United States Department of Agriculture 

Future Scenarios 

A Technical Document Supporting the 

USDA Forest Service 2020 RPA Assessment 

Linda L. Langner, Linda A. Joyce, 

David N. Wear, Jeffrey P. Prestemon, 

David Coulson, Claire B. O’Dea 

Forest Service Gen. Tech. Rep. RMRS-GTR-412 May 2020 
Citation

Langner, Linda L.; Joyce, Linda A.; Wear, David N.; Prestemon, Jeffrey P.; 

Coulson, David; O’Dea, Claire B. 2020. Future scenarios: A technical document 

supporting the USDA Forest Service 2020 RPA Assessment. Gen. Tech. Rep. 

RMRS-GTR-412. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 34 p. https://doi.org/10.2737/RMRSGTR-412.

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 2020 RPA Assessment will 

evaluate the future of the Nation’s renewable natural resources through 2070. 

This publication describes the process used to select a set of four integrated 

scenarios to represent a plausible span of socioeconomic and climate futures 

that underpin the natural resource analyses. These four scenarios, and their 

associated assumptions about population change, economic growth, and 

climate change, are also described in this document. 

Keywords: RPA Assessment, scenarios, Representative Concentration 

Pathways, Shared Socioeconomic Pathways
Authors LINDA L. LANGNER is the retired National Program Leader for the 

Resources Planning Act Assessment with the USDA Forest Service, 

Washington, DC. 

LINDA A. JOYCE is an Emeritus Scientist with the USDA Forest Service, 

Rocky Mountain Research Station in Fort Collins, Colorado.

DAVID N. WEAR is a retired Senior Scientist with the USDA Forest Service, 

Southern Research Station in Research Triangle Park, North Carolina. He is 

currently a Nonresident Senior Fellow with Resources for the Future.

JEFFREY P. PRESTEMON is a Project Leader and Senior Scientist with 

the USDA Forest Service, Southern Research Station in Research Triangle 

Park, North Carolina.

DAVID COULSON is a retired Statistician with the USDA Forest Service, 

Rocky Mountain Research Station in Fort Collins, Colorado.

CLAIRE B. O’DEA is the Assessment Program Manager with the USDA 

Forest Service, Washington, DC.

Acknowledgments We thank our reviewers for their comments, which improved the final 

report: Leslie Brandt (Forest Service), Robert Haight (Forest Service), 

Philip Morefield (U.S. Environmental Protection Agency), and Chris Weaver 

(U.S. Environmental Protection Agency). We wish to acknowledge John 

Abatzoglou and Katherine Hegewisch for their advice and consultation on the 

use of the MACA climate data. 
CONTENTS

INTRODUCTION ....................................... 1

 Framing the RPA Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

 Scenarios for the RPA Assessment . . . . . . . . . . . . . . . . . . . . . . . . 2

CLIMATE SCENARIOS FOR THE 2020 RPA ASSESSMENT . . . . . 4

 Representative Concentration Pathways . . . . . . . . . . . . . . . . . . . . 4

NATIONAL CLIMATE PROJECTIONS . . . . . . . . . . . . . . . . . . . . . . . 7

SOCIOECONOMIC SCENARIOS FOR THE 2020 RPA 

ASSESSMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 Global Socioeconomic Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . 11

 National Socioeconomic Projections . . . . . . . . . . . . . . . . . . . . . . 14

2020 RPA ASSESSMENT SCENARIOS . . . . . . . . . . . . . . . . . . . . . 19

 Criteria for 2020 RPA Assessment Scenarios . . . . . . . . . . . . . . . 19

 Linking 2020 RPA Assessment Scenarios to Natural Resource

 Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
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 forest 

and rangeland conditions in the United States and trends on all ownerships, 

identifies drivers of change, and projects renewable resource conditions and 

trends 50 years into the future. We analyze trends in outdoor recreation, 

fish and wildlife, biological diversity, wilderness, forests, rangelands, 

water, urban forests, and landscape patterns, as well as the potential effects 

of socioeconomic and climate change on these resources. Previous RPA 

Assessments, supporting publications, and links to recorded webinars are 

available at http://www.fs.fed.us/research/rpa. 

The 2020 RPA Assessment is the sixth assessment prepared in response 

to this mandate (USDA Forest Service 1977, 1980, 1989, 2001, 2012a). 

Beginning with the 2010 RPA Assessment, a set of integrated scenarios has 

been used to frame the resource analyses. This report describes the selection 

process for the 2020 RPA Assessment scenarios and the qualitative and 

quantitative components of the chosen scenarios that will be used to support 

2020 RPA Assessment resource analyses.

Framing the RPA Assessment

The original RPA language focused primarily on an economic evaluation 

of whether resource supplies could meet consumer demand. The RPA 

Assessment has broadened to assess resource conditions, ecosystem health, 

and sustainability to recognize the interrelationships between ecological 

and socioeconomic conditions in meeting the needs of the American public. 

While improving the ability to report on economic supply and demand, this 

broader and more flexible approach enhances our ability to evaluate current 

and future conditions of the Nation’s forests and rangelands. 

The 2020 RPA Assessment framework builds on the 2010 RPA Assessment, 

which was designed to better incorporate the global links and interactions 

between natural resources, extend our analytical capability to evaluate the 

potential effects of climate change across resources, and describe more 

clearly the complexity and uncertainty associated with projecting future 

conditions and trends (USDA Forest Service 2012b). For the 2020 RPA 

Assessment we continue this approach to select future scenarios for the 

United States. These scenarios are tied to global scenarios that depict a 

coherent interdependent future for global and U.S. population dynamics, 
2 Future Scenarios

socioeconomic factors, and climate change. These scenarios provide both 

qualitative and quantitative connections for the domestic resource analyses, 

which project resource conditions and trends for 50 years. 

Scenarios for the RPA Assessment

Scenarios are used to explore alternative futures and are intended to provide 

a 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 et al. 2003; IPCC 2007). 

A globally linked scenario approach is important for the RPA Assessment 

because global conditions and trends increasingly affect domestic natural 

resources. Well-defined global scenarios provide a coherent framework for 

evaluating outcomes across resource analyses.

