U.S. Forest Products Module

A Technical Document Supporting

the Forest Service 2010 RPA Assessment

Peter J. Ince, Andrew D. Kramp, Kenneth E. Skog,

Henry N. Spelter, and David N. Wear
September 2011

Ince, Peter J.; Kramp, Andrew D.; Skog, Kenneth E.; Spelter, Henry N.; 

Wear, David N. 2011. U.S. Forest Products Module: A Technical Document 

Supporting the Forest Service 2010 RPA Assessment. Research Paper FPLRP-662. Madison, WI: U.S. Department of Agriculture, Forest Service, 

Forest Products Laboratory. 61 p.

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sions about future trends in wood energy demands and U.S. 

timber markets based on USFPM and GFPM projections. A 

key finding is that projected future trends in U.S. consumption, production, and net trade in forest products are heavily 

influenced by assumptions about future expansion in U.S. 

and global wood energy demands. The projected effects of 

expansion in U.S. and global wood energy consumption are 

to dampen growth in forest product consumption (because 

of price impacts on demands) but also to provide greater 

comparative advantages and enhanced net exports for U.S. 

producers of forest products (because of price impacts on 

foreign producers). Prodigious expansion in wood energy 

demands could cause significant escalation in real U.S. timber prices, but on the other hand, real U.S. timber stumpage 

prices are not projected to increase without fairly substantial 

increases in wood energy consumption. 

Keywords: forest biorefinery model, economic feasibility, 

biomass gasification, biofuels

Abstract

The U.S. Forest Products Module (USFPM) is a partial market equilibrium model of the U.S. forest sector that operates 

within the Global Forest Products Model (GFPM) to provide 

long-range timber market projections in relation to global 

economic scenarios. USFPM was designed specifically for 

the 2010 RPA forest assessment, but it is being used also in 

other applications. Within the GFPM framework of global 

forest product markets and trade, USFPM models aggregate 

U.S. forest product demands and regional forest product 

production, timber harvest, and timber stumpage markets 

in three U.S. subregions: North, South, and West. In each 

subregion, USFPM models timber stumpage markets for 

four categories of timber, including hardwood and softwood 

sawtimber and hardwood and softwood non-sawtimber. 

USFPM models regional timber harvest and transport activities as the conversion of timber stumpage supplies into delivered timber product outputs (sawlogs/veneer logs, pulpwood/composite timber, other industrial roundwood, and 

fuelwood) plus logging residues. USFPM also adds more 

complete product detail in U.S. regions, by differentiating 

hardwood lumber and softwood lumber, OSB structural 

panels and industrial particleboard, and hardwood and softwood plywood. In addition, USFPM models potential future 

supplies of agricultural short-rotation woody crops (SRWC) 

for energy and fiber, along with wood residue byproducts 

of sawmills and plywood mills, and the utilization of those 

materials in production of pulp, wood panels, and energy. 

USFPM thus models all forms of wood biomass feedstock 

that could be used potentially for future energy production in the United States, including fuelwood harvest, other 

roundwood, mill residues, agricultural SRWC, and logging 

residues. This report describes the structure of USFPM in 

detail and presents some examples of results and concluContents

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

Background...........................................................................1

U.S Forest Sector Structure in USFPM ................................3

USFPM Input Data and Parameters......................................7

 Timber Supply...................................................................7

 Timber Harvesting Activities............................................13

 Agricultural Short-Rotation Woody Crop Supply.............14

 Supply of Recovered Paper and Other Fiber Pulp ............15

 Forest Product Production Data and Parameters ..............16

 USFPM Trade in the GFPM Context................................18

 End Product Demand Quantities and Elasticities .............19

GFPM Modifications for USFPM ........................................23

Alternative Scenarios for USFPM/GFPM ............................24

 2010 RPA Global Scenarios..............................................24

 RFS+RES Scenarios.........................................................28

Results...................................................................................31

 U.S. Wood Fuel Feedstock Projections.............................31

 Forest Product Projections................................................33

U.S. Timber Harvest and Market Projections.......................37

Summary and Conclusions ...................................................40

Acknowledgments.................................................................43

References.............................................................................43

Additional Data References..................................................46

Appendix A—Global Forest Products Model (GFPM) 

Formulation and Structure ....................................................47

Appendix B—USFPM/GFPM Projections of Global 

Fuelwood Demands ..............................................................52

English Conversion factor SI unit

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.305 meter (m)

acre 4,046.86 square meter (m2)

Conversion Table
U.S. Forest Products Module

A Technical Document Supporting the 

Forest Service 2010 RPA Assessment

Peter J. Ince, Research Forester, Forest Products Laboratory

Andrew D. Kramp, Associate IP Consultant, University of Wisconsin-Madison

Kenneth E. Skog, Supervisory Research Forester, Forest Products Laboratory

Henry N. Spelter, Economist (ret.), Forest Products Laboratory

David N. Wear, Supervisory Research Forester, Southern Research Station

Introduction

The Forest and Rangeland Renewable Resources Planning 

Act of 1974 (RPA) mandates that the U.S. Forest Service 

shall develop nationwide assessments of forest resource 

demand, supply, and forest resource conditions every 

10 years. Since the 1970s, the Forest Service has developed and applied forest sector market models to produce 

long-range forest resource projections for the periodic RPA 

Assessment. The TAMM-NAPAP-ATLAS (TNA) modeling system evolved as the primary tool used for projecting 

North American timber trends in RPA assessments from the 

late 1970s to 2005 (Adams and Haynes 2007). For the 2010 

RPA forest assessment, the Forest Service conceptualized a 

new modeling system consisting of a group of sub-models 

or modules collectively called the United States Forest Assessment System (USFAS).

This report describes the U.S. Forest Products Module 

(USFPM), a partial market equilibrium model that operates 

within the Global Forest Products Model (GFPM) to produce long-range projections of U.S. forest product markets 

and regional U.S. timber markets in the context of global 

economic scenarios for the RPA Assessment. We designed 

USFPM also to operate within the GFPM, a recursive 

dynamic spatial market equilibrium model of production, 

consumption, trade, and prices for all major forest products 

in 180 countries (Buongiorno and others 2003). The concept 

of developing USFPM within the GFPM originated several 

years ago (Ince and Buongiorno 2007), and USFPM was 

subsequently developed at the U.S. Forest Products Laboratory (FPL) in Madison, Wisconsin. 

We designed USFPM to model U.S. forest product markets 

and regional U.S. timber stumpage markets as part of 

USFAS. Specifically, we designed USFPM to be linked to 

the separate USFAS forest dynamics model developed at the 

USDA Forest Service Southern Research Station to simulate 

dynamics of forest growth and changes in forest inventory 

by forest survey plot, including projected timber harvests in 

relation to projected regional timber prices, and forest plot 

conditions, and associated land use changes.

In the next section of this report, we begin by providing 

some background on development of USFPM in the RPA 

context, and then in subsequent sections we describe in 

more detail the structure of the U.S. forest sector as represented in USFPM, including USFPM input data and parameters. Data inputs or assumptions can be varied to produce 

alternative scenarios for projected U.S. market equilibria 

and trade flows in the GFPM framework. We explain data 

and assumptions for several alternative global economic 

scenarios examined by the 2010 RPA. These scenarios were 

based on global economic scenarios developed by the Intergovernmental Panel on Climate Change (IPCC). We also 

describe another set of scenarios that we developed to project near-term market impacts of alternative biomass energy 

policies in the United States. Last, we present examples of 

results in the form of USFPM/GFPM projections for various 

scenarios in comparison with historical data trends, and we 

present conclusions and a summary based on those results. 

Details regarding IPCC storylines and their adoption in the 

RPA Assessment framework are described in USDA Forest 

Service (In preparation).

Background

RPA Assessments and supporting research are mandated by 

Congress in the Forest and Rangeland Renewable Resources 

Planning Act of 1974 (PL 93-378, United States Code Title 

16). The RPA Assessment mandate is recognized also in the 

National Forest Management Act of 1976, the Forest and 

Rangeland Renewable Resources Research Act of 1978, 

and subsequent amendments (United States Code Title 16, 

Chapter 36). RPA Assessments are required every 10 years. 

RPA forest assessments are periodic research reports prepared by the U.S. Forest Service that describe in detail the 

nationwide forest resource situation, with long-range projections of timber market and forest resource trends that extend 

50 years into the future. The accomplishments of RPA forest 

sector market modeling are well documented in a recently 

published book (Adams and Haynes 2007), and in previous RPA timber assessment reports (e.g., Haynes and others 

2007, Haynes 2003).
Research Paper FPL–RP–662

2

The GFPM was developed in the 1990s at the University of 

Wisconsin-Madison (UW). The GFPM has been used by the 

Food and Agriculture Organization of United Nations (FAO) 

and other organizations (Buongiorno and others 2003). The 

current version of the GFPM is based on QPELPS, Quadratic Price-Endogenous Linear Programming System 

(Zhu and others 2006). The GFPM projects production, 

consumption, trade, and prices for forest products among 

180 countries, with 14 products that include industrial 

roundwood, fuelwood, sawnwood, several categories of 

wood panels, several categories of paper and paperboard, 

and also intermediate wood fiber products such as wood 

pulp and recycled paper. The GFPM is a spatial market 

equilibrium model with endogenously derived shifts in 

timber supplies and exogenously specified shifts in product 

demands. It calculates spatial market equilibria among all 

countries linked by trade in a base year (2006) and in subsequent years over a multi-decade projection period. The primary source of market quantity and price data for the GFPM 

is the FAO online statistical database, FAOSTAT (http://

faostat.fao.org/site/626/default.aspx#ancor).

The U.S. Forest Assessment System (USFAS) was conceptualized and designed as a new modeling framework for 

the 2010 RPA forest assessment (Wear 2011). USFAS was 

designed to link analyses in three separate domains: Forest 

Uses, Forest Dynamics, and Ecosystem Services. Figure 1 

illustrates conceptually the three domains of USFAS. The 

Forest Uses Domain focuses on timber demand, prices, 

and consumption, while also projecting future land uses in 

relation to projected timber markets and other economic assumptions. The Forest Dynamics Domain focuses on simulating resource management decisions such as timber harvest in relation to price and simulating biological and physical development of forest resource conditions in response to 

projected changes in forest uses and the environment (such 

as projected climate change, for example). The Ecosystem 

Services Domain focuses on translating projected changes 

in forest conditions and land use into meaningful estimates 

of effects on the ecosystem services of forests such as water 

resources, biodiversity, and carbon storage. The USFPM 

operating within the GFPM is intended to be an integral 

element of the Forest Uses Domain, providing projections 

of U.S. and global wood product markets and regional U.S. 

timber market trends, and interacting via market projections 

with the Forest Dynamics Domain and Forest Ecosystem 

Services Domain.

Figure 2 further illustrates how RPA models and analysis 

were organized to produce projections of future forest resource trends in support of the 2010 RPA Assessment. RPA 

future scenarios were derived largely from IPCC and included basic economic assumptions (e.g., U.S. and global GDP 

growth), technology assumptions (e.g., U.S. and global expansion of biomass energy production), population growth 

assumptions, future climate data, and forest inventory data, 

including Forest Service Forest Inventory and Analysis 

(FIA) data for the United States and FAO global forest inventory data. The RPA scenario assumptions are fed into 

the RPA models, which include the USFPM/GFPM 

Forest Dynamics Domain

Forest area 

Forest conditions

Forest Use Domain

Land uses

Wood product markets

Resource management

USFAS

United States

Economy

Global economy Climate

Forest Ecosystem

Services Domain

Carbon

Wildlife 

Water

Trade

Figure 1—Conceptual structure of U.S. Forest Assessment System (USFAS).
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

3

forest product and timber market models, the Forest Dynamics Model that models dynamics of U.S. forest growth, forest inventory, and timber harvests, and the Land Use Model, 

which projects changes in land use in response to urbanization as driven by projected population and income changes. 

The RPA models are used to analyze the scenarios and project future U.S. forest uses and conditions, including future 

wood product output, regional timber harvests, and timber 

market trends, changes in U.S. forest inventory, and changes 

in U.S. forest land use. The RPA scenarios and projected 

changes in forest uses and conditions are then used to project trends in forest ecosystem services in the United States, 

including for example water resource projections, wildlife 

projections, and forest carbon projections.

U.S. Forest Sector Structure in 

USFPM 

The USFPM provides much more regional and commodity detail for the United States than the UW version of the 

GFPM. In the UW version of the GFPM, each country 

including the United States, is represented as just a single 

region. In building USFPM within the GFPM, we started 

by creating several U.S. subregions in order to model U.S. 

timber markets in more complete regional detail as required 

for RPA. USFPM is thus basically an expansion of the U.S. 

market region within the GFPM. We also added a new timber commodity structure for U.S. subregions, featuring a 

more complete representation of timber stumpage supply 

and harvest activities. Because USFPM is entirely integrated into the GFPM, we run an entire global analysis of 

trade with GFPM whenever we run USFPM. For all other 

countries, we generally retained the existing structure of 

the GFPM along with the original GFPM data for all the 

other countries, although we modified the global fuelwood 

demand assumptions to reflect IPCC biomass energy projections. In essence, USFPM fits a more disaggregated regional 

U.S. forest sector market model for the RPA assessment into 

the broader global modeling framework of the GFPM. For 

reference, note that the UW still maintains the original version of the GFPM (which does not contain USFPM) and of 

course, the UW version of GFPM can be run independently 

without USFPM, although results and data assumptions 

are different (such as assumptions about global fuelwood 

demands).

The structure of USFPM reflects important diverse elements 

of the U.S. forest products industry and unique requirements 

of RPA assessments. In U.S. regions for example, unlike 

the original GFPM, USFPM differentiates hardwood timber 

from softwood timber, and also differentiates hardwood 

lumber from softwood lumber, OSB structural panels from 

industrial particleboard, and hardwood plywood from softwood plywood. In addition, USFPM models logging residues as byproducts of timber harvest activities, and wood 

mill residues as byproducts of sawmills, plywood mills, and 

pulp mills, and it models current and future use of those 

residues in production of pulp, wood panels, and energy. 

Note that assessment of potential for increased use of forest 

and wood product wastes (i.e., wood residues) is among the 

specific requirements of RPA legislation, which necessitates 

many of the unique structural features of USFPM 

RPA future scenarios RPA models

Projected forest uses

and conditions

Projected forest

ecosystem services

Climate data

Forest

inventory data

U.S. Forest

products

model

(USFPM/GFPM)

Forest 

dynamics 

model

Land 

use

model

Wood

products 

projections

Forest

inventory

projections

Land use

projections

Water

projections

Wildlife 

projections

Carbon

projections

Economic 

assumptions

Technology

assumptions

Population

assumptions

Figure 2—RPA models and analysis in support of the 2010 RPA Assessment.
Research Paper FPL–RP–662

4

(Renewable Resources Planning Act of 1974 (RPA). Title 16 

United States Code §1600(c)(2)). 

In USFPM, we model timber supply, timber harvest, and 

forest product production in each of three U.S. subregions: 

North, South, and West. Figure 3 shows state boundaries of 

the three U.S. subregions. End product demand of the United States is modeled at the national level as a single demand 

region, obtaining products via shipments from U.S. subregions and via imports from the rest of the world (as modeled 

by the GFPM). In addition, roundwood, recovered paper, 

and wood pulp intermediate products can be shipped from 

one U.S. subregion to another, and each U.S. subregion also 

exports end products to the rest of the world. 

The timber commodity structure of the United States as 

represented in the U.S. subregions of USFPM was designed 

to reflect the basic structure of regional U.S. forest resource 

and timber utilization data as reported by the U.S. Forest 

Service for the 2010 RPA Assessment (Smith and others 

2009). The Forest Service resource data derive from national compilations of state-level forest surveys conducted 

by Forest Inventory and Analysis (FIA) researchers. In 

USFPM, we use FIA regional data on timber inventory 

volumes and harvests by timber species group (hardwood 

and softwood) and by merchantability class (sawtimber and 

non-sawtimber), and we also use FIA data on timber harvest 

volumes and corresponding timber product output volumes 

that are used as raw materials for specific forest product 

categories, including lumber, veneer, wood pulp, composite 

wood products, miscellaneous wood products, and 

fuelwood. 

In addition, we expanded the forest product structure in 

USFPM by disaggregating several GFPM end products 

within U.S. regions to provide a more complete analysis 

of hardwood and softwood timber markets. Expansion of 

the GFPM end product categories in USFPM is illustrated 

in Figure 4. Within U.S. regions, we expanded the “sawnwood” product of the GFPM into hardwood lumber and 

softwood lumber, and we expanded “plywood/veneer” into 

softwood plywood/veneer and hardwood plywood/veneer. 

In addition, “particleboard” was expanded into oriented 

strandboard (OSB) and industrial particleboard. These disaggregated USFPM end products actually have rather different end use markets and distinctly varied wood raw material 

input requirements, which we recognize in USFPM. We 

facilitate USFPM trade in the disaggregated products with 

other GFPM countries by aggregating products into GFPM 

product categories for U.S. export and disaggregating U.S. 

imports, using product shares obtained from recent U.S. International Trade Commission (USITC) trade data (product 

shares could be changed but in this report they are held constant across scenarios and over time).

We model timber harvest and transport activities in USFPM 

as the conversions of sawtimber and non-sawtimber stumpage by species group into four aggregated categories of 

West

North

South

AK

OR

WA

ID

MT

WT

ND

SD

CA

NV

AZ

UT

CO

NM

KS

NE

MN

WI

IA

IL

MI

IN

MO

OH

WV

PA

NY

VT ME

NH

MA

RI CT

NJ

DE

MD DC

FL

AL GA

LA TX

OK AR

MS

KY

TN

VA

NC

SC

HI

Figure 3—Three U.S. Forest Products Module (USFPM) supply and production subregions: North, South, 

and West.
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

5

delivered timber product outputs: (1) sawlogs/veneer logs, 

(2) pulpwood/composite, including timber for pulp and 

composite wood products, (3) other industrial roundwood, 

including posts, poles, pilings, and miscellaneous products, 

and (4) fuelwood. To maintain precise consistency with FIA 

data, we model regional U.S. timber supply in USFPM by 

precisely the same timber species groups and merchantability classes as described in the FIA data, with base-year 

(2006) USFPM regional timber harvest volumes and timber 

product output data calibrated precisely to the FIA data 

(Smith and others 2009). 

The Forest Service RPA forest resource data specifically 

define sawtimber and non-sawtimber in terms of tree size 

class and merchantability. Sawtimber is defined as trees that 

appear capable of yielding sawlogs, and greater than 9-in. 

dbh for softwood trees and greater than 11-in. dbh for hardwood trees (Smith and others 2009). Smaller trees, trees that 

do not appear capable of yielding sawlogs, or non-growing 

stock trees (live cull trees and dead trees) are categorized 

as non-sawtimber. In actuality and as programmed into harvest activity parameters of USFPM (based on FIA timber 

harvest data), sawlogs are recoverable from both sawtimber 

and non-sawtimber, although of course the recovery ratio 

of sawlogs is much higher from sawtimber trees than from 

non-sawtimber trees, whereas recovery of pulpwood and 

fuel feedstock is proportionately higher from non-sawtimber 

trees. 

In USFPM, each U.S. subregion supplies four categories of 

timber stumpage: hardwood sawtimber, softwood sawtimber, hardwood non-sawtimber, and softwood non-sawtimber. 

These are converted by harvest activities into four USFPM 

categories of delivered timber product outputs (sawlogs/

veneer logs, pulpwood/composite, other industrial 

roundwood and fuelwood) plus logging residues (which can 

be used as fuel, although residue recovery adds to harvest 

costs). All harvest data and conversion factors match FIA 

timber harvest data (Smith and others 2009). The industrial 

timber product outputs (sawlogs/veneer logs, pulpwood/

composite, and other industrial roundwood) are inputs to 

USFPM forest product manufacturing activities. By contrast, the GFPM does not model timber harvest activities. 

In all other countries in the GFPM, wood supply consists 

of three delivered commodities: “industrial roundwood,” 

“other industrial roundwood,” and “fuelwood,” undifferentiated by species group or source of timber, and calibrated 

to FAO roundwood and fuelwood production data. Thus, 

a more complete model of the timber supply chain for the 

United States is represented in USFPM than in the original 

GFPM, including timber stumpage markets, sawtimber, and 

non-sawtimber harvest activities, and conversions into recognized categories of delivered timber product outputs plus 

logging residues. 

The addition of timber supply detail with regional timber 

harvest and product recovery calibrated to FIA forest resource data (Smith and others 2009) are important features 

of USFPM in the RPA assessment context because it allows 

for analysis of potential for increased wood residue utilization (a legal requirement of the RPA), and it allows for a 

more precise linkage of USFPM (and the GFPM) to the 

USFAS forest dynamics model, which models dynamics of 

U.S. forest growth, forest inventory, and timber harvests by 

species group and merchantability class based on FIA forest survey data. The plot transition model is designed, for 

example, to simulate regional forest growth, forest inventory, and harvests of hardwood and softwood sawtimber 

and non-sawtimber by forest plot in relation to regional 

timber stumpage prices by species group while accounting 

for climate, natural succession, and land-use change effects. 

USFPM provides projected regional timber stumpage demand and prices for the forest dynamics model, and in turn 

the forest dynamics model provides to USFPM the regional 

timber supply functions in terms of projected price elasticities of timber stumpage supply and projected shifts in timber 

stumpage supply curves. An iterative solution procedure can 

be used to derive convergent solutions of USFPM and the 

forest dynamics model for specific RPA scenarios.

Another feature we added to USFPM was the potential 

future supply of agricultural SRWC. We model future agricultural SRWC supply by region using regional estimates 

of feasible delivered wood costs and crop yields, along with 

assumptions regarding limits (upper bounds) on available 

cropland acreage for SRWC. The agricultural SRWC supply 

represents potential future supply of tree crops that could 

be grown on agricultural land (as opposed to forest land). 

Based on historical yield studies with hybrid poplars on 

agricultural land, the future SRWC wood harvest volume 

was specified to be 75% pulpwood and 25% fuelwood, but 

Sawnwood

Plywood/veneer

Particleboard

Fuelwood

GFPM USFPM

Softwood lumber

Hardwood lumber

Softwood plywood/veneer

Hardwood plywood/veneer

Oriented strandboard (OSB)

Industrial particleboard

Fuel feedstock (broader)

Other end products (identical in both models):

Fiberboard

Newsprint

Printing and writing paper

Other paper and board

Other industrial roundwood

Figure 4—USFPM expansion of the GFPM end product 

categories.
Research Paper FPL–RP–662

6

alternatively all of the SRWC harvest can go to fuel feedstock depending on projected market demands (e.g., if the 

projected price of fuel feedstock exceeds pulpwood price). 

Figure 5 compares the U.S. regional wood supply structure 

that we developed in USFPM with the nationwide wood 

supply structure of the original GFPM, where nationwide 

supply functions represent delivered industrial roundwood, 

fuelwood, and other industrial roundwood. Note that the 

GFPM also models change in forest stock and forest area, 

and how this influences industrial roundwood supply 

(Turner and others 2006). USFPM represents timber stumpage supply functions and tree harvest volumes by species 

group and by tree merchantability class (sawtimber and 

non-sawtimber), along with harvested and delivered timber 

product outputs (sawlogs/veneer logs, pulpwood, fuelwood 

and other industrial roundwood timber products). USFPM 

also adds logging residue and mill residue byproducts to 

the wood supply structure. All USFPM timber supply data, 

wood residue recovery data, and base-year solutions for 

timber harvest, wood residues, and timber product output 

quantities were calibrated precisely to RPA forest resource 

data on U.S. regional timber harvest volumes, timber product output volumes, and residue volumes, by species group 

(Smith and others 2009).

USFPM includes all primary categories of wood residues, 

identical in quantity and definition to FIA data: 

•	 logging residues generated as byproducts of timber harvest activities that require added cost to be recovered

•	 fiber residues from lumber and plywood/veneer production representing wood chips and coarse wood residues 

used conventionally along with pulpwood as raw material inputs to wood pulp, particleboard, or fiberboard 

production, and 

•	 fuel residues from lumber, plywood/veneer, and pulp production representing bark, wood fines, and other wood 

residues typically used as fuel. 

Fiber residues are an important feedstock to pulp, particleboard, and fiberboard products in the United States, whereas 

fuel residues accounted for around 60% of reported U.S. 

wood fuel feedstock consumption in the base year, 2006 

(Smith and others 2009). 

Logging residues (typically branches, broken stems, and 

other logging debris) are the volumes of wood removals 

currently left in the forest after timber harvest operations 

and are conventionally uneconomical to recover. Logging 

residue volumes are modeled in USFPM as byproducts of 

timber harvest activities, with regional output volumes calibrated to actual historical logging residue volume data from 

FIA. Future logging residues may be partially recovered and 

used as wood fuel feedstock in USFPM if future demand for 

wood fuel feedstock raises the price high enough to pay the 

extra cost of recovering logging residues. However, we 

Stumpage supply

GFPM

(not represented)

USFPM

Sawtimber trees

(softwood and hardwood)

Non-sawtimber trees

(softwood and hardwood)

(timber harvest and transport)

Agricultural SRWC

(softwood and hardwood)

Delivered wood/fiber Intermediate products

Industrial roundwood

Other industrial roundwood

Fuelwood

Sawlogs/veneer logs

(softwood and hardwood)

Pulpwood/composite

(softwood and hardwood)

Logging residues

Fuelwood

Other industrial roundwood

Wood pulp

Mill

fiber residues

Wood pulp

Fuel residues

End products (demands)

Lumber, wood panels

Paper and paperboard

Other industrial roundwood

Fuelwood

Lumber, plywood, and veneer

(softwood and hardwood)

Non-structural panels

Paper & paperboard

OSB

Fuel feedstock

Other industrial roundwood

(poles, posts, etc.)

Figure 5—Comparison of the GFPM and USFPM wood supply structures.
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

7

constrain future recovery of logging residues to not more 

than 60% of available residue volumes for reasons that are 

both economical (higher costs of recovering additional volumes) and practical in terms of forest management (leaving 

some residues in the forest for nutrient cycling, wildlife 

habitat protection, etc.) Note that the 60% recovery limit 

on logging residue volumes is not a suggested regulatory 

limit, nor does it imply that logging residue recovery above 

or below 60% would be infeasible or inappropriate in some 

cases. The assumption is simply a reasoned approximation 

of likely but unknown future limits on logging residue recovery, consistent with assumptions by Conner and Johnson 

(2011) and Perlack and others (2005).

In addition to disaggregating several GFPM end products 

and adding timber stumpage and wood residues to wood 

supply, we also added to USFPM the feature of “cascading” 

raw material substitution possibilities, which means allowing economic substitution of higher value timber product 

outputs for lower value materials if projected market conditions favor such substitution. Historically, higher value 

timber product outputs like sawlogs or veneer logs were 

too valuable to be used in place of lower value materials 

like pulpwood or fuelwood. However, in scenarios with 

increased demand for lower value products like fuelwood, 

their prices can increase enough to make such product substitution economical. Thus, USFPM allows sawlogs/veneer 

logs to be used as pulpwood/composite timber, and in turn 

pulpwood/composite, logging residues, and fiber residues 

can be used as fuel feedstock, but only if it becomes economical to do so (if projected equilibrium prices for fuel 

feedstock reach levels that permit economical substitution). 

The cascading (one-way) wood raw material substitution 

possibilities in USFPM are illustrated in Figure 6.

Cascading wood raw material substitution is a feature that 

enables USFPM to simulate economic substitution possibilities that could arise, for example, in scenarios with 

significant future expansion in wood biomass energy demand. Higher wood energy demands generally tend to 

increase projected market competition and prices for wood. 

If projected equilibrium prices for fuel feedstock increase 

sufficiently, then logging residues, pulpwood, agricultural 

SRWC, and fiber residue may be utilized for energy, and 

ultimately sawlogs/veneer logs will be too, but only if 

projected equilibrium wood energy demands and prices 

are high enough. Thus, in USFPM, wood energy demands 

potentially compete for the same wood resources as other 

wood products, and USFPM is designed to model the market consequences of such potential competition. 

We can also simulate hypothetical biomass supply policies 

in USFPM by constraining or enhancing the cascading substitution activities. For example, we could simulate policy 

restrictions on use of commercial timber for energy by constraining the use of sawlogs/veneer logs and pulpwood for 

energy, or we could simulate a policy promoting the use of 

logging residues by adding a subsidy value to the logging 

residue byproducts of harvesting activities. 

In addition, through our collaboration with developers of the 

GFPM at UW, researchers there followed our lead and have 

incorporated a similar cascading substitution possibility for 

all other countries in the GFPM, allowing industrial roundwood in the other countries to be substituted for fuelwood 

whenever it becomes economical to do so in the projection 

period. This feature has important implications for the 

future wood market outlook, as discussed later in the 

results section.

USFPM Input Data and Parameters 

In this section, we describe USFPM input data and model 

parameters in detail. The input data include U.S. regional 

supplies of timber (timber harvest volumes and prices), 

regional timber product output volumes and input/output 

coefficients for timber harvest activities, timber harvest 

costs, supplies of other fiber inputs used in the forest product sector (recovered paper used for recycling, mill residues, 

and non-wood pulp), forest product production volumes, 

manufacturing costs and fiber input requirements, and U.S. 

demand quantities and prices for forest products. Parameters 

of U.S. demands include price elasticities and elasticities 

with respect to national income (U.S. real GDP) and other 

demand drivers, whereas timber supply parameters include 

price elasticities and relationships to regional timber growth 

and timber inventory. In this section, we also explain how 

USFPM commodities are aggregated or disaggregated to 

model trade between the United States and the rest of the 

world in the GFPM framework.

