FutureScenarios

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theForestService2010RPAAssessment
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Acknowledgments

We would like to acknowledge the primary authors of this 

report: Amy E. Daniels, Linda A. Joyce, and Linda L. Langner. 

We also acknowledge that sections of the report are drawn 

from more detailed RPA Assessment supporting documents 

authored by H. Ken Cordell, Peter J. Ince, Linda A. Joyce, 

David N. Wear, and Stanley J. Zarnoch. 

We thank our peer reviewers for their excellent suggestions: David Darr (retired Forest Service), William Goran (U.S. 

Army Corps of Engineers), James Hrubovcak (USDA Office 

of Chief Economist), Jeffrey Kline (U.S. Forest Service Pacific 

Northwest Research Station), and Jan Lewandrowski (USDA 

Climate Change Program Office). Reviews by Ross Arnold, 

Margaret Connelly, Curtis Flather, and Guy Robertson also 

greatly improved the document. 

USDA Forest Service. 2012. Future scenarios: a technical document supporting the Forest 

Service 2010 RPA Assessment. Gen. Tech. Rep. RMRS-GTR-272. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 34 p. 

Abstract: The Forest and Rangeland Renewable Resources Planning Act of 1974 (RPA) mandates 

a periodic assessment of the conditions and trends of the Nation’s renewable resources on forests 

and rangelands. The RPA Assessment includes projections of resource conditions and trends 50 

years into the future. The 2010 RPA Assessment used a set of future scenarios to provide a unifying framework for resources analyses. Those scenarios, and their associated assumptions about 

population change, economic growth, land use change, bioenergy, and climate change, are described 

in this document. 

Keywords: scenarios, population, income, land use, bioenergy, climate

We also acknowledge the World Climate Research Program for their role in contributing and willingness to share 

the Coupled Model Intercomparison Project phase 3 (CMIP3) 

multi-model dataset through the Program for Climate Model 

Diagnosis and Intercomparison web portal. We also wish to 

acknowledge the efforts of the IPCC Data Distribution Centre (DDC) to archive and share the IPCC Third Assessment 

climate projection data. And we acknowledge the following 

modeling groups for the availability of their data: the Canadian Centre for Climate Modeling and Analysis, the Commonwealth Scientific and Industrial Research Organisation 

in Australia, the Hadley Centre for Climate Prediction and 

Research in the UK, and in Japan, the Centre for Climate 

System Research (CCSR), together with the University of 

Tokyo, and the National Institute for Environmental Studies 

and the Frontier Research Center for Global Change.
Contents

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

Framework for 2010 RPA Assessment ................................................................... 1

Global Linkages............................................................................................. 1

Resource Interactions.................................................................................... 1

Potential Effects of Climate Change ............................................................. 2

Complexity and Uncertainty........................................................................... 2

Scenario Approach in the 2010 RPA Assessment................................................. 3

IPCC Storylines and Emissions Scenarios.................................................... 4

Selecting the Scenarios and Climate Models for the 2010 

RPA Assessment .............................................................................. 6

Scaling and Updating IPCC Data to U.S. and Sub-National Scales..................... 8

U.S. Population Projections........................................................................... 9

U.S. Economic Projections.......................................................................... 10

U.S. Bioenergy Projections ......................................................................... 13

U.S. Land Use Projections .......................................................................... 16

U.S. Climate Projections.............................................................................. 21

Summary ................................................................................................................. 23

Literature Cited....................................................................................................... 25

Appendix A: National Integrated Assessment Models ....................................... 27

Appendix B: Definition of IPCC Macro Regions.................................................. 30

Appendix C. Availability of 2010 RPA Assessment Scenario Data .................... 32

Appendix D: Acronyms.......................................................................................... 34

North Central

Great

Plains

Intermountain

Pacific

Northwest

Northeast

NORTH

SOUTH

Southeast

Pacific

Southwest

South Central

ROCKY MOUNTAIN

PACIFIC COAST

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 1

Introduction

The Forest and Rangeland Renewable Resources 

Planning Act of 1974 (RPA) (P.L. 93-378, 88 Stat 475, 

as amended) mandates a periodic assessment of the 

condition and trends of the Nation’s renewable resources 

on forests and rangelands. The RPA Assessment provides a snapshot of current United States (U.S.) forest 

and rangeland conditions and trends on all ownerships, 

identifies drivers of change, and projects 50 years into 

the future. We analyze trends in outdoor recreation, fish 

and wildlife, biological diversity, wilderness, forests, 

range, water, urban forests, landscape patterns, and the 

potential effects of climate change on these resources.

The 2010 RPA Assessment is the fifth assessment 

prepared in response to this mandate (U.S. Forest Service 1977, 1981, 1989, 2001). This report describes the 

framework for the 2010 RPA Assessment, describes the 

future scenarios, and documents the common assumptions used across resource analyses.

Framework for the 2010 RPA 

Assessment

The framing of the RPA Assessment has evolved to 

respond to changes in natural resource issues and management. The original legislation focused primarily on 

an economic evaluation of whether resource supplies 

could meet consumer demands. As public expectations 

about the role of natural resources broadened to include 

both ecological and socioeconomic considerations, the 

RPA Assessment analyses also broadened to assess 

resource conditions, ecosystem health, and sustainability in recognition of the interrelationships between 

ecological and socioeconomic conditions in meeting the 

expectations of the American public. While maintaining 

the ability to report on economic supply and demand, 

this broader and more flexible approach improves our 

ability to evaluate the future of the Nation’s forests and 

rangelands and adapt to changing information needs.1

1 A description of previous RPA Assessments and the supporting 

publications can be found at http://www.fs.fed.us/research/rpa/what.

shtml. 

The 2010 RPA Assessment framework was designed to:

	 •	 incorporateglobalinteractionsthat affectdomestic

resource conditions and trends,

	 •	 improve analyses ofinteractions among resources,

	 •	 extend our analytical capability to evaluate the

potential effects of climate change across the 

resources, and

	 •	 describe more clearly the complexity and uncertainty associated with projecting future conditions 

and trends.

Global Linkages

Global conditions and trends increasingly affect the 

conditions and trends in domestic natural resources. The 

international context has always been considered in RPA 

Assessment analyses, mainly in terms of the supply and 

demand of wood products. The international context 

is important in other areas, such as destination travel 

for recreation, energy policies, and wildlife. The 2010 

framework improved these linkages through two main 

changes. First, the 2010 RPA Assessment used a set of 

future scenarios for the United States tied to a global set 

of scenarios that provide a coherent interdependent future 

for global and U.S. population dynamics, socioeconomic 

factors and climate change for more than 50 years into 

the future. These scenarios provided both quantitative 

and qualitative connections for the domestic resource 

analyses that project resource conditions and trends for 

50 years. Second, the U.S. forest resource assessment 

nested the U.S. domestic forest products model within 

a global forest trade model quantitatively tied to the 

global IPCC scenarios.

Resource Interactions

Many common stressors impact renewable resources 

and create interactive effects between them. The 2010 

analyses continued the practice of using common historical and projected data for U.S. population, economic 

growth, and land use change across individual resource 

analyses. These socioeconomic variables continue to be 

important drivers of resource change. The 2010 RPA 

Assessment added a common historical and projected 

data set for climate variables, which ensured consistency 

in incorporating climate effects in resource models, 
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

including models for forest inventory projections, water 

yield/use projections, and recreation use projections.

The individual resource analyses for the RPA Assessment are primarily done with a series of independent 

models. The main exception is the forest resource 

modeling system, which has linkages between land use, 

forest inventory, and forest products. The common assumptions about population growth, economic growth, 

land use change, bioenergy use, and climate change are 

used to ensure that all resource projections are based 

on the same drivers of change. The use of the common 

assumptions provides opportunities to create feedback 

loops across the resource analyses.

Potential Effects of Climate Change

A 1990 amendment to the RPA legislation—the only 

substantive amendment to the Assessment mandate since 

enactment—required the Assessment to analyze potential 

effects of global climate change on renewable resources 

and to identify mitigation opportunities. The first RPA 

“climate” report was a special report summarizing 

current knowledge of the effects of climate change on 

forests (Joyce and others 1990). The 1993 RPA Assessment update included the first analysis linking climate 

projections with an ecological model to drive productivity 

changes in a timber assessment model at a national scale 

(Joyce 1995). This timber policy model also linked to a 

carbon accounting model to track carbon inventory of 

U.S. forests. The 2000 RPA Assessment made further 

advances, including ecological models that expanded our 

capacity to look not only at productivity shifts but also 

ecosystem type shifts (Joyce and Birdsey 2000). Other 

advances included refined estimates of all carbon pools 

associated with the U.S. forests and wood products for 

the U.S. carbon inventory and a more detailed analysis 

of the implications of forest management on carbon 

sequestration.

The 2010 RPA Assessment took a fundamentally different approach to examine the effects of climate change. 

A consistent set of U.S. future scenarios were developed 

which include projections for population, economic 

activity, climate, and bioenergy. Where possible, the resource models were revamped to directly include climate 

variables in the same manner that socioeconomic and 

biological variables are used to project future resource 

effects. The climate projections were driven by the 

scenarios described in the scenarios approach section.

Complexity and Uncertainty

The RPA Assessment analyses address a wide range 

of economic and ecological phenomena. Individually, 

the economic, social, and biological systems that affect the provisioning of goods and services are quite 

complex. Integrating effects across these systems adds 

additional complexity. Considerable uncertainty is also 

associated with projections of the future, particularly 

projections that look forward 50 years. Past RPA Assessments typically focused on one “business as usual” 

future, with varying assumptions about future population sometimes being used to create high/medium/

low trajectories of supply and demand. In addition, the 

timber assessment often included various alternative 

futures linked to different policy and/or trade assumptions. Common assumptions were used for population 

change, economic growth rates, and land use change. 

Climate change analyses were restricted to effects on 

timber markets and forest resources.

Using global scenarios to frame the 2010 analyses 

provided a coherent framework for evaluating outcomes 

across resource analyses. Socioeconomic and climate 

variables were all linked through these global scenarios. 

Scenarios were not assigned likelihoods, nor were any 

scenarios intended to be “accurate” per se. Rather, these 

constructed scenarios provided a means of qualitatively 

and quantitatively understanding (a) how different socioeconomic processes interacted to create different 

possible greenhouse gas (GHG) emissions pathways, 

(b) how these emissions pathways drove global climate 

models to project different potential future climates, 

and (c) how various natural resources would respond 

to alternative future climates. Each link in this chain 

of models is subject to uncertainty from a number of 

sources ranging from deliberate modeling assumptions 

(for example, the global population growth rate selected 

for a given scenario), to stochastic processes in the global 

climate, economic, and biological systems themselves. 

The purpose of using future scenarios to drive different resource projections for the 2010 RPA Assessment 

was to facilitate exploring a consistent range of possible 

futures across resource analyses rather than intending 

to actually predict future resource conditions.

Figure 1 presents a schematic that illustrates how global 

scenarios were linked to U.S. data used in the various 

RPA resource analyses. The remainder of this document will focus on the topics in the box labeled “Basic 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 3

Assumptions.” The next section provides details on the 

selection of scenarios for the 2010 RPA analyses, followed 

by descriptions of the components of the assumptions 

including population, economics, bioenergy, land use, 

and climate. The underlying assumptions described in 

this document are key inputs to the resource analyses 

listed in the “Resource Modeling Systems” box. The 

outcome of these analyses is the estimation of future 

goods and services from forests and rangelands, with 

examples shown in the bottom box of figure 1.

Scenario Approach in the 2010 

RPA Assessment

Scenarios are used to explore alternative futures and 

are intended to serve as a counterfactual framework for 

objectively evaluating a plausible range of future resource 

outcomes. This approach is particularly useful when 

there is considerable uncertainty about the trajectory 

of the driving forces behind political, economic, social, 

and ecological changes (Alcamo and others 2003, IPCC 

2007). A scenario approach can use both qualitative and 

quantitative approaches in visualizing alternative futures 

using different socioeconomic or institutional assumptions for the United States. Carpenter and others (2005) 

and Nakicenovic and others (2000) review examples and 

uses of scenario analysis in other applications.

