e.g., CO 2 e per ton of feedstock harvested), the national lev el results can be informative and account for certain impacts that could be lost in a regional-scale analysis (e.g., inter-regional, domestic leakage effects). When using a retrospecti ve approach one might need to establish a causal statistical relationship between the harvest under consideration and resulting emissions changes elsewhere. Ultimately, carbon stocks may be declining in some areas but increasing at a higher rate in other areas, regardless of whether a reference point or future anticipated baseline approach was applied and regardless of biogenic fee dstock demand for energy purposes. Reporting changes in carbon stock at the national scale would mask important regional differences in terms of harvest and growth rates, as well as species compos ition, and climate. The result of a national scaleNovember 2014 C-9

assessment is that the evaluation of one harvest ac tivity could have a very minor or statistically indistinguishable impact on overall national carbon stocks.

Were this same forest harvest reported on a fuelshe d scale (the area required to provide continuous forest-derived biogenic feedstock to a s pecific end user) instead of a national scale, it likely would have a measurable impact because of th e smaller area under consideration. However, this impact would potentially ignore other market a djustments and landscape impacts at the state or regional scale. The actual harvest itself is the same in both scenarios, but the measured impact would be different because of the choice of assessm ent spatial scale.

Similarly, waste-derived materials also may have so me regional variability, including the composition of waste (which can vary from community to community within a region) and regional climate factors that affect methane (CH 4 ) oxidation via cover soils at managed landfills (B ogner et al., 2007; EPA, 2009; Spokas and Bogner, 2011). How ever, there is a lack of literature describing the degree to which composition of municipal solid wast e (MSW) can vary from region to region, and thus this analysis uses a national average composit ion based on EPA data through 2012 in the illustrative calculations in Appendix N (EPA, 2014) . Although composition of MSW may vary from region to region, this mainly contributes to potent ial generation amounts of CO 2 and CH 4 in a given landfill, whereas the goal of the framework methodo logy for waste-derived feedstocks is ultimately concerned with how

the CO 2 and CH 4 from MSW are treated and used in one activity vers us another. From this perspective, CO 2 and CH 4 from MSW can be treated similarly across the Unite d States.



### 2.4. Spatial Scale in the Framework

A spatial scale should be small enough to recognize changes (e.g., carbon stock changes, emissions fluxes), drivers, and trends and large enough to of fer accurate data and be capable of dealing with large stochastic events such as storms. It should h ave the ability to recognize cross-boundary flows. Too large an area and important local or regional t rends could be masked; too small an area and limited data will preclude accurate estimation or w ould overestimate or underestimate the net landscape emissions impacts by ignoring changes in land management at a regional scale. The spatial scale should be determined by a trade-off b etween the statistical precision and data availability for larger regions, against the local specificity and accurate depiction of biophysical attributes of smaller regions.

Ultimately, the choice will depend on the specific context and program, and it may be possible to use different or nested spatial scales within the s ame set of analyses. 7 This framework explores the regional scale further in the sections below to der ive proof-of-concept values.

7 This framework can be customized so individual ent ities using biogenic feedstocks can derive and inpu t entityspecific values into the framework’s equation to ca lculate an individualized BAF (see the main report Part 3 for more on customized feedstock approaches). However, in some policy or program applications or for some entities, this customized approach will not be appropriate or feasible so the framework can be applied at differ ent scales.November 2014 C-10

2.4.1. Assessment at the Regional Scale

The location of regional boundaries should reflect land base ch aracteristics and the spatial distribution of biogenic feedstock characteristics such as species, rates of productivity, similarity of management practices, ownership patterns, and marke t attributes. Regional boundaries can coincide with other administrative or reporting uni ts, because this may increase the likelihood that other relevant data or model outcomes would be summ arized for the regions. Because it is further likely that forest harvests would be tracked by sta te (for tax reporting purposes, for example), the use of state boundaries as regions, where possible, may be advantageous.

The size of the regions should be determined by a trade-off between the statistical and modeling precision offered by larger regions with improved b iophysical information and local specificity of smaller regions. However, the practical implementat ion of an assessment framework must also be a consideration: it is recognized that at larger spat ial scales, implementation becomes simpler.

The actual regional delineations applied to the ref erence point and future anticipated baseline supporting appendices apply slightly different regi onal scales, as discussed below.

Figure C-1. RPA Regions (USDA Forest Service, 2012)

.