Timber Supply

USFPM models regional timber supply by four categories of 

standing timber, differentiated by species group (hardwood 

and softwood) and by tree merchantability class (sawtimber 

and non-sawtimber). Sawtimber is defined by the Forest 

Service as live growing stock trees above a minimum size 

and capable of yielding sawlogs, specifically hardwood trees 

Sawlogs/veneer logs

Pulpwood/composite

Fuel residue

Fuel feedstock

Logging residue

Agricultural

SRWC

Fiber residue

Figure 6—Cascading wood raw material substitution 

possibilities in USFPM.
Research Paper FPL–RP–662

8

above 11-in. dbh and softwood trees above 9-in. dbh. Nonsawtimber refers to all other trees, including poletimber 

growing stock (smaller live trees) plus non-growing stock 

(live cull or dead trees). Timber harvest data show that 

harvest of sawtimber yields proportionately more sawlog 

and veneer log volume, whereas harvest of non-sawtimber 

yields less sawlog and veneer log volume and proportionately more pulpwood and fuelwood volume.

USFPM also models timber harvesting as activities that 

consume timber supplies and jointly produce four categories 

of timber product outputs: 1) sawlogs/veneer logs (which 

include both sawlogs and veneer logs), 2) pulpwood/

composite timber (which includes pulpwood for pulp mills 

plus timber for composite wood products such as OSB), 

3) other industrial roundwood, and 4) fuelwood. Timber 

product outputs represent timber raw materials harvested 

and delivered to market—e.g., sawlogs/veneer logs delivered to lumber and plywood mills, pulpwood/composite 

timber delivered to pulp mills and OSB mills, etc. 

Base-year (2006) regional U.S. timber harvest data and timber product output data in USFPM incorporate the industrial 

timber harvest data and fuelwood harvest data reported in 

the latest Forest Service RPA forest resources report (Smith 

and others 2009, table 39). The RPA data account primarily 

for domestic timber product outputs, whereas USFPM must 

account also for roundwood exports. Thus, we also added 

independent estimates of regional roundwood export volumes from the USITC trade database, http://dataweb.usitc.

gov/) as needed to more fully account for all regional U.S. 

timber harvest in USFPM. 

We used the Forest Service timber product output (TPO) 

reporting tool (RPA database available on-line at http://

srsfia2.fs.fed.us/php/tpo_2009/tpo_rpa_int1.php) to compile 

the basic data on regional timber harvest by sawtimber and 

non-sawtimber categories in 2006 for both hardwoods and 

softwoods and to determine corresponding volumes of timber product outputs (sawlogs/veneer logs, pulpwood/composite timber, other industrial roundwood, and fuelwood), 

plus corresponding logging residue volumes. Table 1 shows 

the 2006 U.S. regional timber product output and logging 

residue data from the TPO reporting tool for sawtimber and 

non-sawtimber harvest. 

Table 2 shows aggregated regional timber harvest data for 

2006, as reported in the RPA forest resources report, corresponding to regional sums of timber product outputs shown 

in Table 1 (not including logging residues). Regional totals 

of timber product output volumes in Table 1 precisely match 

regional timber harvest volumes as reported in the latest 

Forest Service RPA forest resources report (Smith and others 2009, table 39), although the TPO reporting tool was 

needed to disaggregate timber product output and logging 

residue data into the sawtimber and non-sawtimber categories as shown in Table 1. 

To measure regional roundwood, wood chip, and fuelwood 

import and export volumes, we obtained USITC trade volume data by U.S. Customs Districts. We aggregated the data 

to U.S. subregions similar to the way that published historical Forest Service regional trade data have been aggregated 

(Daniels 2008). Daniels (2008) shows that RPA regions 

place Maryland (MD) in the North and North Dakota (ND) 

in the West, whereas previous Forest Service compilations 

placed MD in the South and ND in the North (Daniels 

2008). Table 3 shows the distribution of U.S. Customs 

Districts by RPA region, used for aggregating USITC 

trade data. 

Table 1—Timber product output and logging residue 

volumes associated with regional sawtimber and nonsawtimber harvest activities (2006)

a

Sawtimber

harvest

(×103 m3

)

Non-sawtimber

harvest

(×103 m3

)

Hardwood Softwood Hardwood Softwood

U.S. North

Sawlogs/veneer logs 21,563 8,243 3,193 1,655

Pulpwood/composite 12,577 4,890 12,552 3,927

Other industrial 

roundwood

411 398 204 301

Fuelwood 3,087 80 12,292 841

Logging residue 5,829 771 23,580 7,135

U.S. South

Sawlogs/veneer logs 27,569 88,066 2,762 8,536

Pulpwood/composite 14,673 29,242 16,505 40,466

Other industrial

roundwood

216 2,691 204 1,130

Fuelwood 5,759 572 3,641 552

Logging residue 6,181 4,067 27,708 28,415

U.S. West

Sawlogs/veneer logs 2,360 68,617 113 4,910

Pulpwood/composite 1,380 3,255 42 300

Other industrial

roundwood

13 908 76 656

Fuelwood 26 1,420 2,674 8,924

Logging residue 292 4,926 1,261 18,473

a

Forest Service Timber Products Output (TPO), Smith and others 2009.

Table 2—Timber harvest volumes by timber 

category and species group (2006)a

Timber harvest (×103 m3)

U.S.

North

U.S.

South

U.S.

West

Hardwood

Sawtimber 37,638 48,218 3,779

Non-sawtimber 28,240 23,113 2,905

Softwood

Sawtimber 13,610 120,570 74,201

Non-sawtimber 6,724 50,684 14,791

a

Sources: Forest Service Timber Products Output (TPO) and 

RPA Forest Resources report (Smith and others 2009, table 

39).
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

9

Table 4 shows USITC trade volume data for roundwood, 

wood chips, and fuelwood, aggregated by species group 

(softwood or hardwood) and U.S. subregion (USITC 

roundwood data are in cubic meters, and wood chips and 

fuelwood data are in metric tonnes (t) dry weight basis). We 

compiled USITC trade data by Harmonized Tariff Schedule code, similar to previous Forest Service compilations 

(Daniels 2008), and not by Standard Industrial Classification 

(SIC) code. We combined exports data with Forest Service 

data on timber product outputs to more fully account for 

regional timber supply in USPM. We also included imports 

data in total wood consumption to more fully account for all 

wood inputs used in forest product production activities in 

USFPM (discussed later in more detail). 

Regional surveys provide Forest Service timber harvest and 

timber product output data (Smith and others 2009, table 

39), but survey scope and methods differ slightly among 

U.S. regions (North, South, and West). In the South, Forest 

Service timber product output data derive primarily from 

surveys of domestic mills but not exporters of roundwood. 

We recognized also that exports of roundwood, wood chips, 

and fuelwood from the South derive primarily from timber 

harvest, so we made adjustments to timber harvest for the 

South to account for exports of roundwood, wood chips, and 

fuelwood. Specifically, we added Southern export volumes 

from USITC (Table 4) to Southern timber product output 

volumes as reported in the RPA forest resources report 

(Smith and others 2009, table 39). We added Southern fuelwood exports to Southern fuelwood harvest, Southern wood 

chip exports to Southern pulpwood harvest, and Southern 

roundwood exports to Southern sawlog/veneer log and pulpwood harvest according to USITC specifications (USITC 

identifies pulpwood fractions of roundwood exports as 

shown in Table 4).

In other USFPM regions (North and West), Forest Service 

timber product output surveys include some roundwood 

export volumes, so we assumed that USITC roundwood and 

fuelwood export volumes were included in the timber harvest and timber product output data for the North and West 

as reported in the RPA forest resources report (Smith and 

others 2009, table 39). Also, unlike the South, wood chip 

exports in the other regions may derive from fiber residues, 

particularly in the West (where most pulpwood supply derives from mill residues). Thus, in the North and West, baseyear quantities of fiber residues available for domestic use 

in USFPM include regional fiber residue output volumes as 

reported in the RPA forest resources report (Smith and others 2009, table 42) minus the regional net exports of wood 

chips (Table 4). 

In addition, we used USITC roundwood, wood chip, and 

fuelwood trade data to specify in USFPM the base-year U.S. 

regional import and export volumes for roundwood and fuelwood, replacing the aggregated U.S. trade data from FAO 

that were in the original version of the GFPM. The aggregate U.S. import and export volumes reported by FAO for 

roundwood and fuelwood are nearly identical to the sums 

of the regional imports and exports from USITC. Although 

wood chip trade is not modeled in the GFPM framework, 

we adjusted base-year regional U.S. roundwood export volumes in USFPM to include wood chip exports derived from 

timber harvest (all wood chip exports from the South and a 

Table 3—Distribution by RPA region of U.S. customs 

districts, as applied in aggregating USITC trade data for 

USFPM 

North South West 

Baltimore, MD Norfolk, VA Columbia-Snake River 

Basin 

Portland, ME Wilmington, NC Seattle, WA 

St. Albans, VT Charleston, SC Anchorage, AK 

Boston, MA Savannah, GA Other West 

Providence, RI Tampa, FL San Diego, CA 

Bridgeport, CT Mobile, AL Nogales, AZ 

Ogdensburg, NY New Orleans, LA Los Angeles, CA

Buffalo, NY Port Arthur, TX San Francisco, CA 

New York, NY Laredo, TX Honolulu, HI 

Philadelphia, PA El Paso, TX Great Falls, MT 

Minneapolis, MN San Juan, PR Pembina, ND

Duluth, MN U.S. Virgin Islands 

Milwaukee, WI Miami, FL 

Detroit, MI Houston/Galveston, TX 

Chicago, IL Washington, DC 

Cleveland, OH Dallas/Fort Worth, TX 

St. Louis, MO Savannah, 

GA/Wilmington, NC, 

Norfolk, VA/Mobile, 

AL/Charleston, SC 

Table 4—Trade volume data for roundwood, 

wood chips, and fuelwood by species group 

and U.S. subregion (2006)a

Commodity 

U.S.

North 

U.S.

South 

U.S.

West 

Roundwood exports (×103 m3)

 Softwood roundwood 2,929 331 3,633

 Pulpwood fraction 463 240 32

 Hardwood roundwood 1,118 824 249

 Pulpwood fraction 216 398 16

Roundwood imports (×103

 m3)

 Softwood roundwood 117 40 2,253

 Pulpwood fraction 25 0 24

 Hardwood roundwood 252 9 10

 Pulpwood fraction 72 0 0

Wood chip exports (×103

 t, dry) 

 Softwood chips 202 106 881

 Hardwood chips 124 224 37

Wood chip imports (×103

 t, dry) 

 Softwood chips 15 1 38

 Hardwood chips 13 433 118

Fuelwood trade (×103

 t, dry) 

 Fuelwood exports 14 3 1

a

U.S. International Trade Commission (USITC). 
Research Paper FPL–RP–662

10

Table 5—USFPM base-year (2006) estimates of total timber product 

output and logging residue volumes associated with sawtimber and 

non-sawtimber harvest activities 

Commodity 

Sawtimber 

harvest 

(×103

 m3)

Non-sawtimber 

harvest 

(×103 m3)

Hardwood Softwood Hardwood Softwood 

U.S. North 

 Sawlogs/veneer logs 21,563 8,243 3,927 1,655 

 Pulpwood/composite 12,577 4,890 12,552 3,193 

 Other industrial 

 roundwood 

411 398 204 301 

 Fuelwood 3,087 80 12,292 841 

 Logging residue 5,829 771 23,580 7,135 

U.S. South 

 Sawlogs/veneer logs 27,956 88,149 2,801 8,544 

 Pulpwood/composite 14,860 29,342 16,715 40,605 

 Other industrial 

 roundwood 

216 2,691 204 1,130 

 Fuelwood 5,777 573 3,652 554 

 Logging residue 6,181 4,067 27,708 28,415 

U.S. West 

 Sawlogs/veneer logs 2,360 68,617 113 4,910 

 Pulpwood/composite 1,380 3,255 42 301 

 Other industrial 

 roundwood 

13 908 76 656 

 Fuelwood 26 1,420 2,674 8,924 

 Logging residue 292 4,926 1,261 18,473 

share of exports from other regions). This is a minor adjustment, because Southern wood chip exports have declined to 

fairly negligible volumes in recent years, and in the West, 

timber harvest accounts for only a small fraction of wood 

chip exports.

Table 5 shows the USFPM base-year data for timber product 

output and logging residue volumes associated with 

sawtimber and non-sawtimber harvest, which take into account Forest Service data on timber product outputs and 

logging residues (from Table 1), plus our additions of regional exports of harvested roundwood, wood chips, and 

fuelwood. 

Table 6 shows USFPM base-year estimates of total timber 

supply (or timber harvest) by species group and region and 

by sawtimber and non-sawtimber categories. Timber harvest 

volumes in Table 6 correspond to the total output of industrial timber products and fuelwood shown in Table 5, but do 

not include logging residues (which are conventionally not 

part of timber harvest). USFPM base-year timber harvest estimates (Tables 5 and 6) are somewhat higher than the Forest Service timber harvest data (Tables 1 and 2) because the 

USFPM data include our adjustments for regional exports 

of roundwood, wood chips, and fuelwood. 

In addition to regional timber supply quantities (Table 6), 

USFPM base-year timber supply data include regional sawtimber and non-sawtimber stumpage prices. There is no singular source of nationwide data on timber stumpage prices, 

although there are various regional timber price reporters 

and also various sources of nationwide data on delivered 

timber product output prices such as pulpwood. We considered a variety of sources on timber prices (Cochran 2007, 

International Woodfiber Report 2007, Log Lines 2006, 

Timber Mart North 2006, and Timber Mart-South 2006). 

For USFPM, we derived regional timber stumpage price 

estimates using a residual value method by calculating the 

average value of delivered timber product outputs recovered 

Table 6—USFPM base-year (2006) 

timber supply or timber harvest 

(excluding logging residue) by region, 

species group, and timber category 

(sawtimber and non-sawtimber) 

Commodity 

Timber

harvest 

(×103

 m3)

Hardwood Softwood 

U.S. North 

 Sawtimber 37,638 13,610 

 Non-sawtimber 28,240 6,724 

U.S. South 

 Sawtimber 48,810 120,754

 Non-sawtimber 23,373 50,833 

U.S. West 

 Sawtimber 3,779 74,201 

 Non-sawtimber 2,905 14,791 
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

11

from sawtimber and non-sawtimber in each region (Table 

5), and subtracting estimated harvest and delivery costs. 

Figure 7 shows our derived base-year regional stumpage 

price estimates for sawtimber and non-sawtimber. We adjusted USFPM harvest cost assumptions so that our derived 

base-year stumpage price estimates for sawtimber and 

non-sawtimber in the South would be closest in value to 

reported “sawtimber” and “pulpwood” stumpage prices for 

the Southern region (2006 stumpage prices as reported in 

Timber Mart-South). 

In general, timber supply models provide estimates of future 

timber harvests in response to price indices and other variables describing the condition of forest inventories. Supply 

relationships aggregate the response of multiple decision 

makers, in this case the response of forest land owners controlling forests with a variety of conditions (i.e., forest types, 

ages, productivity) and timber prices. 

Timber supply is estimated in two ways for the USFPM 

regions. For the South, supply functions are constructed by 

summarizing harvest results of the forest dynamics model 

simulations. Harvest responses are modeled using an econometric model of harvest choices for all FIA plots in the 

region across multiple price realizations for all 13 southern 

states (Polyakov and others 2010). These models estimate 

the probability of harvests as a function of potential revenues for harvest of two types (partial and full harvest) or for 

delaying harvest. These stochastic models are run several 

times to generate multiple estimates of harvests in response 

to future price conditions and the results are summarized 

using the aggregator function.

The regional response is then aggregated using constant 

elasticity models for each time period to provide estimates 

of the aggregate South-wide supply elasticities for the four 

categories of timber supply (hardwood and softwood sawtimber and non-sawtimber). Because the harvest model 

is embedded in the Southern forest dynamics model, the 

harvest choice models yield different supply outcomes in 

the various periods of the simulations. In effect, the price 

quantity relationship shifts in response to changes in the 

inventory that are caused not only by harvesting but also 

by changes driven by climate, forest succession, plot aging, 

and forest type changes. These supply shifts are summarized 

by horizontal shifts (shifts in the quantity intercept) of the 

constant-elasticity aggregation functions (see Table 7 for 

average projected supply shifts).

In the South, supply responses are also scaled by projections of land-use changes. For each RPA storyline (A1B, A2, 

and B2), population and income forecasts drive forecasts of 

urbanization at the county level. Resulting forest land-use 

changes are also influenced by the price path of timber products and each simulated price realization engages a land-use 

forecast consistent with that price path (Wear 2011). Supply 

functions for the South therefore depend on the population, 

income, and climate forecasts specified for the given storyline. In developing the timber supply models, we considered 

only one climate projection for each storyline. Our analysis 

indicated that removal forecasts were not substantially influenced by the climate model used. Gross changes in forest 

land area (2010–2060) are also summarized in Table 7.

For the North and the West, harvest choice modeling approaches comparable to the South proved infeasible for estimating timber supply. In the North, harvest models yielded 

31.10

41.77

70.10

43.86

33.85

53.56

10.28 8.06

20.62

6.26 7.59

3.41

0

10

20

30

40

50

60

70

80

North South West

Softwood sawtimber

Hardwood sawtimber

Softwood non-sawtimber

Hardwood non-sawtimber

Timber stumpage price estimates ($/m3

) Figure 7–USFPM base-year (2006) timber stumpage price estimates ($/m3) by region, for 

sawtimber and non-sawtimber.
Research Paper FPL–RP–662

12

no significant price impacts on harvest choices.We posit that 

this may be because plot data were less frequently measured 

in the North or that product specialization, especially in 

hardwoods, precludes a precise estimate of revenues from 

harvests. Irrespective of cause, price variables using various 

specifications did not yield significant price responses. In 

the West, especially Rocky Mountain West, the interval between FIA inventories was too infrequent to support estimation of comparable models. For these two regions then, we 

adopted more traditional aggregate supply models to simulate timber supply. A time-series model of timber harvests as 

a function of stumpage price and other “shifter” 

variables provides an aggregate model of these regions’ 

supply responses. 

In the North and West, timber supply models have significant but inelastic price responses. In the West, variables describing total standing timber inventory also shift supply of 

timber from the Pacific Northwest. Whereas price responses 

are significant in the North, estimated models revealed no 

clear relationship between aggregate supply of timber and 

aggregate quantities of various inventory variables. These 

findings are likely related to the small shares of inventories 

harvested annually in those regions or confounding effects 

of other types of land uses. In other words, at least over the 

range of inventory conditions observed the past 30 years, 

harvests responded to price signals but supply did not shift 

outward in response to expansion of timber inventory. Other 

variables that might explain the availability of inventory 

for harvest or management, such as population density and 

land uses, also proved insignificant in explaining aggregate 

supply responses. In these regions then, little change in supply would be expected except as dictated by demand-driven 

scarcity.

Table 7 summarizes our regional timber supply functions 

including own-price elasticities for the four categories of 

timber in each of the three U.S. regions. These derive from 

the supply models described above but assume that the estimated elasticity for softwood sawtimber applies to all other 

products in the U.S. West. The harvest quantities of those 

other products are very small relative to national totals, so 

data were unavailable to develop empirical models. Average annual shifts recorded for the simulation period in the 

South reflect a variable time path of supply shifts across the 

decades. For the West, supply shifts outward based on a projected average growth trajectory for forest inventories. The 

supply functions in Table 7 govern total public and private 

timber supply in USFPM, although shifts in supply were 

determined largely by analysis of supply on private lands.

For non-RPA scenarios, USFPM can alternatively employ 

the GFPM approach to endogenously model timber inventory growth and related supply shifts in U.S. regions. Endogenous shifts in timber inventories are computed by region in 

USFPM on the basis of specified annual timber growth rates 

minus depletions of inventory that result from projected 

regional timber harvests, while also adjusting timber inventory to account for any projected changes in forest land area 

(see Appendix A, or Turner and others 2006). Thus, endogenously computed timber growth, timber harvest, and land 

area changes may be used optionally as long-run shifters of 

timber supply, using a fixed elasticity of supply with respect 

to timber inventory (e.g., an elasticity of 1.5 that is used for 

other GFPM countries). 

Initial forest inventory data at the beginning of year 2006 

for all other countries in the GFPM were obtained from the 

Table 7—Regional timber supply parameters, 

including stumpage price elasticities, average 

shifts of timber supply, changes in timberland 

area, and base-year timber inventory data 

U.S.

North 

U.S. 

South 

U.S.

West 

Price elasticities of stumpage supplya

 Softwood sawtimber 0.5 0.22 0.34 

 Softwood non-sawtimber 0.5 0.19 0.34

 Hardwood sawtimber 0.48 0.31 0.34 

 Hardwood non-sawtimber 0.64 0.26 0.34 

A1B scenario average annual shifts of stumpage supply,

2006–2060 (%) 

 Softwood sawtimber 0.0 0.24 0.42

 Softwood non-sawtimber 0.0 1.40 0.42 

 Hardwood sawtimber 0.0 0.73 0.42 

 Hardwood non-sawtimber 0.47 0.10 0.42 

A2 scenario average annual shifts of stumpage supply, 

2006–2060 (%) 

 Softwood sawtimber 0.0 0.67 0.44 

 Softwood non-sawtimber 0.0 1.20 0.44 

 Hardwood sawtimber 0.0 0.79 0.44 

 Hardwood non-sawtimber 0.52 0.15 0.44 

B2 scenario average annual shifts of stumpage supply, 

2006–2060 (%) 

 Softwood sawtimber 0.0 0.51 0.71 

 Softwood non-sawtimber 0.0 1.15 0.71 

 Hardwood sawtimber 0.0 0.79 0.71 

 Hardwood non-sawtimber 0.56 0.15 0.71

Gross changes in private timberland area, 

2010–2060 (%) 

 A1B scenario –6.6 –10.0 –5.1 

 A2 scenario –5.5 –8.1 –5.3 

 B2 scenario –3.3 –5.5 –3.3

Estimated timber inventories, 2006 (×106

 m3)

 Softwood growing stock 1,547 3,293 9,927 

 Hardwood growing stock 5,295 4,757 1,128 

Net annual growth in timber inventories, 

2006 (% change) 

 Softwood growing stock 2.72 6.56 1.75 

 Hardwood growing stock 2.72 3.36 1.95 

a

Price elasticities in Table 7 were applied to all three RPA 

scenarios, except for the B2 scenario where price elasticities of 

non-sawtimber supply were adjusted slightly to 0.18 for 

softwood and 0.27 for hardwood in the South. 
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

13

FAO Global Forest Resources Assessment (FAO 2006, table 

11). FAO’s U.S. forest inventory data include commercial 

and non-commercial “growing stock.” For example, the 

“growing stock” of the United States was reported by FAO 

to be 35,118 million cubic meters in 2005, and 78.7% (or 

27,640 million cubic meters) was reported to be “commercial” (FAO 2006, table 11). The latest RPA forest resources 

report indicates that the volume of timber growing stock on 

all U.S. timberland was 932,096 million cubic feet in 2007, 

or 26,394 million cubic meters (Smith and others 2009, 

table 17). Thus, the FAO “commercial” forest inventory data 

appear to be roughly consistent with the RPA data on timber 

growing stock volume on all timberland. Areas such as national parks or wilderness areas where commercial timber 

harvesting is not permitted are not considered “timberland” 

by the Forest Service, and trees in those areas are not considered to be commercial timber.

Regional timber inventories modeled endogenously in 

USFPM are calibrated to Forest Service data on timber 

growing stock on timberland (Smith and others 2009; Smith 

and others 2004). Thus, USFPM timber inventories do not 

include non-commercial tree volumes on non-timberland 

areas (such as parks, wilderness areas, other reserved areas, 

or unproductive lands), nor do the USFPM inventories include non-growing stock timber volumes. Table 8 shows, for 

example, 2007 timber growing stock inventory data by 

USFPM region and species group as reported in the RPA 

forest resources report (Smith and others 2009, table 17). 

Timber Harvesting Activities

Timber harvesting activities in USFPM broadly represent all 

commercial harvesting of timber (sawtimber and non-sawtimber), including also the joint recovery, transport, and delivery of all timber product outputs from timber harvest (in 

the categories of sawlogs/veneer logs, pulpwood/composite 

timber, other industrial roundwood, and fuelwood). USFPM 

timber harvesting activities are thus defined by material input and output coefficients and estimated harvest costs. 

The timber harvest input and output coefficients in USFPM 

reflect the harvesting efficiencies and timber product output 

ratios that are embodied in actual historical timber harvest 

and timber product output data. The harvesting efficiencies 

and product recovery coefficients could be changed over 

the projection period (e.g., to reflect technological changes 

in harvesting systems) but in lieu of such assumed future 

changes, the model operates with a set of harvest coefficients that were calibrated to the USFPM base-year data 

on timber product outputs and timber harvest volumes by 

region (Table 5).

In USFPM, harvesting of sawtimber is always accompanied 

by simultaneous (joint) harvesting of non-sawtimber, the 

smaller trees, thinnings, and cull trees that are typically harvested along with sawtimber harvest within a given region. 

However, USFPM allows flexibility in the future to harvest 

less sawtimber than the historical sawtimber harvest ratios, 

via the activity of harvesting only non-sawtimber. Thus, 

there are four timber harvest activities modeled in each U.S. 

region, including joint harvesting of hardwood sawtimber 

and non-sawtimber, joint harvesting of softwood sawtimber 

and non-sawtimber, harvesting of only hardwood non-sawtimber, and harvesting of only softwood non-sawtimber. The 

input and output coefficients for all four timber harvesting 

activities are determined precisely by actual ratios of timber 

product outputs to timber harvest volumes by region for 

each category of timber (Table 5). 

Table 9 shows USFPM material input and output coefficients for the four alternate timber harvest activities. In all 

four harvest activities, the primary timber products are sawlogs/veneer logs (those are generally the most valuable timber products), whereas the other timber products are modeled as “co-products” of the timber harvest activities. Thus, 

USFPM input coefficients for harvest activities (Table 9) 

specify volumes of sawtimber and/or non-sawtimber input 

needed per unit of primary product output (sawlogs/veneer 

logs); in other words, the cubic meter volume of sawtimber 

and/or non-sawtimber trees that must be cut down in order 

to harvest one cubic meter of sawlogs/veneer logs. For example, in the joint harvest of hardwood sawtimber and nonsawtimber in the U.S. North, 1.496 cubic meters of hardwood sawtimber plus 1.1 cubic meters of hardwood nonsawtimber must be cut to yield one cubic meter of hardwood 

sawlog/veneer log output. In addition, the harvest activity 

yields the co-products of 0.979 cubic meters of hardwood 

pulpwood, 0.594 cubic meters of hardwood fuelwood, 

0.024 cubic meters of other industrial roundwood, and 

1.135 cubic meters of logging residue. All coefficients are 

calibrated to FIA timber harvest and timber product output 

data (Smith and others 2009).

Pulpwood/composite timber, fuelwood, other industrial 

roundwood, and logging residues are modeled in USFPM 

as “byproducts” of timber harvest activities, while sawlog/

veneer log output is the primary product. Thus, output coefficients for the harvesting activities (Table 9) are the cubic 

meters of pulpwood/composite timber, fuelwood, other industrial roundwood, and logging residue “byproducts” that 

are produced per cubic meter of primary product (sawlog/

Table 8—U.S. timber growing stock inventory 

by USFPM region (2007), based on the RPA 

Forest Resources reporta

Timber inventory 

(×106

 m3)

U.S. 

North 

U.S.

South 

U.S.

West 

Softwood growing stock 1,582 3,355 10,049

Hardwood growing stock 5,441 4,815 1,153

a

Smith and others 2009, table 17. 
Research Paper FPL–RP–662

14

veneer log) output. Again, all harvesting activity coefficients 

(Table 9) are based precisely on actual timber harvest and 

timber product output data (Table 5), and thus USFPM timber harvest coefficients reflect actual regional averages for 

timber harvesting efficiency, timber product recovery, and 

logging residue output. 

Timber harvest cost parameters in USFPM are based on a 

timber harvest cost formula that takes into account average 

tree diameter, stand density, and log skidding distance to 

roadside (Keegan and others 2002). The timber harvest cost 

equation is the following:

Harvest Cost = β0 + β1 × (Avg. Tree Diameter) 

 + β2 × (Tons/Acre) (1)

 + β3 × (Skidding Distance)

Equation (1) computes timber harvest cost per thousand cubic feet, with β0 a numeric constant (28.04), β1 a coefficient 

(–1.272) multiplied by average tree diameter in inches, β2

a coefficient (–0.058) multiplied by stand density in green 

tons per acre, and β3 a coefficient (0.0069) multiplied by 

log skidding distance from stump to roadside in feet. We 

increased costs from the formula by 52% to account for 

timber product transport from roadside landing to the mill 

(transport costs are about one-third of total harvest and delivery cost). Table 10 shows total timber harvest and transport costs for USFPM for sawtimber and non-sawtimber 

($/m3). The harvest and transport costs are applied to the 

four discrete timber harvesting activities in proportion to the 

quantities of sawtimber and non-sawtimber harvested. Tree 

diameter, stand density, and skidding distance assumptions 

used in the formula are shown also for reference in Table 10. 