A scenario approach can use both qualitative and quantitative methods 

in visualizing alternative futures based on different socioeconomic or 

institutional assumptions. The use of the term “scenario” can be confusing 

because scenarios can be used for various purposes, or in reference to 

specific types of scenarios (see Moss et al. 2008; USGCRP 2010). For 

the RPA Assessment, we have adopted the scenario approach used by 

the Intergovernmental Panel on Climate Change (IPCC). This scenario 

approach is intended to provide a better understanding of where there 

may be identifiable thresholds in biophysical and human systems. The 

scenarios are intended to represent plausible futures to better understand 

how systems may respond to different rates of change or how different 

decisions may alter resource trajectories (Moss et al. 2008). Scenarios are 

not assigned likelihoods, nor are any scenarios intended to be “accurate” 

per se. Rather, these constructed scenarios provide a means of qualitatively 

and quantitatively understanding how a range of socioeconomic and climate 

conditions through time interact to create different natural resource futures.

Though other research groups have constructed their own global scenarios 

(see Kok et al. 2015 for an evaluation of global scenarios), the combination 

of the IPCC-based climate and socioeconomic scenarios was chosen as the 

basis for the 2020 RPA Assessment for several reasons. These scenarios 

provide quantitative data on both climate and socioeconomic variables over 

the needed time horizon, are well documented in the scientific literature, have 

been widely used across a large range of impact studies, and are more current 

than other sources.

The 2010 RPA Assessment followed the framework used by the IPCC for the 

Third Assessment Report (TAR) (IPCC 2001) and Fourth Assessment Report 

(AR4) (IPCC 2007). Carpenter and others (2005) and Nakićenović and others 

(2000) review examples and uses of scenario analysis in other applications. 

In the TAR and AR4, greenhouse gas emissions scenarios were developed 

that were driven by socioeconomic scenarios that in turn drove the climate 
RMRS-GTR-412 3

projections. As a result, global climate and socioeconomic projections were 

quantitatively linked in these scenarios. The 2010 RPA Assessment used 

three of these scenarios and nine associated climate projections to frame the 

U.S. analyses (USDA Forest Service 2012b).

The 2020 RPA Assessment will rely on the scenario approach used in the 

IPCC Fifth Assessment Report (AR5) (IPCC 2014). Unlike the sequential 

approach for scenario development used in TAR and AR4, AR5 used 

a parallel process (Moss et al. 2010). Four new scenarios representing 

alternative climate futures were developed independently of the five 

socioeconomic scenarios (Nakićenović et al. 2014; O’Neill et al. 2014).

The remainder of this document describes the process used to select 

global climate and socioeconomic scenarios in order to downscale global 

projections to the national scale and subnational scale, and to select 

combinations of climate and socioeconomic projections that will be used 

in the 2020 RPA Assessment. The selected scenarios set the socioeconomic 

and biophysical context for evaluating resource futures in the 2020 RPA 

Assessment.
4

CLIMATE SCENARIOS FOR THE 2020 RPA ASSESSMENT

In this section we first describe the new global climate scenarios developed 

as part of the Intergovernmental Panel on Climate Change (IPCC) Fifth 

Assessment Report (AR5) and the process we used to select national-scale 

climate data and to select the manageable set of climate projections to be 

used in the 2020 Resources Planning Act (RPA) Assessment. More details 

about the analysis and selection of the climate models used in the 2020 RPA 

Assessment can be found in Joyce and Coulson (in press).

Representative Concentration Pathways 

Scenarios have been a component of the IPCC reports since 1992. As the 

scientific understanding of climate and the physics in climate models has 

improved, the process for creating scenarios has changed accordingly (Moss 

et al. 2010). For the AR5, a new process was developed for climate scenarios, 

beginning with Representative Concentration Pathways (RCPs) (Moss et al. 

2010; USGCRP 2017). Representative Concentration Pathways are designed 

to capture the spread of radiative forcing levels found in the emissions 

literature, and to provide information on components of radiative forcing 

that are used as input for climate modeling (emissions of greenhouse gases, 

air pollutants, and land use) (van Vuuren et al. 2011). A large radiative force 

implies a larger change in the climate. Four RCPs were chosen to encompass 

a range of radiative forcing levels: a very low forcing1

 level (RCP 2.6, or 

2.6 Watts per square meter [W m-2]); two medium stabilization scenarios 

(RCP 4.5 and RCP 6.0); and one high forcing level (RCP 8.5). The RCP 2.6 

scenario is a peak-decline pathway, where radiative forcing peaks at 3.0 W 

m-2 and then declines to 2.6 W m-2 by 2100 (fig. 1). Both RCP 4.5 and RCP

6.0 are considered stabilization pathways; that is, the trajectory of radiative

forcing over time remains below either 4.5 or 6.0 W m-2, respectively, and

stabilizes at those levels by 2100. Finally, RCP 8.5 is a rising radiative

forcing pathway, meaning that radiative forcing continues to increase through

2100. The RCPs provide the basis for exploring possible climate futures over

a wide range of emission levels (van Vuuren et al. 2011), and are intended

to aid in the creation of a new set of integrated socioeconomic and climate

scenarios (Moss et al. 2008). For more information about RCPs, see Hayhoe

et al. (2017).

1

Radiative forcing is the change in the net (downward minus upward) radiative flux (expressed 

in Watts per square meter) at the tropopause or top of atmosphere due to a change in an 

external driver of climate change, such as a change in the concentration of carbon dioxide or 

in the output of the Sun (USGCRP 2017).
RMRS-GTR-412 5

Figure 1—Trends in radiative forcing, 2000–2100, associated with each 

Representative Concentration Pathway (RCP) scenario. Gray area indicates the 

98th (light gray) and 90th (dark gray) percentiles of the literature. Green line: RCP 

2.6; red line: RCP 4.5; black line: RCP 6.0; blue line: RCP 8.5. Forcing is relative to 

preindustrial values and does not include land use (albedo), dust, or nitrate aerosol 

forcing. (Source: van Vuuren et al. 2011.) 

The four RCPs provide time-dependent trajectories of atmospheric 

greenhouse gas concentrations, land use, and land cover. Several climate 

modeling institutions across the world used the RCP data to undertake 

coordinated experiments with different global climate models. As a result, 

there can be 20 or more climate projections per RCP that are available as part 

of the Coupled Model Intercomparison Project, Phase 5 (CMIP5) (https://

esgf-node.llnl.gov/projects/cmip5/) (Hayhoe et al. 2017; Knutti and Sedlack 

2013). The projected range of average global temperature change for three of 

the four RCP scenarios is shown in figure 2. These scenarios, based on more 

than 20 models used in CMIP5, quantify the impact of human choices and 

natural variability on future climate (USGRP 2017).