The challenge of incorporating global interactions in 

the 2010 RPA Assessment led to the search for a set of 

comprehensive global scenarios to serve as anchors for 

the RPA Assessment analyses. These global scenarios 

would provide the context and quantitative linkages 

between U.S. and global trends. We identified several 

criteria for evaluating and selecting global scenarios:

	 •	 Scenarios must be globally consistent.

	 •	 Scenarios must be scientifically credible and

well-documented.

Figure 1—RPA Assessment scenario analysis and modeling systems. 

Global IPCC Scenarios

Basic Assumptions

Socioeconomic

Bioenergy

Data

Climate

Updated &

Downscaled

Examples of Projected Ecosystem Services/Responses

Forest Range

Forage Supply

Urban

Forests

Tree Cover

Water Wildlife

Climate Stress

Recreation Landscape

Wood Markets Pattern

Condition

Carbon

Canopy Change

Uses

Supply

Vulnerability

Bird Diversity

Participation

Amenity

Migration

Fragmentation

Mosaics

Resource Modeling Systems

Forest Assessment

Range Assessment

Urban Forest Assessment

Water Assessment

Wildlife Assessment

Recreation Assessment

Landscape Pattern

Carbon

Sequestration

US Scenarios

Macroeconomic

Demographic

Climate

Land Use
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

	 •	 Scenarios must include assumptions about key

driving forces of resource change:

o Population and economic growth

o Land use change

o Climate change

o Energy use

	 •	 Globally consistent data must be available to link

to U.S.-scale analyses.

We reviewed a number of scenario-based approaches 

as potential anchors for the 2010 RPA Assessment: the 

Millennium Ecosystem Assessment (Alcamo and others 

2003, Carpenter and others 2005), the Intergovernmental Panel on Climate Change (IPCC) (Nakicenovic and 

others 2000), the “Mapping the Global Future” project 

(National Intelligence Council 2004), and the United 

Nations Environmental Program’s Global Environmental 

Outlook (UNEP 2002, 2007). Although these studies 

exhibited wide variations in approach and objectives, 

all focused on a similar set of driving forces that shape 

the global future.

We selected emissions scenarios used in both the IPCC 

third and fourth Assessment Reports (known as TAR 

and AR4, respectively) to provide the global scenarios 

for the 2010 RPA Assessment. The Special Report on 

Emissions Scenarios (SRES) from the TAR (Nakicenovic 

and others 2000) provides detailed documentation of 

these scenarios. The overwhelming advantage of using 

these IPCC emissions scenarios as the basis for the 2010 

RPA Assessment was the level of scientific rigor and 

acceptance surrounding their development, the degree 

of documentation, and the facilitated access to the data. 

The availability of socioeconomic and associated climate 

data at global, regional, and country scales was a critical decision factor. The range of scenarios considered 

in the IPCC Assessments provided a broad spectrum of 

potential futures from which we could select a subset to 

evaluate potential U.S. future resource conditions and 

trends.

IPCC Storylines and Emissions Scenarios

Established in 1988 as a scientific intergovernmental 

body, the IPCC assesses the latest scientific, technical, 

and socioeconomic literature relevant to understanding 

the risk of human-induced climate change, its observed 

and projected impacts, and options for adaptation and 

mitigation (http://www.ipcc.ch/about/index.htm). The 

IPCC produced its first Assessment Report in 1990; its 

second Assessment Report in 1995; the TAR in 2001; 

and the AR4 in 2007. These IPCC assessments revolve 

around projections, and associated implications, of how 

different socioeconomic scenarios translate into GHG 

emissions pathways that feed into global circulation 

models (GCMs). GCMs simulate climate change resulting from the varying concentrations of GHG as specified 

by different emissions scenarios. While the emphasis of 

the IPCC is climate change, the usefulness of the IPCC 

assessments for the RPA Assessment was based on the 

linkage between socioeconomic and climate variables.

Several generations of IPCC-developed emissions 

scenarios exist, each increasing in sophistication. Emissions scenarios developed for the TAR and AR4 represent 

the latest. Similarly, each successive IPCC assessment 

represents advancements in the GCMs developed by 

the nearly two-dozen major climate modeling groups 

around the world. To obtain the kinds of projected 

climate variables useful as inputs to RPA analyses, a 

specific emissions scenario must be selected and paired 

as a “driver” of a specific GCM, resulting in many possible combinations. In the sections below, we describe 

the TAR/AR4 IPCC storylines and emissions scenarios 

and our process for selecting emissions scenarios for the 

2010 RPA Assessment.

IPCC storylines

The IPCC based the TAR and AR4 on a set of four 

storylines depicting potential future states of the world. 

The storylines are qualitative descriptions of the world 

that are internally consistent stories about how the 

future might evolve. The four families of storylines 

developed for use in the TAR and AR4 were A1, A2, 

B1, and B2. Within the A1 storyline, sub-storylines 

focused on different future uses of energy sources: A1B 

(balanced energy), A1F1 (fossil fuel intensive), and A1T 

(predominantly non-fossil fuel).

The storylines can be grossly characterized and categorized by assumptions about global population and 

gross domestic product (GDP) (table 1). Nakicenovic 

and others (2000) provide the full documentation of the 

storylines.

The A1 storyline describes a 21st century of very rapid 

economic growth with a global population that peaks 

in mid-century and then declines. The A1F1 scenario 

carries through a fossil-fuel intensive economy for as 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 5

long as possible, while A1T makes the shift to non-fossil 

intensive technology more rapidly than A1F1 or A1B. 

The A1B option within this storyline takes a more balanced approach between dependence on fossil fuel and 

early switching to other energy sources, so that the A1F1 

and A1T scenarios tend to bracket the results of A1B.

The A2 storyline has a continuously increasing global 

population and more regionally oriented economic 

growth. It has the highest global population growth, but 

the lowest long-term economic growth. The emissions 

are less than A1F1, but higher than A1T.

The B1 storyline shares the same population trend 

as A1, but with an economic future of rapid change 

toward a service and information economy and a strong 

emphasis on clean and resource-efficient technologies. 

GDP growth is lower, but the greatest divergence with 

A1 does not occur until after 2060.2

 Global GHG emissions are not very different between B1 and A1T.

The B2 storyline is similar to A2 in that regional and 

local institutions are emphasized over global integration, 

with intermediate economic growth and a growing global 

population. Per capita income is comparable to A2 and 

B1, but population growth is significantly lower than all 

other scenarios. B2 also has the lowest projected growth 

in biomass energy in the global region that includes the 

United States.

2 The RPA Assessment projection period ends in 2060, while the 

IPCC AR4 projections end in 2100.

Emissions scenarios

The IPCC storylines were then used to develop emissions scenarios for the TAR and AR4. Quantifying 

the population and GDP projections of each storyline 

transformed the qualitative storylines into quantitative 

emissions scenarios evaluated through six integrated 

assessment models (IAMs). For each of the four storylines (A1, A2, B1, and B2) and A1 sub-storylines (A1B, 

A1F1, A1T), IAM modeling teams used the population 

and GDP projections specified by the storyline and then 

proceeded to further interpret, model, and quantify 

other necessary variables for estimating GHG emissions 

consistent with the storyline. For example, modeling 

teams developed quantitative assumptions about technology change, energy sources, and land use change. 

Each IAM had a different “focus” (see Appendix A for 

more information about the six IAMs). Quantification 

of the storylines through IAMs resulted in a suite of 

40 emissions scenarios across the four storylines that 

provided a broad range of future outcomes in terms of 

GHG emissions through 2100. Each emissions scenario 

was considered an equally possible future and linked different levels of GHG emissions to global environmental 

outcomes, including climate.

Each IAM group had the responsibility to create a 

“marker” emissions scenario for a storyline or substoryline. These marker emissions scenarios were harmonized, meaning they used common assumptions about 

the main driving forces within the storylines. Marker 

scenarios were not intended to be mean or median 

scenarios across the range of scenarios. Rather, they 

illustrated their respective storylines and were subjected 

Table 1—Global population and Gross Domestic Product (GDP) projections 

for IPCC storylines, 2010-2100.

Storyline 2010 2020 2040 2060 2100

Global population (millions)

A1 6,805 7,493 8,439 8,538 7,056

A2 7,188 8,206 10,715 12,139 15,068

B1 6,892 7,618 8,547 8,671 7,047

B2 6,891 7,672 8,930 9,704 10,414

Global GDP (2006 trillion USD)

A1 54.2 80.6 181.8 336.2 756.5

A2 45.6 57.9 103.4 145.7 347.2

B1 53.3 75.2 144.0 245.5 469.6

B2 67.1 72.5 133.3 195.6 335.9
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

to more intensive review and tests of reproducibility 

than the other emissions scenarios (Nakicenovic and 

others 2000). As such, marker scenarios are most widely 

used for the study of climate change impacts and other 

applications.

To reiterate an important point, none of the storylines 

or emissions scenarios were considered to reflect the 

“most likely” or “business as usual” future. The TAR 

and AR4 deliberately avoided judging the likelihood 

of future scenarios. Although covering a wide range of 

alternative futures, the storylines and resulting emissions scenarios deliberately excluded global disaster 

scenarios. Scenarios simply provide a tool to explore a 

range of future outcomes without judgment about the 

desirability of the outcomes (Nakicenovic and others 

2000).

Selecting the Scenarios and Climate 

Models for the 2010 RPA Assessment

Selecting the scenarios

We analyzed the variation across the marker scenarios, 

evaluating the range of variation in world population, 

U.S. population, world and U.S. GDP, energy futures, 

and climate. We had no pre-determined test of what 

constitutes “sufficient” variation in any of the variables, 

so the basic test was whether a subset of the IPCC scenarios would cover the range of possibilities that were 

likely to drive the greatest variation in resource effects 

in the United States. The B1 storyline was dropped from 

consideration because its population trajectory matched 

A1, and the differences in GDP growth did not diverge 

greatly until after 2060. We initially proposed using 

the following four IPCC storylines/scenarios: A1F1, 

A1T, A2, and B2. However, no marker scenario was 

developed for either A1F1 or A1T; the marker scenario 

was developed for A1B in the A1 family.

After our initial selection of scenarios, we checked 

on the availability of climate projections linked to the 

marker scenarios. In planning for the AR4, the climate 

model community, through the World Climate Research 

Programme’s Working Group on Coupled Modelling, 

organized a coordinated set of experiments exploring 

climate variability and change that could be performed 

by modeling groups with state-of-the art global coupled 

climate models. These coordinated and structured experiments conducted by a diversity of modeling groups 

facilitated a greater understanding of climate and 

climate modeling. A critical part of this process was 

the archiving of climate model output data so that the 

international climate science community would have 

access for analysis (Meehl and others 2007a), which 

was undertaken by the Program for Climate Model 

Diagnosis and Intercomparison (PCMDI). Choices 

about the experiments influenced the availability of 

the scenario-based climate projections. The list of experiments included only the emissions scenarios A1B, 

A2, and B1 (Meehl and others 2007a). Consequently, 

no climate projections for A1T, A1F1, or B2 scenarios 

were archived through PCMDI (http://www-pcmdi.llnl.

gov/ipcc/data_status_tables.htm). As a result we decided 

to use the A1B scenario instead of A1T and A1F1. We 

did not want to drop the B2 scenario, so we turned to 

the TAR archived projections for B2.

Selecting the climate models

Scenario A2 shows the greatest global warming of 3.4 

degrees C by 2100, followed by A1B with 2.8 degrees 

C and B2 with 2.4 degrees C when averaged across all 

AR4 models (IPCC 2007). Not only do projections of 

the global surface warming vary by emissions scenario 

over the next 100 years, the projections vary by the 

individual climate model. Based on an analysis of a 

variety of different climate models, the uncertainty for 

each of these scenarios can range from nearly 1 degree 

C below the average to 2 degrees C above the average; for example, for the 3.4 degree C mean of the A2 

scenario, the uncertainty range is 2.0 to 5.4 degrees C 

(Meehl and others 2007b). Individual climate models 

reflect a common understanding of climate processes 

held by the international community and also individual 

understanding of these processes by each modeling 

group. Hence the models respond slightly differently 

when forced by the atmospheric chemistry projected 

for the future. Since the results varied by GCM within 

each scenario, we considered it important to identify 

several climate models to include in the RPA analyses 

to provide variation in the resource projections based 

on climate variables.