We adjusted average tree diameter assumptions to calibrate 

the harvest costs and obtain accurate estimates of residual 

timber stumpage values (shown previously in Fig. 7). 

Agricultural Short-Rotation Woody 

Crop Supply

In addition to supply of timber from timberland, USFPM 

also models potential future supply of agricultural SRWC 

from agricultural land. SRWC supply in USFPM represents 

potential supply of fast-growing tree crops on agricultural 

land, such as hybrid poplars or genetically selected loblolly 

pine. The area of agricultural land dedicated to such tree 

crops in the United States is currently rather small. The 2007 

USDA Census of Agriculture reported that the acreage of 

SRWC on farms with 250 acres or larger was about 136,000 

acres, or less than one-tenth of 1% of U.S. agricultural land 

area. However, agricultural SRWC could potentially expand under certain economic circumstances; for example, if 

wood energy demands escalated and wood feedstock prices 

Table 10—USFPM timber harvest and delivery costs 

($/m3

) and related assumptions by region and 

species group for sawtimber and non-sawtimber 

(2006) 

U.S.

North

U.S.

South 

U.S.

West 

Sawtimber harvest and transport costsa

 Hardwood 24 19 19 

 Softwood 18 16 13 

Non-sawtimber harvest and transport costs 

 Hardwood 37 34 29 

 Softwood 35 31 30 

Assumptions for computing harvest costs 

Average tree diameter (in., dbhb

)

 Hardwood sawtimber 14.0 15.0 14.5

 Softwood sawtimber 15.0 15.5 15.5

 Hardwood non-sawtimber 9.2 9.7 10.3

 Softwood non-sawtimber 7.8 8.9 8.5

Stand density (green t/acre) 36 51 85 

Skidding distance (ft) 450 450 550 

a

2006$/m3 of timber harvest. c

Diameter at breast height. 

Table 9—Material input and output coefficients for 

USFPM timber-harvesting activities by region and 

species group 

U.S.

North 

U.S.

South 

U.S.

West 

 (m3 harvest/m3 sawlog–veneer output)

Joint harvest of hardwood sawtimber and non-sawtimber 

Timber harvest (input) coefficients 

 Hardwood sawtimber 1.496 1.587 1.562 

 Hardwood non-sawtimber 1.100 0.760 1.081 

Co-product (output) coefficients 

 Hardwood pulpwood/composite 0.979 1.027 0.611 

 Hardwood fuelwood 0.594 0.305 0.998 

 Other industrial roundwood 0.024 0.014 0.033 

 Logging residue 1.135 1.101 0.574 

Joint harvest of softwood sawtimber and non-sawtimber 

Timber harvest (input) coefficients 

 Softwood sawtimber 1.293 1.249 1.037 

 Softwood non-sawtimber 0.599 0.526 0.196 

Coproduct (output) coefficients 

 Softwood pulpwood/composite 0.760 0.724 0.081 

 Softwood fuelwood 0.075 0.012 0.132 

 Other industrial roundwood 0.057 0.040 0.020 

 Logging residue 0.648 0.336 0.299 

Harvest of hardwood non-sawtimber only 

Timber harvest (input) coefficients 

 Hardwood non-sawtimber 8.529 8.344 23.557 

Co-product (output) coefficients 

 Hardwood pulpwood/composite 3.790 5.973 0.390

 Hardwood fuelwood 3.678 1.298 21.552 

 Other industrial roundwood 0.061 0.073 0.615 

 Logging residue 7.055 9.881 10.161 

Harvest of softwood non-sawtimber only 

Timber harvest (input) coefficients 

 Softwood non-sawtimber 3.583 5.953 2.938 

Co-product (output) coefficients 

 Softwood pulpwood/composite 2.024 4.756 0.102 

 Softwood fuelwood 0.412 0.065 1.710 

 Other industrial roundwood 0.147 0.132 0.126 

 Logging residue 3.495 3.329 3.540 
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

15

reached levels where it would become feasible for U.S. 

farmers to shift more agricultural land from conventional 

food and fiber crops to fast-growing tree crops. 

We modeled potential SRWC supply in each U.S. subregion 

using regional estimates and assumptions regarding the reserve prices for production of SRWC on agricultural land, 

the regional SRWC yields, limits on agricultural land available for SRWC, assumed distributions of land area dedicated to SRWC by species group and region, and costs of 

capacity expansion for SRWC on agricultural land. Table 11 

summarizes the regional supply parameters and assumptions 

for SRWC in USFPM. 

The reserve prices for production of SRWC (Table 11) are 

estimates of real price levels at which production becomes 

economically attractive, i.e., minimum prices needed for 

farmers to produce SRWC. In addition, future expansion of 

SRWC output is limited in USFPM by a dynamic capacity constraint, with production capacity changing over time 

according to a Tobin Q-formula approach, where capacity 

change is proportionate to the ratio of the shadow price of 

new capacity to cost of capacity expansion, such that SRWC 

output capacity will increase in proportion to its profitability 

(Zhang and others 1993). 

Potential SRWC supply in both the short-run and the 

long-run in USFPM may be illustrated conceptually by an 

“L-shaped” supply curve as shown in Figure 8, where the 

flat horizontal portion of the supply curve is defined by the 

reserve price, and the vertical portion of the supply curve is 

an upper bound on supply. In effect, the upper bound is determined in the short run by the dynamic production capacity constraint, which may increase over time, but in the long 

run, the upper bound is ultimately limited by the assumed 

regional area of agricultural land available for SRWC and 

the estimated average yields of SRWC per unit of land area 

(Table 11).

SRWC harvest volume in USFPM is assumed to be 75% 

pulpwood/composite timber and 25% fuelwood, based on 

harvest recovery studies for short-rotation hybrid poplar 

(Hartsough and others 2000). Alternatively, all of the volume can go to bioenergy in USFPM via the cascading wood 

raw material substitution possibilities (Fig. 6), so projected 

supply and utilization of SRWC in USFPM really depends 

on projected demands and market conditions, which will 

vary with alternative economic scenarios. In any case, 

projected regional prices of either pulpwood or wood fuel 

feedstock must reach the reserve prices before any supply 

of SRWC occurs in USFPM. The reserve prices are actually higher than real prices of pulpwood or fuelwood in the 

base-year, and thus SRWC supply will expand in the model 

only if real prices of either pulpwood or fuel feedstock are 

projected to increase in the future (an outcome that depends 

on supply and demand growth assumptions of a particular 

economic scenario).

Supply of Recovered Paper and Other 

Fiber Pulp

In addition to wood supply from timber harvesting activities and potential SRWC supply, USFPM includes also the 

regional supplies of two other categories of fiber inputs 

used in papermaking, recovered paper (used for paper recycling) and other fiber pulp (chiefly cotton fiber pulp used for 

specialty printing and writing paper products in the United 

States). Recovered paper is an important fiber input to paper 

and paperboard production in the United States, and also a 

large U.S. export item, while the non-wood fiber is a relatively small fiber input in the United States (but proportionately a larger and more important fiber input in some other 

countries modeled in the GFPM). 

Table 11—Regional supply parameters and 

assumptions for agricultural short-rotation woody 

crops (SRWC) in USFPM 

U.S.

North 

U.S.

South 

U.S.

West 

Reserve prices for productiona

 Hardwood SRWC 75 65 80 

 Softwood SRWC 75 65 80 

SRWC yields (average dry t of harvestable wood/acre/y) 

 Hardwood SRWC 6 7 6 

 Softwood SRWC 4 8 5 

Available agricultural land area (thousands of acres) 

 Hardwood SRWC 3,000 5,000 1,000 

 Softwood SRWC 1,000 10,000 1,000 

Land area dedicated to SRWC in 2006 (thousands of acres) 

 Hardwood SRWC 60 30 40 

 Softwood SRWC 1 5 1 

Cost of capacity expansion (2006 $/acre) 

 Hardwood SRWC 500 500 500 

 Softwood SRWC 500 500 500 a

2006 $/dry t of harvested and delivered wood. 

1

Quantity

Price

Figure 8–Conceptual illustration of “L-shaped” supply 

curve in USFPM.
Research Paper FPL–RP–662

16

The tons of recovered paper consumed for recycling at U.S. 

paper and paperboard mills has stabilized at a level equal to 

37% of total paper and paperboard output in recent years, 

after having experienced a 10% gain in the 1990s when U.S. 

mills expanded their use of recovered paper (AF&PA 2009). 

However, the tons of paper recovered for recycling and 

exported from the United States has continued to increase, 

with increasingly efficient paper recovery systems, reaching 

a recovery rate equal to 53% of paper and paperboard consumption in 2006 and over 57% by 2008 (AF&PA 2009). 

Exports accounted for 33% of U.S. recovered paper supply 

in 2006 and 38% of supply by 2008 (AF&PA 2009). Exports to China in particular have increased significantly in 

recent years, and according to data from U.S. Department of 

Commerce, China consumed about two-thirds of total U.S. 

recovered paper exports in 2009.

Following the GFPM approach, regional recovered paper 

supply in USFPM is modeled as a single commodity, with a 

price elasticity of 1.0 and an exogenously specified growth 

rate (equal to just above 1%/y in U.S. regions). This supply 

growth rate assumes continued gains in efficiency of paper 

recovery for recycling. Thus, the U.S. paper recovery rate 

climbs from around 60% to around 80% to 90% by 2060 

(highest in the B2 scenario), whereas projected consumption 

of recovered paper for recycling is determined by specified 

fiber input coefficients in paper and paperboard production 

(discussed later). We derived base-year (2006) regional supply quantities of recovered paper for USFPM from data on 

domestic recovered paper consumption by U.S. subregion 

plus net exports of recovered paper by region (AF&PA 

2007). Base-year average supply price for recovered paper 

was adapted from the GFPM data (based on recovered paper 

commodity values reported by FAO). Table 12 shows the 

USFPM base-year recovered paper supply estimates. 

Among U.S. regions, production of other fiber pulp (nonwood pulp) occurs only in the U.S. South as cotton fiber 

pulp, which is relatively small in comparison to wood pulp 

output, at just 245 thousand metric tonnes in 2006 (equivalent to less than one-half of 1% of Southern U.S. wood pulp 

production). In the U.S. paper industry, the cotton fiber pulp 

is used mainly in producing specialty grades of printing and 

writing paper (e.g., cotton bond paper). In USFPM, the supply of cotton fiber pulp in the South is represented by a fixed 

reserve price ($850/t) and an upper bound on supply, which 

effectively creates an “L-shaped” supply curve (Fig. 8). The 

upper bound starts at the base-year production level in 2006 

and increases at about 1% per year (in line with the GFPM 

assumption for expansion of non-wood pulp). As with recovered paper, the projected consumption of other fiber pulp 

is determined by specified fiber input coefficients in paper 

and paperboard production (discussed in the following 

section).

Forest Product Production Data and 

Parameters

Forest product production data and parameters in USFPM 

include base-year (2006) forest product production volumes, 

wood and fiber raw material input coefficients, manufacturing costs, and wood and bark residue byproduct coefficients. 

Base-year forest product production data in USFPM correspond to actual 2006 volumes of forest product output as reported for the United States by leading forest industry trade 

associations and by U.S. Department of Commerce. Forest 

product production activities are represented in USFPM by 

a combination of wood and fiber raw material input coefficients (specified quantities of wood or other fiber inputs 

required per unit of forest product output), wood and bark 

residue byproduct coefficients, and estimates of other manufacturing costs per unit of product output (a singular net cost 

factor for each product representing all other production 

costs besides the costs of the wood and fiber inputs). 

In developing USFPM for RPA we wanted to ensure that 

projected regional volumes of wood and fiber inputs consumed and volumes of residue byproducts produced would 

match regional data on wood and fiber supply, including 

data on wood harvests, residue supplies, and wood raw material imports. Thus, we calibrated the wood and fiber input 

coefficients and fiber residue byproduct coefficients in 

USFPM so that regional wood and fiber input volumes and 

residue output volumes determined by regional production of all forest products in the base year (2006) matched 

precisely the totals of actual data on timber product outputs 

from timber harvesting and data on fiber residue outputs 

by region as reported in the RPA forest resources report 

(Smith and others 2009, tables 39 and 42) plus added regional roundwood and wood chip import data from USITC. 

We also calibrated the fuel residue byproduct coefficients 

so that fuel residue output volumes determined by regional 

production of all forest products in the base year matched 

precisely the actual aggregate data on fuel residue outputs 

by region as reported in the RPA forest resources report 

(Smith and others 2009, table 42) plus regional fuelwood 

import data from USITC. In the calibration process, we also 

took into account regional differences in wood densities by 

species group and corresponding conversions from volume 

to weight measures. 

Estimated forest product production quantities by U.S. subregion for 2006 are shown in Table 13 for all forest products 

represented in USFPM. Miscellaneous wood products produced from “other industrial roundwood” (posts, poles, 

Table 12—USFPM regional recovered 

paper supply estimates (2006)

U.S.

North

U.S.

South

U.S.

West

Supply quantities

(×103 t)

17,172 15,734 14,070

Supply prices

(2006 $/t, average)

123 123 123
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

17

cooperage, etc.) are not represented as products in USFPM, 

but USFPM models aggregate U.S. demand for other industrial roundwood, as the GFPM models trade and demand in 

other countries for “other industrial roundwood.” 

Table 13 was based primarily on production data from industry trade associations. Data sources included the Western 

Wood Products Association for softwood lumber (WWPA 

2006), APA-The Engineered Wood Association for softwood 

plywood and OSB (APA-The Engineered Wood Association 

2007), Composite Panel Association for particleboard and 

medium density fiberboard (Howard 2007), and the American Forest & Paper Association for pulp, paper, paperboard 

and insulating board (AF&PA 2009). Production data for 

solid-wood products in the United States are reported in 

various units of measure (nominal board feet for softwood 

lumber, full-dimension board feet for hardwood lumber, and 

square feet of various thickness standards for wood panels). 

We used standard conversion factors (Howard 2007) to 

translate production data into solid cubic meters, the unit of 

measure for solid wood products in the GFPM and USFPM. 

Similarly, U.S. production data for pulp, paper, and paperboard are reported in short tons, which we converted to 

metric tonnes (1 short ton = 0.90718 t). In some cases (e.g., 

softwood lumber and structural wood panels) the production 

data were reported by U.S. subregion, while in other cases, 

production data were available only for the United States as 

a whole (not by subregion), and in those cases we allocated 

U.S. production data to the subregions according to regional 

distributions of production capacity, with some minor adjustments for wood pulp production. We shifted the equivalent of 2% of U.S. chemical and semichemical wood pulp 

output from the West to the North and South (1% each) to 

balance fiber supply with mill closures and downtime in the 

West. Production data for some products such as hardwood 

lumber were derived from U.S. Department of Commerce 

(Howard 2007).

Wood and fiber input coefficients and residue byproduct coefficients for the various forest products are among the more 

complex data incorporated into USFPM, partly because 

there are real structural differences among U.S. subregions 

and products in terms of wood or fiber inputs. For example, 

newsprint production in the U.S. North utilizes mainly recovered paper and little wood pulp as fiber input, whereas 

in the South and West newsprint utilizes a larger share of 

wood pulp. Oriented strandboard (OSB) uses only harvested 

pulpwood/composite timber as input, whereas particleboard 

uses primarily fiber residues. We incorporated such differences into the wood and fiber input coefficients of USFPM, 

so that the model has an accurate representation of regional 

variations in wood and fiber consumption relative to forest 

product output. As mentioned previously, we calibrated all 

input and output coefficients in USFPM so that computed 

base-year wood raw material consumption and residue outputs by region match actual data on U.S. timber consumption, trade, and wood residue production by region. Table 14 

summarizes the calibrated base-year (2006) wood and fiber 

input coefficients and residue byproduct coefficients that we 

programmed into USFPM.

We included some alternative wood or fiber input coefficients in USFPM to allow raw material substitution possibilities in some products where appropriate. For example, 

the primary raw material for particleboard and fiberboard 

production in the United States is fiber residues (mill residues from sawmills and plywood mills), but it is technically 

feasible to substitute harvested pulpwood/composite timber 

for fiber residues (although residues are currently cheaper 

and therefore the primary raw material for particleboard 

and fiberboard). In USFPM, we allow substitution of harvested pulpwood/composite timber for fiber residues in 

particleboard and fiberboard production to facilitate future 

economic substitution if projected prices change. Similarly, 

for wood pulp production we allow the option of using either harvested pulpwood/composite timber or fiber residues. 

We programmed this type of raw material flexibility into 

the model by allowing particleboard, fiberboard, and wood 

pulp production activities to use either harvested pulpwood/

composite timber or fiber residues, as alternative “input 

mix” options. Of course, we do not allow technologically infeasible substitution possibilities, such as direct substitution 

of pulpwood for saw logs/veneer logs as inputs to lumber or 

plywood production, to occur in USFPM.

We also introduced production efficiency gains for most forest products over the 50-year projection period in USFPM, 

based on global efficiency gains that were programmed by 

UW researchers into the GFPM. A general theory about 

efficiency gains is that global free trade makes access to 

advanced production technology more globally diffuse. 

Globalization and the prolific worldwide spread of advanced 

forest product production technology is a recognized structural change that has been under way in the forest sector for 

some time (Ince and others 2007). Therefore, less efficient 

Table 13—U.S. forest product production volumes 

by USFPM region (2006)

U.S.

North

U.S.

South

U.S.

West

Solid-wood products (×106 m3)

Softwood lumber 3,465 31,645 30,438

Hardwood lumber 15,411 10,471 1,074

Softwood plywood/veneer 32 9,933 4,416

Hardwood plywood/veneer 581 966 54

Oriented strandboard (OSB) 3,321 9,919 0

Industrial particleboard 578 3,422 3,177

Fiberboard 1,821 3,305 2,056

Pulp, paper, and board products (×106 t)

Chemical/semi-chemical wood pulp 7,553 36,411 5,285

Mechanical wood pulp 1,092 2,088 782

Newsprint 409 2,862 1,468

Printing and writing paper 13,224 8,162 1,267

Other paper and paperboard 15,089 35,794 5,897
Research Paper FPL–RP–662

18

producers are expected to adopt more efficient production 

technology over time. Thus, wood and fiber input coefficients and manufacturing costs in the GFPM and also in 

USFPM are programmed to gradually shift over the projection period toward the most efficient levels (of the most advanced countries) for each forest product. 

USFPM Trade in the GFPM Context

USFPM models and projects net trade flows between the 

United States and the rest of the world as determined by the 

GFPM and its market equilibrium solution for global net 

trade. The GFPM models global forest product production, 

consumption, and net trade for 180 countries including the 

United States. The global market equilibrium is solved using 

economic optimization techniques (Appendix A describes 

the GFPM formulation, structure, and global market equilibrium solution).

A simplifying structural feature of the GFPM is that instead 

of modeling all bilateral trade flows among countries, the 

trade flows of each country are modeled only with respect to 

an intermediate dummy region (so-called “world” region). 

The GFPM allows the option of specifying bilateral trade 

flows between selected subsets of countries (http://fwe.wisc.

edu/facstaff/Buongiorno/book/GFPM.htm) but no bilateral 

trade flows were incorporated in the UW version of the 

GFPM that we used in developing USFPM. USFPM was 

developed within the structure of the GFPM, so U.S. trade 

flows are modeled entirely as trade with the rest of the world 

in aggregate (not as bilateral trade with individual countries). This structural feature combined with the optimizing 

solution technique compels each country in the GFPM to 

become either a global exporter or global importer of any 

commodity at the market equilibrium, unless trade flows 

are constrained with GFPM “trade inertia bounds.” Trade 

inertia bounds are constraints on changes in trade flows that 

can force both imports and exports to remain simultaneously 

positive. Like the GFPM, USFPM has highly constrained 

international trade flows in the base year, along with inertia 

constraints over the projection period. The constraints on 

U.S. trade are ±5% change per year for import and export 

quantities (and for pulp transported between regions). Inertia constraints are not applied to U.S. trade in roundwood, 

logs, other fiber pulp and recovered paper.

End products in USFPM are imported to the U.S. aggregate 

demand region and exported from U.S. subregions: North, 

South, and West. U.S. subregions also import intermediate 

products, logs and pulp, and each subregion can trade pulp 

with other subregions. This permits more realistic trade 

solutions in which net trade flows can vary among U.S. subregions. For example, USFPM simulates accurately that the 

U.S. South and West are net exporters of wood pulp, whereas the U.S. North is a net importer of wood pulp from other 

countries and from other U.S. subregions. Tables 15 and 16 

summarize U.S. base-year (2006) foreign export and import 

quantities specified in the USFPM data input. 

Table 14—Base-year (2006) wood and fiber input 

coefficients and residue by-product coefficients for forest 

product production activities by region in the USFPMa

Coefficients 

U.S.

North

U.S.

South 

U.S.

West 

 (m3/m3 product output) 

Softwood (SW) lumber 

 SW sawlog/veneer log input 2.142 2.378 2.026

 SW fiber residue byproduct 0.352 0.754 0.662

 Fuel residue byproduct 0.388 0.589 0.348

Hardwood (HW) lumber 

 HW sawlog/veneer log input 1.669 2.086 2.465

 HW fiber residue byproduct 0.256 0.486 0.391

 Fuel residue byproduct 0.359 0.637 0.082

Softwood plywood/veneer 

 SW sawlog/veneer log input 2.640 2.567 2.796

 SW fiber residue byproduct 0.352 0.754 0.662

 Fuel residue byproduct 0.388 0.589 0.348

Hardwood plywood/veneer 

 HW sawlog/veneer log input 2.526 2.526 2.526

 HW fiber residue byproduct 0.256 0.486 0.391

 Fuel residue byproduct 0.359 0.637 0.082

Oriented strandboard (OSB) 

 HW pulpwood/composite timber input 1.651 0.760 1.572

 SW pulpwood/composite timber input 0.019 0.883 0.085

Industrial particleboard 

 HW fiber residue input or harvested 

 HW pulpwood/composite timber input 

1.468 0.245 0.019

 SW fiber residue input or harvested 

 SW pulpwood/composite timber input 

0.301 1.309 1.844

Fiberboard 

 HW fiber residue input or harvested 

 HW pulpwood/composite timber input 

0.714 0.308 0.009

 SW fiber residue input or harvested 

 SW pulpwood/composite timber input 

0.128 0.483 0.746

Chemical/semi-chemical wood pulp 

 HW fiber residue input or harvested 

 HW pulpwood/composite timber input 

2.562 0.724 0.079

 SW fiber residue input or harvested 

 SW pulpwood/composite timber input 

1.078 2.283 3.395

 Fuel residue byproduct 0.392 0.325 0.026

Mechanical wood pulp 

 HW fiber residue input or harvested 

 HW pulpwood/composite timber input 

1.433 1.123 1.620

 SW fiber residue input or harvested 

 SW pulpwood/composite timber input 

0.907 0.810 0.821

 Fuel residue byproduct 0.392 0.339 0.059

Newsprint (t/t product output) 

 Chemical/semi-chemical wood pulp input 0.041 0.070 0.100

 Mechanical wood pulp input 0.096 0.365 0.456

 Recovered paper input 1.001 0.677 0.573

Printing and writing paper 

 Chemical/semi-chemical wood pulp input 0.650 0.756 0.529

 Mechanical wood pulp input 0.072 0.133 0.093

 Other fiber pulp (non-wood) input 0.012 0.000 0.000

 Recovered paper input 0.070 0.015 0.325

Other paper and paperboard 

 Chemical/semi-chemical wood pulp input 0.194 0.741 0.793

 Mechanical wood pulp input 0.002 0.001 0.000

 Recovered paper input 0.718 0.332 0.700

a

Note that wood and fiber input coefficients for wood pulp, industrial 

particleboard, and fiberboard in USFPM allow for substitution between fiber 

residue and harvested pulpwood/composite timber within each species group 

(HW and SW), as alternative “input mix” options. 
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

19

As shown earlier in Table 13, for example, the U.S. North 

had a fairly large share of U.S. paper and paperboard production in the base year, but a much smaller share of U.S. 

wood pulp production, necessitating import of wood pulp to 

the U.S. North from foreign countries and also from other 

U.S. regions. For example, in the base year, nearly three 

million tons of wood pulp (market pulp) was shipped from 

the U.S. South to the U.S. North, and two million tons were 

shipped from the U.S. West to U.S. North.

In USFPM, U.S. trade in products such as hardwood and 

softwood lumber, hardwood and softwood veneer/plywood, 

and OSB and industrial particleboard, are aggregated into 

corresponding GFPM product categories for export (e.g., 

sawnwood, veneer/plywood, and particleboard) or disaggregated for import, using base-year product trade shares. 

U.S. trade data for lumber, plywood, and OSB generally 

correspond to data sources reported by Howard (2007), 

while trade data for industrial particleboard, fiberboard, and 

pulp come from FAO, and paper and board trade data come 

from the U.S. Department of Commerce (as reported also by 

AF&PA). Base-year (2006) export and import prices for individual commodities in USFPM match the base-year prices 

as specified in the UW version of the GFPM (derived from 

FAO data on value of shipments). Weighted averages of 

GFPM prices are used in cases where USFPM commodities 

are disaggregated from GFPM commodities.

End Product Demand Quantities and 

Elasticities

USFPM data inputs related to end product demands include 

base-year (2006) U.S. demand quantities (consumption) and 

prices, price elasticities of demand, GDP elasticities, and 

other demand elasticities for all end products. The base-year 

forest product demand quantities are apparent consumption estimates, derived by adding total U.S. production 

(Table 13) to net imports (imports minus exports) for each 

commodity (Tables 15, 16). The exceptions were demand 

quantities for other industrial roundwood and fuel feedstock. 

Other industrial roundwood products (poles, posts, etc.) are 

not traded in significant volumes internationally, so the U.S. 

demand quantity is simply equivalent to the sum of U.S. 

regional harvest volumes for other industrial roundwood 

(Table 5). The base-year fuel feedstock demand quantity is 

a combination of several elements: 1) roundwood fuelwood 

harvest volumes from regional FIA harvest data, 2) regional 

mill residue fuel byproduct volumes from FIA data, and 

3) net trade in fuelwood. 

Table 17 summarizes the base-year (2006) U.S. demand 

quantities and prices for end products as specified in the 

USFPM data input. Product demand prices were derived 

from various sources. Softwood lumber, softwood plywood, 

OSB, and industrial particleboard prices were derived from 

U.S. domestic prices reported in Random Lengths Yearbook 

(2007). The hardwood lumber price was obtained from 

William Luppold (Luppold and Bumgardner 2007). The 

hardwood plywood price was calculated as the unit value 

of imports from the Bulletin of Hardwood Market Statistics. For all other products, we used demand prices from 

the GFPM model (derived from FAOSTAT product export 

unit value data). For all of the solid wood products and 

composite wood products, prices are measured in dollars 

per solid cubic meter (m3), and quantities are measured in 

thousands of solid cubic meters, including lumber, plywood/

veneer, other industrial roundwood, fuel feedstock, OSB, 

particleboard, and fiberboard. For the paper and paperboard 

products, newsprint, printing and writing paper, and other 

paper and paperboard, quantities are measured in thousands 

of dry metric tonnes and prices are measured in dollars per 

dry metric tonne. 

Table 15—U.S. foreign export quantities (2006) 

Commodity 

U.S. 

North

U.S.

South 

U.S.

West 

Quantity 

(×103

 m3)

 Softwood (SW) sawlogs 2,466 91 3,601 

 Hardwood (HW) sawlogs 902 426 233 

 SW pulpwood 463 240 32 

 HW pulpwood 216 398 16 

 SW lumber 436 911 848 

 HW lumber 1,843 954 576

 SW veneer/plywood 0 427 147 

 HW veneer/plywood 2,466 91 3,601 

 Oriented strandboard (OSB) 135 24 0 

 Industrial particleboard 68 10 273 

 Fuelwood and charcoal 32 6 2

 Fiberboard 343 219 255 

Quantity 

(×103

 t) 

 Mechanical pulp 68 0 17 

 Chemical/semi-chemical pulp 595 4,159 360 

 Other fiber pulp 32 102 0 

 Recovered paper 5,210 1,793 8,909 

 Newsprint 28 262 296 

 Printing and writing paper 949 532 257 

 Other paper and paperboard 1,664 4,260 1,397 

Table 16—U.S. foreign import quantities (2006) 

Commodity 

U.S.

Aggregate

U.S.

North

U.S.

South 

U.S.