For the RPA Assessment, we needed to decide how many of the RCPs we 

would use as the basis for RPA projections. From a scientific viewpoint, 

exploring all available alternative futures is desirable. But resource and time 

constraints, as well as communication challenges, require a narrowing of 

choices for the RPA Assessment. We chose to follow the Fourth National 
6 Future Scenarios

Climate Assessment2 approach for framing impacts of climate change by 

using RCP 4.5 and RCP 8.5 as the two bounding scenarios. RCP 4.5 will be 

the lower radiative forcing scenario, and RCP 8.5 will be the higher scenario. 

RCP 2.6 is primarily interesting as a scenario requiring extensive mitigation 

policy to achieve the lower radiative forcing levels. The RPA Assessment 

emphasizes futures with no significant change from current policy, so we 

chose to not include RCP 2.6. We also did not consider RCP 6.0 because 

resource effects from that scenario are likely to fall between RCP 4.5- and 

RCP 8.5-based analyses. We believe using a lower end and a higher end 

scenario will provide a wide range of long-term outcomes and will help 

communicate findings.

Figure 2—Multimodel simulated time-series, 1900–2100, for the change in 

global annual mean surface temperature relative to 1901–1960 for a range of the 

Representative Concentration Pathways (RCPs). These scenarios account for the 

uncertainty in future emissions from human activities (as analyzed with the 20+ 

models from around the world used in the most recent international assessment). 

Solid lines show the mean; associated uncertainties are depicted by shading, showing 

±2 standard deviations (5–95 percent) across the distribution of individual models 

based on the average over 2081–2100. (Source: USGCRP 2017.)

2

The memo detailing the choice of RCPs 4.5 and 8.5 to frame the impact analyses for the 

Fourth National Climate Assessment can be found at https://scenarios.globalchange.gov/sites/

default/files/External%20memo%20NCA4%20scenarios%20framing_20150506.pdf.
7

NATIONAL CLIMATE PROJECTIONS

Concern over resource and time constraints also affected the number of 

climate models and projections selected. To choose the individual climate 

models and associated downscaled projections for the 2020 RPA Assessment, 

we first identified the climate variables needed for the resource analyses and 

then developed criteria for selecting the climate models and the projections. 

The 2020 RPA Assessment climate projections must provide sufficient 

information and data to explore the effect of climate change on multiple 

resources, including wildlife, rangeland and forest condition, water, 

urban forests, forest products, and recreation. Scientists coordinating the 

underlying resource analyses identified a set of required climate variables 

and their spatial and temporal grain and extent. The spatial extent for the 

RPA Assessment is the United States; however, few models are available 

that link climate with resource production in Alaska, Hawaii and the Pacific 

island territories, the U.S. Virgin Islands, and Puerto Rico. Consequently, 

the climate analyses in the 2020 RPA Assessment focus on the conterminous 

United States. The spatial grain of data required was as fine as possible. 

The spatial scale of the global model output can range from 30 miles (48 

kilometers [km]) to 200 miles (322 km) on a side. Several downscaling 

methods have been developed to bring global model output closer to the 

scale of historical weather observations and the scale needed for resource 

management and planning. The set of required climate variables was used to 

evaluate the ability of existing downscaled climate datasets to meet the needs 

of the 2020 RPA Assessment with respect to climate scenarios (Joyce and 

Coulson, in press). 

Based on the resource analysis needs of the 2020 RPA Assessment, the 

downscaled dataset selected was MACAv2-METDATA (Abatzglou 2013; 

Abatzglou and Brown 2012). This dataset contained statistically downscaled 

projections from 20 different global climate models, each run under RCP 4.5 

and RCP 8.5. The spatial scale for this downscaled dataset was 4 km [2.5 

miles], meeting the fine-scale needs for RPA Assessment resource analyses. 

Both daily and monthly downscaled projections were provided, as well as the 

historical observed climate that was used in the downscaling process. 

Although using as many climate models and projections as available can 

provide a greater sense of the future range of temperature and precipitation, 

combining for example two RCP scenarios with multiple climate projections 

per RCP and numerous socioeconomic scenarios and projections can quickly 

escalate into an unmanageable number of scenarios and projections. As a 

result, we developed 3 criteria to screen the 20 climate models, and limited 
8 Future Scenarios

the number of projections to 5 that would provide an approximation of the 

overall projection space encompassed by the entire set of models. The five 

projections for each RCP would become the minimum core set for the 2020 

RPA Assessment analyses.

Three criteria were used to screen individual climate models. The first 

criterion was to use information on historical model performance to 

eliminate from further consideration those models consistently rated as poor 

performers. We chose to use the regional rankings developed by Rupp (Rupp 

2014, 2016; Rupp et al. 2013), where a consistent methodology was used 

across three regions in the conterminous United States. Rupp and others’ 

analysis focused on temperature and precipitation variables, important 

climate variables in RPA Assessment analyses. On the basis of this analysis, 

4 climate models were eliminated, reducing the set to 16 models. 

The objective of selecting five models was to identify projections that capture 

the magnitude of change in temperature and precipitation across the entire 

set. Four projections were identified that represented the least change and the 

greatest change in temperature (least warm, hottest) and the largest decrease 

and greatest increase in precipitation (driest, wettest) for the conterminous 

United States. Ensembles have often been used to reduce the number of 

projections; ensembles are the average of a number of projections and as 

such have reduced variability. We chose not to use an ensemble, as the 

individual model variability may be important when these projections are 

used as input in resource modeling efforts such as for water, forest condition, 

range, and wildlife. A fifth projection was selected that would be close to the 

mean change in temperature and precipitation of all model projections. Two 

additional criteria were used to screen the models as the projections were 

being selected. Only one model from a modeling institution was selected to 

reduce the influence of modeling institution on the 10 projections. If possible, 

the same model was chosen for RCP 4.5 and RCP 8.5 to reduce model 

variability across the RCPs. This core set of 5 models provides a reasonable 

approximation of the overall space encompassed by the larger set of 20 

models, but a greatly reduced total effort, thereby making the subsequent 

analysis feasible. 