We selected AR4 climate models that developed global 

projections for A1B and A2 from the PCMDI Climate 

Model Intercomparison Project 3 (CMIP3) website, and 

the TAR climate models that developed global projections 

for B2 from the IPCC Date Distribution Centre. Three 

GCMs were chosen for each marker scenario based on 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 7

the availability of the projections in the CMIP3 database 

at the time this study started and the variables needed 

for the RPA Assessment and assessments being done in 

Canada (Price and others 2011) (table 2). For the TAR 

climate models, a suite of climate models projecting 

the B2 scenario had been downscaled using the same 

procedure described here and had been used to assess 

the impact of climate effects on vegetation (Bachelet 

and others 2008, Price and others 2004). Hence these 

same models were selected. The global projections from 

these models capture a range of future climates.

The reliability of individual climate models has been 

explored using several different approaches: how well 

they simulate historical climate, whether they capture 

well-known regional climate phenomena such as the El 

Nino-Southern Oscillation, or how sensitive they are to 

the GHG changes. The three AR4 models used in this 

current study were included in the Multi-Model Data 

study that tested the climate sensitivity across GCMs 

(Randall and others 2007): CGCM3.1, MIROC3.2, and 

UKMO_HADCM3. Their climate sensitivities were 

about and above the mean. Reichler and Kim (2008) 

attempted to quantify agreement between model and 

observations for several generations of climate models. 

They conclude that the ability to simulate present-day 

mean climate has improved over these three generations. 

The most recent generation represented in AR4 would 

generally simulate present day climate more realistically 

than the models associated with TAR. In their approach, 

index values less than one indicate more accurate models, 

and the three AR4 models used in this study fall at or 

below one in their study.

Table 2—IPCC scenarios and GCM climate models used for the 2010 RPA 

Assessment. 

Integrated Assessment

Scenario Model (IAM) GCMs1 Model Vintage

A1B AIM CGCM3.1(T47) AR4

 MIROC3.2(medres) 

 CSIRO-Mk3.5

A2 ASF CGCM3.1(T47) AR4

 MIROC3.2(medres) 

 CSIRO-Mk3.5

B2 MESSAGE CGCM2 TAR 

 CSIRO-Mk2 

 UKMO-HadCM3

1 CGCM3.1—Canadian Centre for Climate Modeling and Analysis (CCCma) Coupled 

Global Climate Model (CGCM3), Medium Resolution (T47). http://www.cccma.

bc.ec.gc.ca/models/cgcm3.shtml

CSIRO-Mk3.5—Commonwealth Scientific and Industrial Research Organization (CSIRO) 

(Australia), CSIRO Mk3 Climate System Model. http://www.cmar.csiro.au/e-print/

open/gordon_2002a.pdf

MIROC3.2MR—Center for Climate System Research (CCSR), University of Tokyo; 

National Institute for Environmental Studies (NIES) and Frontier Research Center 

for Global Change (FRCGC) (Japan), Model for Interdisciplinary Research on 

Climate (MIROC) Version 3.2 Medium Resolution. http://www.ccsr.u-tokyo.ac.jp/

kyosei/hasumi/MIROC/tech-repo.pdf 

CGCM2—Coupled Global Climate Model, Medium Resolution (T47). Canadian Centre for 

Climate Modelling and Analysis http://www.cccma.bc.ec.gc.ca/models/cgcm2.shtml

CSIRO-Mk2—Australia’s Commonwealth Scientific and Industrial Research Organisation 

Australia (CSIRO) http://www.cmar.csiro.au/e-print/open/hennessy_1998a.

html#ccm

UKMO-HadCM3—Hadley Centre for Climate Prediction and Research UK (HCCPR) http://

cera-www.dkrz.de/IPCC_DDC/IS92a/HadleyCM3/hadcm3.html 
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

Scaling and Updating IPCC Data 

to U.S. and Sub-National Scales

The combination of an emissions scenario and a GCM 

provided the global demographic, macroeconomic, and 

climate assumptions for the various component analyses 

comprising the 2010 RPA Assessment. Here we describe 

the procedures used to develop national and sub-national 

projections of population, GDP, income, bioenergy use, 

land use change, and climate for the U.S. and RPA regions 

(figure 2) that are linked to the IPCC Assessment.

The IPCC scenario-based projections provided data 

at the global and macro-region level (Appendix B), 

with further disaggregation to the country level for 

population and GDP. The socioeconomic data from the 

IPCC marker scenarios served as drivers of the selected 

GCMs, which were then downscaled for RPA Assessment climate projections. We chose to update the IPCC 

projections of U.S. population and GDP projections with 

more recent U.S. data. In doing so, we maintained the 

trends and cross-scenario relationships of IPCC scenarios 

as explained below. We disaggregated these updated 

estimates to obtain county-level income and population 

data for the RPA Assessment analyses.

Projecting population and economic information 

at the county-level involved a number of simplifying 

assumptions. Accounting for the various state and local events that govern the change and development of 

towns and counties is impossible. As a result, the RPA 

county-level projections presented here for the national 

assessment should not be taken as statistically reliable 

projections of possible economic or demographic futures 

for specific counties. Rather, the overall spatial pattern 

of change in response to alternative scenarios is more 

important in our analyses, displaying the heterogeneity 

that would not be evident if projections were made only 

at RPA regional or national levels.

North Central

Great

Plains

Intermountain

Pacific

Northwest

Northeast

NORTH

SOUTH

Southeast

Pacific

Southwest

South Central

ROCKY MOUNTAIN

PACIFIC COAST

Figure 2—RPA Assessment regions.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 9

U.S. Population Projections

The IPCC A1B population projections for the United 

States were based on 1990 Census Bureau projections. 

The 1990 Census projections are lower than more recent 

Census projections (U.S. Census Bureau 2004). Therefore, we used the 2004 Census population series for 

2000-2050 to replace the original IPCC U.S. population 

projection for A1B. The difference in U.S. population is 

quite small from an IPCC regional or global perspective, 

but population is a strong driver of resource change for 

the United States. Therefore, updating the U.S. data 

for A1B to match the newer Census projections was 

important. Since Census projections ended in 2050, we 

extrapolated the Census projection to 2060, the end of 

the RPA projection period.

The 2004 Census population projection (with the 

extrapolation to 2060) served as the U.S. population 

projection for the RPA A1B scenario. We then revised 

the population projections for the A2 and B2 storylines 

to begin at the same starting point as the updated 

A1B. The A2 and B2 projections were adjusted relative to the updated A1B projection to maintain the 

same proportional relationship as among the original 

IPCC projections. Table 3 shows the original IPCC 

U.S. population projections and the updated U.S. 

population projections for the 2010 RPA Assessment. 

Figure 3 illustrates the updated projections for the 

three RPA scenarios relative to historical population 

trends in the United States.

Table 3—IPCC U.S. population projections and updated RPA U.S. population 

projections, 2000- 2060 by scenario (millions of people).

2000 2010 2020 2030 2040 2050 2060

IPCC

A1B 277 300 324 347 367 383 396

A2 278 306 334 363 390 417 447

B2 278 299 322 337 343 348 351

RPA

A1B 282 309 336 364 392 420 447

A2 282 315 346 380 416 457 505

B2 282 308 334 353 366 381 397

300

400

500

600

Historic

RPA A1B

RPA A2

0

100

200

196019651970197519801985199019952000200520102015202020252030203520402045205020552060

RPA B2

Million people

Figure 3—Historic U.S. population and projected U.S. population to 2060 by RPA scenario.
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

U.S. population at the county level

A number of RPA Assessment analyses require 

county-level population projections. No readily 

available county-level projections exist that extend to 

2060. Several commercial vendors offer county-level 

projections for a shorter time period, but these single 

projection series do not link to the scenarios chosen for 

the 2010 RPA Assessment. However, these types of projections can be a starting point for the RPA county-level 

projections. We describe the process for disaggregating 

the U.S. population projections to the county scale briefly 

below; detailed documentation is available in Zarnoch 

and others (2010).

We used the Woods & Poole Economics, Inc. (WP) 

projection series to provide the starting point for the 

county-level projections from 2010-2030 (Woods & 

Poole Economics Inc. 2006). From the WP county-level 

projections, we calculated county shares of the WP national population total, which differs slightly from the 

Census U.S. population total. Because Census projections were the basis for the adjusted A1B projection, we 

then applied the WP county shares to the Census U.S. 

population totals. The result was county-level population projections through 2030 based on the WP county 

shares, but that sum to the 2004 Census U.S. population 

projections.

The second step was to develop county-level projections from 2035-2060 in five-year time steps consistent 

with an extrapolation of the WP projection series and 

constrained by national population totals from A1B. 

The projections from 2035 to 2060 used the previous 

five to 10-year absolute growth for a given county and 

adjusted it such that the sum of the projections across 

all counties equaled the A1B-projected national total 

for that projection year. We performed this additivity 

adjustment for each time interval since the growth in 

the previous five to 10-year period may not necessarily 

have equaled the growth in the next period.

The additivity adjustment factor was either positive or 

negative. For a positive additivity adjustment factor, all 

counties with increasing populations had their growth 

increased by this factor. Similarly, all counties with 

decreasing populations had their growth decreased. 

The additivity adjustment factor for any of the five-year 

time steps was obtained by equating the A1B national 

population in that year to the sum of the increasing and 

decreasing county populations in the same year. The 

result was that both increasing and decreasing counties 

were adjusted with the same proportional adjustment. 

This adjustment only applied to the change in county 

population between time periods and not to the total 

county population. This ensured that increasing counties 

remained increasing and decreasing counties remained 

decreasing after adjustment.

Our system for these projections was simplistic and 

did not take into account natural density-dependent 

mechanisms that affect human demographic dynamics. 

As such, high density counties with high rates of growth 

exhibited explosive population increases while high 

density counties with high rates of negative growth 

tended toward complete depopulation. We developed 

a modification to dampen the extreme increases and 

decreases.

We defined three fast increasing groups and three fast 

decreasing groups based on each county’s percentile 

rank for the two criteria, population density and density 

growth rate. These groups were then assigned dampening factors that would adjust their growth slightly by 

decreasing positive growth and decreasing negative 

growth. Experimenting with the cut points for these 

groups led to a set of final criteria for dampening factors and resulting population projections through their 

application.

We obtained county population projections from 

2035-2060 for the A2 and B2 scenarios directly from 

the county shares from the A1B county projections 

using the same additivity adjustment and dampening 

methodology outlined previously. Using this approach, 

we preserved the proportionate relationship between 

projected total national population for the A1B, A2, and 

B2 scenarios. This assured that the three projections for 

a single county did not unexpectedly cross, which could 

occasionally occur if not constrained in this way.

U.S. Economic Projections

Macroeconomic outlook

Macroeconomic trends (for example, trends in GDP, 

disposable personal income, and labor productivity) 

have a critical influence on the supply of and demand 

for renewable resources. The IPCC projections of GDP 

were based on data developed in the early 1990s. Similar 

to the rationale for updating the U.S. population projections, we decided to update the GDP projections to reflect 

more current trends. Therefore, the U.S. GDP projections 

were updated with more current data, relative to the 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 11

data used in the emissions scenarios, and all monetary 

projections were converted to 2006 USD instead of the 

original 1990 USD used in the emissions scenarios.

The Forest Service commissioned a U.S. macroeconomic outlook report from the Economic Research 

Service (ERS) to project U.S. macroeconomic trends 

through 20603

. We used the official U.S. GDP value 

for 2006 as the new starting point for the A1B, A2, and 

B2 projections (U.S. Department of Commerce 2008a). 