West

Quantity (×103 m3)

 Fuelwood and charcoal 128 — — — 

 Sawnwood 55,524 — — — 

 Veneer/plywood 7,121 — — — 

 Particleboard 10,247 — — — 

 Fiberboard 3,292 — — — 

 Sawlogs and pulpwood — 369 49 2,262

Quantity (×103

 t) 

 Newsprint 4,923 — — — 

 Printing and writing paper 8,250 — — — 

 Other paper and paperboard 3,329 — — —

 Mechanical pulp — 0 85 20

 Chemical/semi-chemical pulp — 4,175 941 666

 Other fiber pulp — 41 2 0

 Recovered paper — 211 9 217
Research Paper FPL–RP–662

20

U.S. demand elasticities for end products are summarized 

in Table 18. Some of the elasticities were obtained from the 

GFPM, while elasticities for other products were estimated 

or obtained from different sources, as explained below. The 

GFPM elasticities were used for non-structural wood panels 

(industrial particleboard and fiberboard), hardwood lumber, 

hardwood plywood/veneer, other paper and paperboard, 

and other industrial roundwood. The GDP elasticity for fuel 

feedstock demand (“x” in Table 18) is a control variable 

that is adjusted to create alternative wood energy demand 

scenarios.

For newsprint and printing and writing paper (communication paper grades), elasticities with respect to price, GDP, 

advertising spending in print media, and advertising spending in electronic media were obtained from Wongcharupan 

and Ince (2003, 2004). That analysis revealed that U.S. 

demands for communication paper grades are primarily 

dependent on trends in advertising expenditures, and notably the shift in advertising expenditures from traditional 

print media to electronic media (such as television and the 

internet). This ongoing structural change in U.S. communication paper demands has been noted also by others (Soirinsuo 2009). Statistical analysis has shown that projected U.S. 

consumption of communication papers depends on assumed 

future trends in advertising expenditures in print media and 

electronic media, and generally more recent trends point 

toward lower future demands for communication papers. We 

applied future assumptions in USFPM for change in advertising expenditures as determined by recent historical trends, 

including, for example, –1% per year for newspaper advertising based on advertising expenditures data from 2000 to 

2007 as reported by Newspaper Association of America, 

and +6% per year for electronic media advertising, based on 

2006 average growth for TV, radio, and Internet.

We estimated U.S. demands for softwood lumber and structural panels (softwood plywood and OSB) using annual 

consumption data and a Cobb-Douglas (CES) equation that 

included as independent variables the real producer price 

index for each commodity group (U.S. Bureau of Labor Statistics (BLS)), U.S. real GDP (Bureau of Economic Analysis 

(BEA)), and U.S. single-family housing starts (U.S. Census 

Bureau), for the years 1973 through 2008. Before we estimated the demand equations, the Hausman specification 

test was used to test for endogeneity of the price variable 

(Hausman 1978). The test determines whether ordinary least 

squares regression is likely to be statistically inconsistent 

because of endogeneity, a statistical feedback effect that 

may occur with price variables in demand equations. The 

Hausman test was performed by first regressing annual price 

data (price indexes) for both softwood lumber and structural 

panels on annual data for the other exogenous variables 

(GDP and housing starts). The respective consumption data 

were then regressed on residuals obtained from the previous 

step. The Hausman test results based on t statistics for the 

residual coefficients provided only weak evidence of no 

endogeneity in the case of softwood lumber (p-value 

Table 17—U.S. demand prices and 

quantities for end products (2006)

Commodity

Price

($/m3

)

Quantity

(×103 m3)

Softwood (SW) lumber 192 117,077

Hardwood (HW) lumber 271 25,384

SW plywood/veneer 305 16,371

HW plywood/veneer 504 4,999

Oriented strandboard (OSB) 209 22,055

Industrial particleboard 204 8,099

Fuel feedstock 25 113,255

Other industrial roundwood 80 7,208

Fiberboard 345 9,657

Price

($/t)

Quantity

(×103 t)

Newsprint 587 9,078

Printing and writing paper 908 29,165

Other paper and paperboard 805 52,788

Table 18—U.S. demand elasticities for USFPM end products 

Commodity Price GDP 

Housing

starts 

Advertising 

spending in 

print 

media 

Advertising

spending in

electronic

media 

Softwood (SW) lumber –0.14 0.39 0.49 — —

Hardwood (HW) lumber –0.10 0.22 — — —

SW veneer/plywood –0.65 0.55 0.69 — —

HW veneer/plywood –0.29 0.41 — — —

Oriented strandboard (OSB) –0.65 0.55 0.69 — —

Industrial particleboard –0.29 0.54 — — —

Fuel feedstock –0.50 X — — —

Other industrial roundwood –0.05 –0.58 — — —

Fiberboard –0.46 0.35 — — —

Newsprint –0.68 0.77 — 1.35 –1.00 

Printing and writing paper –0.42 0.60 — 1.00 –0.55 

Other paper and board –0.23 0.43 — — — 
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

21

of 0.056). However, to avoid the endogeneity issue in any 

case, we applied the regression method of two-stage least 

squares (2SLS) with instrumental variables to estimate demand equations for lumber and structural panels.

Using a log-log model in the first stage, annual softwood 

lumber and structural panel price indexes were first regressed on GDP, single-family housing starts, average size 

in square footage of new single-family homes (as reported 

by U.S. Census Bureau), the all-commodities PPI, and the 

all-commodities CPI. In the second stage, again using a 

Cobb–Douglas model, softwood lumber and structural panel 

consumption were separately regressed on the predicted 

price indexes from the first stage, real GDP, single-family 

housing starts, and average size of new homes to estimate 

elasticities for the demand equations. In the case of structural panels, the demand elasticities were all statistically 

significant (except for the elasticity with respect to size of 

new homes, which was only marginally significant), and all 

of the estimated elasticities had the correct or expected sign 

(negative for price and positive for all other variables). In 

the case of softwood lumber, demand elasticities for price 

and size of new homes had incorrect signs (+/–) and were 

also not statistically significant. However, other studies have 

mostly found that price elasticity of U.S. softwood lumber 

demand is fairly inelastic, generally less in absolute value 

than 1.0 (Song and others 2011). Therefore, in this case 

we substituted a restricted regression of softwood lumber 

consumption on real GDP and housing starts in the second 

stage, fixing lumber demand price elasticity at a value of 

–0.141. This elasticity value was from a recent U.S. lumber 

demand and supply analysis using co-integration in dynamic 

equations (Song and others 2011). We included the size of 

new homes, although the elasticity for that variable was not 

statistically significant. Our regression results and resulting 

estimates of demand elasticities are shown in Table 19, including results of 2SLS regression for both softwood lumber 

and structural panel demands. 

Estimating combined structural panel demand (softwood 

plywood and OSB together using a consumption-weighted 

price series) yielded acceptable results (Table 19) but separate regressions tend to yield poor results or results without 

expected signs on estimated elasticities. This is because the 

demands are influenced by ongoing product substitution. 

OSB has been steadily substituting for softwood plywood 

in housing construction since the late 1970s, with OSB increasing from 0% of consumption in 1976 to 63% in 2006. 

Thus, softwood plywood’s share of structural panel demand 

has declined steadily since 1976, losing about 2% of market share per year until 1997 when the decline slowed to 

about 1% per year to 2010. In USFPM, this decline for the 

plywood market share is projected to continue. However, 

softwood plywood is projected to ultimately retain a 20% 

market share because of specialty applications that require 

plywood (plywood siding, marine plywood, concrete forms, 

etc.), whereas OSB approaches a market share of 80% by 

2060. 

The U.S. market shares for OSB and softwood plywood are 

modeled in USFPM by adding a dummy demand shifter to 

OSB and softwood plywood demands (reducing the plywood market share to 20% by 2060) while both products 

still share the same demand elasticities with respect to price, 

GDP, housing starts, and size of new houses (Table 19). The 

dummy demand shifter was generated by using a lagged 

variable Equation (2) with a dampening coefficient to best 

approximate the current historical product substitution trend 

beginning in 2011, which is the 2010 point minus 1%. Figure 9 shows the actual historical softwood plywood share of 

total U.S. structural panel consumption along with the projected share as determined by the lagged variable 

Equation (2):

(2)

where xn is softwood plywood percentage of structural panel 

consumption in year n, 

 x1 – x0 = 0.01, 

 and d = 0.05

xn = xn–1 – (xn–2 – xn–1) 

1+ d

Table 19—Regression results for U.S. softwood 

lumber and structural panel demandsa

Regression of 

Producer Price Indexes 

(first stage) 

Softwood 

lumber 

Structural 

panels 

Variable Coefficient

Standard

Error Coefficient

Standard

Error

Intercept 4.701 3.948 –3.333 4.367 

Real GDP –0.512 0.609 0.395 0.673 

Single-family 

housing starts 

0.363 0.121 0.369 0.133 

Size of new houses 0.348 1.011 0.543 1.118

PPI –1.215 0.399 0.361 0.441 

CPIb 1.093 0.382 –0.799 0.423 

Adjusted R2

 0.477 0.377 

Regression of 

consumption quantities (second stage) 

Softwood 

lumber 

Structural 

panels 

Variable Coefficient

Standard

error Coefficient

Standard

error 

Intercept 4.496 0.348 0.552 1.471 

Real price –0.141 (fixed) –0.652* 0.134 

Real GDP 0.386* 0.032 0.551* 0.173 

SF housing starts 0.490* 0.045 0.691* 0.070 

Size of new homes — — 0.387 0.396 

Adjusted R2

 0.919 0.975 

a

Use of the asterisk (*) denotes statistically significant elasticities at 

95% or higher confidence. 

b

Consumer Price Index. 
Research Paper FPL–RP–662

22

With housing starts a significant long-run shifter of softwood lumber and structural panel demands (Table 19), 

USFPM requires long-range projections of U.S. housing 

starts. Housing projections may be obtained from sources 

such as the 2010 U.S. Annual Energy Outlook (DOE 2010a) 

that we used for our RFS+RES scenarios. However, for the 

2010 RPA scenarios, we generated preliminary projections 

of single-family housing starts based on housing needs as 

determined by RPA population projections and derived 

demographic trends (explained in the following section of 

text). Sources for data used in projecting U.S. single-family 

housing starts for the RPA scenarios are shown in Table 20. 

Projections of U.S. single-family (single-unit) housing 

“needs” were based on RPA population assumptions that 

were deconstructed into demographic elements. This began 

by segregating the population into discrete age cohorts or 

age classes, as follows: 

Total population = Pop age class1 + Pop age class2 

 + ... + Pop age classn

The n cohorts were arrayed by ascending age with “1” being the youngest and “n” the oldest. Using data from 2000 

to 2006, we computed “headship rates” for each age cohort 

(the fraction of persons who are labeled heads of households) by dividing the number of household heads by the 

number of people in the cohort as follows:

Headship rate age class1 = Household heads age 

 class1/Pop age class1

The number of projected households within each cohort 

at any time is then the number of people in the age cohort 

times the computed headship rate:

 Household age class1 = Pop age class1

 × Headship rate age class1 

Summing households over the age cohorts gives the total 

number of households:

 Total households = Hhold age class1 

 + Hhold age class2 

+ ... +Hhold age classn

To obtain the projected future populations in each age cohort, we started with the initial age distributions and moved 

them forward through time. Over time an age cohort loses 

all its graduating members who move into the next older age 

cohort but gains the survivors from the next younger age 

cohort (e.g., as follows):

 Pop age class2 in year2 = Pop age class1 in year1

 – mortality age class1 in year1

To these were added the net number of people who immigrated into the U.S. (number of immigrants less the number 

of emigrants):

 Pop age class2 in year2 = Pop age class2 in year2

 + Net influx in year2.

The net influx of immigrants was the slack variable we 

used to make the population totals conform to the various 

RPA scenarios. By definition, the household count equals 

the minimum housing stock that is needed. Thus, the new 

housing needed each year equals the change in the number 

of households (determined by population’s growth and demographic distributions) adjusted for changes in housing 

vacancies and removals each year. Not all houses are occupied year round (some are seasonal homes) nor do houses 

last forever. From historical data, we calculated the normal 

number of housing units that are vacant. The changes in 

vacancies together with the numbers of houses that are typically demolished each year (removals) represent additional 

increments of new housing needs:

Housing needs in year2 = Total households in year2 

 – Total households in year1

 + Change in vacancies 

 + Removals in year2

0

10

20

30

40

50

60

70

80

90

100

19761980198419881992199620002004200820122016202020242028203220362040204

4

2048205220562060

Projected

Historical

Softwood plywood share (%)

Figure 9–Softwood plywood share (percentage) of U.S. 

structural panel consumption, historical data 1976–2005 

(Howard 2007), and projected trend, 2006–2060 (based on 

Eq. (2)).

Table 20—Data elements and sources used in singlefamily housing start projections

Element Source

U.S. population growth RPA scenario assumptions

based on U.S. Census and IPCCa

U.S. population by age U.S. Census Bureau,

Statistical Abstract of United States

Households by age of head U.S. Census Bureau,

Current Population Survey

Net migration U.S. Census Bureau

Mortality rates U.S. Center for Disease Control

Ownership per age cohort H. Spelter estimates

(Forest Products Laboratory)

Removals and vacancies H. Spelter estimates

(Forest Products Laboratory)

a Intergovernmental Panel on Climate Change.
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

23

Next, these data were combined with assumptions derived 

from historical data regarding the tendency of households 

of different age classes to occupy single-family (single-unit) 

dwellings versus dwellings in multi-unit structures. These 

assumptions generated our projected single-family housing 

“needs” for each RPA scenario.

RPA scenarios focus on a long-range projection period from 

2020 to 2060, rather than the recent historical period from 

2006 to 2010 when a significant downturn in U.S. housing 

construction occurred or the near-term period from 2010 to 

2020 (when a rebound in housing is forecast to occur). RPA 

scenarios assume that housing starts will follow the projected “needs” levels from 2020 to 2060, with housing needs 

linked demographically to RPA population projections.

Last, we note that projected housing needs or housing starts 

exhibit wider variation than projected population trends, as 

expected. This is because housing starts represent marginal 

inflows of new housing stock relative to a large existing 

housing stock. For example, with a static population marginal needs for new housing would be essentially zero except 

for replacement needs arising from removals or vacancies, 

but small increments of added population growth can greatly 

multiply the marginal need for new housing. Thus, modest 

adjustments in population growth translate into more significant adjustments in projected housing needs as illustrated in 

Figure 10, which shows historical data for U.S. single-family (single-unit) housing starts from 1960 to 2010 along with 

our preliminary projections of single-family housing needs 

for the three selected RPA scenarios. In Figure 10, we also 

show for comparison a December 2010 single-family housing starts forecast to 2012 from the National Association of 

Home Builders (NAHB).

Our assumption about the future average size of new singlefamily homes (a shifter of structural panel demands) recognizes that the average size of new homes as reported by the 

Census Bureau increased historically in recent decades but 

then leveled out and declined modestly in the past decade. 

Average size of new homes is therefore generally assumed 

to remain constant at base-year (2006) levels in most USFPM scenarios. Among RPA scenarios (discussed below 

in more detail) the B2 scenario has a “sustainable development” theme, so in that scenario we assume that average 

new home size will continue to gradually decline, by 10% 

over the decade from 2010 to 2020, and by just 5% in the 

subsequent 40-year period from 2020 to 2060.

GFPM Modifications for USFPM 

We modified the global roundwood supply elasticity for 

foreign countries in the GFPM to maintain consistency between the GFPM and the RPA timber supply elasticities 

that were estimated for USFPM. The overall design and 

structure of the GFPM have been described elsewhere 

(Buongiorno and others 2003, Turner and others 2007, 

and Raunikar and others 2010, Buongiorno and Zhu 2011), 

and the UW GFPM website provides updates of the GFPM 

(http://forestandwildlifeecology.wisc.edu/facstaff/Buongiorno/book/GFPM.htm). 

In the 2010 UW version of the GFPM, short-run price elasticity of supply was specified to be 1.0 for industrial roundwood and fuelwood in all countries. In addition, the GFPM 

models change in forest inventory in each country as a longrun determinant of supply, and the initial wood supply quantities, prices, and forest inventories vary among countries, so 

long-run supply adjustments to price vary from one country 

to another, even though price elasticities are the same. However, specification of identical price elasticities represents an 

implicit assumption that relative short-run responsiveness 

of wood supply quantity to price is proportionately the same 

for all countries. To maintain the functionality of the GFPM, 

it is important to retain this feature of the model, but introducing U.S. regional stumpage supplies in USFPM leads to 

differing short-run timber supply elasticities (not the same 

as the original 1.0 supply elasticity of the UW GFPM). 

Since USFPM and the RPA forest assessment focus mainly 

on U.S. timber stumpage markets with empirically derived 

or model-based estimates of U.S. timber stumpage supply 

elasticities, it was appropriate for us to modify the global 

industrial roundwood supply elasticity so that foreign countries would share the same relative responsiveness of industrial roundwood supply to price as estimated for commercial 

timber supply in the U.S. regions. However, that result 

cannot be obtained by simply applying U.S. timber stumpage price elasticities to industrial roundwood in foreign 

countries, because prices for delivered industrial roundwood 

in other countries are not all the same, and the delivered 

roundwood prices are generally higher than U.S. stumpage 

prices for timber. 

0.0

0.5

1.0

1.5

2.0

2.5

19601970198019902000201020202030204020502060

Housing starts per year (millions)

Historical data

NAHB forecast

A2(p)

A1B(p)

B2(p)

Figure 10—Historical U.S. annual single-family housing 

starts, 1960–2010, NAHB forecast to 2012, and preliminary 

(p) projections for three selected scenarios (2020–2060).
Research Paper FPL–RP–662

24

To obtain the same relative price responsiveness for industrial roundwood supply in all countries, we had to modify 

foreign industrial roundwood price elasticities by taking 

into account the average U.S. timber stumpage price elasticity and the ratio of foreign industrial roundwood prices to 

average U.S. timber stumpage price. When we calibrated 

the USFPM/GFPM model in 2010, the weighted average of 

our timber stumpage price elasticities for the RPA scenarios 

was 0.28, and weighted average timber stumpage price was 

$34.62/m3. Multiplying average U.S. stumpage price elasticity (0.28) times the ratio of foreign industrial roundwood 

price to average U.S. stumpage price resulted in modified 

industrial roundwood price elasticities for foreign countries 

ranging from 0.65 (at a roundwood price of $80/m3) to 

1.03 (at a roundwood price of $126.90/ m3).

Alternative Scenarios for 

USFPM/GFPM

This section explains how we developed alternative global 

scenarios for USFPM/GFPM. Subsequent sections explain 

results of analysis for these scenarios. The 2010 RPA scenario data and results presented in this report are preliminary, 

not final. At the time this report was prepared, the 2010 RPA 

assessment was in progress and analysis was subject to possible revision. The scenarios presented here include global 

scenarios developed for the 2010 RPA Forest Assessment, 

and also other scenarios related to near-term projections of 

wood energy use.

2010 RPA Global Scenarios

The integrated modeling framework and analysis developed 

for the 2010 RPA Assessment operates with quantitative assumptions about global economic growth, population changes, and climate change. Consideration of global change led 

to a decision by RPA staff in 2006 to derive the 2010 RPA 

scenarios from the set of global climate and carbon emission scenarios developed for the Intergovernmental Panel 

on Climate Change, or IPCC. The RPA staff decided to use 

IPCC scenarios in 2006, a year before the IPCC and Albert 

Gore Jr. were awarded the 2007 Nobel Peace Prize for their 

efforts to disseminate knowledge about manmade climate 

change and to lay foundations for the measures needed 

to counteract such change. Three global scenarios were 

selected for the 2010 RPA from among global scenarios 

documented in the Special Report on Emissions Scenarios 

(SRES) prepared for IPCC (Nakicenovic and Swart 2000). 

We selected the IPCC marker scenarios for A1B, A2, and 

B2 storylines (specifically the “A1B AIM,” “A2 ASF,” and 

“B2 MESSAGE” scenarios), which were subjected also to 

some further harmonization and down-scaling. We adopted 

the IPCC storyline names for these scenarios, and hence we 

describe the three RPA scenarios as A1B, A2, and B2. 

The three scenarios include a range of assumptions about 

future economic growth, population growth, and climate 

change, expected to have different effects on projected 

future U.S. resource conditions and trends. In general, the 

A1B scenario incorporates elements of globalization and 

economic convergence with social development themes of 

economic growth and new technologies. The A2 incorporates heterogenic regionalism and less trade with social development themes of self-reliance and preservation of local 

identities. The B2 incorporates localized solutions and slow 

change with social development themes of sustainable development and diversified technology. A more in-depth discussion of the RPA scenarios and assumptions is provided in 

a separate RPA report in process (USDA Forest Service, in 

preparation).

Global data from the IPCC scenarios were downscaled to 

U.S. national and sub-national levels to facilitate resource 

analyses for the 2010 RPA Assessment, and some minor 

adjustments were made to U.S. population and economic 

growth assumptions. U.S. GDP was updated to a 2006 base 

year, and projections for the A1B scenario were based on 

projections provided by the USDA Economic Research Service. The U.S. population projection for A1B was updated to 

be consistent with the U.S. Census Bureau projections based 

on 2000 Census data. The projections for the A2 and B2 scenarios were then adjusted to maintain the same proportional 

difference with A1B across the projection period as reported 

in the SRES. 

Although IPCC SRES scenarios and the RPA adjustments 

to the U.S. GDP and population projections were all developed prior to the recent economic recession (2008–2009), 

the growth assumptions of all three scenarios were not 

adjusted in any way to reflect the economic recession, in 

order to maintain precise consistency with the SRES global 

economic and climate assumptions. Nevertheless, note that 

U.S. GDP growth of the A1B scenario remains consistent 

with the long-term U.S. real GDP growth trendline based 

on data that extend through the recent recession (through 

2009), while U.S. GDP growth projections of A2 and B2 

scenarios are lower than the trendline. U.S. GDP growth 

of the A1B scenario (from USDA Economics Research 

Service) coincides almost precisely to a logarithmic trendline based on real U.S. GDP data from 1950 to 2006, and 

inclusion of more recent U.S. GDP data (2006–2009) only 

slightly changes the trendline. Using annual U.S. GDP data 

from 1950 to 2006 the logarithmic trendline is: Growth = 

–0.0077 × Ln(Year) + 0.05685 (where “Year” starts at 1950 

= 0). Recomputed with GDP data from 1950 through 2009, 

the growth formula changes slightly [Growth = –0.0091 × 

Ln(Year) + 0.0618] and still closely matches the U.S. real 

GDP growth rates of the A1B scenario. On the other hand, 

unlike the A1B scenario, the A2 and B2 scenarios assume 

slower per capita real income growth relative to historical 

income growth since the mid-20th century. Thus, the A2 and 

B2 both have substantially lower long-term U.S. and global 

GDP growth rates than the A1B scenario (and lower than the 
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

25

GDP growth trendline based on data from 1950 to 2009). 

Thus, even though growth assumptions for all scenarios 

were developed prior to the most recent economic recession, 

the three scenarios still subtend a wide range of GDP growth 

assumptions by current standards, with A2 and B2 below the 

current trendline.

The A1B scenario has midrange U.S. population growth, 

whereas A2 has higher population growth, and B2 has lower 

growth (Fig. 11). However, A1B has the highest projected 

average personal income, followed by B2 and A2 (Fig. 12). 

In A1B, by 2060 the United States has 446 million people 

and real personal income averages nearly $81,000 per capita 

(in 2006 $). The A2 scenario has the highest projected U.S. 

population, over 505 million people in 2060, but the lowest 

real personal income per capita, only around $56,000 by 

2060. The B2 has lowest population growth but mid-level 

personal income, with a U.S. population of 397 million people and real personal income per capita of around $60,000 

in 2060.

The energy outlook and climate change projections of the 

IPCC SRES scenarios also reflect a general consensus 

among other global energy studies that production of renewable energy such as biomass will expand in the coming decades, although rates of expansion vary by scenario. Other 

global studies that have similarly predicted this trend include a recent Massachusetts Institute of Technology (MIT) 

study projecting world biomass energy production increasing from 45 exajoules (1 × 1018 J or EJ) currently to a range 

between 221 and 267 EJ by 2050 (Gurgel and others 2007), 

and an International Energy Agency (IEA) study predicting 

potential biomass energy production increasing to 

200 to 300 EJ by 2050 (Faaij 2007). In general, IPCC 

scenarios and other global energy studies that project large 

increases in biomass energy production share a common 

view that global petroleum production will peak sometime 

within the next couple of decades (e.g., by 2020 to 2030) 

leading generally to expansion in other forms of energy production including renewable energy production. 

Meanwhile, the United States and many other countries 

have established near-term targets and mandates for biofuel 

production, as noted in the roadmap report on biofuels for 

transport by the IEA, which determined a global biofuel capacity target of 250 billion gallons of gasoline equivalent by 

2030 (IEA 2011). In addition, policies that promote biomass 

energy use for thermal and electric power generation are 

also found in various countries and also in the United States, 

where a majority of states have adopted renewable portfolio standards requiring that electricity providers obtain a 

minimum percentage of their power from renewable energy 

resources by a certain date, which vary by state but cluster 

generally in the area of a 20% requirement by 2020. The 

U.S. Department of Energy provides an on-line summary of 

state renewable portfolio standards (see http://apps1.eere.

energy.gov/states/maps/renewable_portfolio_states.cfm).

Figure 13 displays charts that show for comparison the 

historical and projected trends in global energy production (in exajoules) reported in the IPCC Special Report on 

Emissions Scenarios (SRES) database for each of the three 

selected RPA scenarios. In all three scenarios shown in the 

charts there is a continuity of the historical pattern of expansion in global energy production along with shifting sources 

of energy, but the three scenarios vary in their overall levels 

of projected global energy production and corresponding 

levels of biomass energy production. 

The three scenarios all project a peaking of global oil production around 2020 to 2030, but as shown in Figure 13, 

the scenarios vary in their responses to subsequent declines 

in energy production from oil. For example, A1B has the 

highest projected overall energy production and therefore 

the highest projected expansion in biomass energy production, in line with A1B themes of economic growth and new 

technologies. The A2 has lower overall energy production 

and also higher energy production from coal, and therefore 

0

100

200

300

400

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600

2006 2020 2030 2040 2050 2060

A1B

A2

B2

U.S. population (millions)

Figure 11–U.S. population projections, 2006 to 2060 for 

three RPA scenarios.

0

10,000

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30,000

40,000

50,000

60,000

70,000

80,000

90,000

2006 2020 2030 2040 2050 2060

A1B

A2

B2

Personal income projections ($2006)

Figure 12–U.S. Per capita personal income projections, 

2006 to 2060, for three RPA scenarios.
Research Paper FPL–RP–662

26

the lowest projected global expansion in biomass energy 

production. The B2 has the lowest overall energy production 

and consumption, in line with its theme of sustainable development, but B2 has relatively slow changes among energy sources as compared with the other scenarios. Therefore, 

global biomass energy production levels of B2 are slightly 

higher than the A2 scenario.

Developing global fuelwood demand assumptions for the 

RPA scenarios based on the IPCC SRES scenarios required 

first calibrating the primary biomass energy production from 

corresponding IPCC scenarios to a common historical basis, 

because the three selected IPCC scenarios (A1 AIM, A2 

ASF, and B2 MESSAGE) were actually developed using 

different economic and biophysical models, and hence their 

reported historical biomass energy production levels were 

not the same (Nakicenovic and Swart 2000). We observed 

that the reported historical global biomass energy production levels of the B2 scenario (46 EJ in 1990 and 

43 EJ in 2000) were closest to historical global biomass 

energy production reported in the literature (e.g., Gurgel and 

others 2007) and as reported in data adapted from the International Energy Agency (Openshaw 2010). Thus, we adjusted base-level biomass energy production levels of the A1B 

and A2 scenarios to match the B2 scenario for the years 

1990 and 2000, but for all three scenarios we retained the 

same increases in biomass energy production as projected 

originally by SRES. We also made adjustments for missing 

data in some global regions. Table 21 shows the calibrated 

global primary biomass energy production in exajoules (EJ) 

for the three RPA scenarios and corresponding global expansion factors from 2000 to 2060. 

Table 22 provides a summary of the three alternative RPA 

scenarios in terms of their general description, their economic and social development themes as outlined by IPCC, 

and their basic assumptions about population growth, economic growth, and future global expansion of biomass 

energy production.

To summarize the RPA scenarios, the A1B has the highest 

global economic growth coupled with slowing global population growth, and it assumes continued economic globalization and global economic convergence, generally consistent 

with global real GDP growth since the mid-19th century of 

about 3% per year. Although global crude oil production 

is projected to peak in the decade between 2020 and 2030, 

global energy resources are taken to be plentiful in the A1B 

scenario by assuming large future availability of coal, unconventional oil, and natural gas as well as high levels of 

improvement in efficiency of energy exploitation, energy 

conversion, and transport technologies. High economic 

growth and high energy consumption and energy conversion 

in the A1B scenario propel the highest levels of biomass 

energy production according to the SRES (with global expansion of 5.9× from 2000 to 2060). 

The A2 and B2 scenarios have considerably lower global 

real GDP growth, slower growth in energy consumption, 

0

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800

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19902000201020202030204020502060

A1B

0

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A2

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19902000201020202030204020502060

B2

Other renewables 

Biomass

Nuclear

Gas

Oil

Coal

Global projected primary energy production (EJ)

Figure 13–Global primary energy production (EJ) by 

source, 1990 to 2060, for three RPA scenarios, as projected 

by IPCC (Nakicenovic and Swart 2000).
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

27

and also more modest expansion in global biomass energy 

production (3.1× and 3.2×, respectively). The A2 is differentiated by considerably higher global population growth 

than other scenarios, but the lowest global GDP growth, and 

hence lowest global per capita income of the three scenarios 

(Fig. 12). The B2 assumes more diversified global technology and a theme of sustainable development, resulting in the 

lowest global population growth and mid-level global GDP 

growth, but also the lowest U.S. real GDP growth. 