Based on the criteria and analysis described, we selected five models for 

each RCP that represent the least warm projection, the hottest projection, 

the driest projection, the wettest projection, and the middle of the projected 

temperature and precipitation space of the 16 models (table 1, fig. 3). We 

were able to select the same model for both RCP 4.5 and RCP 8.5. Although 

these models represent the magnitude of change for one climate variable, 

the user should be aware of what each model projects for the other climate 

variable. Models selected to represent the magnitude of change for one 

climate variable (such as temperature) may not project the mid-range value 

for the other climate variable (such as precipitation). The historical modeled 

and projected climate data used in the RPA Assessment are archived in the 

USDA Forest Service’s Research Data Archive (Coulson and Joyce 2020; 

Joyce and Coulson, in press; Joyce et al. 2018). 
RMRS-GTR-412 9

A

B

Figure 3—Precipitation change (percent) plotted against temperature difference (oC) at mid-century (2041–2070) from the 

historical period (1971–2000) under (A) scenario RCP 4.5 and (B) scenario RCP 8.5 for all 16 models at the scale of the 

conterminous United States. Individual models are denoted by open circles in the RCP 4.5 scenario and open triangles in the 

RCP 8.5 scenario. The black ‘X’ represents the mean temperature and precipitation change for all models in each scenario. 

Four of the 20 screened models were eliminated based on selection criteria. The core model projections are identified as filled 

circles or triangles and noted Least Warm, Hot, Dry, Wet, and Middle in legend. (Source: Joyce and Coulson, in press.) 

Table 1—Core set of climate model projections for mid-century analysis in the 2020 RPA Assessment (Source: 

Joyce and Coulson, in press). 

Least Warm Hot Dry Wet Middle

RCP 4.5 MRI-CGCM3 HadGEM2-ES IPSL-CM5A-MR CNRM-CM5 NorESM1-M

RCP 8.5 MRI-CGCM3 HadGEM2-ES IPSL-CM5A-MR CNRM-CM5 NorESM1-M

Climate 

model 

institution

Meteorological 

Research Institute, 

Japan

Met Office 

Hadley Centre, 

United Kingdom

Institut Pierre 

Simon Laplace, 

France

National 

Centre of 

Meteorological 

Research, 

France

Norwegian 

Climate Center, 

Norway
10 Future Scenarios

The RPA Assessment focuses on the next 50 years (through 2070), so we 

selected models that provided the temperature and precipitation range 

through 2070 compared to the historical period, which we defined as 1971 

through 2000. We also evaluated whether model selection based on behavior 

at 2070 would be different from behavior at end of century (2070–2099). 

We concluded that the same core models could be used for end-of-century 

analysis to capture the range of climate futures. These core models were 

selected at the conterminous U.S. scale. We also evaluated their utility as 

core models for least warm, hottest, driest, wettest, and middle within regions 

of the National Forest System. At the regional scale, the relative comparisons 

are appropriate for all regions; that is, the hottest core model is always hotter 

than the least warm projection and the wettest projection is always wetter 

than the driest core model in each region. Additional details on all of the 

analyses can be found in Joyce and Coulson (in press).
11

SOCIOECONOMIC SCENARIOS FOR THE 2020 RPA ASSESSMENT 

In this section we first describe the global socioeconomic scenarios 

developed in parallel to the Representative Concentration Pathways (RCPs) 

and how we selected the socioeconomic pathways to be used in the 2020 

Resources Planning Act (RPA) Assessment. The remainder of the section 

describes the process to create nationally downscaled socioeconomic data 

and selection of a subset of socioeconomic projections to represent the 

potential range of socioeconomic change. 

Global Socioeconomic Scenarios

Shared Socioeconomic Pathways (SSPs) were developed in parallel to the 

RCPs to provide scenarios of plausible alternative pathways of societal 

development (O’Neill et al. 2014). They are composed of storylines that 

vary across dimensions of variables characterizing uncertainty about the 

future: population trajectories, economic growth, technology, trade, and 

governance. Five SSPs were developed, and each was described in terms 

of the difficulties, costs, research and development investment levels, or 

degree of policy changes (“challenges”) involved in mitigating or adapting 

to climate change. Four of the SSPs describe the range of high challenges 

(difficult, costly, and entailing large policy shifts) and low challenges for 

global adaptation and mitigation, while a fifth SSP defines an intermediate 

case. The SSPs do not include climate feedbacks or specific policy options 

(O’Neill et al. 2014). 

The SSPs can be described across a selected set of quantitative or qualitative 

characteristics (table 2). The characterization of change is relative both to 

current conditions and to change among the SSPs. Each SSP has a narrative 

and associated characteristics that make the pathway distinct from the others, 

although there can be considerable regional variability within a particular 

SSP. The trend for each scenario characteristic shown in table 2 is the general 

global trend. For example, global population growth is the lowest under 

SSP5, but population growth in the United States is highest under this SSP. 

Similarly, economic growth under SSP1 is higher in low-income countries 

than in high-income countries (see O’Neill et al. 2017 for more detailed 

SSP descriptions). To develop national socioeconomic scenarios linked 

to the global SSPs, we focused on the SSP variation in demographic and 

economic characteristics, which have been quantified at the country level 

(data available on the SSP public database at https://tntcat.iiasa.ac.at/SspDb/

dsd?Action=htmlpage&page=welcome).
12 Future Scenarios

Table 2—Selected global characteristics of Shared Socioeconomic Pathways (SSPs) (Source: based on O’Neill et al. 2017).

SSP element SSP1 SSP2 SSP3 SSP4 SSP5

Population growth Low Medium High Medium-high Low

Migration Medium Medium Low Medium High

Urbanization High Medium Low Medium-high High

Per capita economic growth Medium-high Medium Low Low-medium High

International trade Moderate Moderate Strongly 

constrained

Moderate High

Globalization Connected 

markets

Semiopen 

globalized 

economy

Deglobalizing Limited global 

connections

Strongly 

globalized

Technology development Rapid Medium, uneven Slow Slow to rapid by 

sector

Rapid

Energy technology Emphasis on 

efficiency and 

renewables

Continued 

reliance on fossil 

fuels 

Slow change, 

directed to 

domestic energy 

Diversified, with 

efficiency and 

low-carbon

Directed to 

fossil fuels

Carbon intensity Low Medium Depends on 

domestic sources

Low-medium High 

Energy intensity Low Uneven High Low-medium High 

The country-level projections of both population and income consistent 

with SSP global narratives were undertaken by three modeling groups. 