We used GDP growth rates projected in the ERS report 

to develop an updated projection of GDP for the A1B 

scenario. The updated GDP for A1B was slightly higher 

than the IPCC projection. We revised the A2 and B2 

GDP projections to maintain the same proportional 

relationship to the updated A1B GDP trajectory as the 

one defined by the original IPCC GDP projections. 

Table 4 shows the differences between the original 

IPCC projections for U.S. GDP compared to the updated 

GDP figures, as well as the national-level projections 

for U.S. personal income and U.S. disposable personal 

income used in the RPA scenarios. Figure 4 shows the 

3 Torgerson, D. 2007. US macroeconomic projections to 2060. On 

file with the U.S. Forest Service Quantitative Sciences Staff. 

differences among the three RPA scenario projections 

for updated GDP in comparison to historical U.S. GDP.

U.S. income projections

GDP is a useful aggregate variable used primarily 

in the forest products models as a demand driver, but 

modeling choices at finer scales for other resources 

required a more disaggregated measure of economic 

growth. Measures of personal income and disposable 

personal income were used as drivers in other resource 

analyses, such as land use change, water use, and recreation use. Similar to the process used for updating the 

GDP projections, the official U.S. 2006 statistics for 

personal income (PI) and disposable personal income 

(DPI) were used to start the updated RPA projections 

(U.S. Department of Commerce 2008b).

The ERS macroeconomic report projected national 

growth rates in disposable personal income (DPI) 

through 2060. These growth rates were applied to both 

the 2006 PI and DPI starting point to derive projections 

for total national PI and DPI for the A1B scenario (table 

4). We calculated the A2 and B2 projections for PI and 

DPI to maintain the same proportional relationship across 

scenarios that were used in calculating the trajectories 

for GDP.

Table 4—IPCC U.S. GDP projections and updated RPA U.S. GDP, personal income, and disposable personal 

income projections by scenario, 2010-2060 (billion 2006 USD). 

2000 2010 2020 2030 2040 2050 2060

GDP — IPCC

A1B 10,654 13,456 16,888 21,093 26,112 31,117 38,524

A2 10,282 12,484 14,986 18,061 21,436 24,825 30,330

B2 11,297 14,586 17,017 18,905 21,193 23,466 25,640

GDP — RPA

A1B 13,195 14,736 19,029 23,424 28,835 35,496 43,696

A2 13,195 13,679 16,890 20,057 23,683 28,313 34,401

B2 13,195 15,974 19,164 20,990 23,416 26,778 29,084

RPA Personal

Income

A1B 10,768 12,073 15,969 19,322 23,785 29,280 36,043

A2 10,768 11,207 13,932 16,544 19,353 23,354 28,376

B2 10,768 13,087 15,808 17,313 19,315 22,088 23,990

RPA Disposable 

Personal Income

A1B 9,629 10,796 14,036 17,278 21,269 26,182 32,230

A2 9,629 10,022 12,458 14,794 17,469 20,884 25,374

B2 9,629 11,702 14,136 15,482 17,275 19,751 21,452
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

U.S. income at the county level

The national DPI and PI projections were disaggregated 

to the county level to facilitate use in the RPA Assessment 

models. To accomplish this, we first derived per capita 

PI (PCPI) and per capita DPI (PCDPI) for individual 

counties based on the Woods & Poole 2006 county level 

income data and 2006 county population levels. The 2006 

county-level PCPI and PCDPI were then multiplied by 

projected county population to derive a base estimate 

of total county PI and DPI for the projection period. 

These base estimates were then adjusted to sum to the 

projected total national PI and DPI levels described in 

the previous section. This approach maintains a constant relationship between county per capita measures 

and the national averages (for example, a county with 

a PCPI at 90 percent of the national level in 2006 will 

maintain that 90 percent ratio in 2060), and it assumes a 

constant ratio of DPI to PI at the per capita and national 

aggregate levels (in other words the tax rate is constant). 

At the same time, it assures that all national aggregates 

equal the estimated income projections described above. 

These simplifying assumptions allow for a relatively 

straightforward estimation of county-level income that 

depends on county-level population projections and 

national-level income projections.

Issues with updating the U.S. population and 

economic projections

Population, GDP, and income are important variables 

in determining GHG emissions levels. Although the U.S. 

population accounts for a small proportion of the world 

population, the U.S. contribution to emissions is much 

higher than its percentage of world population. Therefore, updating the U.S. population and GDP projections 

could lead to slightly higher global emissions than were 

projected in the IPCC emissions scenarios. The impact 

of underestimating the climate forcing captured by the 

emissions scenarios included in the RPA analyses is 

unknown. Regardless, we considered re-aligning the 

IPCC U.S.-level economic and population data with 

more recent data to be critical for projecting national 

resource effects within the RPA resource modeling systems. Even if GHG emissions would increase under the 

adjusted U.S. projections, we assumed that the relative 

resource impacts between scenarios would not change.

The IPCC emissions scenarios and the adjusted U.S. 

projections for the 2010 RPA scenarios were completed 

before the global economic downturn. We chose 2006 

as the base year for the U.S. economic variables because 

they were the most recent data available when the scenarios were constructed. The projection trend line for 

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Historic

RPA A1B

RPA A2

0

5,000

10,000

15,000

196019651970197519801985199019952000200520102015202020252030203520402045205020552060

RPA B2

Billion dollars

Figure 4—Historic U.S. GDP and projected U.S. GDP to 2060 by RPA scenario.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 13

the United States from 2006 to 2010 does not account 

for the downturn in GDP and other economic variables 

through 2010, creating a discontinuity in the early years 

of the projection period.

Generally, long-term projections are not intended to 

predict temporary ups and downs, meaning recessions 

would not be part of the projected trend. However, 

the global recession was severe with varied recovery 

around the world, where developing countries have 

recovered more rapidly than the developed world. The 

change in focus for the 2010 RPA Assessment from a 

“most likely” approach to a “scenario” approach provided a more robust set of projections about the range 

of potential futures. The scenario approach recognizes 

that the scenarios are not intended to predict a single 

future. Likewise, economic conditions at present may 

have few long-run effects on forest conditions or renewable resources. Current conditions should be averaged 

into the last several years of historical experience, and 

specific attention to the values foreseen in the next five 

years is not warranted.

U.S. Bioenergy Projections

We linked assumptions about the role of biomass in 

bioenergy projections to the IPCC emissions scenarios 

as we did with other RPA Assessment assumptions discussed in this document. The assumptions for bioenergy 

projections were input to the RPA Forest Assessment 

Modeling System that includes the Global Forest Products Model (GFPM) and the U.S. Forest Products Module 

(USFPM). Our approach accounted for relevant IPCC 

macro region land use projections as well as regional 

biomass energy projections provided by IPCC emissions 

scenarios and their supporting database (Nakicenovic and 

others 2000). For a detailed explanation of the RPA Assessment bioenergy assumptions, see Ince and others 

(2011).

The IPCC emissions scenario database provided global 

land use projections for each of its four major modeling regions (called “macro” regions – see Appendix B). 

The land use projections associated with the emissions scenarios included several categories of land use 

that produce biomass for energy, including non-forest 

biomass energy plantations, agricultural cropland, and 

forest land. Furthermore, these projections provided 

changes in land use and biomass energy production for 

each of the four macro regions, from which we deduced 

relationships between projected land use and biomass 

energy production.

In all three emissions scenarios considered in the 

2010 RPA Assessment, expansion of biomass energy 

plantation area projected in the IPCC macro regions was 

directly correlated with projected regional expansion 

in primary biomass energy production. Comparing 

across scenarios, A1B had the 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 

were both smaller in the A2 and B2 scenarios. Also, 

note that biomass energy plantation area only began to 

expand by the year 2020 in all scenarios, coinciding 

with projected regional expansion in biomass energy 

production, while the area of forest land and cropland 

in macro regions remained relatively static throughout 

the projection period in all three scenarios. We deduced 

that non-forest biomass energy plantations were an 

important element of biomass energy supply according 

to emissions scenarios.

Having concluded that non-forest biomass energy plantations, agricultural crops and biomass residues are all 

important elements of global biomass energy supply along 

with fuelwood in the RPA scenarios, we applied a direct approach to projecting the expansion of roundwood fuelwood 

consumption. Specifically, we deducted estimated energy 

supply from biomass plantations, agricultural crops, and 

residues from the projected total biomass energy production, 

yielding a remainder, which was the implicit consumption 

of roundwood fuelwood.

Toward this end, we first computed projected production and consumption of biomass from non-forest 

biomass energy plantations based on IPCC projections 

of regional biomass energy plantation area multiplied 

by conventional estimates of biomass energy plantation 

productivity per macro region. Next, we estimated other 

non-roundwood biomass used for energy consumption, 

which mainly consisted of biomass from agricultural 

crops and crop residues, plus other non-roundwood 

sources, including wood residues. We estimated biomass 

energy from cropland and residue biomass by macro 

region for the year 2000 by subtracting the energy 

equivalent of roundwood fuelwood consumption in the 

year 2000 from the biomass energy production in the 

year 2000. We then projected the cropland and residue 

biomass output based on IPCC projections of cropland 

area.
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

Next we computed global and regional projections of 

roundwood fuelwood consumption for the three RPA 

scenarios based on the IPCC projections of biomass 

energy production with deductions for the projected 

biomass consumption from all non-fuelwood sources 

as described above. Roundwood fuelwood consumption estimates were also calibrated to precisely match 

historical roundwood fuelwood consumption data by 

macro region as reported by Food and Agricultural 

Organizations Statistics (FAOSTAT) (on-line FAO forestry database, at http://faostat.fao.org/site/626/default.

aspx#ancor). Figure 5 illustrates the estimates of global 

biomass consumption for energy by RPA scenario.

Using the imputed estimates of roundwood fuelwood 

consumption, we derived preliminary expansion factors 

for projecting fuelwood consumption out to 2060 for 

each macro region and emissions scenario (table 5). 

6

8

10

12

14

0

2

4

Millions of metric tons

Roundwood

Fuelwood from

forests

Cropland Crops,

Residues & Other

Biomass Energy

Crops

B2 Global Biomass Consumption by Source

6

8

10

12

14

0

2

4

Millions of metric tons

Roundwood

Fuelwood from

forests

Cropland Crops,

Residues & Other

Biomass Energy

Crops

A2 Global Biomass Consumption by Source

6

8

10

12

14

0

2

4

Millions of metric tons

Expansion Factor for

Roundwood Fuelwood

(2006-2060): 4.1 X

Roundwood

Fuelwood from

forests

Cropland Crops,

Residues & Other

Biomass Energy

Crops

1990 2000 2010 2020 2030 2040 2050 2060

A1B Global Biomass Consumption by Source

Expansion Factor for

Roundwood Fuelwood

(2006-2060): 1.9 X

Expansion Factor for

Roundwood Fuelwood

(2006-2060): 1.5 X

1990 2000 2010 2020 2030 2040 2050 2060

1990 2000 2010 2020 2030 2040 2050 2060

Figure 5—Estimated global biomass consumption for energy (millions of metric tons per year) by source for three RPA scenarios, 

based on interpretation of IPCC emissions scenario land use projections and other information. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 15

Secondly, we also applied common regional elasticities of fuelwood demand with respect to the demand 

growth rates to calibrate the model solution. The final 

target expansion factors for fuelwood consumption in 

each country within each macro region were computed 

by multiplying the preliminary expansion factor for 

the region times the country’s share of regional GDP 

in 2060, divided by the country’s share of regional fuelwood consumption in 2006. Compound growth rates 

for fuelwood demand in each country were computed 

on the basis of the expansion factors 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, while 

regional totals were also calibrated to the imputed regional shares of fuelwood consumption derived from 

emissions scenarios.