USFPM/GFPM models global fuelwood production and 

consumption (not total biomass energy production), and the 

IPCC SRES did not specify precisely how much of their 

projected biomass energy production consists of fuelwood 

from forests, so we had to develop approaches for modeling fuelwood production or consumption that reasonably 

reflect the biomass energy outlook of the RPA scenarios as 

projected by SRES. The available information in the SRES 

report and its supporting database (Nakicenovic and Swart 

2000) include projections of total primary biomass energy 

production globally and in four large global regions, called 

“macro” regions. The regional information indicated for 

example that the macro region encompassing South America 

and Africa was projected to account for the largest share 

of expansion in global biomass energy production, similar 

to findings of other global biomass energy studies (e.g., 

Gurgel and others 2007; Faaij 2007). In addition, the SRES 

report and database provided projections of land use for 

each scenario by macro region, including projected areas 

of forest land, cropland, and non-forest land dedicated to 

energy crops (“energy biomass land”). Thus, we were able 

to consider and explore several options for projecting global 

fuelwood demand in USFPM/GFPM based on the IPCC 

scenarios (described as follows):

Option 1: One option that has been employed previously in 

the GFPM is to model fuelwood demands of each country 

with a common price elasticity of demand (–0.5) and exogenously shift the demand over time so that global expansion 

in roundwood fuelwood consumption matches the SRES 

global expansion in primary biomass energy production by 

scenario, while each country’s share of global fuelwood 

consumption is adjusted to converge upon and match each 

country’s share of global GDP by the end of the projection 

period (Raunikar 2010). This option results in very large 

expansion of fuelwood consumption for countries like the 

United States that have a small current share of global fuelwood consumption but a large share of projected global 

GDP. Also, this option does not take into account available 

information from the SRES on regional land use and biomass energy production by macro region, so the projections 

of fuelwood consumption by macro region do not align with 

the SRES regional allocation of biomass energy production 

(e.g., compared with the SRES biomass energy projections, 

there is proportionately much less expansion of fuelwood 

production in South America and Africa under this option).

Option 2: Another option is to model fuelwood demands in 

each country using econometric demand functions that are 

based on statistical analysis of historical data, with a price 

Table 22—Summary of the three alternative RPA scenarios

Scenario A1B A2 B2

General description Globalization,

economic convergence

Heterogenic regionalism,

less trade

Localized solutions,

slow change

Social development themes Economic growth, new technologies,

capacity building

Self-reliance, preservation

of local identities

Sustainable development,

diversified technology

Global real GDP growth,

(2010–2060)

High (6.2×) Medium (3.2×) Medium (3.5×)

Global population growth,

(2010–2060)

Medium (1.3×) High (1.7×) Medium (1.4×)

U.S. GDP growth,

(2006–2060)

Medium (3.3×) Low (2.6×) Low (2.2×)

U.S. population growth,

(2006–2060)

Medium (1.5×) High (1.7×) Medium (1.3×)

Global expansion

of primary biomass energy

production, (2000–2060)

High (5.9×) Medium (3.1×) Medium (3.2×)

Table 21—Calibrated global primary 

biomass energy production for RPA 

scenarios

Global primary 

biomass energy production,

measured in exajoules (EJ) 

Year 

Scenario

A1B

Scenario

A2

Scenario

B2

1990 46.00 46.00 46.00 

2000 43.10 43.10 43.10 

2020 69.50 52.22 52.60

2060 254.54 131.80 135.90 

Expansion factor,

 2000–2060 

5.9× 3.1× 3.2× 
Research Paper FPL–RP–662

28

elasticity of fuelwood demand and an econometric relationship of fuelwood demand to GDP in each country. This option has also been explored with the GFPM. Under this option, the fuelwood consumption in each country responds to 

the alternative GDP growth assumptions of the three different RPA scenarios (Table 22), but in general the econometric 

relationships between fuelwood demand and GDP growth 

result in very modest expansion of fuelwood consumption 

because in many countries consumption of fuelwood has 

been receding as a share of total energy output in recent 

decades. Thus, under this option the projected expansion of 

global fuelwood consumption falls far short of the expansion in biomass energy production as projected by SRES in 

all scenarios.

Option 3: A third option is to model fuelwood demands 

by SRES macro region, taking into account regional landuse projections and regional biomass energy projections 

provided by SRES for each scenario. This option is implemented by modeling fuelwood demands of each country 

with a common price elasticity of demand (–0.5) and exogenously shifting demands over time so that regional expansion in fuelwood consumption follows SRES projections of 

regional net biomass energy consumption after deducting 

regional estimates of biomass energy from biomass plantations, agricultural crops, and residues. Biomass energy outputs from biomass plantations are computed by multiplying 

SRES projections of biomass energy plantation area times 

conventional yield assumptions for biomass energy crops by 

region. Biomass energy output from cropland and residues 

are based on historical output of biomass energy from cropland and residues, with projections based on SRES regional 

cropland area projections. We deduct the biomass energy of 

biomass plantations, agricultural crops, and residues from 

total biomass energy production as projected by SRES within each macro region, yielding as a remainder the imputed 

regional consumption of forest-based fuelwood. We then 

compute matching growth shifters for fuelwood demand in 

each region for each RPA scenario, and we distribute the 

growth among countries within each region in proportion to 

each country’s share of regional GDP in 2060. We also apply common regional elasticities with respect to the growth 

shifters to adjust or fine-tune the regional fuelwood projections until USFPM/GFPM projections of regional fuelwood 

consumption match the regional fuelwood consumption 

targets. Under this option, we take into account the large 

share of projected biomass energy production that is supplied by non-forest biomass energy plantations and cropland 

residues based on SRES land area projections. Thus, option 

3 produces projections of global fuelwood consumption in 

USFPM/GFPM that are somewhat lower than option 1, but 

higher than option 2. In this report, we focus on USFPM/

GFPM results obtained using the option 3 approach, which 

is described in more detail in Appendix B, but for comparison we also produced results for an “A1B-Low Fuelwood” 

scenario, where the U.S. and global fuelwood demands were 

determined by the option 2 approach (via historical GDP 

growth relationships).

RFS+RES Scenarios

While the 2010 RPA assessment focused USFPM/GFPM on 

long-range global climate and emissions scenarios developed by the IPCC, we have also explored other alternative 

scenarios with USFPM/GFPM. This section describes a set 

of scenarios that we developed recently in collaboration 

with the Pinchot Institute for Conservation in a special study 

commissioned by the Forest Service Research Executive 

Team (Ince and others 2011). These scenarios and related 

assumptions were developed in collaboration with V. Alaric 

Sample of the Pinchot Institute for Conservation in Washington D.C. Do-il Yoo (University of Wisconsin graduate 

student) assisted in structuring the data input and running 

the scenarios with USFPM/GFPM. 

This set of alternative scenarios focuses on potential implications of near-term biomass energy policies, specifically 

the U.S. Renewable Fuels Standard (RFS) that promotes 

expansion of advanced biofuels based on biomass, and 

also potential federal renewable energy standards (RES) 

for electric power production from renewable sources. We 

developed four alternative RFS+RES scenarios based in 

part on U.S. renewable energy projections from the 2010 

U.S. Annual Energy Outlook (AEO) (DOE 2010a). The 

RFS+RES scenarios differ from one another mainly in terms 

of assumptions about future expansion in U.S. wood energy 

consumption through 2030. Also, unlike the RPA scenarios, 

the RFS+RES scenarios take into account the recent economic recession and the downturn in U.S. housing construction from 2006 to 2010, with adjustments in U.S. and 

global GDP. The sources of basic assumptions for the four 

RFS+RES scenarios are summarized in Table 23. 

All of the RFS+RES scenarios assumed future levels of 

U.S. cellulosic biofuel output under the Renewable Fuels 

Standard policy (RFS) as projected by the 2010 AEO (DOE 

2010a). The scenario labeled “HP” assumed the higher biofuel output projection of the AEO “High Oil Price” (HP) 

case, whereas the other three scenarios assumed the RFS 

biofuel projection of the AEO reference case. All of the 

scenarios also included additional biomass energy consumption under hypothetical national renewable energy standards 

(RES) requiring either 10% (RES10) or 20% (RES20) 

of electric power to be generated from non-hydroelectric 

renewable energy sources by 2030. The scenario labeled 

“RES20+EFF” assumed a similar energy policy but allowed 

half of the non-hydro renewable energy to be more efficient 

combined heat and power, therefore requiring somewhat 

less biomass input for energy production than the RES20 

scenarios. 

We assumed in all RFS+RES scenarios that wood will account for 1/3 of the biomass required in the United States 

to meet RFS and RES energy goals (other sources of 
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

29

biomass, chiefly agricultural, were assumed to account for 

the remaining 2/3). Our assumption that wood will account 

for one-third of projected expansion in U.S. biomass energy 

is consistent with estimates from recent national energy 

studies. For example, wood was projected to account for 

about 30% of expansion in U.S. biomass energy according 

to the 2010 Annual Energy Outlook (DOE 2010b). Also, 

in a separate study, wood biomass supply needed to meet a 

25% renewable portfolio standard for electric power combined with a 25% renewable fuel standard for transportation fuels was projected to be 28% of total biomass, while 

energy crops were 41%, a portion of which were short 

rotation woody crops (DOE 2007). The same study also 

projected wood energy use at about 160 million cubic meters above current use, which is also close to the expansion 

projected in our highest wood energy demand scenario (an 

expansion of about 200 million cubic meters by 2030 in our 

RFS+RES20+HP scenario).

In summary, abbreviations applied to the policy scenarios 

have the following meanings: 

RFS—U.S. cellulosic biofuel output under the U.S. RFS 

obtains the levels projected by AEO 2010; 1/3 of required 

biomass is wood.

RES10—Hypothetical Renewable Energy Standard, 10% of 

U.S. electric power output is non-hydro renewable energy 

by 2030; 1/3 of required biomass is wood.

RES20—Hypothetical Renewable Energy Standard, 20% of 

U.S. electric power output is non-hydro renewable energy 

by 2030; 1/3 of required biomass is wood.

RES20+EFF—Similar to RES 20, but half of biomass energy output is more efficient combined heat and power, reducing biomass consumption; 1/3 of required biomass is wood.

HP—U.S. GDP growth and higher biofuel output based 

AEO High Oil Price case.

Among the RFS+RES scenarios, the RFS+RES10 scenario 

was posited as a baseline wood energy scenario because it 

largely reflects existing U.S. energy policies. It included 

projected U.S. cellulosic biofuel output under the U.S. RFS 

policy projected by the 2010 Annual Energy Outlook Reference Case outlook (DOE, 2010a). It also included additional 

biomass consumption sufficient to meet a national Renewable Energy Standard (RES) requiring 10% of electric 

power to be non-hydro renewable by 2030. This reflects that 

some but not all U.S. states have RES policies, known as renewable portfolio standards, which vary by state but cluster 

generally in the area of a 20% requirement by 2020. 

The baseline RFS+RES10 scenario projects a 51% expansion in U.S. consumption of wood for energy by 2030. That 

represents a modest increase in the wood energy share of 

total U.S. primary energy consumption from 1.0% in 2006 

to 1.3% in 2030, based on total U.S. energy consumption 

as projected by IEA. The alternative scenarios project the 

wood energy share of total U.S. energy consumption by 

2030 to be 1.6% in the RFS+RES20+EFF scenario, 1.8% in 

the RFS+RES20 scenario, and 2.5% in the RFS+RES20+HP 

scenario (Table 23). Thus, our policy-driven RFS+RES 

scenarios for U.S. wood energy consumption vary from an 

increase of 51% (from 2006 to 2030) in the RFS+RES10 

baseline scenario to 83% in the RFS+RES20+EFF 

scenario, 101% in the RFS+RES20, and 178% in our 

RFS+RES20+HP scenario. 

For all RFS+RES scenarios, future U.S. GDP growth and 

U.S. housing starts assumptions (key drivers of U.S. forest 

product demands) were based on projections of the 2010 

Annual Energy Outlook (AEO) (DOE 2010a). The AEO 

housing starts data include also the historical decline in U.S. 

housing starts of over 70% from 2005 to 2009, along with 

projected gradual recovery in housing starts from 2010 to 

2015. The first three scenarios use the U.S. real GDP growth 

projections of the AEO Reference case, while the “HP” 

Table 23—Four RFS+RES scenarios and their sources of basic assumptions 

Scenario RFS+RES10 RFS+RES20+EFF RFS+RES20 RFS+RES20+HP 

Basis for U.S. wood energy 

 projections 

AEO reference case RFS

 and hypothetical RES10

AEO reference case RFS

 and hypothetical 

 RES20+EFF 

AEO reference case RFS 

 and hypothetical 

 RES20 

AEO high oil price case RFS

 and hypothetical RES20 

Wood percentage of U.S. 

 primary energy consumption 

 in 2030 

1.3 1.6 1.8 2.5 

U.S. GDP growth AEO reference case AEO high oil price case 

U.S. housing starts AEO reference case 

Foreign GDP growth IMFa

 (2006–2014), IPCC B2 MESSAGE (2015–2030)a

Fuelwood consumption in 

foreign countries 

Fuelwood consumption as a percentage of primary energy consumption remains constant (2006–2030) 

 while energy consumption increases based on IEA projections, resulting in a 65% increase in fuelwood 

 consumption volume in total for all other countries. 

a

Note: Global GDP growth of the B2 scenario after 2015 dovetailed with International Monetary Fund (IMF) growth to 2014. 
Research Paper FPL–RP–662

30

scenario uses the slightly different U.S. GDP projections 

of the AEO “High Oil Price” case. All RFS+RES scenarios 

use real GDP data and projections from 2006 to 2014 for 

other countries from the International Monetary Fund World 

Economic Outlook (IMF 2010), coupled with longer-term 

GDP growth assumptions of the IPCC B2 Message scenario 

(IPCC 2000). Because we included actual historical GDP 

data from 2006 to 2009 for all countries, the RFS+RES scenarios take into account the recent global recession, and also 

incorporate an assumption of gradual economic recovery in 

U.S. and global GDP growth as projected by AEO and IMF. 

We derived the U.S. single-family housing starts projection 

for the RFS+RES scenarios from the projection of total U.S. 

housing starts reported in the AEO 2010 Reference Case 

(DOE 2010) by adjusting for the multi-family share of housing starts. The total housing starts projections of the AEO 

High Oil Price scenario and Reference Case scenario were 

nearly identical (DOE 2010), so we used the Reference Case 

projections in all RFS+RES scenarios. Not surprisingly, the 

housing starts projection based on the AEO did not match 

precisely the preliminary projections of any of the 2010 RPA 

scenarios, but the AEO housing outlook came closest to the 

RPA B2 projection after 2015, actually overlapping the preliminary B2 projection as shown in Figure 14. Thus, in the 

RFS+RES scenarios, the U.S. housing starts assumptions 

are similar to the projections of the RPA B2 scenario after 

2015, but lower than the housing starts projections of the 

A1B or A2 scenarios. In addition, the U.S. GDP growth assumptions of the RFS+RES scenarios take into account the 

recent economic recession and therefore the GDP growth 

of the RFS+RES scenarios is lower than the RPA scenarios 

from 2006 to 2015, but similar to the B2 scenario from 2015 

to 2035.

We also applied several other general assumptions to the 

RFS+RES scenarios. First, for U.S. regions, we assumed 

an elasticity of timber supply with respect to timber growing stock inventory (ε = 1.5), and we applied initial timber 

growth assumptions that matched recent data for timber 

growing stock by U.S. region and species group (Smith and 

others 2009). Future changes in regional U.S. timber growth 

rates are modeled endogenously in relation to changes in 

growing stock density using the same general approach 

applied to other countries in the GFPM (Appendix A), 

where growth in forest stock is a function of forest stocking 

density and industrial roundwood supplies have a long-run 

supply elasticity of 1.5 with respect to change in forest 

stock (Raunikar and others 2010; Turner and others 2006). 

Second, we did not include any biomass supply policies or 

incentives in our scenarios, such as biomass subsidies, carbon credits, or offset values for forest carbon sequestration 

or any constraints on use of pulpwood or mill residues for 

energy. We plan to evaluate impacts of biomass supply and 

climate policies in future studies. Last, for the RFS+RES 

scenarios we applied projections of U.S. forest land area 

changes by region as derived recently by Alig and others 

(2010) instead of using the 2010 RPA land area projections 

or the endogenously determined projection of forest land 

area based on the “Kuznets curve” approach that is applied 

to other countries in the GFPM.

For the RFS+RES scenarios, fuelwood demands in all other 

countries were programmed to grow at rates that would 

maintain a constant fuelwood share of the total energy 

consumption in each country from 2006 to 2030 (based 

on IEA global energy projections). Under this assumption, 

the volume of fuelwood consumption of all other countries 

increases in aggregate by around 65% by 2030 because of 

projected increases in total energy consumption. This assumption for fuelwood is actually a real departure from 

historical fuelwood trends for most countries, where the 

fuelwood share of total energy consumption has generally 

declined in recent decades, although trade in certain wood 

energy products such as wood fuel pellets has increased in 

recent years (Spelter and Toth 2009). 

In summary, the RFS+RES scenarios differ from the RPA 

scenarios in several ways. We designed the RFS+RES scenarios to project impacts of biomass energy policies over a 

shorter period, from 2010 to 2030 (rather than the longerrange focus of the RPA assessment from 2020 to 2060). 

Also, unlike the 2010 RPA scenarios, the RFS+RES scenarios take into account the effects of the recent economic 

recession on U.S. and global GDP, using actual GDP data 

from 2006 to 2010 along with U.S. GDP projections from 

the 2010 Annual Energy Outlook (EIA 2010), and global 

GDP data and projections for foreign countries from the 

IMF World Economic Outlook database (IMF 2010). Thus, 

RFS+RES scenarios explicitly incorporate the recent global 

recession, the downturn in U.S. housing construction since 

2006 and resulting impacts on timber markets, as well as 

assumptions about near-term economic recovery. 

0.0

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Housing starts per year (millions)

Historical data

AEO 2010

A2(p)

A1B(p)

B2(p)

Figure 14— Historical U.S. annual single-family housing 

starts, 1960–2010, with projections based on Annual Energy 

Outlook (AEO) 2010, and preliminary (p) projections for RPA 

scenarios.
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

31

Results

Results of analysis are presented here in the form of USFPM/GFPM projections for the 2010 RPA scenarios and 

selected RFS+RES scenarios. In most cases, projections 

are shown graphically in charts that also include historical 

data series for comparison to the projections. Results shown 

here for the 2010 RPA scenarios are preliminary, as the RPA 

forest assessment was still in process at the time this report 

was written. However, these results provide indications of 

the kinds of projections that USFPM/GFPM produces under the specific scenarios and data assumptions described 

in this report. Our presentation of results focuses here on 

USFPM/GFPM projections for the United States rather than 

the global outlook. A parallel global outlook for wood and 

forests based on the RPA scenarios was developed with the 

UW version of the GFPM and published separately (Raunikar and others 2010).

U.S. Wood Fuel Feedstock Projections

A striking feature of USFPM/GFPM scenarios is the large 

projected expansion relative to historical trends in U.S. consumption of wood fuel feedstock (including fuelwood, fuel 

residues, and other wood materials projected to be used for 

energy). Figure 15 shows the historical trend in total U.S. 

wood fuel feedstock consumption along with preliminary 

USFPM/GFPM projections of consumption for the three 

RPA scenarios and also for two selected RFS+RES scenarios (RFS+RES10 and RFS+RES20+HP). The global expansion in fuelwood consumption of the RPA scenarios is similar the GFPM results reported previously (Raunikar and others 2010). However, projected rates of expansion for U.S. 

fuelwood consumption are generally higher than the global 

rate of expansion for the RPA scenarios because forests are 

projected to provide a large share of biomass for energy production in the Organization for Economic Cooperation and 

Development (OECD) region (including United States) according to the SRES projections, and also because the U.S. 

share of regional fuelwood consumption in 2060 is assumed 

to match the relatively large U.S. share of regional GDP by 

2060 (see Appendix B for more details). Thus, in the RPA 

scenarios, U.S. consumption of wood fuel feedstock is projected to expand from 2006 to 2060 by a factor of 15.7× in 

the A1B scenario, 9.4× in the A2 scenario, and 3.7× in the 

B2 scenario (Figure 15; see also Appendix Figure B3). 

Note also that the RFS+RES scenarios subtend a range of 

projected expansion in U.S. wood fuel feedstock consumption that overlaps the RPA B2 projection (from 2020 to 

2030), but the A1B and A2 projections of fuel feedstock 

consumption are higher than the RFS+RES projections 

(Fig. 15). Thus, an implicit result is that the IPCC SRES 

A1B and A2 scenarios envision higher U.S. wood energy 

consumption than indicated by the 2010 Annual Energy 

Outlook, even after taking into account the AEO high oil 

price scenario for biofuel production and a hypothetical 

20% renewable energy standard by 2030.

Although global timber supply and fuelwood trade projections influence global allocation of fuelwood production in 

USFPM/GFPM, projected U.S. wood fuel feedstock production nevertheless follows closely the projected trends in U.S. 

consumption. Figure 16 shows historical data on total U.S. 

wood fuel feedstock production along with USFPM/GFPM 

projections of U.S. wood fuel feedstock production for the 

three RPA scenarios and the two selected RFS+RES scenarios (RFS+RES10 and RFS+RES20+HP).

Historically, U.S. wood fuel feedstock production has consisted mostly of roundwood fuelwood harvest and fuel residues (mill residues used as fuel). Fuel residues accounted 

for about 60% of U.S. wood fuel feedstock production in 

2006, whereas roundwood fuelwood harvest including 

bark accounted for about 40% (Smith and others 2009). 

However, non-conventional sources of fuel feedstock tend 

to dominate in the projections, especially in scenarios with 

0

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A2(p)

B2(p)

RFS+RES20+HP

RFS+RES10

Historical data

U.S fuel feedstock consumption 

(million m3/y)

Figure 15—Annual U.S. wood fuel feedstock consumption, 

1970–2010, with USFPM/GFPM projections for selected 

RFS+RES scenarios and preliminary (p) projections for 

three RPA scenarios (million m3/y).

1970198019902000201020202030204020502060

A1B(p)

A2(p)

B2(p)

Historical Data

RFS+RES20+HP

RFS+RES10

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

U.S fuel feedstock production 

(million 

m3/y)

Figure 16—Annual U.S. wood fuel feedstock production, 

1970–2010, with USFPM/GFPM projections for selected 

RFS+RES scenarios and preliminary (p) projections for 

three RPA scenarios (million m3/y).
Research Paper FPL–RP–662

32

large increases in wood energy consumption. Figure 17 

shows historical data and preliminary projections of U.S. 

wood fuel feedstock production by source for the A1B scenario. Conventional fuelwood harvest and fuel residues expand, but non-conventional sources of wood fuel feedstock 

become dominant in the projections as consumption and 

price of fuel feedstock increase. Non-conventional sources 

include harvest residues, and hardwood (HW) and softwood 

(SW) pulpwood along with mill fiber residues that would be 

conventionally used at wood pulp mills and particleboard 

mills. Projections for the other RPA scenarios are generally 

much lower, as shown for example in Figure 18 for the B2 

scenario, where expansion of fuel feedstock consists mainly 

of harvest residues.

Thus, the RPA scenarios encompass a wide range in projected U.S. production of wood energy from non-conventional 

sources, such as pulpwood, harvest residues, and fiber residues, from modest expansion in use of harvest residue for 

energy in the B2 scenario (Fig. 18) to really significant 

expansion in the A1B scenario (Fig. 17). In the A1B scenario, for example, the projected consumption of pulpwood and 

fiber residues for energy climbs to over 900 million cubic 

meters per year by 2060 (Fig. 17), a quantity several times 

higher than current total U.S. timber harvest. Thus, projected impacts on timber markets are significant in the A1B 

scenario. On the other hand, in the B2 scenario, projected 

consumption of pulpwood and fiber residues for energy is 

less than one-tenth as large as in the A1B scenario (Fig. 18). 

Furthermore, because harvest residues provide most of the 

projected expansion in U.S. wood energy consumption in 

the B2 scenario (Fig. 18), the impacts of wood energy production on conventional timber markets and conventional 

forest product markets are much smaller in the B2 scenario 

than in the A1B scenario.

The alternative projections of U.S. wood fuel feedstock 

consumption in the RPA scenarios result in divergent trajectories for U.S. wood fuel feedstock prices and industrial 

roundwood prices as projected by USFPM/GFPM. 

Figure 19 shows the USFPM/GFPM preliminary projections of U.S. wood fuel feedstock demand price and U.S. 

industrial roundwood price for the three RPA scenarios. All 

price projections are in terms of 2006 real prices (the model 

generally ignores inflation). U.S. industrial roundwood 

price is the average of delivered regional prices for sawlogs, 

pulpwood, and other industrial roundwood as projected by 

USFPM. In recent years, bulk wood fuel feedstocks (such as 

green fuel residues or whole-tree chips) have been selling in 

the United States at prices in the vicinity of $15 to $25 per 

cubic meter (Timber Mart-South 2006) and in USFPM the 

base-period (2006) demand price for wood fuel feedstock 

was set at $25 per cubic meter (Table 17). The expansion in 

consumption of wood energy results in a projected real price 

of wood fuel feedstock more than five times higher by 2060 

in the A1B scenario, but the smaller expansion in wood 

energy demand of the A2 and B2 scenarios results in much 

smaller increases in wood fuel feedstock price. Parallel price 

trends are projected for industrial roundwood (Fig. 19).

In the B2 scenario, as noted previously, most of the expansion in U.S. wood fuel feedstock consumption consists of 

increased use of harvest residues (Fig. 18), which has little 

impact on conventional timber prices, because the harvest residues are produced as byproducts of conventional 

timber harvesting activities and only a nominal additional 

cost is required to recover the harvest residues. In fact, the 

increased utilization of harvest residues for energy boosts 

revenues for timber stumpage and harvest activities and 

thus tends to increase timber supply. Hence, there is little 

increase in projected U.S. industrial roundwood price in 

the B2 scenario (Fig. 19), while the price is projected to be 

somewhat higher in the A2 scenario and much higher in 

the A1B scenario, with higher wood energy consumption 

and expanded competition for non-conventional sources of 

wood fuel feedstock such as pulpwood and fiber residues 

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U.S. fuel feedstock production 

(million 

m3/y)

Figure 17— Annual U.S. wood fuel feedstock production, 

1970–2010, and USFPM/GFPM preliminary (p) projection of 

production by feedstock source for A1B scenario (million 

m3/y).

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(million 

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0

Figure 18—Annual U.S. wood fuel feedstock production, 

1970–2010, and USFPM/GFPM preliminary (p) projection 

of production by feedstock source for B2 scenario (million 

m3/y).
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

33

(and eventually agricultural short-rotation woody crops toward the end of the projection period in the A1B scenario). 

The larger expansion in wood fuel feedstock consumption 

and expanded use of non-conventional supply sources drives 

up the projected real price for wood fuel feedstock and industrial roundwood, and most significantly in the A1B 

scenario (Fig. 19).

USFPM/GFPM projections of average global real price 

increases for fuelwood and industrial roundwood outside 

of the United States are somewhat different, as shown in 

Figure 20. In the A1B scenario, global wood prices increase 

more than in the United States, but the A2 scenario has 

the lowest projected increase in global price for fuelwood 

(just a $32 increase from 2006 to 2060). This is because 

the A2 scenario has the lowest projected global expansion 

in fuelwood consumption (Appendix B, Figure B3). Thus, 

there are notable differences in the RPA scenarios between 

projected increases for U.S. fuel feedstock and industrial 

roundwood price (Fig. 19) and projected increases for global 

fuelwood price (Fig. 20). In the A1B scenario, projected 

price increases for wood fuel are much higher globally 

(+$200) than in the United States (+$129). The lowest projected global fuelwood and industrial roundwood prices 

occur in the A2 scenario, whereas the lowest projected U.S. 

fuel feedstock and industrial roundwood prices occur in the 

B2 scenario. 

The alternative projections of U.S. and global fuelwood 

demands and industrial roundwood prices have large and 

divergent impacts on the overall U.S. forest product production and trade outlook, as explained in the next section, 

because demands for fuelwood and resulting roundwood 

price impacts influence comparative advantages in production and trade. In particular, as will be shown in the forest 

product projections, U.S. producers of forest products tend 

to gain comparative advantage in the A1B and B2 scenarios, 

where projected global roundwood prices generally increase 

much more than the projected increase in U.S. domestic 

wood prices (see Figs. 19 and 20). On the other hand, foreign producers of forest products tend to gain comparative 

advantage in wood costs in the A2 scenario where there is 

less expansion in global fuelwood demand, lower global 

increases in wood prices, and less global competition for 

wood raw material.