While the projections of population were consistent across all three groups, 

there were variations in projections of gross domestic product (GDP). In 

the following comparisons, we relied on the economic projections provided 

by the International Institute for Applied Systems Analysis (IIASA 2018) 

because IIASA included more country-level projections that are important 

for modeling international trade flows as applied in RPA modeling of global 

wood products markets. The IIASA modeling methodology is described in 

Cuaresma (2017). 

The SSP data for global and U.S. population and GDP are projected through 

2100 to be consistent with the IPCC climate change projections (fig. 4). The 

2020 RPA Assessment will focus on the time period 2020 to 2070. As seen 

in figure 4, divergence across the SSPs tends to accelerate to the end of the 

century.

Global and U.S. trends do not necessarily follow the same trajectory across 

SSPs. As shown in figure 4, global population trends and U.S. population 

trends diverge, with the highest population growth scenario for the United 

States having very little population growth globally (SSP5). Trends in GDP 

growth are more consistent between U.S. and global trends, although for 

the United States the SSP projecting the lowest economic growth is SSP3, 

while globally it is SSP4. In SSP1, world population begins declining after 

2050, whereas U.S. population continues to grow until about 2090 and then 

flattens. U.S. trends for SSP2 are almost identical to SSP1, whereas globally 

the two SSPs diverge markedly in population but are similar in GDP trends. 
RMRS-GTR-412 13

0

100

200

300

400

500

600

700

800

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

U.S. Population (millions)

Global population (millions)

Year

G-SSP1

G-SSP2

G-SSP3

G-SSP4

G-SSP5

US-SSP1

US-SSP2

US-SSP3

US-SSP4

US-SSP5

A

0

20,000

40,000

60,000

80,000

100,000

120,000

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

U.S. GDP (trillion US 2005 dollars)

Global GDP (trillion 2005 US dollars)

Year

G-SSP1

G-SSP2

G-SSP3

G-SSP4

G-SSP5

US-SSP1

US-SSP2

US-SSP3

US-SSP4

US-SSP5

B

Figure 4—(A) Global (G) and U.S. population projections and (B) global and U.S. gross domestic product (GDP) projections 

by Shared Socioeconomic Pathway (SSP), 2010–2100. 
14 Future Scenarios

SSP5 and SSP3 bracket the high and low changes in population and GDP

for the United States, with the highest population growth associated with the 

highest economic growth (SSP5) and the lowest economic growth associated 

with a declining population (SSP3) (fig. 5). These patterns are tied to several 

interacting assumptions about economic growth, fertility and mortality, 

migration patterns, and the openness of the global economy.

National Socioeconomic Projections

The range of socioeconomic trends described by the five SSPs represents 

highly divergent futures to consider when we are creating national 

socioeconomic scenarios for the 2020 RPA Assessment. As with the RCPs, 

we must select SSPs to use as a basis for domestic scenarios. Before making 

that choice, we developed a methodology to downscale the country-level 

SSP data to a finer spatial scale. As described in the previous section, 

considerable effort by the climate science community has been devoted to 

downscaling climate projections. The availability of downscaled data has 

eliminated the need to develop our own downscaled climate data for the 

2020 RPA Assessment. No similar effort has been devoted to socioeconomic 

scenarios—specifically to jointly downscaling the SSP-based population 

and economic projections. Projections of population and income that 

are downscaled using a consistent approach are critical inputs to various 

modeling systems.

Socioeconomic assumptions have long been a part of RPA Assessment 

assumptions, as they play a central role in natural resource impacts across 

the United States. The U.S. Census Bureau routinely updates official national 

population projections for the United States. The census projections have 

been used to downscale national population projections to county-level 

projections, but these downscaled projections provide only one projection 

for less than 50 years, and the methodology is proprietary (Woods and Poole 

Economics, Inc. 2020). The U.S. Environmental Protection Agency (2020) 

has provided SSP-based downscaled population projections for SSP2 and 

SSP5, but these projections do not extend to economic variables.

Socioeconomic projections at a finer than national spatial scale were not 

available to meet the needs for RPA Assessment analyses; consequently, we 

developed county-level projections of population and income change for 

each SSP. The method that we used to project these variables was based on 

economic theory and is consistent with county-scale historical patterns of 

change (Wear and Prestemon 2019). 

Our approach, derived from economic growth theory (Solow 1956), assumes 

that population and income changes are interrelated and generally consistent 

with per capita income convergence across locations over time. The concept 

of convergence posits that factors of production, including labor and capital, 

will move from locations of relative abundance, where earnings are low, 

to locations of relative scarcity, where earnings are high. Capital and labor 
RMRS-GTR-412 15

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Proportional change from 2015 value

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U.S. Gross Domestic Product Trend

SSP1 SSP2 SSP3 SSP4 SSP5 CS-1 CS-2 CS-3 CS-4 CS-5

B

Figure 5—Projections of (A) U.S. population and (B) U.S. gross domestic product. Lines with solid circles are based on the 

IIASA Integrated Assessment Model for five Shared Socioeconomic Pathways (SSPs). Hollow symbols are total values from 

the calibrated simulations (CS) of county-level changes for the SSPs. 
16 Future Scenarios

mobility converge toward equal relative factor abundance and earnings 

across locations. One measurable effect of convergence is that locations 

which today have lower per capita income will experience faster future rates 

of per capita income growth compared to locations which today have higher 

per capita income. If economic growth theory applies, fine-scale allocations 

of population and income across space should be jointly determined in 

the long run. Such a connection could mean that models of downscaled 

population and income that recognize their joint relationship would provide 

more accurate projections of population and income changes at fine spatial 

scales compared to methods that ignore their relationship. However, previous 

efforts to downscale national-scale population and income to spatial units 

that are county scale or smaller in the United States (e.g., Jones and O’Neill 

2013; McKee et. 2015) have not linked population and income in a manner 

consistent with per capita income convergence.