This approach resulted in projected U.S. and OECD90 

region (see Appendix B) roundwood fuelwood consumption expanding at a rate that was higher than the global 

average rate of expansion. This is partly because the 

fuelwood expansion factors for the OECD90 region are 

higher than other regions for two of the three scenarios, 

and because the U.S. share of roundwood fuelwood consumption in the OECD90 region was only 12 percent in 

2006, but the projected U.S. shares of regional GDP in 

2060 are much higher (42 percent in the A1B scenario, 

44 percent in the A2 scenario, and 49 percent in the B2 

scenario). Thus, the U.S. share of regional fuelwood 

consumption had to increase to match U.S. regional 

GDP shares in 2060. In addition, the United States has 

a fairly abundant wood supply, which also helps to support expansion in U.S. fuelwood consumption relative 

to other countries and regions.

For the United States and for many other countries 

the resulting rates of expansion in wood energy consumption are prodigious in all RPA scenarios, and by 

far the highest in the A1B scenario, followed by the A2 

scenario, and lowest in the B2 scenario (figure 6). In the 

A1B scenario, for example, U.S. wood fuel feedstock4

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 just slightly higher than all other commercial 

uses. Although the projected rates of expansion in wood 

energy consumption are higher in the United States than 

worldwide (figure 6), global wood fuel consumption 

quantity is currently much higher than in the United 

States (especially in developing countries), and wood 

energy consumption in the rest of the world remains 

quantitatively higher than U.S. consumption throughout 

the projection period in all scenarios.

4 U.S. wood fuel feedstock includes conventional fuelwood harvest, as well as other wood materials projected to be used for energy 

that have not been used historically (harvest residues, pulpwood 

roundwood, fiber resides, and sawlogs). See Ince and others (2011) 

for more details. 

Table 5—Preliminary expansion factors and final expansion 

factor targets for global and IPCC regional roundwood fuelwood consumption for RPA scenarios 

(2006 to 2060).

Preliminary Final Targets

GLOBAL

A1B (AIM) 4.1 X 4.5 X

A2 (ASF) 1.9 X 2.2 X

B2 (MESSAGE) 1.5 X 2.6 X

OECD 90

A1B (AIM) 12.8 X 14.0 X

A2 (ASF) 7.4 X 8.1 X

B2 (MESSAGE) 2.6 X 3.0 X

REF

A1B (AIM) 7.5 X 7.5 X

A2 (ASF) 4.2 X 4.2 X

B2 (MESSAGE) 3.7 X 3.7 X

ASIA

A1B (AIM) 1.7 X 1.7 X

A2 (ASF) 0.9 X 0.9 X

B2 (MESSAGE) 1.6 X 1.6 X

ALM 

A1B AIM) 4.8 X 4.8 X

A2 (A2 ASF) 1.8 X 1.8 X

B2 (MESSAGE) 3.4 X 3.4 X
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

Scenario A2

Scenario A1B

Scenario B2

US Roundwood

Fuelwood, Including

Logging Residues

Total US Wood Fuel

Feedstock, Including Mill

Residues

World Roundwood

Fuelwood (Excluding

Residues)

0 X 10 X 20 X 30 X

2.7

5.4

11.5

2.2

9.4

21.4

4.5

15.8

34.2

U.S. Land Use Projections

Land use change is a major driver and manifestation 

of resource change (for example, Alig and Butler 2004, 

Lubowski and others 2006). These changes are responsive to changes in population and economic growth, and 

in turn, they interact with the conditions and trends of 

renewable resources. Land use change analyses provide 

a critical link to other models that analyze resource 

changes, such as wildlife habitat and timber availability.

Land use projections were used as an independent 

variable in several of the resource analyses for the 

2010 RPA Assessment. The IPCC emissions scenarios 

included projections of forestland area for the world 

and the OECD90 region. However, we decided not to 

link our land use projections to the IPCC projections 

directly because data were not available at the country 

level. The United States has land use data that can be 

used as a starting point for projections that provides a 

better basis for the analysis. The land use model includes 

both population and income variables that came from 

the county-level population and income projections 

described earlier, so the RPA land use projections are 

linked to the IPCC scenarios through those variables. 

The land use projections are documented in more detail 

in Wear (2011). We were unable to incorporate climate 

effects into the land use change models for the RPA 

Assessment. Depending on the changes in projected 

temperature and precipitation, it is possible that agriculture and forestry production possibilities may change in 

some regions. Consistent county-level data on potential 

changes in productivity and/or associated returns to rural 

land uses as a result of climate across the United States 

were not available to facilitate such analyses.

We projected land use distributions at the county level 

for all counties in the conterminous United States using 

econometric models to fit to historical data. Separate 

models were developed for the four RPA Assessment 

regions (figure 2) with two exceptions. Texas and 

Oklahoma were split between regions, with the forested 

eastern portions of each state included in the South’s 

model and the remainder in the Rocky Mountain model. 

For model estimation then, Texas and Oklahoma counties were included with regions with most similar conditions, but for all reporting we aggregate all of Texas 

and Oklahoma into the South, consistent with the RPA 

Assessment regions shown in figure 2.

The land use model had two major components. The 

first used county-level population changes and personal 

income to simulate future urbanization, because urban 

Figure 6—Projected expansion factors from 2006 to 2060 in the volumes of wood consumed for energy by RPA 

scenario, including total U.S. wood fuel feedstock consumption, U.S. roundwood fuelwood consumption, 

and world roundwood fuelwood consumption.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 17

uses were assumed to be the dominant land type in all 

land use conversions (in other words land is converted 

to urban, but urban is not converted to other land uses). 

The second component allocated the remaining rural 

land among competing uses based on economic returns 

to the various rural land uses.

The econometric models were fit to land use change 

data from 1987 and 1997 to ensure the projected land 

use changes were generally consistent with observed 

urbanization intensities and rural land use changes. 

We held constant the real rents of both agricultural 

and forest land uses—in effect assuming that the relative returns to these uses remains constant through the 

forecast period. Historical land use data were derived 

from the National Resources Inventory (NRI) survey 

of land uses conducted for the years 1987 and 1997. 

The NRI provides the only consistent, repeated, and 

exhaustive measure of nonfederal land in the United 

States. Unfortunately, 1997 was the last year for which 

the detailed county-level data were made available. We 

used NRI county estimates of the areas of nonfederal 

land in pasture, cropland, forest, range, and urban uses. 

All land use change occurs within this “mutable” land 

base; all other uses are held constant over the projection 

period, including federal land, water area, enrolled Conservation Reserve Program lands, and utility corridors 

(table 6).

Table 6A—U.S. major land use projections for RPA scenario A1B by RPA region, 1997-2060. 

RPA Land Use Category Total

Region Year Urban Cropland Forest Pasture Range Area

- - - - - - - - - - - - - - - Thousand acres - - - - - - - - - - - - - - - 

North

1997 28,929 142,190 149,747 36,063 86 357,015

2010 33,445 140,183 147,763 35,538 86 357,015

2020 37,590 138,350 145,928 35,060 86 357,015

2030 41,316 136,706 144,267 34,640 86 357,015

2040 45,488 134,857 142,413 34,171 86 357,015

2050 50,172 132,771 140,338 33,648 86 357,015

2060 55,441 130,417 138,007 33,064 86 357,015

Pacific

Coast 1997 6,997 19,770 38,433 4,115 32,983 102,298

2010 8,736 19,414 37,736 4,030 32,382 102,298

2020 9,880 19,182 37,262 3,975 31,999 102,298

2030 10,958 18,962 36,813 3,923 31,642 102,298

2040 12,109 18,727 36,331 3,870 31,261 102,298

2050 13,339 18,476 35,818 3,814 30,850 102,298

2060 14,662 18,206 35,267 3,756 30,406 102,298

Rocky

Mountain 1997 6,851 123,385 28,744 15,596 256,332 430,907

2010 9,138 122,728 28,484 15,467 255,091 430,907

2020 10,694 122,267 28,302 15,382 254,261 430,907

2030 12,154 121,845 28,127 15,303 253,477 430,907

2040 13,727 121,386 27,952 15,218 252,623 430,907

2050 15,475 120,874 27,761 15,128 251,669 430,907

2060 17,375 120,299 27,556 15,032 250,645 430,907

South

1997 29,879 84,292 175,812 61,191 111,854 463,029

2010 38,368 81,736 171,837 60,109 110,979 463,029

2020 44,923 79,842 168,482 59,285 110,497 463,029

2030 50,770 78,213 165,481 58,497 110,068 463,029

2040 57,083 76,462 162,178 57,701 109,606 463,029

2050 63,966 74,563 158,544 56,831 109,124 463,029

2060 71,630 72,498 154,434 55,863 108,604 463,029

Total

1997 72,656 369,637 392,736 116,965 401,255 1,353,249

2010 89,687 364,061 385,820 115,144 398,538 1,353,249

2020 103,087 359,641 379,974 113,702 396,843 1,353,249

2030 115,198 355,726 374,688 112,363 395,273 1,353,249

2040 128,407 351,432 368,874 110,960 393,576 1,353,249

2050 142,952 346,684 362,461 109,421 391,729 1,353,249

2060 159,108 341,420 355,264 107,715 389,741 1,353,249
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

Table 6B—U.S. major land use projections for RPA scenario A2 by RPA region, 1997-2060. 

RPA Land Use Category Total

Region Year Urban Cropland Forest Pasture Range Area

- - - - - - - - - - - - - - - Thousand acres - - - - - - - - - - - - - - - 

North 

1997 28,929 142,190 149,747 36,063 86 357,015

2010 32,704 140,549 148,053 35,623 86 357,015

2020 36,105 139,072 146,524 35,228 86 357,015

2030 39,140 137,762 145,143 34,884 86 357,015

2040 42,375 136,363 143,673 34,518 86 357,015

2050 46,182 134,710 141,945 34,091 86 357,015

2060 50,687 132,750 139,897 33,595 86 357,015

Pacific 

Coast 1997 6,997 19,770 38,433 4,115 32,983 102,298

2010 8,741 19,409 37,735 4,029 32,384 102,298

2020 9,858 19,178 37,270 3,974 32,017 102,298

2030 10,930 18,955 36,821 3,923 31,669 102,298

2040 12,081 18,715 36,336 3,869 31,297 102,298

2050 13,397 18,440 35,782 3,809 30,870 102,298

2060 14,928 18,119 35,137 3,742 30,372 102,298

Rocky 

Mountain 1997 6,851 123,385 28,744 15,596 256,332 430,907

2010 8,981 122,805 28,491 15,476 255,155 430,907

2020 10,383 122,419 28,316 15,400 254,388 430,907

2030 11,706 122,071 28,149 15,329 253,652 430,907

2040 13,107 121,709 27,981 15,254 252,856 430,907

2050 14,644 121,297 27,798 15,177 251,991 430,907

2060 16,500 120,802 27,578 15,085 250,942 430,907

South 

1997 29,879 84,292 175,812 61,191 111,854 463,029

2010 37,852 81,753 172,413 60,489 110,993 463,029

2020 43,710 79,989 169,675 59,576 110,549 463,029

2030 48,709 78,651 167,252 58,748 110,140 463,029

2040 53,837 77,287 164,747 57,913 109,716 463,029

2050 59,699 75,747 161,861 56,953 109,240 463,029

2060 66,452 73,975 158,498 55,889 108,686 463,029

Total 

1997 72,656 369,637 392,736 116,965 401,255 1,353,249

2010 88,278 364,516 386,692 115,617 398,618 1,353,249

2020 100,056 360,658 381,785 114,178 397,040 1,353,249

2030 110,485 357,439 377,365 112,884 395,547 1,353,249

2040 121,400 354,074 372,737 111,554 393,955 1,353,249

2050 133,922 350,194 367,386 110,030 392,187 1,353,249

2060 148,567 345,646 361,110 108,311 390,086 1,353,249
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 19

Table 6C—U.S. major land use projections for RPA scenario B2 by RPA region, 1997-2060. 