Forest Product Projections

Before discussing timber market projections, we present the 

U.S. forest product market projections, which also influence 

the overall U.S. timber market outlook. Figure 21 shows the 

historical trend for annual total U.S. lumber consumption 

(hardwood and softwood sawnwood) along with USFPM/

GFPM projections of total U.S. lumber consumption for 

the RFS+RES10 scenario and preliminary projections for 

the three RPA scenarios. As shown by the historical trend in 

Figure 21, U.S. lumber consumption was severely affected 

by the collapse in U.S. housing starts from 2005 to 2009 and 

the recent economic recession, with total lumber consumption dropping by around 50% from 2005 to 2009, and just 

beginning to recover in 2010. For USFPM/GFPM, the principal drivers of U.S. lumber demand include housing starts 

projections (Fig. 10) and GDP growth assumptions (Table 

22), which vary by RPA scenario and thus result in divergent 

lumber consumption projections for the RPA scenarios, as 

shown in Figure 21. By contrast, the RFS+RES scenarios 

take into account the housing downturn and economic recession along with gradual recovery as projected by the AEO 

2010, resulting in a somewhat lower trajectory for U.S. lumber consumption (illustrated by the RFS+RES10 scenario in 

Fig. 21).

Projected lumber consumption for all RFS+RES scenarios 

were nearly identical to the RFS+RES10 projection shown 

in Figure 21 because of nearly identical housing starts assumptions and GDP growth assumptions among RFS+RES 

scenarios. Also, harvest residues account for most of projected expansion in wood fuel feedstock production in 

all RFS+RES scenarios (similar to B2 scenario) so wood 

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+$7

+$37 (2006 dollars per m3

)

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(2006 dollars per m3

2006 Base price )

A1B(p)

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B2(p)

−$11

+$10

+$119

Figure 19—USFPM/GFPM preliminary (p) projections of 

U.S. wood fuel feedstock real price and industrial 

roundwood price increases for RPA scenarios (2006 $/m3).
Research Paper FPL–RP–662

34

energy has a small impact on lumber production in the 

RFS+RES scenarios. RPA scenarios provide a wider range 

of alternatives in U.S. lumber consumption because the 

RPA scenarios incorporated higher levels of wood energy 

consumption and divergent assumptions about U.S. housing 

starts and U.S. GDP growth.

The basic assumptions of the A1B and A2 scenarios result in 

relatively high U.S. lumber consumption projections that are 

also quite similar from 2020 to 2045. This similarity is because U.S. lumber demand is driven both by projected housing starts and real GDP growth (about half of lumber is used 

in housing construction and half in a broad array of other 

end uses that follow GDP growth), so the higher U.S. GDP 

growth of the A1B compensates for the higher housing starts 

of the A2. Among the RPA scenarios, the B2 has the lowest 

projections of U.S. lumber consumption because the B2 has 

the lowest U.S. housing starts projections (Fig. 10) and the 

lowest rate of growth in U.S. GDP (Table 22). 

However, USFPM/GFPM projections of U.S. lumber production do not exactly follow consumption because of the 

influence of global trade and wood energy demands. 

Figure 22 shows historical U.S. lumber production along 

with USFPM/GFPM projections for selected RFS+RES scenarios and preliminary projections for RPA scenarios. Of the 

RPA scenarios, A1B has the highest projected U.S. lumber 

production, partly because A1B has high lumber consumption (Fig. 21) but also because A1B has highest projected 

global fuelwood demand and fuelwood prices (Fig. 20), 

which create competing global demand and higher global 

prices for industrial roundwood that limit global lumber exports to the United States. Hence, U.S. lumber producers 

obtain comparative advantage and high levels of production 

in the A1B scenario. By contrast, A2 has the lowest projected expansion in global wood energy consumption 

(Fig. B3) so there is less competition from wood energy for 

industrial roundwood in foreign countries. Thus, foreign 

lumber producers do not lose comparative advantage in 

the A2 scenario, resulting in lower projected levels of U.S. 

lumber production (Fig. 22). In this regard, the RFS+RES 

scenarios were more similar to the A1B scenario, but with 

higher U.S. net exports of lumber. These findings were more 

in line with recent trends (Fig. 22). The reason for the similarity is that RFS+RES scenarios incorporate the collapse in 

housing from 2006 to 2010, which affords higher exports in 

conjunction with lower domestic consumption (Fig. 21).

Figure 23 shows USFPM/GFPM projections of U.S. lumber 

net exports (annual exports minus imports) again showing 

clearly that the A2 scenario affords the most comparative 

advantage to foreign lumber producers, because of lowest 

global fuelwood consumption and relatively little global 

competition for industrial roundwood from wood energy.

Beyond lumber, the next leading category of U.S. solidwood products is the structural wood panel products, 

including chiefly oriented strandboard (OSB) and softwood 

plywood, both of which are used primarily in housing and 

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Foreign avg. fuelwood price

(2006 dollars per m3

)

2006 Base price

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+$200

+$80

+$36

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Foreign avg. industrial roundwood price

(2006 dollars per m3

2006 Base p )rice

A1B(p)

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B2(p)

+$150

–$10

+$30

Figure 20—USFPM/GFPM preliminary (p) projections of 

average global fuelwood real price and industrial roundwood price increases outside of the United States for RPA 

scenarios (2006 $/m3).

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0

U.S. lumber consumption 

projections (million m3/y)

Figure 21—Annual U.S. lumber consumption, 1970–

2010, with RFS+RES10 projections and preliminary (p) 

projections for RPA scenarios (million m3/y).
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

35

other construction applications, as well as various industrial end uses. Figure 24 shows historical data for total U.S. 

structural wood panel consumption (OSB and softwood 

plywood) along with USFPM/GFPM projections of U.S. 

structural panel consumption for the RFS+RES10 scenario 

and preliminary projections for the RPA scenarios. As was 

the case for lumber consumption, the A1B and A2 scenarios 

have highest projected structural wood panel consumption because those scenarios have the highest GDP growth 

(A1B) and highest housing starts projections (A2), whereas 

B2 has the lowest GDP growth and housing starts.

However, as with lumber production, projections of U.S. 

structural wood panel production do not exactly follow consumption projections because of the influence of trade and 

global wood energy demands. Figure 25 shows historical 

U.S. structural wood panel production and USFPM/GFPM 

projections of production. As was the case for lumber, the 

A2 scenario affords more comparative advantage to foreign 

producers of structural panels because the A2 scenario has 

the lowest projected global expansion in wood energy consumption (Appendix B, Figure B3) and thus there is little 

global competition for industrial roundwood from wood 

energy. The A2 scenario also has the highest levels of U.S. 

housing starts, which propel the highest levels of structural 

wood panel demand and high levels of wood panel imports 

(lowest net exports), as shown in Figure 26. As was the case 

for lumber, the A1B scenario affords comparative advantage 

to U.S. producers of structural wood panels and high levels 

of production, at least for several decades, but toward the 

end of the projection period (after 2040) U.S. production 

declines in the A1B scenario as OSB output is negatively 

impacted by high levels of pulpwood consumption for energy (Fig. 17). The RFS+RES scenarios exhibit even higher 

U.S. net exports of structural wood panels (more in line 

with recent net export trends, as shown in Fig. 26) because 

RFS+RES scenarios incorporate the recent collapse in U.S. 

housing from 2006 to 2010, which affords higher exports in 

conjunction with lower domestic consumption.

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Lumber production

(million m3/y)

Figure 22—Annual U.S. lumber production, 1970–2010, with 

RFS+RES projections and preliminary (p) projections for 

RPA scenarios (million m3/y).

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Net exports

 (million m3/y)

Figure 23—Annual U.S. net exports of lumber, 1970–2010, 

with RFS+RES projections and preliminary (p) projections 

for RPA scenarios (million m3/y).

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Structural wood panel 

consumption (million m3/y)

Figure 24—Annual U.S. structural wood panel consumption, 1970–2010, with RFS+RES10 projections and preliminary (p) RPA projections (million m3/y).

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Structural wood panel 

production (million m3/y)

Figure 25—Annual U.S. structural wood panel production, 

1970–2010, with RFS+RES projections and preliminary (p) 

projections for RPA scenarios (million m3/y).
Research Paper FPL–RP–662

36

Figure 27 shows the historical trend for total paper and paperboard consumption in the United States, along with USFPM/GFPM projections for the RFS+RES10 scenario, and 

preliminary projections for the three RPA scenarios. Again, 

in the RFS+RES scenarios, unlike RPA scenarios, projections of product consumption incorporate the recent economic recession and are nearly identical for all RFS+RES 

scenarios because of nearly identical GDP assumptions. 

Also wood energy demands exert little influence on timber 

markets in the RFS+RES scenarios because harvest residues supply most of the expansion in wood fuel feedstock 

production. Although divergent projections of paper and 

paperboard consumption would be expected in the RPA scenarios because of divergent GDP growth assumptions, the 

projections are also impacted by competing consumption of 

pulpwood for wood energy, especially in the A1B scenario.

Of the three RPA scenarios, the A1B has highest U.S. GDP 

growth, so not surprisingly the A1B has the highest projected U.S. consumption of paper and paperboard (Fig. 27), 

but consumption in the A1B is only slightly higher than the 

other scenarios. U.S. wood energy demands in the A1B scenario consume large volumes of pulpwood (Fig. 18), which 

increases the price for pulpwood and dampens projected 

growth in U.S. paper and paperboard consumption, although 

inelasticity of paper and paperboard demands with respect 

to price (Table 18) tends to limit the demand response to 

higher wood costs. There is generally less competition for 

pulpwood from wood energy in the A2 and B2 scenarios. 

Total U.S. paper and board consumption is projected to 

gradually decline in all scenarios, including the RFS+RES 

scenarios, with consumption declining primarily in newsprint and printing and writing paper grades.

Figure 28 shows the historical trend of total annual U.S. paper and paperboard production, along with USFPM/GFPM 

projections. U.S. production peaked historically in 1999. 

Although projected U.S. paper and paperboard consumption 

is only modestly higher in the A1B than the other RPA scenarios (Fig. 27), there is nevertheless a wider divergence in 

projected U.S. production, especially between A1B and A2 

scenarios (Fig. 28). This divergence is attributable to trade 

impacts of high global fuelwood demand in the A1B scenario versus low global fuelwood demand in the A2 

(Appendix B, Fig. B3). 

With highest global fuelwood demands and prices (Fig. 20), 

A1B has the most global competition for roundwood from 

fuelwood among the RPA scenarios. Thus, U.S. producers 

of pulp and paper products gain comparative advantage relative to foreign producers because of strong global competition for roundwood in the A1B scenario. There is also similarly a gain in comparative advantage for U.S. paper and 

paperboard producers in the RFS+RES scenarios because 

of their prodigious 65% expansion in foreign fuelwood consumption by 2030 (Table 23). The A2 scenario has the least 

global competition for roundwood from fuelwood demands, 

so U.S. producers of paper and paperboard do not gain 

comparative advantage in that scenario. Historical data on 

U.S. paper and paperboard net exports and projections of net 

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Net exports

(million m3/y)

Figure 26—Annual U.S. net exports of structural wood panel 

products, 1970–2010, with RFS+RES10 projections and 

preliminary (p) projections for RPA scenarios (million m3/y).

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Figure 27—Annual U.S. paper and paperboard consumption, 1970–2010, with RFS+RES10 projections and preliminary (p) RPA projections (million t/y).

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Production 

(million t/y)

Figure 28—Annual U.S. paper and paperboard production, 1970–2010, with RFS+RES projections and preliminary (p) RPA projections (million t/y).
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

37

exports are shown in Figure 29. Some of the recent gains in 

U.S. net exports of paper and paperboard are attributable to 

a weaker dollar in recent years, as well as productivity gains 

for U.S. producers. USFPM/GFPM can analyze effects of 

changes in currency exchange rates, but future adjustments 

of currency exchange rates were not programmed into the 

current version of the model. Projected U.S. production of 

paper and paperboard is highest in the A1B and RFS+RES 

scenarios (Fig. 28) because of the positive projected U.S. 

trade balances for paper and paperboard in those scenarios, 

while projected production is lowest in the A2 scenario with 

the lowest projected net exports (Fig. 29). Clearly there is a 

positive correlation between high global fuelwood consumption (high competing demands for roundwood at the global 

level) and higher projected U.S. net exports and production 

of paper and paperboard.

A key finding of this analysis is that projected U.S. consumption, production, and net trade in forest products are 

heavily influenced by assumptions about future expansion in 

U.S. and global wood energy demands. This finding extends 

to both the solid-wood products (e.g., lumber and wood 

panel products) and to the pulp and paper sector. Higher 

U.S. wood energy consumption and higher roundwood prices tend to dampen domestic demands for forest products, 

as competing energy demands for wood biomass generally 

drive up the projected U.S. and global prices for timber and 

forest products. On the other hand, higher global fuelwood 

consumption (as in the A1B scenario) also increases global 

competition for industrial roundwood and boosts comparative advantages of U.S. producers of forest products. Across 

the spectrum of RPA scenarios, the projected effects of 

expansion in U.S. and global wood energy consumption are 

to dampen expected growth in forest product consumption 

(because of price impacts on demands) but also to provide 

greater comparative advantages and enhanced net exports 

for U.S. producers of forest products (because of price impacts on foreign producers).

Among the RPA scenarios, projected U.S. real prices for forest products were generally the highest in the A1B scenario 

(with highest competing demands for wood energy and 

highest roundwood prices). Projected product prices were 

generally lower in the A2 and B2 scenarios. For example, 

projected real prices of softwood lumber were more than 

50% higher by 2060 in the A1B as in the B2. Real price of 

softwood lumber was projected to decline over the projection period in the A2 and B2 scenarios, but projected to increase in the A1B with increasing timber prices. Real prices 

trends for paper and paperboard products were generally 

projected to be flat to modestly declining in all scenarios, 

but were still about 15% to 25% higher by 2060 in the A1B 

than in the A2 or B2 scenarios. Figure 30 shows charts that 

illustrate projected U.S. real price trends (weighted averages) for several aggregate categories of primary forest 

products, including lumber (softwood and hardwood), paper 

and paperboard (newsprint, printing and writing paper, and 

all other paper and board), and structural panels (OSB and 

softwood plywood). With the highest production levels and 

projected prices, the A1B scenario generates the highest revenues for U.S. forest product producers, whereas the A2 and 

B2 scenarios have lower prices, lower production levels, 

and lower projected forest product revenues.

U.S. Timber Harvest and Market 

Projections

U.S. timber harvest and market projections for most of our 

USFPM/GFPM scenarios depart from historical timber 

trends of recent decades because of projected expansion in 

wood energy consumption. Total U.S. timber harvest has 

declined since the late 1980s, but projections for the RPA 

and RFS+RES scenarios generally point to increasing future 

timber harvest volumes. Figure 31 shows historical annual 

U.S. timber harvest volumes based on interpolation of FIA 

timber harvest data (Smith and others 2009) and USFPM/

GFPM projections of U.S. timber harvest. Among RPA 

scenarios, the largest projected expansion in U.S. timber 

harvest occurs in the A1B scenario, followed by the A2 

and B2 scenarios, which mainly reflect relative magnitudes 

of projected expansion in wood fuel feedstock production 

(see Fig. 16). However, even in the B2 scenario, harvest is 

projected to reach levels well above the peak harvests of 

recent decades (in the 1980s). We also show in Figure 31 

the U.S. timber harvest projection for the “A1B-Low Fuelwood” scenario (where fuelwood demands are based on 

historical relationships and not IPCC SRES global biomass 

energy projections, but all other assumptions are identical 

to the A1B scenario). The “A1B-Low Fuelwood” projection 

shows clearly that most of the projected expansion in timber 

harvest of the A1B scenario is a result of the projected expansion in fuelwood demand, without which the projected 

trend for U.S. timber harvest is much more subdued.

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Historical

Net exports

(million t/y)

Figure 29—Annual U.S. net exports of paper and paperboard, 1970–2010, with RFS+RES projections and preliminary (p) RPA projections (million t/y).
Research Paper FPL–RP–662

38

Among U.S. regions, the U.S. South has accounted for the 

largest regional share of U.S. timber harvest in recent decades, accounting for 57% of U.S. timber harvest volume 

in 2006 for example (Smith and others 2009). Figure 32 

shows the historical trend in total timber harvest volume 

of the U.S. South, along with USFPM/GFPM projections. 

Unlike the U.S. timber harvest trend that has been declining 

since the 1980s, timber harvest in the U.S. South has declined only since the mid-1990s. USFPM/GFPM projections 

indicate that the U.S. South will continue to be the largest 

timber-producing region of the United States, generally accounting for around half or more of total U.S. timber harvest 

throughout the projection period in all three RPA scenarios 

(Fig. 32). Also, as with total U.S. timber harvest, the projected timber harvest trends of the U.S. South generally 

reflect the relative magnitudes of projected increases in regional fuel feedstock production, and the South is projected 

to be the largest regional producer of wood fuel feedstock in 

the future, as well as largest regional producer of timber in 

general.

Figure 33 shows historical annual U.S. softwood timber harvest volumes along with corresponding USFPM/GFPM projections, whereas Figure 34 shows historical data and projections for U.S. hardwood timber harvest. Hardwood has 

some advantages as a wood fuel feedstock because of higher 

density and thus higher energy content per unit volume, 

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B2(p)

Figure 30—Preliminary (p) RPA projections of average 

U.S. demand prices for lumber, paper and paperboard, 

and structural panels.

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RFS+RES10

RFS+RES20+HP

Historical

Timber harvest (million m3/y)

Figure 31—Historical total annual U.S. timber harvest 

volume, 1986 to 2006, with RFS+RES projections and 

preliminary (p) RPA projections (million m3/y).

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Historical

Timber harvest volume 

 (million m3/y)

Figure 32—Historical total annual timber harvest volume 

in U.S. South, 1986 to 2006, with RFS+RES projections and 

preliminary (p) RPA projections (million m3/y).
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

39

although in the United States softwoods have been more 

commonly grown in industrial timber plantations. Both 

hardwood and softwood timber harvest are projected to increase with expansion of U.S. wood energy consumption. 

In the B2 scenario, with the smallest projected expansion 

in U.S. fuel feedstock consumption, the projected softwood 

timber harvest is relatively flat, while hardwood timber 

harvest volume nearly doubles over the projection period. 

With larger increases in wood energy consumption in the 

A1B scenario, softwood timber harvest more than doubles 

and hardwood timber harvest roughly triples. In the A1B 

scenario, a large share of the projected increase in hardwood 

and softwood timber harvest is attributable to expanded use 

of hardwood and softwood pulpwood as fuel feedstock 

(Fig. 17). The higher softwood timber harvest of the 

RFS+RES scenarios (Fig. 33) is attributable to higher projected net exports and production of conventional forest 

products such as lumber and structural wood panel products 

(Figs. 22 and 25).

High levels of expansion in U.S. timber demand and timber 

harvest that are associated with high levels of wood energy 

demand generally result in projected increases in timber 

stumpage prices, for both sawtimber and non-sawtimber 

stumpage, but particularly so for non-sawtimber stumpage 

prices that have been historically much lower than sawtimber stumpage prices. Non-sawtimber consists primarily of 

pulpwood and fuelwood that traditionally have had lower 

prices than sawlogs and veneer logs, which make up a larger 

share of sawtimber volume (Table 5). However, as wood 

fuel feedstock demand increases, there are greater demands 

and higher prices for fuelwood and pulpwood (for energy), 

and thus higher prices for non-sawtimber stumpage. Eventually the higher prices and timber substitution possibilities 

impinge also upon sawtimber prices, but the proportional 

increases in sawtimber prices are generally less than the increases for non-sawtimber prices.

A set of charts in Figure 35 shows the USFPM/GFPM 

projections of real sawtimber and non-sawtimber stumpage prices for hardwoods and softwoods in the U.S. North, 

whereas similar charts show projected timber stumpage 

prices for the U.S. South in Figure 36, and for the U.S. West 

in Figure 37. Again, all price projections are in 2006 real 

prices (the model generally ignores inflation). By far the 

largest projected increases in real timber stumpage prices 

occur in the A1B scenario, which has the largest projected 

expansion in U.S. and global wood energy demand. Both 

sawtimber and non-sawtimber prices are projected to increase prodigiously in the A1B scenario, and the price of 

non-sawtimber climbs higher than the price of sawtimber 

in the A1B scenario by around 2040 and beyond. This is 

because of larger fuelwood and harvest residue components 

and higher bark content of non-sawtimber, which eventually 

afford a higher market value (per cubic meter of wood) for 

non-sawtimber in a scenario with very high wood energy 

demands. 

On the other hand, the projected sawtimber price trends 

are relatively flat in the A2, B2, and A1B-Low Fuelwood 

scenarios, and in those scenarios non-sawtimber prices are 

projected to increase much more modestly than in the A1B 

scenario. In the B2 scenario, the projected real price trends 

for timber are mostly flat to declining, despite the fact that 

total U.S. timber harvest volume is projected to increase in 

the B2 scenario (Fig. 31). Note also that the timber price 

trend projections of the RFS+RES scenarios (not shown in 

the figures) were also generally flat to declining (similar to 

the B2 projections).

Thus, the prodigious projected timber price increases of the 

A1B scenario must be viewed as counter-balanced by fairly 

flat to declining price trends of the B2, A1B-Low Fuelwood, 

and RFS+RES scenarios. In all scenarios, the overall U.S. 

consumption of industrial roundwood for forest products is 

projected to gradually decline (coincidentally more or less 

in line with the historical U.S. timber harvest trends). Thus 

an important observation here is that U.S. timber demands 

and timber prices are not projected to increase without 

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20102020

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A2(p)

B2(p)

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RFS+RES10

RFS+RES20+HP

Historical

Softwood timber harvest volume

(million m3/y)

Figure 33—Annual U.S. softwood timber harvest volumes, 

1986 to 2006, with RFS+RES projections and preliminary (p) 

RPA projections (million m3/y).

0

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600

800

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2010

2020

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RFS+RES20+HP

Historical

Hardwood timber harvest volume

(million m3/y)

Figure 34—Annual U.S. hardwood timber harvest volumes, 

1986 to 2006, with RFS+RES projections and preliminary (p) 

RPA projections (million m3/y).
Research Paper FPL–RP–662

40

substantial increases in wood energy consumption (e.g., at 

least equal to or greater than the assumed increases of the 

B2 scenario). In essence, barring significant and unforeseen 

structural changes in U.S. forest product demands, substantial increases in real U.S. timber prices are not expected to 

occur without substantial increases in wood energy demand.

The projected increases in timber stumpage prices in the 

A1B scenario result also in a projected structural change 

in U.S. wood supply, specifically expansion in supply of 

agricultural SRWC as higher prices for wood biomass make 

agricultural SRWC more economically feasible in the latter 

decades (beyond 2040) when timber prices rise substantially 

above historical levels. Figure 38 shows projected U.S. 

roundwood supply by source for the A1B scenario including 

agricultural SRWC (but excluding bark and wood residue 

volumes), while Figure 39 shows projected roundwood supply for the B2 scenario. With much lower projected timber 

prices, the B2 scenario does not result in any agricultural 

SRWC supply. However, as noted previously, we applied 

fixed estimates of SRWC yields and also upper limits on 

agricultural land available for SRWC (Table 11), and those 

limits constrain SRWC supply in the A1B scenario. If we 

were to assume more significant gains in productivity for 

SRWC or more land available for SRWC, it would tend to 

increase the future role of agricultural SRWC, especially 

in the A1B scenario.

Summary and Conclusions

USFPM/GFPM is now operational, providing a tool for 

detailed analysis of regional U.S. timber markets in the context of global forest product trade and global scenarios for 

economic development and biomass energy. We tested and 

calibrated the model to obtain accurate base-period solutions, and we produced preliminary projections for RPA scenarios and other near-term wood energy scenarios (Ince and 

others 2011). We believe the model behaves appropriately, 

providing rational behavioral responses to market stimuli 

and reasonable projections of forest product and timber 

market trends in relation to wide-ranging assumptions about 

future U.S. and global economic growth and future wood 

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2006 Base price

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20202030

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Softwood sawtimber Softwood non-sawtimber

Hardwood sawtimber Hardwood non-sawtimber Projections (2006$) Projections (2006$)

Projections (2006$) Projections (2006$)

A1B(p)

A2(p)

B2(p)

A1B-low fuelwood(p)

2006 Base price

A1B(p)

A2(p)

B2(p)

A1B-low fuelwood(p)

2006 Base price

A1B(p)

A2(p)

B2(p)

A1B-low fuelwood(p)

2006 Base price

Figure 35— USFPM/GFPM projections of real sawtimber and non-sawtimber stumpage prices for 

hardwoods and softwoods in the U.S. North (2006 $/m3).
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

41

energy demands. We acknowledge that some future scenario 

assumptions may be outside the bounds of historical experience, such as future levels of U.S. and global wood energy 

consumption in the A1B scenario. However, we showed that 

global energy trends of the RPA scenarios are in line with 

historical expansion in global energy output and shifting 

energy resources (Fig. 13). We also showed results for a scenario with much lower projected trends in global fuelwood 

consumption (A1B-Low Fuelwood scenario).

A key general finding is that projected future trends in U.S. 

consumption, production, and net trade in forest products 

and projected timber market trends are heavily influenced 

by assumptions about future expansion in U.S. and global 

wood energy demands. The projected effects on forest product markets of expansion in U.S. and global wood energy 

consumption are to dampen growth in forest product consumption (because of price impacts on demands) but also to 

provide greater comparative advantages and enhanced net 

exports for U.S. producers of forest products in scenarios 

that feature larger increases in foreign roundwood prices 

because of increased global fuelwood demand. Thus, the 

advantages of being able to analyse the U.S. forest sector 

within the global context of the GFPM may be regarded as 

an advancement of RPA modeling capabilities, with the importance of the global analysis borne out in the results regarding the impact of expansion in global fuelwood demand 

on U.S. forest sector comparative advantage.

Projected trends in U.S. real timber stumpage prices are 

generally flat to declining in the B2 scenario (with low but 

still significant 4-fold projected expansion in U.S. wood 

energy consumption), but timber stumpage prices follow 

steeply upward trends in the A1B scenario (with highest 

projected expansion in U.S. wood energy consumption). On 

the other hand, in the A1B-Low Fuelwood scenario, with 

identical economic growth but with global fuelwood demands constrained to historical trends, the timber stumpage 

price projections are much lower than the A1B (and even 

lower than the B2 scenario for softwood stumpage prices). 

0

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2020203020402050

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Projections (2006$)

Softwood sawtimber Softwood non-sawtimber

Hardwood sawtimber Hardwood non-sawtimber Projections (2006$)

Projections (2006$) Projections (2006$)

A1B(p)

A2(p)

B2(p)

A1B-low fuelwood(p)

2006 Base price

A1B(p)

A2(p)

B2(p)

A1B-low fuelwood(p)

2006 Base price

A1B(p)

A2(p)

B2(p)

A1B-low fuelwood(p)

2006 Base price

A1B(p)

A2(p)

B2(p)

A1B-low fuelwood(p)

2006 Base price

Figure 36— USFPM/GFPM projections of real sawtimber and non-sawtimber stumpage prices for hardwoods 

and softwoods in the U.S. South (2006 $/m3).
Research Paper FPL–RP–662

42

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Projections (2006$)

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Hardwood sawtimber Hardwood non-sawtimber Projections (2006$)

Projections (2006$) Projections (2006$)

A1B(p)

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B2(p)

A1B-low fuelwood(p)

2006 Base price

A1B(p)

A2(p)

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2006 Base price

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2006 Base price

A1B(p)

A2(p)

B2(p)

A1B-low fuelwood(p)

2006 Base price

Figure 37— USFPM/GFPM projections of real sawtimber and non-sawtimber stumpage prices for 

hardwoods and softwoods in the U.S. West (2006 $/m3).

0

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1,200

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2000201020202030204020502060

Agricultural SRWC

Non-sawtimber

Sawtimber

Roundwood

(million m3/y)

Figure 38— USFPM/GFPM preliminary (p) A1B scenario 

projections of U.S. roundwood supply by source, excluding 

bark and harvest residue (million m3/y). SWRC is shortrotation woody crops.

0

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600

800

1,000

1,200

1,400

2000201020202030204020502060

Non-sawtimber

Sawtimber

Roundwood 

(million m3/y)

Figure 39— USFPM/GFPM preliminary (p) B2 scenario 

projections of U.S. roundwood supply by source, excluding 

bark and harvest residue (million m3/y).
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

43

Again, all price projections are in 2006 real prices (ignoring 

inflation). In the A1B scenario, real sawtimber stumpage 

prices reach levels that are far higher than the historical 

range of U.S. sawtimber prices in modern times by around 

2040, indicating that wood energy demands of the A1B scenario could lead to unprecedented timber market conditions 

or otherwise imponderable outcomes during the projection 

period. Long-run responses to such high timber prices (as in 

the A1B scenario) would logically include increased investment in timber planting and silviculture to increase timber 

output capacity, but economic sustainability of such high 

timber prices is also questionable if the primary future use 

of wood is energy. 

Commercial production of biofuels or energy from wood 

is currently prohibitive at such high timber prices (e.g., as 

projected in A1B scenario). Technology capable of profitably transforming such high-priced timber or biomass into 

commercially viable forms of energy has yet to be demonstrated. Instead, biomass energy technology today relies 

on relatively low cost feedstock. The affordability of wood 

for energy at such prices depends on fairly uncertain future 

outcomes, such as the future efficiencies of biomass energy 

technologies, future prices for energy in general, and the 

effectiveness of future mandates or incentives for development of biomass energy. Our analysis points to a primary 

conclusion that expansion of wood energy consumption 

within the context of biomass energy projections developed 

for IPCC could result in either very substantial increases in 

wood energy demand and real timber prices (as in the A1B 

scenario), or more modest increases in wood energy demand 

and little change in real timber prices (as in the A2, B2, or 

A1B-Low Fuelwood scenarios).