Our approach to downscaling population and income is based on models that 

incorporate their joint relationships. Observations are explained statistically 

with fixed-effects cross-sectional time series panel models of population and 

per capita personal income at the county level, with data from 1970 to 2010 

on a 5-year time-step. Furthermore, our method recognizes that econometric 

estimation of models based on spatially arranged data needs to account for 

the spatial arrangement explicitly by including spatial weights matrices (e.g. 

Anselin 1988), and account for temporal change processes for population 

and per capita income by including time lags of both variables. Given that 

we cannot predict which model specification would work best in producing 

long-term projections of population and income at fine spatial scales, we use 

a model averaging approach to include information from alternative models. 

Because our approach involves uncertainty about the model functional form 

most likely to minimize bias and forecast error, we sought to minimize 

these twin criteria by using this approach, rather than model selection 

(Hansen 2007). Averages of projections from a set of competing models 

have been shown to perform better than individual models selected using 

model selection criteria such as the Akaike information criterion or Bayesian 

information criterion (Berge 2015). Performance could improve because of 

the overlapping information sets involved in each individual specification 

(Bates and Granger 1969) or because their combination alleviates the effects 

of model misspecifications (Hendry and Clements 2004; Stock and Watson 

2004; Timmermann 2006). Variations of the model averaging approach are 

widespread in the econometrics literature (e.g., Hoeting et al. 1999).

We applied the methodology to estimate county-level projections for all five 

SSPs. In these projections, the rate of personal income change nationwide 

(summed across all counties) was constrained to match the rate of GDP

change nationwide as projected by IIASA (2018) for the United States 

for each of the SSPs. Under SSP3, population grows slowly to a peak in 

2035 and then gradually declines to 2010 population levels by 2070, while 

income grows steadily at about 1 percent per year (from about $13 billion in 
RMRS-GTR-412 17

2010 to about $24 billion in 2070). Under SSP5, population expands by 86 

percent, from 313 million to 581 million between 2010 and 2070, while real 

GDP grows at a rate of 2.5 percent per year between 2010 and 2070, more 

than quadrupling over this period. SSPs 1, 2, and 4 provide intermediate 

projections with population growing to between 390 million and 451 million 

people in 2070 and annual GDP growth rates of between 1.4 and 1.8 percent. 

The cumulative distributions of county populations for the historical period 

and the projection period show overall outward shifts in the cumulative 

distribution of population for all SSPs, although the degree of shifts 

varies across SSPs. Maps of change in population density (fig. 6) show 

the influence of spatial factors and differences in growth across scenarios. 

Historical patterns of population growth indicate increasingly concentrated 

populations over time in the United States, a result aligned with the spatial 

and temporal autocorrelation identified in our statistical models. Likewise, 

our projections indicate an ongoing process of interregional population 

movement. We project a shift in the Nation’s population away from the 

Northeast and Midwest and toward the South and West, although the rates 

of such interregional population shifts vary across SSPs; the smallest shift 

occurs under the lowest population growth rate. Comparing the lowest 

population growth scenario (SSP3) with the highest population growth 

scenario (SSP5) (fig. 6) demonstrates how populations would expand from 

existing metropolitan cores into but not much beyond the closest surrounding 

rural counties. A large share of the current rural United States experiences 

either new or continued population losses or stable population across all 

scenarios. See Wear and Prestemon (2019) for a comparison of projections 

across scenarios and for details on model specification, performance, and 

potential limitations.

Though we developed county-level downscaled data for all five SSPs, we 

decided to limit the number of SSPs to be considered in the selected RPA 

Assessment scenarios. We chose SSP3 and SSP5 because they bound the 

demographic and economic change for the United States and capture most 

of the range in global change as well. SSP4 has lower growth in global GDP

than SSP3, but the difference is relatively small compared to variation across 

the other SSPs. SSP1 and SSP2 follow similar trajectories for the United 

States and globally; however, the underlying narrative for these pathways 

offers opportunities to explore differences among resource- and sectorspecific variables that could have different implications for natural resources. 

For example, the narrative for SSP1 is more focused on low-emission energy 

sources, whereas SSP2 is more tightly linked to historical patterns of energy 

use. Therefore, we decided to retain both SSP1 and SSP2. We eliminated 

SSP4 because its trajectory falls between SSP3 and SSP2.
18 Future Scenarios

Figure 6—Projected change in population density (people per square mile, ppsm), 2010–2070, for Shared Socioeconomic 

Pathways (SSPs): (A) SSP3 (low growth), (B) SSP2 (moderate growth), and (C) SSP5 (high growth).
19

2020 RPA ASSESSMENT SCENARIOS

The Shared Socioeconomic Pathways (SSPs) and Representative 

Concentration Pathways (RCPs) were developed to provide a scenario 

matrix architecture to assist in the development of common scenarios that 

can be used across different climate change research communities. The SSPs 

provide a range of socioeconomic trajectories that can be combined with the 

RCP-based climate projections to explore climate impacts (van Vuuren and 

Carter 2014; van Vuuren et al. 2014). While the emphasis of these products is 

climate change, the scenario matrix architecture is a useful mechanism to link 

global and U.S. scenarios, and it defines a set of integrated scenarios for the 

2020 Resources Planning Act (RPA) Assessment.

The RPA Assessment requires national-scale socioeconomic futures that can 

be generally mapped to global climate outcomes, and it requires that data 

are downscaled to fine grains to match the economic and ecological context 

of the resource analyses. It is unreasonable to assume that U.S. economic 

and demographic changes can be considered outside the context of global 

conditions. In addition, the set of chosen scenarios should span a broad range 

of plausible futures to avoid anchoring in too narrow a range of effects.

Criteria for 2020 RPA Assessment Scenarios

While the RPA Assessment scenarios need to link at least at the broad 

qualitative level to the general worldviews of the SSP and RCP futures, 

they also must provide a compelling range of futures for the United States. 

Several criteria were considered in selecting the combination of climate 

and socioeconomic futures that define the 2020 RPA Assessment scenarios. 

As described in the Representative Concentration Pathways section, the 

selection of climate “futures” was limited by using RCP 4.5 and RCP 8.5 

as bounding pathways, followed by the selection of five core global climate 

models per RCP to represent the potential range of climate change across 

precipitation and temperature gradients within those RCPs. 