RPA Land Use Category Total

Region Year Urban Cropland Forest Pasture Range Area

- - - - - - - - - - - - - - - Thousand acres - - - - - - - - - - - - - - - 

North 

1997 28,929 142,190 149,747 36,063 86 357,015

2010 34,555 139,669 147,290 35,416 86 357,015

2020 37,662 138,310 145,906 35,051 86 357,015

2030 39,237 137,630 145,199 34,863 86 357,015

2040 41,135 136,789 144,367 34,638 86 357,015

2050 43,695 135,652 143,238 34,344 86 357,015

2060 45,437 134,901 142,450 34,142 86 357,015

Pacific 

Coast 1997 6,997 19,770 38,433 4,115 32,983 102,298

2010 8,859 19,393 37,687 4,025 32,334 102,298

2020 9,860 19,186 37,270 3,976 32,006 102,298

2030 10,528 19,045 36,985 3,942 31,797 102,298

2040 11,158 18,913 36,716 3,911 31,601 102,298

2050 11,911 18,755 36,397 3,876 31,359 102,298

2060 12,590 18,608 36,108 3,844 31,147 102,298

Rocky 

Mountain 1997 6,851 123,385 28,744 15,596 256,332 430,907

2010 9,411 122,617 28,462 15,451 254,966 430,907

2020 10,699 122,260 28,304 15,382 254,263 430,907

2030 11,536 122,060 28,192 15,337 253,783 430,907

2040 12,424 121,828 28,084 15,291 253,281 430,907

2050 13,512 121,529 27,960 15,235 252,671 430,907

2060 14,438 121,312 27,846 15,190 252,121 430,907

South 

1997 29,879 84,292 175,812 61,191 111,854 463,029

2010 40,288 80,986 170,996 60,315 110,915 463,029

2020 45,768 79,338 168,435 59,459 110,499 463,029

2030 48,739 78,604 167,025 58,892 110,242 463,029

2040 52,043 77,754 165,351 58,365 109,987 463,029

2050 56,234 76,644 163,196 57,743 109,683 463,029

2060 59,318 75,864 161,660 57,232 109,426 463,029

Total 

1997 72,656 369,637 392,736 116,965 401,255 1,353,249

2010 93,113 362,665 384,435 115,207 398,301 1,353,249

2020 103,989 359,094 379,915 113,868 396,854 1,353,249

2030 110,040 357,339 377,401 113,034 395,908 1,353,249

2040 116,760 355,284 374,518 112,205 394,955 1,353,249

2050 125,352 352,580 370,791 111,198 393,799 1,353,249

2060 131,783 350,685 368,064 110,408 392,780 1,353,249
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

Patterns of rural uses reflect biome boundaries 

(e.g., natural boundaries between grassland and forestland) and productivity determined by biophysical 

conditions along with comparative advantages for 

producing various goods and services determined by 

cost and return attributes. Forest uses dominate the 

South, the Northeast, the Lake States and the Pacific 

Northwest. Cropland is concentrated in the Plains and 

Midwest, while rangeland is concentrated in the High 

Plains and Intermountain West. Urban land, the least 

abundant land use, corresponds with the nation’s cities, and the largest area of pastureland is found at the 

boundary between grassland and forest biomes from 

eastern Texas to northern Missouri.

The changes in major land uses over the projection 

period for scenario A1B are summarized in figure 7. 

The pattern of change is similar for the other scenarios, 

but the change in acres is smaller in both A2 and B2. 

Scenario A1B, with an intermediate level of population 

growth but strong growth in personal income, yields the 

highest rate of urbanization, while scenario B2 has the 

lowest. The total area of urbanization is similar across 

the three scenarios until 2040, after which urbanization diverges more strongly across scenarios, generally 

reflecting the increased divergence in population and 

income growth across scenarios in the later decades 

of the projection period. The total area of urban land 

increases the most in the South, followed by the North. 

However, the rate of increase in urban land is highest 

in the Rocky Mountain region.

In all scenarios, only urban land area increases over 

the projection period, while all other land uses experience losses. Forest losses vary regionally. The South has 

the largest loss of forest, reflecting both an abundant 

forest resource and the region with the highest projected 

population growth and urbanization (figure 8). The North 

has the second greatest loss of forest land. Because the 

majority of forest land in the West is public, and therefore 

held fixed, the projected change in forest area for the 

western regions is relatively small. Cropland losses are 

greatest in the eastern United States, while rangeland 

losses are concentrated in the Rocky Mountain region.

2010-2020

Range

Pasture

Forest

Cropland

Urban

-40,000 -20,000 0 20,000 40,000 60,000 80,000

2010-2030

2010-2040

2010-2050

2010-2060

Figure 7—Projected cumulative change in the areas of major nonfederal land uses in conterminous United 

States for RPA scenario A1B (thousand acres).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 21

U.S. Climate Projections

Global climate projections – from emissions 

scenarios to future climate

In order to determine how emissions influence atmospheric chemistry and consequently the climate, emissions from the IAMs were converted to atmospheric 

concentrations of carbon dioxide. Two different carbon 

cycle models (ISAM and Bern-CC) were used to model 

the dynamics of the global carbon cycle, and determine, given the carbon dioxide emissions, the future 

atmospheric CO2 concentrations (Prentice and others 

2001). These projections of atmospheric concentrations 

of carbon dioxide and other GHGs (Ebhalt and others 

2001) are used to alter the atmospheric chemistry in the 

GCMs so that the effects of GHG climate forcing on 

the global climate can be quantified (Meehl and others 2007b). The atmospheric concentrations of carbon 

dioxide associated with each emissions scenario based 

on the carbon cycle models are found at http://www.

ipcc-data.org/ddc_co2.html. As mentioned previously, 

global climate projections from the many GCMs using 

these emissions scenarios have been archived: for the 

TAR at the IPCC Data Distribution Centre (DDC); for 

the AR4, at the Climate Model Intercomparison Project 

(CMIP3) website. Climate models used in AR4 differed 

from the climate models used in the three previous IPCC 

reports and represent many improvements in describing 

climate processes (Le Treut and others 2007).

Downscaling the IPCC global 

climate projections

The resolution of GCM global projections stored in 

either the IPCC DDC or at the CMIP3 website range 

from 250 to 600 km on the side of the grid, far coarser 

than that typically used in many impact assessments 

including the RPA Assessment. Because of the spatial 

scale of the socio-economic data (U.S. county), most 

analyses in the RPA Assessment are being conducted 

at that spatial scale. Hence this necessitated climate 

projection data at that spatial scale. The development 

of downscaled climate data is an active area of research 

that attempts to meet the needs of the climate impact 

community by providing finer scale climate projections 

-12,000

-10,000

-8,000

-6,000

-4,000

-2,000

-20,000

-18,000

-16,000

-14,000

A1B A2 B2

0

Change in thousand acres

South North Pacific Rocky Mountain

Figure 8—Change in conterminous U.S. nonfederal forest area from 2010-2060 by RPA Assessment region 

and RPA scenario (thousand acres).
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

(Fowler and others 2007, Hayhoe 2007, Wilby and others 2004). A variety of techniques have been developed 

to “downscale” the global climate projections to finer 

spatial scales associated with climate impact studies. 

Because the techniques introduce some variability, we 

needed consistency in the downscaling of the climate 

projections for the RPA Assessment. More detailed 

documentation of the development of the RPA climate 

projections and downscaling process can be found in 

Joyce and others (in review).

We used the delta or change factor method for the 

downscaling technique, which has been adopted for 

interpolating both climate observations and climate 

model output to fine spatial resolutions over large regions 

(Hamlet and Lettenmaier 1999, Miller and others 2003, 

Price and others 2004, Ray and others 2010). Monthly 

time-series data for the conterminous United States and 

Alaska were obtained for specific realizations (runs) 

from each GCM, representing both the simulated 20th

century (20C3M realizations for the 1961–2000 period) 

and the selected emissions scenarios A1B, A2, and B2 

for the 21st century (2001-2100). The change factor at 

each GCM grid node was computed by either subtracting from (projected temperature variables) or dividing 

by (other projected climate variables) the mean of that 

month’s values for the simulated historical period. We 

used the historical period 1961–1990 as the baseline. 

The simulated historical GCM data was available for 

this period as were observed historical climate data. 

The global grid change factor data were spatially interpolated. ANUSPLIN was used to fit a two-dimensional 

spline “surface” function uniquely to each month’s data 

(deltas) for each of the six normalized climate variables 

(ANUSPLIN, developed by M Hutchinson and colleagues at Australian National University in Canberra 

(http://cres.anu.edu.au/outputs/anusplin.php; see also 

McKenney and others 2006). The fitted spline functions 

were, in turn, used to create gridded data sets for each 

monthly variable covering North America at a spatial 

resolution of standard 5 arc-minute spatial resolution 

(0.0833 degree ≈ 10 km at mid-latitudes) using latitude

and longitude as independent variables.

Because climate models typically have very low horizontal resolution, their representation of topographic 

effects on local climate is necessarily poor. For this 

reason, the normalized and interpolated climate model 

data (delta values) were combined with climatological 

data for the reference period interpolated to the same 

resolution, using data from the Parameter-elevation 

Regressions on Independent Slopes Model (PRISM) 

(Daly 1994). The normalization (computation of the change 

factors), the interpolation procedures, and the use of 

observed historical climatology effectively removed 

model biases associated with the global model means 

and allowed direct comparison of the downscaled projections for different scenarios and different GCM runs.

Climate projections for the U.S. at the 5 arcminute grid and U.S. county level

Four climate variables were downscaled for the RPA 

scenarios using the PRISM historical climatology: 

monthly mean daily maximum temperature, monthly 

mean daily minimum temperature, monthly precipitation, and monthly mean daily potential evapotranspiration. The mean annual temperature for all scenarios 

increases at the scale of the conterminous United States 

over the next 100 years from the historical mean of 10.86 

degrees C to a range of 14.65 degrees C for the B2 scenario, 14.94 degrees C for the A1B scenario, and 15.95 

degrees C for the A2 scenario. Each scenario summary 

represents the mean of the three climate models used 

in this study for each scenario. While A2 shows the 

greatest surface warming by 2100, the relative degrees 

of warming vary over the projection period (Joyce and 

others, in review.). For example, at 2060, the A1B scenario shows the greatest increase in the mean annual 

temperature (figure 9). The warming trends are similar 

across the scenarios until 2060 when the scenario temperatures begin to depart from each other. While the 

climate projections extend to 2100, the RPA Assessment 

resource analyses stop at 2060.

For the 2010 RPA Assessment projection period, the 

A1B scenario mean represents the warmest and driest 

scenario at the scale of the United States (figure 9). The 

A2 scenario becomes the wettest, although the precipitation changes at the scale of the U.S. are small at 2060. 

Regional differences in precipitation projections vary 

greatly. The B2 scenario projects the least warming of 

these three scenarios. The individual model projections 

vary across the individual scenario. For example, within 

the A2 scenario, the CGCM3.1 model projects the least 

warming and the MIROC3.2 model projects the greatest warming within this scenario (Joyce and others, in 

review).

For purposes of the RPA Assessment, county delineation is critical. We used the Forest Inventory and 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 23

Analysis (FIA) Survey Unit and County Coverage as the 

GIS delineation for counties. An overlay file between 

the 5 arc-minute grid and the County boundaries was 

developed in ArcGIS 9.2. This resulting layer was merged 

with the projected climate data. Once merged, the county 

means for the climate variables were calculated using 

a weighted mean value of the underlying 5 arc-minute 

grids within the county. With the overlay of the county 

shape file on the grid shape file, some grids are assigned 

to more than one county. During the overlay process the 

area of each grid falling wholly or partially within the 

county was calculated. These areas were used as weights 

to calculate the county means for respective climate 

variables. Note that to obtain state or U.S. temperature 

and precipitation values, the county or the grid data 

should be area-weighted in those calculations, as county 

sizes vary greatly across the United States.

Figure 9—U.S. temperature and precipitation changes from the historical period (1961-1990) to the decade 

surrounding the year 2060 (2055-2064). The scenario-GCM pairs are defined in table 2. 