Acknowledgments

The authors gratefully acknowledge the collaboration of 

colleagues who supported and contributed to development 

of the U.S. Forest Products Module (USFPM) and related 

applications. We gratefully acknowledge funding from the 

RPA Assessment (Forest Service Washington Office). We 

also gratefully acknowledge the contributions of Dr. Linda 

Langner of the Forest Service Washington Office staff in 

selecting and developing the 2010 RPA scenarios from the 

Special Report on Emissions Scenarios of the Intergovernmental Panel on Climate Change. In addition, we gratefully acknowledge the contributions of Professor Joseph 

Buongiorno and his former graduate Ph.D. students at 

University of Wisconsin-Madison (UW) who developed the 

Global Forest Products Model (GFPM), and who assisted 

us at various stages with understanding how to operate the 

model, including Dr. Ron Raunikar, Dr. James Turner, and 

Dr. Shushuai Zhu. Our UW colleagues also wrote the text in 

Appendix A that describes the mathematical structure of the 

GFPM. We also acknowledge that recent development of the 

GFPM at UW for the 2010 RPA was supported and coordinated by Dr. Jeffrey Prestemon of the USDA Forest Service, 

Southern Research Station. We also acknowledge James L. 

Howard of the FPL for his contributions of forest product 

data and assistance in assembling historical data series, 

and Dr. Prakash Nepal who helped estimate demand equations. Furthermore, we gratefully acknowledge funding we 

received from the Forest Service Research Executive Team 

to develop additional analysis of alternative wood energy 

scenarios (RFS+RES scenarios), and collaboration in development of those scenarios with Al Sample of the Pinchot 

Institute for Conservation. We also gratefully acknowledge 

the contributions of UW students who worked with us at the 

FPL, including Dan Toth, who assisted Henry Spelter in developing projections of U.S. housing needs for various scenarios based on U.S. population projections, and also Do-il 

Yoo, who helped develop the RFS+RES scenarios. Finally, 

we acknowledge continued support for our work provided 

by the FPL from our Director, Christopher Risbrudt (ret.), 

our Assistant Director, Michael Ritter, and FPL research 

support staff.

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Assessment. Gen. Tech. Rep. RMRS-GTR-XXX. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. XX p. 

USDA Forest Service. 2010. Timber product output (TPO) 

reports. http://srsfia2.fs.fed.us/php/tpo_2009/tpo_rpa_int1.

php. (Accessed 12 July 2011) Washington, D.C.: USDA 

Forest Service.

Wear, David N. 2011. Forecasts of county-level land uses 

under three future scenarios: a technical document supporting the Forest Service 2010 RPA Assessment. Gen. Tech. 

Rep. SRS-141. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station. 41 p.

Wear, David N.; Huggett, Robert; Li, Ruhong; Perryman, 

Benjamin; and Liu, Shan. In preparation. Regional forecasts 

of forest conditions under future scenarios : a technical document supporting the Forest Service 2010 RPA Assessment. 

Gen. Tech. Rep. SRS-xxx. Asheville, NC: Department of 

Agriculture Forest Service, Southern Research Station. xxx 

p. [Not cited in text]

Wear, David N.; Huggett, Robert; Li, Ruhong; Perryman, 

Benjamin; and Liu, Shan. In preparation. Forecasts of forest conditions under future scenarios : a technical document 

supporting the Forest Service 2010 RPA Assessment. Gen. 

Tech. Rep. SRS-xxx. Asheville, NC: Department of Agriculture Forest Service, Southern Research Station. xxx p. [Not 

cited in text]

Wongcharupan, M.; Ince, P.J. 2004. Analysis of U.S. printing and writing paper demand. Unpublished manuscript. On 

file with: P. Ince, Forest Products Laboratory, One Gifford 

Pinchot Drive, Madison, WI 53726. 26 p.

Wongcharupan, M.; Ince, P.J. 2003. Error correction models 

and directed graphs of U.S. newsprint demand. In: Moffat, 

S.O., ed. Proceedings of the 2003 southern forest economics 

workers annual meeting; March 17–18, 2003; New Orleans, 

LA. Asheville, NC: U.S. Department of Agriculture, Forest 

Service, Southern Research Station: 47–55. 416 p. (Published on CD).

WWPA. 2006. Statistical yearbook of the western lumber 

industry. Portland, OR: Western Wood Products Association. 

12 p.

Zhang, D.; Buongiorno, J.; Ince, P.J. 1993. PELPS III: a 

microcomputer price endogenous linear programming system for economic modeling, v. 1.0. Res. Pap. FPL–RP–526. 

Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 43 p.

Zhu, S.; Buongiorno, J.; Turner, J.; Li, R. 2006. Calibrating 

and updating the Global Forest Products Model (GFPM version 2006). Staff Pap. Series No. 59. Madison, WI: 
Research Paper FPL–RP–662

46

University of Wisconsin, Department of Forest Ecology and 

Management. 62 p.

Additional Data References

AF&PA. 2006–2007. Paper, paperboard, and wood pulp statistics. (Various eds.). Washington, DC: American Forest & 

Paper Association. 12 p. (monthly)

CPA. 2005. North American capacity report. Particleboard 

MDF hardboard. Leesburg, VA: Composite Panel Association. 

DOC. New privately owned housing units started. Washington, DC: U.S. Department of Commerce, Bureau of the 

Census. http://www.census.gov/const/www/newresconstindex_excel.html. (Accessed 12 July 2011).

DOC. Real gross domestic product. Chained dollars. Table 

1.1.6. Washington, DC: U.S. Department of Commerce, 

Bureau of Economic Analysis. http://www.bea.gov/national/

nipaweb/SelectTable.asp?Selected=N. (Accessed 12 July 

2011). 

DOL. Producer price index database. Washington, DC: U.S. 

Department of Labor, Bureau of Labor Statistics. http://

www.bls.gov/ppi. (Accessed 12 July 2011). 

FRA. 2005. Annual pulpwood statistics summary. Rep. 06-

A-7. Rockville, MD: Forest Resources Association, Inc. 37 

p.

Illinois Agricultural Statistics Service. Illinois timber prices. 

(Various issues). Urbana, IL: University of Illinois Extension. http://web.extension.uiuc.edu/forestry/il_timber_prices. (Accessed 12 July 2011). 

MillsOnline. The center for paper business and industry 

studies. http://www.cpbis.gatech.edu/data/mills-online. (Accessed 12 July 2011). 

Ohio State University. Ohio timber price report. Columbus, 

OH: Ohio State University. http://www.oardc.ohio-state.edu/

ohiowood/timberarchive.htm. (Accessed 12 July 2011). 

Pennsylvania State College. Published stumpage and mill 

price report data. Timber market report, Pennsylvania woodlands. University Park, PA: Pennsylvania State College of 

Agricultural Sciences, Cooperative Extension. http://www.

sfr.psu.edu/TMR/TMR.htm. (Accessed 12 July 2011). 

Random Lengths. 2007. Forest product market prices and 

statistics yearbook 2006. Vol. XLII. Eugene, OR: Random 

Lengths Publications, Inc. 

RISI. 2003. North American lumber forecast. Boston, MA: 

Resource Information Systems, Inc. (Paperloop.com). (July 

2003). 

USITC. Interactive tariff and trade data web. Washington, 

DC: U.S. International Trade Commission. http://dataweb.

usitc.gov/scripts/user_set.asp. (Accessed 12 July 2011). 

WWPA. 2006. Statistical yearbook of the western lumber 

industry. Portland, OR: Western Wood Products Association. 
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

47

Appendix A—Global Forest 

Products Model (GFPM) 

Formulation and Structure

This appendix contains an overview of the mathematical 

structure and formulation of the GFPM, adapted from a recent overview written by Zhu and others (2010). This overview describes formulas that are used in solving the global 

spatial market equilibrium problem each period (objective 

function, constraints, and manufacturing activities), formulas that control market dynamics or exogenous shifts from 

period to period (demand shifts, supply shifts, changes in 

forest area and forest stock, changes in manufacturing costs 

and inputs, and other exogenous changes), and methods for 

linear approximation and implementation of trade inertia 

constrains. Also included in this appendix are lists of commodities and countries represented in the GFPM structure.

Global Spatial Market Equilibrium

Objective Function

 

(A1)

where

i,j is country, 

k product, 

P price in U.S. dollars of constant value, 

D final product demand, 

S raw material supply, 

Y quantity manufactured, 

m manufacturing cost, 

T quantity transported, and 

c cost of transportation, including tariff. 

All variables refer to a specific year. In making predictions, 

the period between successive equilibria may be multiple 

years. 

End Product Demand

(A2)

where

D*

 is current demand at last period’s price, 

P–1 last period’s price, and 

δ price elasticity of demand. 

As shown in the section on market dynamics, below, D*

depends on last period’s demand, and the growth of GDP 

in the country. In the base year, D* is equal to the observed 

base-year consumption, and P–1 is equal to the observed 

base-year price.

Primary Product Supply

 (A3)

where

S*

 is current supply at last period’s price and λ is price 

 elasticity of supply.

As shown in the section on market dynamics, below, S*

depend on last period’s supply, and on exogenous or endogenous supply shifters. In the base year, S* is equal to the 

base-year supply, and P–1 is equal to the observed base-year 

price.

Total Wood Supply

 (A4)

where

r is industrial roundwood,

n other industrial roundwood,

f fuelwood,

θ fraction of fuelwood that comes from the 

 forest.

Also, where Ii is forest stock.

Material Balance Constraint

A material balance constraint is imposed to ensure that regional shipments and consumption of each commodity are 

in balance with supply and production:

(A5)

where aikn is input of product k per unit of product n.

In addition, byproducts that result from the production of a 

manufactured commodity satisfy the constraint

where bikl is the amount of byproduct l that can be recovered 

per unit of production of manufactured commodity k. 

Trade Inertia Constraint

Trade inertia constraints limit the range of variability in 

trade flows from one period to the next, and for each commodity they are defined in terms of upper and lower bounds 

on transportation quantities

(A6)

where the superscripts L and U refer to a lower bound and 

upper bound, respectively. The upper and lower bounds on 

trade flows for each period are actually computed from a 

user-specified constraint on the periodic change in the trade 

flow quantity, specified as a decimal fraction (such as for 

example 0.05, which would set upper and lower bounds on 

− ∑∑ ∫ − ∑∑∑

= ∑∑ ∫ − ∑∑ ∫

i k i j k ijk ijk

Yik

ik ik ik

i k

Dik

i k

Sik

ik ik ik ik ik ik

m Y dY c T

Z P D dD P S dS

0

0 0

( )

max ( ) ( )

ik

ik

ik

ik ik P

P S S λ ( )

, 1

*

−

=

i ir in i if S = S + S +θ S

i i S ≤ I

T S Y D a Y T i k n j ik ik ik ikn in ijk j jik ∑ + + − − ∑ − ∑ = 0 ∀ ,

Y b Y i k l il ikl ik − = 0 ∀ , ,

ik

ik

ik

ik ik P

P D D δ ( )

, 1

*

−

=

U

ijk ijk

L Tijk ≤ T ≤ T
Research Paper FPL–RP–662

48

the trade flow at 5% above and 5% below the trade flow in 

the previous period).

Prices

The shadow prices of the material balance constraints 

(Eq. (A5)) give the market-clearing prices for each commodity and country.

Manufacturing Cost

Manufacturing is represented by activity analysis, with 

input-output coefficients and a manufacturing cost. The 

manufacturing cost is the marginal cost of the inputs not recognized explicitly by the model (labor, energy, capital, etc.):

(A7)

where

m* is current manufacturing cost, at last period’s output, 

 and 

s elasticity of manufacturing cost with respect to out

 put.

As shown in the next section, m* depend on last period’s 

quantity manufactured, and on the exogenous rate of change 

of manufacturing cost. In the base year, m* is equal to the 

observed base-year manufacturing cost and Yik,–1 is equal to 

the observed base-year quantity manufactured.

Transport Cost

The transport cost for commodity k from country i to country j in any given year is

(A8)

where

c is transport cost, per unit of volume, 

f freight cost, per unit of volume, I

t import ad-valorem tariff, and 

P–1 last period’s equilibrium export price predicted 

 endogenously by the model. In the base year, P–1 is 

 equal to the observed base-year price.

Market Dynamics

Unless otherwise indicated, variables refer to one country, 

one commodity, and one year. Rates of change refer to a 

multi-year period. All periodic exponential rates of change, 

rp, are defined by the annual exponential rate of change, ra, 

as

 where p is the length 

 of a period, in years. (A9)

All periodic linear changes, Δvp are defined by the corresponding annual linear change, Δva, as:

 (A10)

Shifts of Demand

(A11)

where 

gy is GDP periodic growth rate, and

α elasticity. 

In the GFPM, the assumed growth rate for real GDP in each 

country is the only shifter of forest product demands, but in 

the USFPM we also added some other shifters of demands 

in the United States, such as projected housing starts as an 

additional shifter of softwood lumber and structural panel 

demands.

Shifts of Supply

Industrial roundwood and fuelwood:

 for k = r, n, f (A12)

Where

gI is periodic rate of change of forest stock (endogenous, 

see below), 

gy' periodic rate of change of GDP per capita, and 

β elasticity.

Waste paper and other fiber pulp:

(A13)

Changes in Forest Area and Forest Stock

(A14)

where

A is forest area, and 

ga periodic rate of forest area change based on the 

period length, p, Equation (A9) and the annual rate 

of forest area change, gaa, defined by

 for y’≤ y’*, else gaa = 0 (A15)

where, for each country, α0 is calibrated so that in the base 

year the observed gaa is equal to the gaa predicted by Equation (A15) given the income per capita y’. 

y' is income per capita, predicted from 

(A16)

 y'* is defined by 

 and (A17)

Forest stock evolves over time according to a growth-drain 

equation:

 (A18)

ik s

ik

ik

ik Y

Y

m m ( )

, 1

*

−

=

(1 ) 1

*

y y D = D +α g −

(1 ) 1 ' '

*

I I y y S = S + β g + β g −

'* '* 0 2 gaa =α0 +α1 y +α2 y =

1 2 2 y'*> −α α

( ) = + ijk + ik ,−1

I cijk fijk t jk f P

= (1+ ) −1 p

p a r r

(1 ) 1

*

y y S = S + β g −

1 (1 ) = + A− A ga

2

0 1 2 g y' y' aa =α +α +α

' 1 ' (1 ) ' = + − y g y y

p a ∆v = p∆v = −1 + −1 − −1 I I G pS
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

49

where 

 is the periodic change of forest 

 stock without harvest, 

gu periodic rate of forest growth on 

 a given area, without harvest, 

 and 

gu* adjustment of periodic rate of 

 forest growth on a given area, 

 without harvest. 

The last is exogenous, for example to represent the effect of 

invasive species, or of climate change. 

The periodic rate of forest growth, gu, is based on the annual 

rate of forest growth, gua, defined by

 (A19)

where σ is negative, so that gua decreases with stock per unit 

area. For each country, γ0 is calibrated so that in the base 

year the observed gua is equal to the gua predicted by Equation (A19) given the stock per unit area, I/A.

The periodic rate of change of forest stock net of harvest, 

used in Equation (A12) is then

 

(A20)

Changes in Manufacturing Coefficients and Costs

The input-output coefficients a (in equation [A5]), may be 

changed exogenously over time, for example to reflect increasing use of recycled paper in paper manufacturing:

(A21)

where Δa is an exogenously specified periodic change in the 

input-output coefficient. 

Likewise, the manufacturing cost for any manufacturing 

activity (m in Equation (A7) may be adjusted exogenously 

over time:

(A22)

where gm is an exogenously specified periodic rate of change 

in manufacturing cost. 

The current versions of the GFPM and USFPM incorporate 

adjustments in the material input coefficients (in Equation 

(A21) for forest product manufacturing processes. The adjustments are designed to gradually shift the wood and wood 

fiber input requirements toward the more technologically 

efficient levels, with target levels determined by those of the 

most efficient countries at present. In other words the model 

assumes that efficient technology will be adopted globally 

over the 50-year projection period such that eventually the 

production efficiencies in all countries will match the current efficiencies of the most efficient countries.

Changes in Freight Cost and Tariff

The freight cost and the import tariffs in Equation (A8) may 

be changed exogenously over time:

 , (A23) 

where Δf and Δt are periodic changes in freight cost and 

tariff, respectively. This optional feature of the model was 

not actually applied in the current scenarios developed with 

the GFPM and USFPM (i.e., import tariffs and changes in 

freight costs were not applied in USFPM/GFPM) but this is 

a feature that could be activated in future applications of the 

model.

Changes in Trade Inertia Bounds

 

 (A24)

ε is absolute value of maximum annual relative change in 

trade flow (exogenous). This feature is applied in USFPM 

to “loosen” the trade inertia constraints in the projection 

period. As with the GFPM, the USFPM trade flows are 

highly constrained in the base period by very tight inertia 

constraints (e.g., not allowing more than 1% variation in 

trade flow quantities relative to the actual base-year trade 

flows), but this constraint is relaxed in the projection period 

(beyond 2010) by allowing up to 5% variation in trade flow 

quantities each period.

Linear Approximation of Demand, Supply, 

Manufacturing Cost

Supply and demand equations and manufacturing cost functions in the GFPM (and USFPM) are actually linear approximations of the equations specified by the elasticities. For 

example, considering a demand equation such as Equation 

(A2), and omitting the subscripts for region and product, the 

inverse demand equation in any given year is

(A25)

The linear approximation is then the following:

 with and 

for D*>1, else (A26)

b = 0 if σ = 0. The same method of approximation is used 

for the supply equations, and the manufacturing cost equations. Linear approximation of the equations allows for 

more efficient solution of the mathematical optimization 

problem.

1

1

−

− − = II I

gI

a = a−1 + ∆a

(1 ) 1

*

m m = m + g −

f = f + ∆f −1 t = t + ∆t −1

1/σ

1 * ( ) D

D P = P−

P = a + bD a = P−1 −bD* *

1

D

P b

σ

− =

σ

−1 = P b

G g g g I a u u ( )

* = + +

σ γ ( ) 0 A

I

gua =

p T T

p T T

U

L

(1 ) 1

(1 ) 1

ε

ε

+ − =

− − =
Research Paper FPL–RP–662

50

Implementation of Trade Inertia Bounds

To avoid infeasibilities due to inconsistent bounds, the inertia constraints (Equation (A6)) are implemented as

(A27)

where ΔTL, ΔTU is the amount by which trade falls short of 

the lower bound, or exceeds the upper bound. These two 

variables appear in the objective function:

 (A28)

where W is an arbitrarily large number.

GFPM Commodities and Countries

Table A1 lists the commodities represented in the GFPM 

(the USFPM has somewhat more detailed commodity 

structure as described previously), and Table A2 lists the 

countries that are represented in the GFPM. The reader is 

referred to GFPM documentation for additional information 

on the model structure (Buongiorno and others 2003; Zhu 

and others 2006; Zhu and others 2010).

Table A1—Commodities represented in the 

GFPMa

Code Commodities Units

80 Fuelwood and charcoal ×103 m3

81 Industrial roundwood ×103 m3

82 Other industrial roundwood ×103 m3

83 Sawnwood ×103 m3

84 Veneer and plywood ×103 m3

85 Particleboard ×103 m3

86 Fiberboard ×103 m3

87 Mechanical wood pulp ×103 t

88 Chemical and semi-chemical wood pulp ×103 t

89 Other fiber pulp ×103 t

90 Waste paper ×103 t

91 Newsprint ×103 t

92 Printing and writing paper ×103 t

93 Other paper and paperboard ×103 t

a

The listed commodities are default commodities in GFPM. It 

is possible to add or remove commodities (see Zhu and others

2008).

U

ijk

U

ijk ijk

L

ijk

L

ijk ijk

T T T

T T T

− ∆ ≤

+ ∆ ≥

max ... ( )... , ,

U

ijk i j k

L Z = − ∑ W ∆Tijk + ∆T
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

51

Table A2—Countries represented in GFPMa

Code Country Code Country Code Country Code Country

Africa North and Central

America

Asia Europe

A0 Algeria F0 Bahamas I5 Afghanistan N5 Albania

A1 Angola F1 Barbados I6 Bahrain N6 Austria

A2 Benin F2 Belize I7 Bangladesh N7 Belgium

A3 Botswana F3 Canada I8 Bhutan N8 Bosnia and Herzegovina

A4 Burkina Faso F4 Cayman Islands I9 Brunei Darussalam N9 Bulgaria

A5 Burundi F5 Costa Rica J0 Cambodia O0 Croatia

A6 Cameroon F6 Cuba J1 China O1 Czech Republic

A7 Cape Verde F7 Dominica J2 Cyprus O2 Denmark

A8 Central African 

Republic

F8 Dominican Republic J3 Hong Kong O3 Finland

A9 Chad F9 El Salvador J4 India O4 France

B0 Congo, Republic of G0 Guatemala J5 Indonesia O5 Germany

B1 Côte d’Ivoire G1 Haiti J6 Iran, Islamic

Republic of

O6 Greece

B2 Djibouti G2 Honduras J7 Iraq O7 Hungary

B3 Egypt G3 Jamaica J8 Israel O8 Iceland

B4 Equatorial Guinea G4 Martinique J9 Japan O9 Ireland

B5 Ethiopia G5 Mexico K0 Jordan P0 Italy

B6 Gabon G6 Netherlands Antilles K1 Korea, Democratic 

People’s Republic

P1 Macedonia, The Former

Yugoslav Republic of

B7 Gambia G7 Nicaragua K2 Korea, Republic of P2 Malta

B8 Ghana G8 Panama K3 Kuwait P3 Netherlands

B9 Guinea G9 Saint 

Vincent/Grenadines

K4 Laos P4 Norway

C0 Guinea-Bissau H0 Trinidad and Tobago K5 Lebanon P5 Poland

C1 Kenya H1 United States of 

America

K6 Macau P6 Portugal

C2 Lesotho South America K7 Malaysia P7 Romania

C3 Liberia H2 Argentina K8 Mongolia P8 Slovakia

C4 Libyan Arab 

Jamahiriya

H3 Bolivia K9 Myanmar P9 Slovenia

C5 Madagascar H4 Brazil L0 Nepal Q0 Spain

C6 Malawi H5 Chile L1 Oman Q1 Sweden

C7 Mali H6 Colombia L2 Pakistan Q2 Switzerland

C8 Mauritania H7 Ecuador L3 Philippines Q3 United Kingdom

C9 Mauritius H8 French Guiana L4 Qatar Q4 Serbia and Montenegro

D0 Morocco H9 Guyana L5 Saudi Arabia Former USSR

D1 Mozambique I0 Paraguay L6 Singapore Q5 Armenia

D2 Niger I1 Peru L7 Sri Lanka Q6 Azerbaijan, Republic of

D3 Nigeria I2 Suriname L8 Syrian Arab Republic Q7 Belarus

D4 Réunion I3 Uruguay L9 Thailand Q8 Estonia

D5 Rwanda I4 Venezuela, Republic 

of Bolivia

M0 Turkey Q9 Georgia

D6 Sao Tome and Principe M1 United Arab Emirates R0 Kazakhstan

D7 Senegal M2 Viet Nam R1 Kyrgyzstan

D8 Sierra Leone M3 Yemen R2 Latvia

D9 Somalia Oceania R3 Lithuania

E0 South Africa M4 Australia R4 Moldova, Republic of

E1 Sudan M5 Cook Islands R5 Russian Federation

E2 Swaziland M6 Fiji Islands R6 Tajikistan

E3 Tanzania, United 

Republic of

M7 French Polynesia R7 Turkmenistan

E4 Togo M8 New Caledonia R8 Ukraine

E5 Tunisia M9 New Zealand R9 Uzbekistan

E6 Uganda N0 Papua New Guinea

E7 Congo, Democratic

Republic of

N1 Samoa ZY Dummy Region

E8 Zambia N2 Solomon Islands ZZ World

E9 Zimbabwe N3 Tonga

N4 Vanuatu

a

The listed countries are the default countries in GFPM. It is also possible to add or remove countries; see Zhu and others (2008).
Research Paper FPL–RP–662

52

Appendix B—USFPM/GFPM Projections of Global Fuelwood Demands

This appendix describes an approach to projecting global 

fuelwood demands for the 2010 RPA scenarios (described 

in the text as “option 3”). This approach is an extension of 

the approach used previously in the GFPM (Raunikar and 

others 2010). We have considered and explored different 

options for projecting global fuelwood demands in USFPM/

GFPM. We believe the approach described here should 

come closer to representing fuelwood consumption levels 

implicit in the RPA scenarios because the scenarios were 

based on IPCC SRES scenarios and because this approach 

takes into account relevant regional land use projections as 

well as regional biomass energy projections provided by 

scenario in the IPCC SRES report and its supporting database (Nakicenovic and Swart 2000).

Projected Global Land Use and Biomass 

Energy Production by Macro Region

The IPCC SRES database provides projections of global 

land use for each of four major global regions (called “macro” regions). The four macro regions are OECD 90 (Organization for Economic Cooperation and Development) member states as of 1990, including chiefly countries of Western 

Europe, the United States, Canada, Japan, Australia, and 

New Zealand), ALM (Africa, Latin America, and Middle 

East including South America and Caribbean countries), 

REF (Central and Eastern Europe and newly independent 

states of the former Soviet Union), and ASIA (remaining 

countries of Asia, including China, India, and other south 

and east Asian countries but excluding the Middle East and 

Japan). Table B1 shows the countries within each SRES 

macro region.

The SRES land use projections include several categories of 

land use that produce biomass for energy, including non-forest biomass energy plantations (or “energy biomass” lands 

as described in the SRES report), agricultural cropland, 

and forest land. Furthermore, the SRES provides projected 

changes in land use and biomass energy production for each 

of the four macro regions, from which we can deduce relationships between projected land use and biomass energy 

production. Table B2 shows SRES projections of regional 

biomass energy plantation area for the three RPA scenarios, 

while Table B3 shows SRES projections of total biomass 

energy production by macro region for the three RPA scenarios.

In all three scenarios, projected regional expansion of biomass energy plantation area is directly correlated with projected regional expansion in primary biomass energy production. For example, the A1B scenario has largest regional 

expansion in the area of biomass energy plantations and also 

biomass energy production, while expansions of biomass 

energy plantation area and biomass energy production are 

both smaller in the A2 and B2 scenarios. It can be noted also 

that biomass energy plantation area only begins to expand 

by the year 2020 in all scenarios, coinciding with projected 

regional expansion in biomass energy production, while the 

regional areas of forest land and cropland remain relatively 

static throughout the projection period in all three scenarios. 

Figure B1 illustrates SRES projections of these categories 

of global land use for the three RPA scenarios. Therefore, 

we deduce that biomass energy plantations are an important 

element of biomass energy supply in the RPA scenarios according to SRES. 

In addition to recognizing that biomass energy plantations 

are an important element of biomass energy supply in the 

RPA scenarios according to SRES, we have observed also 

that biomass energy plantations are an important element of 

future biomass supply according to other studies that have 

analyzed the potential for significant expansion in global 

biomass energy production (e.g., Gurgel and others 2007; 

Faaij 2007; Hoogwijk and others 2005). Fuelwood and other 

fuel feedstocks from forests are currently the largest global 

source of biomass used for energy via direct combustion, 

but significant future expansion in use of biomass for energy and biofuels would entail expansion in biomass supply 

from non-forest biomass energy plantations according to the 

SRES and other global studies. 

Furthermore, the energy equivalent of global roundwood 

fuelwood consumption is well below the estimated global 

production of biomass energy, indicating clearly that other 

non-forest sources of biomass are already important contributors to biomass energy production. Those other sources 

of biomass energy include agricultural crops (e.g., corn or 

sugar cane used for ethanol production), cropland residues, 

and also various forms of wood residues used for energy. 

For example, in the year 2000, global consumption of 

roundwood fuelwood was 1,825,914,364 cubic meters according to the FAOSTAT global database. This is equivalent 

in energy to approximately 26 EJ, which is much less than 

the 43.1 EJ of global primary biomass energy production in 

2000 (Table B3), and thus the remainder or approximately 

17 EJ of biomass energy must be attributable to biomass 

other than roundwood fuelwood, specifically sources such 

as agricultural crops, cropland residues, and wood residues 

(biomass energy plantations are not yet a significant conventional source of biomass for energy).

Projecting Implicit Fuelwood Consumption by 

Macro Region for RPA Scenarios 

Having concluded that biomass energy plantations, agricultural crops and biomass residues are important elements of 

global biomass energy supply along with fuelwood in the 

RPA scenarios, we apply a direct approach to projecting 

the expansion of fuelwood consumption for RPA scenarios. 