After choosing four SSPs, we had a choice of eight possible RCP–SSP 

combinations for the 2020 RPA Assessment scenarios (two RCPs and four 

SSPs). With 5 climate projections for each of the 8 scenarios, there were 

potentially 40 future socioeconomic-climate outcomes for the United States. 

We did not have a predetermined number of desired scenarios, but were 

concerned about having a manageable number of futures that would not 

exceed computational and personnel resources for conducting the analyses 

and communicating the results.
20 Future Scenarios

Aside from these practical considerations, we concentrated on assessing the 

plausibility of potential RCP–SSP combinations—a challenging task. By 

plausibility, we mean that the degree of global warming indicated by the RCP 

is consistent with the emissions generated by the socioeconomic activity 

depicted in the storyline for each SSP. The development of the RCPs and 

SSPs and their associated projections assumed that any combination of RCP

and SSP is conceptually plausible. However, adjustments to assumptions, 

which could include policy assumptions, might be necessary for some RCP–

SSP combinations (van Vuuren et al. 2014). The RPA Assessment analyses 

are traditionally based on continuation of current policies, so we preferred 

to select RCP–SSP combinations that did not require assumptions which 

would indicate large departures from current policies for the RPA Assessment 

scenarios.

Recent experiments that tested potential consistency between RCPs and 

SSPs provided additional information to help judge the plausibility of 

different RCP–SSP combinations. As summarized in Riahi et al. (2017), 

several Integrated Assessment Model teams undertook a set of analyses 

that characterized the SSPs using both the quantitative socioeconomic 

data described previously and quantitative interpretations of key SSP 

characteristics such as energy use, land use, and technology change. The 

modeling teams began with “baseline” scenarios that used the quantitative 

data from each SSP and assumed continuation of current climate policies. 

They then developed mitigation scenarios that explored the implications 

of various climate mitigation policies on emissions, for example by 

implementing a carbon price or imagining alternative mixes of energy 

sources. The data output from these analyses are available on the SSP 

database (https://tntcat.iiasa.ac.at/SspDb) at the global level and for five 

global regions, including the Organisation for Economic Cooperation and 

Development region that includes the United States3

.

The results of these experiments were instructive for selecting RPA 

Assessment scenario combinations. The SSP baseline scenarios were of 

most interest because they provide an estimate of carbon dioxide equivalent 

concentrations and associated radiative forcing levels that are generally 

consistent with current policies. The SSP baseline scenarios had greenhouse 

concentrations that translated to a radiative forcing level by 2100 ranging 

from 5.0 to 8.7 W m-2. The highest and lowest radiative forcing levels 

across the baseline results are shown in figure 7 for the four SSPs being 

used in the 2020 RPA Assessment. The results for both 2070 and 2100 are 

displayed. Across all of the baseline runs to 2100, only the SSP5 baseline 

scenario reached a forcing level of RCP 8.5, but all the others exceeded 

3

A special issue of Global Environmental Change describes these experiments (2017; Issue 

42).
RMRS-GTR-412 21

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SSP1-B SSP2-B SSP3-B SSP5-B

Total Radiative Forcing (W/m2)

Shared Socioeconomic Pathway Baseline

High 2070 Low 2070 High 2100 Low 2100

Figure 7—Range of total radiative forcing levels associated with Integrated Assessment Model baseline results for selected 

Shared Socioeconomic Pathways (SSPs) in 2070 and 2100. 

RCP 4.5 forcing levels by 2100. Only SSP1 remained under RCP 4.5 by 

2070 under some baseline results. The results of the baseline analyses 

indicate that achieving consistency with the RCPs of 6.0 and below would 

require additional technical or policy assumptions to reduce greenhouse gas 

emissions (Riahi et al. 2017). 

SSP1 is the only baseline scenario that resulted in radiative forcing close to 

the RCP 4.5 level, and was judged to be the only SSP that could plausibly 

link with RCP 4.5 for RPA Assessment purposes. Combining any of the 

remaining SSPs with RCP 4.5 would require varying levels of technology or 

policy assumptions that are beyond the scope of RPA Assessment analyses 

except when the RPA framework is used specifically for policy analysis. 

Combining SSP5 and RCP 8.5 is plausible according to the baseline results. 

The remaining SSPs, SSP2 and SSP3, produced forcing levels between RCP 

6.0 and RCP 8.5. Pairing these SSPs with RCP 8.5-based climate projections 

could overstate climate influence in RPA resource models. In summary, 

pairing SSPs and RCPs and assessing the mutual consistency of their 

assumptions is an inexact science, and various choices are probably equally 

justified in a scenario-driven assessment such as the RPA Assessment.
22 Future Scenarios

Therefore, four RCP–SSP combinations were chosen as plausible to underpin 

2020 RPA Assessment analyses of resource effects without significant policy 

changes (fig. 8). Only one RCP–SSP combination is based on RCP 4.5. 

Rather than focusing on future outcomes that equally consider the full range 

of scientific uncertainty, we are focusing more attention on outcomes with 

higher societal risk from changing socioeconomic and climate conditions. 

Low probability, high impact outcomes tend to carry the greatest societal 

risk. Those risks are highest under scenarios of the greatest climate change, 

such as those associated with RCP 8.5. Decisionmakers are likely to be most 

interested in understanding higher impact futures so that they can plan for the 

most extreme outcomes (Weaver et al. 2017). 

LM

Lower warming

and moderate 

U.S. growth 

RCP4.5-SSP1

HH

High warming

and high 

U.S. growth 

RCP8.5-SSP5

HM

High warming 

and moderate 

U.S. growth

RCP8.5-SSP2

HL

High warming 

and low 

U.S. growth 

RCP8.5-SSP3

Low

Future 

Emissions

High Future Socioeconomic Growth

High

Future 

Emissions

Low Future Socioeconomic Growth

Figure 8—Characterization of global warming and U.S. socioeconomic growth characteristics of the four Representative 

Concentration Pathway (RCP)–Shared Socioeconomic Pathway (SSP) combinations underpinning the 2020 RPA Assessment 

scenarios. 
RMRS-GTR-412 23

The range of changes in global and U.S. characteristics is similar between the 

2010 and 2020 RPA Assessment scenarios. The four 2020 RPA Assessment 

scenarios encompass most of the projected range of climate change from 

the RCPs and projected quantitative and qualitative range of socioeconomic 

change from the SSPs, resulting in four distinct futures (table 3). The 

scenario “short names” are linked to their global radiative forcing levels 

and U.S. socioeconomic growth characteristics, as described in the first 

line of table 3. We have described RCP 4.5 as “lower warming” and RCP 

8.5 as “high warming.” For the United States, economic and population 

growth trends initially move in the same direction across scenarios (with 

population growth turning to shrinkage under SSP3 for the U.S. after 2040), 

whereas globally, economic and population growth diverge in three of the 

four scenarios. These quantitative trends and narratives provide a unifying 

framework that organizes the RPA Assessment natural resource sector 

analyses around a consistent set of possible world views.