Summary

The RPA Assessment provides a nationally consistent 

analysis of the status and trends of the Nation’s renewable resources. Variability in data sources and models 

provide a considerable challenge in developing a coherent 

framework across multiple resource analyses. The set 

of underlying assumptions described in this document 

are used to ensure consistency across RPA Assessment 

analyses.

We chose to take a scenario approach for the 2010 

RPA Assessment to provide a shared view of potential 

futures. We linked our scenarios to the emissions scenarios and global climate projections used in the IPCC 

TAR and AR4 to recognize the influence of global 

forces on domestic resource conditions and trends. We 

100

80

60

40

20

0

-20

-40

-60

-80

-100

Precipitation changes (mm) by 2060

Temperature changes (degrees C) by 2060

2 2.5 3 3.5 4 4.5

A2 CSIRO-Mk3.5

A1B CGCM3.1

A2 CGCM3.1

A1B CSIRO-Mk3.5

B2 CSIRO-Mk2

A1B

A2

B2

B2 HadCM3

B2 CGCM2

A2 MIROC3.2

A1B MIROC3.2

Scenario-GCM Scenario mean
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

originally intended to include four IPCC-based scenarios, 

but the availability of global climate projections at the 

time limited selections within the A1 family. Therefore, 

we chose to develop future scenarios linked to IPCC 

scenarios A1B, A2, and B2.

Figure 10 portrays the variation in the common assumptions across the three RPA scenarios. Population 

and income grow under all three scenarios, although at 

varying rates. The A1B scenario has the highest economic growth rate, coupled with a moderate population 

Figure 10—Summary of variation in assumptions across RPA scenarios.

B2

A2

A1B

U.S. Population in 2060 (millions) U.S. GDP in 2060 (trillion USD)

Change in Urban and Developed Area,

2010-2060 (millions of acres)

Change in Forest Area, 2010-2060

(millions of acres)

Expansion Factor of Total U.S. Wood Fuel

Feedstock, 2006-2060

Conterminous U.S. Mean Annual

Temperature in 2060 (degrees C)

0 100 200 300 400 500 600

0 20 40 60 80

0 5 10 15 20 10.86 11.36 11.86 12.36 12.86 13.36 13.86 14.36

-35 -30 -25 -20 -15 -10 -5 0

0 10 20 30 40 50

B2

A2

A1B

B2

A2

A1B

B2

A2

A1B

B2

A2

A1B

B2

A2

A1B
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 25

projection. Economic growth had more influence than 

population growth in projections of land use change: 

scenario A1B had the greatest rate of change, followed 

by A2, even though A2 has the greatest growth in U.S. 

population. Similarly, A1B had the greatest projected 

increase in use of biomass for bioenergy. Mean annual 

temperature increases under all three scenarios in the 

conterminous United States. Scenario A1B shows the 

greatest warming to 2060, although by 2100 (the time 

frame for IPCC projections), A2 has the greatest surface 

warming. Scenario B2 results are consistently lower 

than the other two scenarios, reflecting lower population growth. Even though economic growth is relatively 

low in B2, per capita income for B2 exceeds per capita 

income for A2 by 2060.

The RPA scenarios and the associated socioeconomic 

and climate projections described in this document were 

used in various natural resource models to project a range 

of futures for water yield and water use, forest inventory 

and wood markets, wildlife habitat, recreation use, and 

other resources. Those results will be summarized in 

the forthcoming RPA Assessment summary, with more 

detailed information in a series of supporting technical 

publications.

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Prentice, I.C.; Farquhar, G.D.; Fasham, M.J.R.; and others. 2001. 

The carbon cycle and atmospheric carbon dioxide. Chapter 3 

in: Houghton, J.T.; Ding, Y.; Griggs, D.J.; Noguer, M.; van der 

Linden, P.J.; Dai, X.; Maskell, K.; Johnson, C.A., eds. Climate 

change 2001: the scientific basis. Contribution of Working Group 

I to the Third Assessment Report of the Intergovernmental Panel 

on Climate Change. Cambridge, United Kingdom and New York: 

Cambridge University Press. 881 p.

Price, D.T.; McKenney, D.W.; Papadopol, P.; Logan, T.; Hutchinson, M.F. 2004. High resolution future scenario climate data 

for North America. Pages 7.7-7.7.13 In: Proceedings of the 26th 

Conference on Agricultural and Forest Meteorology: Vancouver, 

British Columbia:, 23–26 August 2004. Boston, Massachusetts: 

American Meteorology Society. Available at: http://ams.confex.

com/ams/AFAPURBBIO/techprogram/paper_78202.htm. [Accessed 28 July 2010.]

Price, D.T.; McKenney, D.W.; Joyce, L.A.; Siltanen, R.M.; Papadopol, P.; Lawrence, K. 2011. High-resolution interpolation of 

climate scenarios for Canada derived from General Circulation 

Model simulations. Inf. Rep. NOR-X-421. Edmonton, Alberta: 

Natural Resources Canada, Canadian Forest Service, Northern 

Forestry Center. 126 p.

Randall, D.A.; Wood, R.A.; Bony, S.; and others. 2007. Climate 

models and their evaluation. In: Solomon, S.; Qin, D.; Manning, 

M.; Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor M.; Miller, H.L., 

eds. Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change. Cambridge, United 

Kingdom and New York: Cambridge University Press.

Ray, A.J.; Barsugli, J.J.; Wolter, K.; Eischeid, J.K. 2010. Rapidresponse climate assessment to support the FWS status review 

of the American pika. Boulder, Colorado: NOAA Earth Systems 

Research Laboratory. Available at: http://www.esrl.noaa.gov/psd/

news/2010/pdf/pika_report_final.pdf. 

Reichler, Thomas; Junsu, Kim. 2008. How well do coupled models 

simulate today’s climate? Bulletin of the American Meteorological Society 89:303-311.

United Nations Environment Program [UNEP]. 2002. Global Environment Outlook 3. London: Earthscan Publications Ltd. 460 p. 

United Nations Environment Program [UNEP]. 2007. Global Environment Outlook 4. Valletta, Malta: Progress Press Ltd. 572 p. 

U.S. Census Bureau. 2004. U.S. interim projections by age, sex, race 

and Hispanic origin. Internet release: March 18, 2004. Available 

online at: http://www.census.gov/population/www/projections/

usinterimproj/natprojtab01a.pdf 

U.S. Department of Commerce. 2008a. National income product 

accounts table 1.15. Gross domestic product. Version: January 30, 

2008. Washington, DC: U.S. Department of Commerce, Bureau 

of Economic Analysis.

U.S. Department of Commerce. 2008b. National income product 

accounts table 2.1. Personal income and its disposition. Version: 

January 30, 2008. Washington, DC: U.S. Department of Commerce, Bureau of Economic Analysis.

U.S. Forest Service. 1977. The nation’s renewable resources-an 

assessment, 1975. Forest Resource Report No. 21. Washington 

DC: U.S. Department of Agriculture, Forest Service, Washington 

Office. 243 p. 

U.S. Forest Service. 1981. An assessment of the forest and range 

land situation in the United States. Forest Resource Report No. 

22. Washington, DC: U.S. Department of Agriculture, Forest 

Service, Washington Office. 352 p. 

U.S. Forest Service. 1989. RPA assessment of the forest and rangeland situation in the United States, 1989. Resource Report No. 

26. Washington, DC: U.S. Department of Agriculture, Forest 

Service, Washington Office. 72 p.

U.S. Forest Service. 2001. 2000 RPA assessment of forest and range 

lands. FS-687. Washington DC: U.S. Department of Agriculture, 

Forest Service, Washington Office. 78 p.

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-141. Asheville, 

North Carolina: U.S. Department of Agriculture, Forest Service, 

Southern Research Station. 41 p.

Wilby, R.L.; Charles, S.P.; Zorita, E.; Timbal, B.; Whetton, P.; 

Mearns, L.O. 2004. Guidelines for use of climate scenarios 

developed from statistical downscaling methods. Available at: 

http://www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf. 

 Woods & Poole Economics Inc. 2006. The 2006 complete economic and demographic data source (CEDDS). Washington, DC. 

Available at: http://www.woodsandpoole.com.

Zarnoch, Stanley J.; Cordell, H. Ken; Betz, Carter J; Langner, Linda. 

2010. Projecting county-level populations under three future 

scenarios: a technical document supporting the Forest Service 

2010 RPA Assessment. E-Gen. Tech. Rep. SRS-128. Asheville, 

North Carolina: U.S. Department of Agriculture, Forest Service, 

Southern Research Station. 8 p. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 27

Appendix A—National Integrated 

Assessment Models

Six integrated assessment models (IAMs) were used to evaluate the emissions scenarios 

developed for the Intergovernmental Panel on Climate Change Third and Fourth Assessment Reports. This appendix briefly describes the six IAMs, based on Nakicenovic and 

others. (2000). More detailed descriptions of the models can be found at http://sedac.

ciesin.org/mva/ and at references included in the individual model descriptions.

Asian Pacific Integrated Model 

The Asian Pacific Integrated model (AIM) from the National Institute of Environmental 

Studies in Japan is a computer simulation model that was developed primarily to examine 

global warming responses in the Asia Pacific region but is linked to a world model that 

allows for global estimates. AIM includes three main models: a GHG emission model, a 

global change model, and a climate change impact model. The AIM model was used for 

the marker scenario for scenario A1B. Further documentation can be found in Morita 

and others (1994). 

Atmospheric Stabilization Framework Model 

The Atmospheric Stabilization Framework model (ASF) from ICF Consulting in the 

U.S.A. includes energy, agricultural, and deforestation GHG emissions and atmospheric 

models and provides emission estimates for nine world regions. The energy component is 

driven primarily by energy supply prices. The agricultural model estimates production of 

major agricultural products, driven by population and economic growth. The agricultural 

model is linked to a deforestation model that estimates deforestation related to population 

growth and demand for agricultural products. The outputs of these models are used to 

estimate GHG emissions. The atmospheric model uses the GHG emissions to calculate 

GHG concentrations and corresponding radiative forcing and temperature effects. The 

ASF model was used to develop the marker scenario for A2. Detailed documentation 

of the ASF model is found in Lashof and Tirpak (1990), Pepper and others (1992, 1998) 

and Sankovski and others (2000). 

Integrated Model to Assess the Greenhouse Effect 

The Integrated Model to Assess the Greenhouse Effect model (IMAGE2) from the 

National Institute for Public Health and Environmental Hygiene in the Netherlands has 

three linked sub-models. The Energy-Industry System computes GHG emissions in the 

13 world regions of the model, based on a simulation model of energy investment decisions, using five economic sectors. The Terrestrial Environment System simulates global 

land-use and land-cover and their effect on GHG emissions and carbon fluxes between 

the biosphere and atmosphere. The Atmosphere-Ocean System uses a two-dimensional 

atmospheric energy model and a separate two-dimensional ocean model. Detailed documentation is available in Alcamo and others (1998) and de Vries and others (1994, 1999, 

2000). 
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

Multiregional Approach for Resource and Industry Allocation 

The Multiregional Approach for Resource and Industry Allocation model (MARIA) 

from the Science University of Tokyo in Japan is an integrated model designed to assess 

technology and policy options for addressing global climate change. MARIA is an intertemporal non-linear optimization model that allows international trading among eight 

global regions. An energy module includes three fossil fuels, biomass, nuclear power, 

and renewable energy sources. A food and land use module is used to assess the potential 

contributions of biomass. A global warming subsystem addresses global carbon emissions. 

Detailed documentation is available in Mori and Takahashi (1999) and Mori (2000).

Model for Energy Supply Strategy Alternatives and 

Their General Environmental Impact 

The Model for Energy Supply Strategy Alternatives and Their General Environmental 

Impact model (MESSAGE) from the International Institute of Applied Systems Analysis 

(IIASA) in Austria is one of the six models that constitute IIASA’s integrated modeling 

framework. Economic and energy development profiles are developed that serve as inputs 

for MESSAGE and their macro-economic model MACRO. MESSAGE is a dynamic linear 

programming model that calculates least-cost supply structures under the constraints of 

resource availability, available technologies, and demand for energy. MACRO estimates 

the relationships between macro-economic development and energy use. MESSAGE and 

MACRO are used in tandem to test scenario consistency. The marker scenario for B2 is 

based on MESSAGE. Further documentation can be found in Messner and Strubegger 

(1995) and Riahi and Roehrl (2000).