2.4.1.1. RPA Regions
For the retrospective reference point baseline appr oach illustrative examples for forest-derived feedstocks provided in subsequent appendices, the r egions follow the region boundaries developedNovember 2014 C-11

by the USDA Forest Service for the Resources Planni ng Act (RPA), resulting in eight regions as shown in Figure C-1.

The RPA regions are based on publicly available dat a on forest resource stock dynamics (inventory, growth, harvest) generated by the FIA program of US DA Forest Service. These regions are designed to reflect the spatial distribution of forest chara cteristics (forest types, rates of productivity, management practices) and follow state boundaries t o increase the likelihood that other relevant data are reported at the same administrative level. The RPA regions form the basis for a wide array of reports on forest resources conditions, markets, and trends (see USDA Forest Service, 2012).

For forest-based operations, one important source o f publicly available data on forest stock dynamics (inventory, growth, harvest) comes from th e FIA. FIA collects and provides information on hundreds of thousands of sample plots nationwide on all types of forest ownerships. FIA data can be used to assess availability of forest-derive d feedstocks and estimate production of harvest residues (Conner and Johnson, 2011; Johnson, 2001). These data can be used to estimate several terms in the BAF

equation using the reference point baseline approa ch.

For forest-derived feedstocks, the smallest spatial scale at which FIA data are reliable may be somewhat larger than a fuelshed. It is possible to use FIA data to narrow down the forestland base within prospective regions to a working forest for each region and then compute variables such as gross growth, removals, and excess growth, along wi th their sampling errors. Sampling errors for basic estimates of overall biomass may be within a few percent at this scale. However, sampling errors on other variables of interest—such as growt h and harvest—will be much higher. For example, the state of New Jersey is about 7,500 squ are miles in size, approximately the size of a 50- mile radius circle that might approximate a fuelshe d. In a recent report (Crocker et al., 2011), the sampling error for the volume of New Jersey’s growi ng stock was 4.6%, but sampling errors for growth and removals were 9.62% and 29.5%, respectiv ely.

Therefore, although the precision of the basic volu me estimate may be acceptable at fuelshed scales, growth and removal metrics related to the b alance of carbon emissions and sequestration will be less reliable, and for that reason larger s patial scales are preferred. At the RPA regional le vel, the spatial scale provides estimates within accepta ble uncertainty ranges.

2.4.1.2. FASOM-GHG Regions
The regional delineation used for the illustrative BAF equation applications for agriculture-derived feedstocks under the retrospective reference point baseline approach and for forest-derived and agriculture-derived feedstocks under the future ant icipated baseline approach (Appendices H through M) is the delineation as used within the U. S. FASOM-GHG. These 11 regions are shown in Figure C-2.November 2014 C-12

Figure C-2. FASOM-GHG Regions.
The FASOM-GHG regions are based on the underlying d atasets used in the FASOM-GHG model. 8 Many of the datasets are resolved at the state and/ or county level and aggregated up to the various FASOM-GHG regions for reporting. Forest-sector data are based on a number of relevant datasets, including the FIA. As noted, the FASOM-GHG regions are similar to the RPA regions in general size and geographical location. Specifically, the FASOM- GHG regions reflect areas that exhibit similar land characteristics, crop types, existing forest r esources, forest/crop yields, forest/agricultural management alternatives, soil types, rainfall, and climate patterns (see Beach and McCarl, 2010).



## 3. Conclusion

The RPA and FASOM-GHG regions are examples of spati al scales that address some of the tradeoffs previously discussed in this appendix. The regions are small enough to recognize trends and changes in growth and removals, yet large enough to offer widely available data and adequate statistical power. Furthermore, the RPA and FASOM-G HG regions are well established in the literature and not as complex as alternative region al delineations (e.g., the eco-regions previously developed by EPA for ecological applications [see B ryce et al., 1999]). The regions also largely follow state boundaries, which allows for easier re porting and greater recognition of crossboundary flows.

That said, although the RPA and FASOM-GHG regions a re used in this report to road test the framework, they are selected for illustrative purpo ses only. Ultimately, any final choice of regional

8 Additional information on the FASOM-GHG model and its application for the technical appendices of thi s report can be found in Appendix L.November 2014 C-13

delineation is a decision for policy makers and sho uld reflect the requirements for a particular
program.


## 4. References

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