Specifically, we deduct estimated energy supply of biomass 

plantations, agricultural crops, and residues from the 
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

53

Table B1—Countries within Special Report on Emissions Scenarios (SRES)a macro regions 

OECD90 Region 

North America (NAM)

Canada Guam Puerto Rico United States 

of America 

Virgin Islands 

Western Europe (WEU)

Andorra Austria Azores Belgium Canary Islands 

Channel Islands Cyprus Denmark Faeroe Islands Finland 

France Germany Gibraltar Greece Greenland 

Iceland Ireland Isle of Man Italy Liechtenstein 

Luxembourg Madeira Malta Monaco Netherlands 

Norway Portugal Spain Sweden Switzerland 

Turkey 

Pacific Organization for Economic Cooperation and Development (PAO)

Australia Japan New Zealand 

Asia Region 

Centrally Planned Asia and China (CPA)

Cambodia China Hong Kong Korea (DPR) Laos (PDR) 

Mongolia Viet Nam 

South Asia (SAS)

Afghanistan Bangladesh Bhutan India Maldives 

Nepal Pakistan Sri Lanka

Other Pacific Asia (PAS)

American Samoa Brunei Darussalam Fiji French Polynesia Gilbert-Kiribati 

Indonesia Malaysia Myanmar New Caledonia Papua New Guinea 

Philippines Republic of Korea Singapore Solomon Islands Taiwan, province of 

China Thailand Tonga Vanuatu Western Samoa 

REF Region 

Central and Eastern Europe (EEU)

Albania Bosnia and 

Herzegovina 

Bulgaria Croatia Czech Republic 

The Former 

Yugoslav Republic 

of Macedonia 

Hungary Poland Romania Slovak Republic 

Slovenia Yugoslavia 

New independent states of the former Soviet Union

Armenia Azerbaijan Belarus Estonia Georgia 

Kazakhstan Kyrgyzstan Latvia Lithuania Republic of Moldova 

Russian Federation Tajikistan Turkmenistan Ukraine Uzbekistan 

ALM Region 

Middle East and North Africa

Algeria Bahrain Egypt (Arab Republic) Iraq Iran (Islamic Republic) 

Israel Jordan Kuwait Lebanon Libya/GSPLAJb

Morocco Oman Qatar Saudi Arabia Sudan 

Syria (Arab 

Republic) 

Tunisia United Arab Emirates Yemen 

Latin America and the Caribbean

Antigua and 

Barbuda

Argentina Bahamas Barbados Belize 

Bermuda Bolivia Brazil Chile Colombia 

Costa Rica Cuba Dominica Dominican Republic Ecuador 

El Salvador French Guyana Grenada Guadeloupe Guatemala 

Guyana Haiti Honduras Jamaica Martinique 

Mexico Netherlands Antilles Nicaragua Panama 

Paraguay Peru Saint Kitts and Nevis Santa Lucia Saint Vincent 

and the Grenadines 

Suriname Trinidad and 

Tobago

Uruguay Venezuela 
Research Paper FPL–RP–662

54

Table B1—Countries within Special Report on Emissions Scenarios (SRES) macro regions—con.

Sub-Saharan Africa

Angola Benin Botswana British Indian

Ocean Territory

Burkina Faso

Burundi Cameroon Cape Verde Central African Republic Chad

Comoros Cote d’Ivoire Congo Djibouti Equatorial Guinea

Eritrea Ethiopia Gabon Gambia Ghana

Guinea Guinea-Bissau Kenya Lesotho Liberia

Madagascar Malawi Mali Mauritania Mauritius

Mozambique Namibia Niger Nigeria Reunion

Rwanda Sao Tome and

Principe

Senegal Seychelles Sierra Leone

Somalia South Africa Saint Helena Swaziland Tanzania

Togo Uganda Zaire Zambia Zimbabwe

a

Nakicenovic and Swart (2000).

b

Great Socialist People's Libyan Arab Jamahiriya.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

19902000

2010

20202030

2040

2050

A1B global distribution 

Millions of hectares

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

1990

2000

2010

2020

2030

2040

2050

2060

A2 global distribution 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

1990

2000

20102020

2030

20402050

2060

B2 global distribution 

Forest land

Biomass energy

plantations

Conventional

cropland

Millions of hectares

Millions of hectares

Figure B1—SRES projections of global land use for the three RPA scenarios of land 

that supply biomass, including forest land, cropland, and biomass energy plantations.
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

55

projected total biomass energy production, yielding a remainder, which is the implicit consumption of fuelwood. 

We start by computing projected production and consumption of biomass from biomass energy plantations based on 

SRES projections of regional biomass energy plantation 

area (Table B2) multiplied times conventional estimates of 

biomass energy plantation productivity by region. Estimates 

of biomass energy plantation productivity by global region 

are available in the literature, and we adopted estimates developed in the recent MIT study (Gurgel and others 2007) 

because the MIT study provided consistent estimates for all 

global regions and also estimated future gains in productivity. According to the MIT study (and similar estimates 

reported in other studies) the highest productivity levels for 

biomass energy plantations are found in Central and South 

America, where current productivity is estimated to be 

15 metric tonnes per hectare per year, with a potential 

for doubling by the year 2100 (Gurgel and others 2007). 

Productivity rates for other global regions as reported in 

the MIT study were used to derive current and projected 

biomass plantation productivity levels for each of the four 

SRES macro regions, as shown in Table B4.

Multiplying biomass plantation productivity estimates 

(Table B4) by SRES projections of biomass energy plantation area (Table B2) gives corresponding estimates of regional biomass production from biomass energy plantations 

for each RPA scenario, which are shown in Table B5. The 

ALM region (Africa and Latin America) followed by Asia 

are the macro regions with the largest projected expansion 

in biomass output from biomass energy plantations, because 

those regions are projected to have the largest expansion in 

biomass energy plantations according to the SRES (Table 

B2) as well as highest estimated average regional productivity levels (Table B4). This result is also similar to findings of 

other global studies (e.g., Gurgel and others 2007, Hoogwijk 

and others, 2005)

After computing estimated regional biomass production 

from biomass energy plantations (Table B5), we computed 

estimates of other non-roundwood biomass consumption for 

energy, representing biomass from agricultural crops and 

crop residues plus other non-roundwood sources including mill residues. We estimated this cropland and residue 

biomass energy output of each region in the year 2000 by 

subtracting the energy equivalent of roundwood fuelwood 

consumption in 2000 from total biomass energy production 

in 2000 (Table B3). We then projected this cropland and residue biomass output based on SRES projections of cropland 

area, and results are shown in Table B6.

Next we computed global and regional projections of 

roundwood fuelwood consumption for the RPA scenarios 

based on the SRES projections of biomass energy production (Table B3) with deductions for the projected biomass 

consumption from biomass energy plantations (Table B5) 

and the projected biomass consumption from cropland and 

residues (Table B6). The roundwood fuelwood consumption 

estimates for the year 2000 were also calibrated to match 

precisely historical roundwood fuelwood consumption 

data by macro region as reported by FAOSTAT. Resulting preliminary global and regional roundwood fuelwood 

consumption estimates (in thousands of cubic meters per 

year) are shown in Table B7. Figure B2 shows preliminary 

projections of global biomass consumption for energy by 

RPA scenario based on our interpretation of SRES data and 

information. Projected global consumption of roundwood 

fuelwood from forests drives the wood energy demands in 

USFPM/GFPM. In USFPM, roundwood that can be used for 

energy includes conventional fuelwood, logging residues, 

and other roundwood such as pulpwood, which are sourced 

from timber harvest or SRWC, while mill residues can also 

be used for energy (compare Fig. 4). 

We used the preliminary estimates of roundwood fuelwood 

consumption (Table B7) to derive preliminary expansion 

factors for fuelwood consumption by macro region for 

each RPA scenario (Table B8). However, we also computed 

unique growth rates for fuelwood consumption in each 

country based on the assumption that each country’s targeted share of regional fuelwood consumption will match its 

projected share of regional GDP in 2060. This assumption is 

analogous to the fuelwood demand and GDP share assumptions applied previously in the GFPM (Raunikar and others 

2010). Secondly, we also applied common regional elasticities of fuelwood demand with respect to the demand growth 

rates to calibrate the model solution. 

Table B2— Special Report on Emissions Scenarios 

(SRES)a

 projections of regional biomass energy plantation 

area (“energy biomass” land) 

Hectares 

(×106

)

World 2000 2010 2020 2030 2040 2050 2060

 A1B (A1 AIM) 0.0 0.0 73.7 157.7 256.8 418.3 484.1

 A2 (A2 ASF) 0.0 0.0 41.2 96.8 144.9 189.7 227.1

 B2 (B2 Message) 0.0 0.0 14.4 86.3 164.6 167.0 271.3

OECD 90 

 A1B (A1 AIM) 0.0 0.0 11.2 24.0 38.9 63.1 73.1

 A2 (A2 ASF) 0.0 0.0 8.4 23.5 37.3 47.2 42.8

 B2 (B2 Message) 0.0 0.0 2.6 11.1 17.5 21.7 26.9

REF 

 A1B (A1 AIM) 0.0 0.0 3.0 6.1 10.1 16.5 19.1

 A2 (A2 ASF) 0.0 0.0 0.0 0.1 0.5 0.8 8.1

 B2 (B2 Message) 0.0 0.0 0.7 3.5 6.3 8.5 11.8

Asia 

 A1B (A1 AIM) 0.0 0.0 17.1 37.0 60.6 99.1 114.7

 A2 (A2 ASF) 0.0 0.0 20.6 44.7 58.2 71.6 72.8

 B2 (B2 Message) 0.0 0.0 3.9 40.2 75.7 67.3 125.0

ALM 

 A1B (A1 AIM) 0.0 0.0 42.5 90.5 147.2 239.5 277.3

 A2 (A2 ASF) 0.0 0.0 12.2 28.5 49.0 70.1 103.4

 B2 (B2 Message) 0.0 0.0 7.1 31.5 65.1 69.5 107.5

a

Nakicenovic and Swart (2000). 
Research Paper FPL–RP–662

56

Final expansion factor targets for roundwood fuelwood consumption by global region are shown in Table B8. Final targets for roundwood fuelwood consumption in each country 

within each macro region were computed by multiplying the 

preliminary expansion factor for the region (Table B8) times 

the country’s share of regional GDP in 2060, divided by the 

country’s share of regional fuelwood consumption in 2006. 

The same formula was used to compute the U.S. wood fuel 

feedstock expansion factor. Compound growth rates for fuelwood demand in each country were computed on the basis 

of the expansion targets for each country. This procedure 

ensured that target expansion factors for fuelwood consumption in each country were calibrated to each country’s regional share of GDP in 2060 (similar to previous GFPM 

approach) while also calibrated to the imputed regional 

shares of fuelwood consumption derived from SRES 

(Table B7). 

The model solution for each scenario was then obtained 

by adjusting regional elasticities with respect to compound 

growth rates for demand, until the USFPM/GFPM projections of global fuelwood consumption matched the final 

targets for expansion in fuelwood consumption. In general, 

the target fuelwood consumption shares of individual countries within each region matched their regional GDP shares 

in 2060. However, projected supplies of wood within each 

country also influence projected equilibrium fuelwood consumption quantities of each country. Thus, each country has 

a unique equilibrium projection of expansion in fuelwood 

consumption, depending on its GDP share of regional GDP 

and depending on its inherent fuelwood supply capacity, 

while regionally and globally the expansion of fuelwood 

consumption is also driven by the overall expansion targets 

shown in Table B8.

This approach resulted in projected U.S. roundwood fuelwood consumption expanding more than the global average rate of expansion. This is partly because fuelwood 

expansion factors for the OECD90 region (which includes 

the United States) are higher than the worldwide expansion factors (Table B8). It is also because the U.S. share of 

roundwood fuelwood consumption in the OECD90 region 

was only 34% in 2006, but projected U.S. shares of regional 

GDP in 2060 are higher (42% in A1B, 44% in A2, and 49% 

in the B2 scenario). Thus, the U.S. share of regional fuelwood consumption must increase to match U.S. regional 

GDP shares in 2060. In addition, the United States has fairly 

Table B3—SRESa projections of total primary biomass energy 

productionb by scenario, with projections adjusted to match B2 

data in 2000

World 2000 2010 2020 2030 2040 2050 2060

A1B (A1 AIM) 43.10 45.60 69.50 93.37 135.86 200.80 254.54

A2 (A2 ASF) 43.10 45.60 52.22 71.87 91.63 111.40 131.80

B2 (B2 Message) 43.10 45.60 52.60 61.10 79.10 104.60 135.90

OECD 90

A1B (A1 AIM) 6.80 6.10 10.20 15.50 22.64 35.20 43.34

A2 (A2 ASF) 6.80 6.10 8.43 14.82 21.22 27.61 27.51

B2 (B2 Message) 6.80 6.10 7.20 7.50 8.50 11.50 15.20

REF

A1B (A1 AIM) 1.20 0.80 1.90 3.20 5.07 8.40 10.50

A2 (A2 ASF) 1.20 0.80 0.80 0.86 1.03 1.21 5.61

B2 (B2 Message) 1.20 0.80 0.80 1.60 2.40 3.90 5.90

Asia

A1B (A1 AIM) 22.40 24.70 26.28 24.50 35.07 51.69 64.40

A2 (A2 ASF) 22.40 24.70 27.22 33.74 40.27 46.79 45.71

B2 (B2 Message) 22.40 24.70 28.00 32.20 40.60 53.70 68.40

ALM

A1B (A1 AIM) 12.70 14.00 31.11 50.17 73.07 105.51 136.30

A2 (A2 ASF) 12.70 14.00 16.92 23.60 30.27 36.94 54.13

B2 (B2 Message) 12.70 14.00 16.60 19.80 27.60 35.50 46.40 a

Nakicenovic and Swart (2000). b

Energy production measured in exajoules (EJ).

Table B4—Current and projected average 

productivity of biomass energy 

plantations by SRESa macro region (t/ha/y)

Region 2010 2020 2030 2040 2050 2060

OECD 90b 6.6 7.4 8.3 9.1 9.9 10.7

REFc 6.8 7.4 8.1 8.8 9.5 10.1

Asiad 10.2 11.3 12.3 13.3 14.3 15.3

ALMe 8.4 9.4 10.4 11.4 12.3 13.3 a

Nakicenovic and Swart (2000).

b

Average for USA, Europe, Canada, Japan, Australia, 

and New Zealand.

c

Average for Former Soviet Union and Eastern Europe.

d

Average for China, India, Indonesia, East Asia, and 

other.

e

Average for Africa, Central and South America, 

Mexico, and the Middle East.
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

57

Table B5—Global and regional biomass production from biomass energy 

plantationsa based on SRESb land use projections

Region 2000 2010 2020 2030 2040 2050 2060

World

A1B (A1 AIM) 0 0 697,527 1,642,259 2,920,218 5,156,163 6,428,115

A2 (A2 ASF) 0 0 409,255 1,039,143 1,672,822 2,365,486 3,035,300

B2 (B2 Message) 0 0 135,927 940,088 1,960,368 2,116,181 3,758,080

OECD 90

A1B (A1 AIM) 0 0 82,833 198,165 353,404 625,046 784,206

A2 (A2 ASF) 0 0 62,351 193,741 338,420 467,631 458,860

B2 (B2 Message) 0 0 19,645 91,892 159,207 215,173 289,017

REF

A1B (A1 AIM) 0 0 22,223 49,669 88,236 155,821 192,929

A2 (A2 ASF) 0 0 0 897 3,986 7,126 82,402

B2 (B2 Message) 0 0 5,316 28,210 55,175 79,967 119,166

Asia

A1B (A1 AIM) 0 0 192,910 454,814 806,022 1,419,983 1,759,926

A2 (A2 ASF) 0 0 231,891 548,860 773,337 1,025,312 1,117,791

B2 (B2 Message) 0 0 44,312 492,910 1,006,763 963,161 1,918,808

ALM

A1B (A1 AIM) 0 0 399,561 939,612 1,672,556 2,955,312 3,691,054

A2 (A2 ASF) 0 0 115,014 295,645 557,079 865,416 1,376,247

B2 (B2 Message) 0 0 66,654 327,076 739,224 857,881 1,431,089

a

Measured in (×103 t/y). b

Nakicenovic and Swart (2000).

abundant wood supply, and in USFPM logging residues also 

contribute to roundwood fuelwood supply, which helps to 

support the higher expansion in U.S. fuelwood consumption 

relative to other countries and regions. 

Total U.S. wood fuel feedstock consumption expands 

less rapidly than U.S. roundwood fuelwood consumption 

because roundwood is only a fraction of total U.S. wood 

fuel feedstock consumption (about 40% in the base year 

including bark). As explained previously, U.S. wood fuel 

feedstock consumption as modeled in USFPM includes both 

roundwood (e.g., harvested roundwood fuelwood and harvest residues that can be used as fuel) and also fuel 

Table B6—Global and regional cropland and residue biomass use for energya

Region 2000 2010 2020 2030 2040 2050 2060

World

A1B (A1 AIM) 875,725 954,125 951,349 949,322 945,413 941,521 937,156

A2 (A2 ASF) 875,725 985,150 1,006,816 1,029,312 1,053,631 1,077,951 1,104,917

B2 (B2 Message) 875,725 981,107 1,000,551 1,020,014 1,038,209 1,056,403 1,076,126

OECD 90

A1B (A1 AIM) 257,439 210,701 209,840 209,360 207,505 205,667 203,021

A2 (A2 ASF) 257,439 216,774 220,563 224,565 229,242 233,918 239,503

B2 (B2 Message) 257,439 216,664 220,972 225,109 228,277 231,445 234,745

REF

A1B (A1 AIM) 15,509 21,133 21,081 21,055 20,985 20,916 20,869

A2 (A2 ASF) 15,509 21,839 22,331 22,844 23,386 23,928 24,526

B2 (B2 Message) 15,509 21,739 22,171 22,625 23,049 23,473 23,940

Asia

A1B (A1 AIM) 537,137 565,352 563,955 562,613 561,499 560,387 559,398

A2 (A2 ASF) 537,137 583,878 597,721 612,033 627,343 642,653 659,072

B2 (B2 Message) 537,137 580,538 591,767 603,095 614,897 626,699 639,438

ALM

A1B (A1 AIM) 65,639 156,908 156,502 156,143 155,758 155,373 155,118

A2 (A2 ASF) 65,639 162,764 166,690 170,717 174,934 179,151 183,725

B2 (B2 Message) 65,639 161,791 165,071 168,342 171,534 174,725 178,259

a

Measured in (×103 t/y). b

2000 data based on historical biomass consumption for energy production minus roundwood fuelwood 

consumption, and projections based on cropland area as projected by SRES (Nakicenovic and Swart 2000).
Research Paper FPL–RP–662

58

residues (mill residues that are used as fuel). In fact about 

60% of U.S. wood fuel feedstock consumption in 2006 was 

fuel residues (total U.S. wood fuel feedstock consumption was 113.3 million cubic meters in 2006, of which only 

about 45 million cubic meters was roundwood fuelwood and 

bark). On the other hand, in USFPM most of the projected 

increase in future U.S. wood fuel feedstock consumption 

comes from roundwood, because supplies of mill residues 

are limited by projected U.S. wood product production volumes. Thus, the target expansion factors in USFPM for total 

U.S. wood fuel feedstock consumption from 2006 to 2060 

are 15.7× (or 5.2% per year) in the A1B scenario, 9.4× (or 

4.2% per year) in the A2 scenario, and 3.7× (or 2.4% per 

year) in the B2 scenario. As explained above, those rates of 

expansion for U.S. fuel feedstock consumption and the target expansion factors for global roundwood fuelwood consumption by macro region (Table B8) are all aligned with 

the SRES projections of regional biomass energy production 

and regional land use by RPA scenario, including the SRES 

projections by scenario of regional land use for non-forest 

biomass energy plantations. 

For the United States and for many other countries the resulting projections of expansion in wood energy consumption are prodigious in all RPA scenarios, but by far the highest in the A1B scenario, followed by the A2 scenario, and 

lowest in the B2 scenario. In the A1B scenario for example 

U.S. wood fuel feedstock consumption climbs to levels 

that dwarf U.S. consumption of wood for all other end uses 

(about five times higher by 2060 than all other wood uses) 

while in the B2 scenario U.S. wood fuel feedstock consumption climbs to a level only moderately higher than all other 

commercial uses. Figure B3 shows final projected rates of 

expansion in volumes of wood consumed for energy by RPA 

scenario from 2006 to 2060, according to USFPM/GFPM 

equilibrium results. 

The A1B scenario has the highest projected global expansion of roundwood fuelwood consumption (4.5× from 2006 

to 2060), while the A2 has the lowest global expansion of 

fuelwood consumption (2.2×). The projected global expansion rates for fuelwood are not exactly the same as expansion rates for biomass energy production as projected by 

SRES (Table 22) because of the adjustment for the output 

of biomass energy plantations and the added requirement 

that each country’s regional share of fuelwood consumption match its regional GDP share in 2060. Thus, as shown 

in Figure B3, the counter-factual assumptions of the RPA 

scenarios and USFPM/GFPM projections provide a wide 

range of perspectives on potential expansion in U.S. and 

global demand for wood energy, while taking into account 

SRES projections of regional biomass energy production 

and regional land use, and specifically taking into account 

SRES projections of non-forest biomass energy plantations 

for each RPA scenario.

Mathematical Summary

Mathematically, our “option 3” approach to projecting 

global fuelwood consumption in USFPM/GFPM can be 

summarized as follows:

Table B7—Imputed global and regional roundwood fuelwood consumption estimatesa for 

RPA scenariosb

World 2000 2010 2020 2030 2040 2050 2060

A1B (A1 AIM) 1,825,914 1,893,180 2,612,634 2,983,116 4,208,244 5,682,124 7,712,877

A2 (A2 ASF) 1,825,914 1,849,745 1,715,082 2,193,072 2,671,559 3,067,467 3,536,509

B2 (B2 Message) 1,825,914 1,855,405 2,133,559 1,582,373 1,403,096 2,965,152 2,855,246

OECD 90

A1B (A1 AIM) 121,096 136,962 312,524 527,026 817,807 1,329,528 1,686,572

A2 (A2 ASF) 121,096 128,459 200,743 463,947 707,603 972,914 970,176

B2 (B2 Message) 121,096 128,614 172,971 87,276 59,412 189,056 343,052

REF

A1B (A1 AIM) 63,260 27,063 73,914 127,580 206,158 347,375 444,456

A2 (A2 ASF) 63,260 26,074 25,386 27,519 34,903 42,215 247,271

B2 (B2 Message) 63,260 26,213 18,167 42,129 60,432 131,346 217,433

Asia

A1B (A1 AIM) 834,161 957,525 801,623 310,249 569,016 888,002 1,313,461

A2 (A2 ASF) 834,161 931,588 765,805 764,068 890,425 978,283 748,832

B2 (B2 Message) 834,161 936,264 1,092,182 745,688 604,579 1,576,715 1,261,887

ALM

A1B (A1 AIM) 807,397 771,675 1,424,532 2,018,472 2,614,796 3,116,068 4,266,635

A2 (A2 ASF) 807,397 763,476 803,981 1,017,864 1,118,360 1,153,191 1,649,071

B2 (B2 Message) 807,397 764,838 851,037 708,461 679,306 1,068,121 1,032,516

a

Measured in (×103 m3/y). b

Based on Food and Agriculture Organization (FAO) of United Nations statistical database (FAOSTAT) data for 2000 

and projections based on Special Report on Emissions Scenarios (SRES,

Nakicenovic and Swart 2000) biomass energy 

production with deductions for biomass production from energy plantations, agricultural crops, and residue.
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

59

We used FAOSTAT data (the same data used conventionally in the GFPM) to determine initial roundwood fuelwood 

consumption in the base year (2006) for each country (i) in 

each macro region (j), and we denote the base-year demand 

quantity as dij, 06. Applying the conventional GFPM approach in USFPM/GFPM, we specify roundwood fuelwood 

demand of each country (dij) with a common global price 

0

2

4

6

8

10

12

14

1990

2000

2010

2020

2030

2040

2050

2060

Million t

A1B global biomass

expansion factor for

roundwood fuelwood

(2006–2060): 4.1×

0

2

4

6

8

10

12

14

1990

2000

2010

2020

2030

2040

2050

2060

Million t

A2 global biomass

expansion factor for

roundwood fuelwood

0

2

4

6

8

10

12

14

1990

2000

2010

2020

2030

2040

2050

2060

Million t

Roundwood

fuelwood from

forests

Cropland crops,

residues and 

other

Biomass energy

crops

B2 global biomass

expansion factor for

roundwood fuelwood

(2006–2060): 1.9×

(2006–2060): 1.5×

Preliminary Preliminary

Preliminary

elasticity (-0.5 for all countries), and we apply long-term 

fuelwood demand growth rates (gi) that are unique to each 

country. We also apply common regional elasticities (ej) to 

the growth rates for all countries within each macro region 

(j). Over time, from year (t) to year (t+1), the global demand 

for fuelwood is thus shifted according to the following 

formula (B1):

Figure B2—Preliminary projections of global biomass consumption for energy (millions of metric tons per 

year) by source for three RPA scenarios, based on interpretation of SRES land use projections and other 

information.
Research Paper FPL–RP–662

60

(B1)

The specified growth rates for fuelwood consumption in 

each country (gi) are compound rates of growth that precisely match targeted expansion factors for fuelwood consumption of each country for each RPA scenario. The target 

expansion factors (Fi) for each country are computed according to the following formula (B2):

 

(B2)

Where GDPj

i 60 is the GDP of country i in macro region j in 

the year 2060, ΣGDPj

i 60 is the sum of GDP for all countries 

within macro region j (so the numerator in the term above 

is the GDP share of country i in region j in 2060), and the 

denominator in the term above is the fuelwood consumption share of country i in region j in 2006, and Fj is the final 

target expansion factor for fuelwood consumption in region 

j for the given scenario (Table B8). 

Thus, in each macro region, the target expansion of fuelwood consumption for each country is based on the target 

expansion for the region adjusted by a ratio of that country’s 

share of regional GDP in 2060 to that country’s share of 

Table B8—Preliminary expansion 

factors and final expansion factor 

targetsa for global and regional 

roundwood fuelwood consumption for 

RPA scenarios (2006–2060)

Preliminary Final targets

World

A1B (A1 AIM) 4.1× 4.5×

A2 (A2 ASF) 1.9× 2.2×

B2 (B2 Message) 1.5× 2.6×

OECD 90

A1B (A1 AIM) 12.8× 14.0×

A2 (A2 ASF) 7.4× 8.1×

B2 (B2 Message) 2.6× 3.0×

REF

A1B (A1 AIM) 7.5× 7.5×

A2 (A2 ASF) 4.2× 4.2×

B2 (B2 Message) 3.7× 3.7×

Asia

A1B (A1 AIM) 1.7× 1.7×

A2 (A2 ASF) 0.9× 0.9×

B2 (B2 Message) 1.6× 1.6×

ALM

A1B (A1 AIM) 4.8× 4.8×

A2 (A2 ASF) 1.8× 1.8×

B2 (B2 Message) 3.4× 3.4×

regional fuelwood consumption in 2006. This approach ensures that the targeted expansion of fuelwood consumption 

for each country converges toward its share of regional GDP 

in 2060, while the aggregate expansion of fuelwood consumption for all countries within a given macro region (j) 

matches appropriate expansion factor for each region (Table 

B8). The fuelwood demand growth rates for each country 

(gi) are simply compound growth rates that correspond precisely to that country’s expansion factor (Fi

), computed as 

Fi to the power of (1/54)-1, where 54 is the number of years 

from 2006 to 2060. 

Operationally, when running USFPM/GFPM to generate 

results for the RPA scenarios, we used only the regional 

elasticity factors (ej) for each of the four macro regions as 

adjustment parameters to raise or lower the equilibrium 

consumption of fuelwood until we obtained model projections that closely matched the targeted global and regional 

expansion factors for fuelwood consumption (Table B8). 

Thus we ensured that the projected expansion of fuelwood 

consumption for the RPA scenarios, both globally and by 

macro region, took into account the IPCC SRES projections 

of biomass energy production by macro region for each scenario (Table B3), and also took into account the IPCC SRES 

projections of biomass energy plantation area by macro 

region (Table B2), corresponding biomass output from 

energy plantations (Table B5), and biomass output from 

cropland (Table B6) based on IPCC SRES cropland area 

projections. We believe that this approach ensures that the 

2.6

2.2

4.5

3.7

9.4

15.7

6.0

21.9

35.0

0 X 10 X 20 X 30 X

Scenario B2

Scenario A2

Scenario A1B

U.S. Roundwood

fuelwood, including

logging residues

Total U.S. wood fuel

feedstock, including

mill and logging residues

World roundwood

fuelwood (excluding

residues)

Figure B3—Projected expansion from 2006 to 2060 in 

volumes of wood consumed for energy by RPA scenario, 

including U.S. roundwood fuelwood consumption, total U.S. 

wood fuel feedstock consumption, and world roundwood 

fuelwood consumption.

∑∑ = ∑ ∑ + ⋅ + i ij t j i j i j ij t d e g d , 1 , (1 )

F

d

d

GDP

GDP

F j

i ij

ij

i i

j

i

j

i

×

∑

∑

=

,06

,06

60

60
U.S. Forest Products Module: A Technical Document Supporting the Forest Service 2010 Resources Planning Act Assessment

61

USFPM/GFPM projections of global and regional fuelwood 

consumption are linked closely to the full array of biomass 

energy information as presented in the IPCC SRES for the 

selected RPA scenarios.




United States Department of Agriculture

Forest Service

Forest Products Laboratory

Research Paper FPL-RP-662

September 2011