Mini Climate Assessment Model (MiniCAM) 

The Mini Climate Assessment Model (MiniCAM) from the Pacific Northwest National Laboratory in the U.S.A. integrates three models. The Edmonds-Reilly-Barns 

energy-economic model represents long-term trends in economic output, energy use, and 

greenhouse gas emissions for nine world regions. The GHG emissions are used in the 

Model for the Assessment of Greenhouse-gas Induced Climate Change and the regional 

climate change Scenario Generator to provide estimates of atmospheric concentration, 

climate change, and sea level rise. More detailed documentation is found in Edmonds 

and others (1994, 1996a, 1996b). 

Literature Cited

Alcamo, J.; Kreileman, E.; Krol, M.; Leemans, R.; Bollen, J.; van Minnen, J.; Schaefer, M.; Toet, S.; de 

Vries, B. 1998: Global modeling of environmental change: an overview of IMAGE 2.1. In Alcamo, J.; 

Leemans, R.; Kreileman, E., eds. Global change scenarios of the 21st century: results from the IMAGE 

2.1 model. Kidlington, Oxford: Elsevier Science: pp. 3-94.

De Vries, B.; Bollen, J.; Bouwman, L.; den Elzen, M.; Janssen, M.; Kreileman, E. 2000. Greenhouse gas 

emissions in an equity-, environment- and service-oriented world: an IMAGE-based scenario for the 

next century. Technological Forecasting and Social Change 63(2-3). 

De Vries, B.; Janssen, M.; Beusen, A. 1999. Perspectives on global energy futures-simulations with the 

TIME model. Energy Policy 27: 477-494.

De Vries, H.J.M.; Olivier, J.G.J.; van den Wijngaart, R.A.; Kreileman, G.J.J.; Toet, A.M.C. 1994. Model 

for calculating regional energy use, industrial production and greenhouse gas emissions for evaluating 

global climate scenarios. Water, Air and Soil Pollution 76: 79-131. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 29

Edmonds, J.; Wise, M.; MacCracken, C. 1994. Advanced energy technologies and climate change: an analysis using the Global Change Assessment Model (GCAM). PNL-9798, UC-402, Richland, Washington: 

Pacific Northwest Laboratory. 

Edmonds, J.; Wise, M.; Pitcher, H.; Richels, R.; Wigley, T.; MacCracken, C. 1996a. An integrated assessment of climate change and the accelerated introduction of advanced energy technologies: an application 

of MiniCAM 1.0. Mitigation and Adaptation Strategies for Global Change 1(4): 311-339. 

Edmonds, J.; Wise, M.; Sands, R.; Brown, R.; Kheshgi, H. 1996b. Agriculture, land-use, and commercial 

biomass energy: a preliminary integrated analysis of the potential role of biomass energy for reducing future 

greenhouse related emissions. PNNL-11155. Washington, DC: Pacific Northwest National Laboratories. 

Lashof, D.; Tirpak, D.A. 1990. Policy options for stabilizing global climate. 21P-2003. Washington, DC: 

U.S. Environmental Protection Agency.

Messner, S.; Strubegger, M. 1995. User’s guide for MESSAGE III. WP-95- 69. Laxenburg, Austria: International Institute for Applied Systems Analysis.

Mori, S. 2000: The development of greenhouse gas emissions scenarios using an extension of the MARIA 

model for the assessment of resource and energy technologies. Technological Forecasting and Social 

Change 63(2-3). 

Mori, S.; Takahashi, M. 1999. An integrated assessment model for the evaluation of new energy technologies and food productivity. International Journal of Global Energy Issues 11(1-4): 1-18. 

Morita, Y.; Matsuoka, Y.; Kainuma, M.; Harasawa, H. 1994. AIM-Asian Pacific Integrated Model for 

evaluating policy options to reduce GHG emissions and global warming impacts. In: Bhattacharya, S.; 

and others, eds. Global warming issues in Asia. Bangkok: AIT: 254-273. 

Pepper, W.J.; Barbour, W.; Sankovski, A.; Braaz, B. 1998. No-policy greenhouse gas emission scenarios: 

revisiting IPCC 1992. Environmental Science and Policy 1: 289-312. 

Pepper, W.J.; Leggett, J.; Swart, R.; Wasson, J.; Edmonds, J.; Mintzer, I. 1992. Emissions scenarios for the 

IPCC: an update. In: Houghton, J.T.; Callandar, B.A.; Varney, S.K., eds. Climate change 1992: supplementary report to the IPCC scientific assessment. Cambridge: Cambridge University Press. 

Riahi, K.; Roehrl, R.A. 2000: Greenhouse gas emissions in a dynamics-as-usual scenario of economic and 

energy development. Technological Forecasting and Social Change 63(2-3). 

Sankovski, A.; Barbour, W.; Pepper, W. 2000. Quantification of the IS99 emission scenario storylines using 

the atmospheric stabilization framework (ASF). Technological Forecasting and Social Change 63(2-3). 
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

Appendix B—Definition of IPCC Macro Regions

OECD90 

REGION

ASIA REGION

North America (NAM) Centrally planned Asia and China (CPA)

Canada 

Guam 

Puerto Rico

United States 

of America 

Virgin Islands

Cambodia 

China 

Hong Kong 

Korea (DPR)

Laos (PDR) 

Mongolia 

Viet Nam

Western Europe (WEU) South Asia (SAS)

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

Afghanistan

Bangladesh 

Bhutan 

India

Other Pacific Asia 

(PAS)

American Samoa

Brunei Darussalam

Fiji 

French Polynesia

Gilbert-Kiribati

Indonesia 

Malaysia 

Myanmar 

New Caledonia

Papua New Guinea 

Maldives 

Nepal 

Pakistan 

Sri Lanka

Philippines 

Republic of Korea 

Singapore 

Solomon Islands

Taiwan, province of 

China

Thailand 

Tonga 

Vanuatu 

Western Samoa

Pacific OECD (PAO)

Australia 

Japan

New Zealand

REF REGION 

Central and Eastern Europe 

(EEU)

Newly independent states 

of the former Soviet Union

Albania 

Bosnia and 

Herzegovina 

Bulgaria 

Croatia 

Czech Republic 

The former 

Yugoslav 

Republic of 

Macedonia

Hungary 

Poland 

Romania 

Slovak 

Republic 

Slovenia 

Yugoslavia

Armenia 

Azerbaijan 

Belarus 

Estonia 

Georgia 

Kazakhstan 

Kyrgyzstan 

Latvia

Lithuania 

Republic of Moldova 

Russian Federation 

Tajikistan 

Turkmenistan 

Ukraine 

Uzbekistan
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 31

Appendix B - continued

ALM REGION

Middle East and North Africa Latin America and the Caribbean 

Algeria 

Bahrain 

Egypt (Arab 

Republic) 

Iraq 

Iran (Islamic 

Republic) 

Israel 

Jordan 

Kuwait 

Lebanon 

Libya/SPLAJ

Morocco 

Oman 

Qatar 

Saudi Arabia

Sudan 

Syria (Arab 

Republic) 

Tunisia 

United Arab 

Emirates

Yemen

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 

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 
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

Appendix C—Availability of 2010 RPA Assessment 

Scenario Data

The detailed data on population, income, land use change, and climate described in this 

document are available to other users. This appendix provides links to those data sets. 

Population, income, and land use data

County level projections of population, personal income, disposable personal income, 

and land use are available on the RPA Assessment website: http://www.fs.fed.us/research/

rpa/pubs-supporting-2010-rpa-assessment.shtml. Population and income projections are 

provided in an Excel spreadsheet, including the 2006 base year data and projections from 

2010 to 2060 at five-year intervals for all 50 States. The land use projections are provided 

on a separate Excel spreadsheet, with county level projections of cropland, pastureland, 

forest, range, and urban and developed land uses from 2010 to 2060 at 10-year intervals 

in the conterminous United States. 

Climate data 

The suite of projected climate variables (monthly mean daily maximum air temperature 

[°C], monthly mean daily minimum air temperature [°C], monthly total precipitation [mm], 

and computed values for potential evapotranspiration), downscaled to the 5 arc-minute 

grid and county scale, are available through the Forest Service archive website. The 

historical data spans 1990-2008 and the projection data spans the 2001 to 2100 period. 

Monthly data for each year is available. Meta-data documentation following international 

standards is available for each climate data set (Coulson and Joyce 2010a, 2010b; Coulson 

and others 2010a, 2010b, 2010 c, 2010d). The climate data, both historical and projected, 

are available at the RMRS archive web site: http://www.fs.fed.us/rm/data_archive/

dataaccess/contents_location.shtml#US.

Literature Cited

Coulson, David P.; Joyce, Linda A. 2010a. Historical climate data (1940-2006) for the conterminous United 

States at the 5 arc minute grid spatial scale based on PRISM climatology. Fort Collins, Colorado: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. Available at: doi:10.2737/

RDS-2010-0011.

Coulson, David P.; Joyce, Linda A. 2010b. Historical Climate data (1940-2006) for the conterminous United 

States at the county spatial scale based on PRISM climatology. Fort Collins, Colorado: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. Available at: doi:10.2737/RDS-2010-0010.

Coulson, David P.; Joyce, Linda A.; Price, David T.; McKenney, Daniel W. 2010a. Climate scenarios for 

the conterminous United States at the 5 arc minute grid spatial scale using SRES scenarios B2 and 

PRISM climatology. Fort Collins, Colorado: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. Available at: http://www.fs.fed.us/rm/data_archive/dataaccess/US_ClimateScenarios_grid_B2_PRISM.shtml.

Coulson, David P.; Joyce, Linda A.; Price, David T.; McKenney, Daniel W. 2010b. Climate scenarios for the 

conterminous United States at the county spatial scale using SRES scenarios B2 and PRISM climatology. Fort Collins, Colorado: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. Available at: doi:10.2737/RDS-2010-0009.

Coulson, David P.; Joyce, Linda A.; Price, David T.; McKenney, Daniel W.; Siltanen, R. Martin; Papadopol, 

Pia; Lawrence, Kevin. 2010c. Climate scenarios for the conterminous United States at the 5 arc minute 

grid spatial scale using SRES scenarios A1B and A2 and PRISM climatology. Fort Collins, Colorado: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Available at: http://

www.fs.fed.us/rm/data_archive/dataaccess/US_ClimateScenarios_grid_A1B_A2_PRISM.shtml.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012 33

Coulson, David P.; Joyce, Linda A.; Price, David T.; McKenney, Daniel W.; Siltanen, R. Martin; Papadopol, 

Pia; Lawrence, Kevin. 2010d. Climate scenarios for the conterminous United States at the county 

spatial scale using SRES scenarios A1B and A2 and PRISM climatology. Fort Collins, Colorado: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Available at: 

doi:10.2737/RDS-2010-0008.
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-272. 2012

Appendix D—Acronyms

AR4 IPCC 4th Assessment

DDC IPCC Data Distribution Centre

DPI Disposable personal income

ERS Economic Research Service

FIA Forest Inventory and Analysis

GCM Global circulation model

GDP Gross domestic product

GHG Greenhouse gas

IAM Integrated assessment model

IPCC Intergovernmental Panel on Climate Change

NRI National Resources Inventory

OECD90 IPCC macro region that includes the United States (See Appendix B)

PCPI Per capita personal income

RPA Forest and Rangeland Renewable Resources Planning Act

TAR Third IPCC Assessment

UNEP United Nations Environmental Program

USD United States dollar

WP Wood-Poole Economics, Inc.

UnitedStatesDepartmentofAgriculture

ForestService

RockyMountainResearchStation

GeneralTechnicalReportRMRS-GTR-272

March2012