Table 3—Characteristics of the four 2020 RPA Assessment scenarios.a

Characteristic

Scenario 

LM

Scenario

HL

Scenario 

HM

Scenario

HH

Global warming and 

U.S. socioeconomic 

growth 

Lower warming 

and moderate 

U.S. growth

High warming and 

low U.S. growth

High warming and 

moderate U.S. 

growth

High warming 

and high U.S. 

growth

Global real GDPb

growth, 2020–2070

Medium 

(4.9X)

Low 

(3.2X)

Medium 

(4.6X)

High 

(6.9X)

Global population 

growth, 2020–2070 

Lowc

(1.2X)

High

(1.6X)

Medium

(1.4X)

Low

(1.2X)

U.S. real GDP 

growth, 2020–2070

Medium

(3.0X)

Low 

(1.9X)

Medium 

(2.8X)

High 

(4.7X)

U.S. population 

growth, 2020–2070

Medium 

(1.5X)

Low 

(1.0X)

Medium 

(1.4X)

High 

(1.9X)

Global emissions Lower High High High

Global scenario links RCP4.5-SSP1 RCP8.5-SSP3 RCP8.5-SSP2 RCP8.5-SSP5

a

 Numbers in parentheses are the factors of change in the projection period. For example, U.S. real gross domestic 

product increases by a factor of 3.0 between 2020 and 2070 in Scenario LM.

b GDP = gross domestic product (based on estimates by the International Institute for Applied Systems Analysis 2018).

c Note: Low population involves initial increase with declines in the latter decades of the projection period.
24 Future Scenarios

Linking 2020 RPA Assessment Scenarios to Natural 

Resource Sectors

Defining the 2020 RPA Assessment scenarios is the beginning of the RPA 

analysis process. The RPA scientists determine how to use the scenario 

data and assumptions in their resource sector analyses. Each analysis uses 

different combinations of the scenario variables and resource-specific 

variables to evaluate future resource outcomes. Examples of connections 

between components of the 2020 RPA Assessment scenarios and RPA 

Assessment resource analyses (fig. 9) illustrate the numerous routes through 

which the scenario variables can influence resource analyses. In some cases, 

both socioeconomic and climate projections are direct inputs to resource 

analyses, including outdoor recreation demand, water vulnerability, and 

forest product supply and demand. In other cases only the climate variables 

are direct inputs to the analyses, for example, in projections of rangeland 

productivity and stress on terrestrial habitats. 

Figure 9—Scenarios for the 2020 RPA Assessment and examples of links to resource analyses.
RMRS-GTR-412 25

Land use and landscape pattern projections are often the intermediary 

between the scenarios and resource-specific effects (fig. 9). The land use 

projections incorporate the U.S. climate and socioeconomic projections. In 

turn, the landscape pattern projections are based on the land use projections. 

Land use projections are strongly influenced by population and economic 

drivers; changes are more rapid and more extensive in futures of higher 

populations or more rapid economic growth (or both). 

Analyses that rely on the land use or landscape patterns incorporate the 

scenario variables indirectly. For example, the forest dynamics model 

uses land use projections to adjust projected future forest land area. The 

landscape pattern projections support projections of forest fragmentation 

and future hotspots for at-risk species. Patterns such as the degree of forest 

fragmentation influence the feasibility of land management options, the 

availability of land resources to support uses such as outdoor recreation, and 

the ability of ecosystems to support biological diversity. 

The examples shown in figure 9 are only a subset of the connections across 

the resource sectors. Daigneault et al. (2019) provide an example of building 

on the SSPs to develop potential future pathways for the global forest sector. 

They use differences in scenario components such as technology adoption 

and energy use and translate them into assumptions about harvest rates, 

biomass demands, and the productivity of forest plantations.

The following narratives provide a broad overview of the four 2020 RPA 

Assessment scenarios, focusing on how the climate and socioeconomic 

projections and qualitative aspects of each scenario may affect natural 

resource conditions and trends. It is premature to predict the direction of 

change in resource conditions before the analyses have been completed 

because underlying drivers may interact in unexpected ways. 

Scenario LM (RCP 4.5–SSP1): Lower Global Warming and Moderate U.S. 

Socioeconomic Growth

Scenario LM (RCP 4.5–SSP1) has a lower level of global warming and 

a moderate level of economic growth globally and in the United States, 

implying shifts from historical trends in development patterns, commodity 

demand, technology, trade, and energy use. The emphasis on sustainability 

is consistent with maintaining natural environments, including forests 

and rangelands. Under this scenario, the United States will face continued 

pressure for development in response to continued population growth. More 

compact development will reduce pressure on natural landscapes compared 

to historical patterns of extensive development in the wildland-urban 

interface.

Growing incomes will increase demand for both natural resource 

commodities and amenities. Growth in U.S. demand for housing will be 

moderate, in line with population and income growth; increased interest in 

wood as a building material, combined with new wood-based technologies 
26 Future Scenarios

could create demand for new wood product construction. Demand for 

agricultural commodities usually increases with rising incomes, but 

population-driven demand will be moderate. As world populations shrink, 

the demand for cropland may decline, reducing pressure on other natural 

land covers. Demand for livestock generally increases with rising incomes, 

possibly increasing demand for livestock grazing on rangelands, or creating 

pressure to convert rangelands to more productive pasture. With lower levels 

of global warming, effects on water will be less pronounced than under high 

warming scenarios, but water availability is still likely to be a constraint 

in some regions of the United States, affecting choices in agricultural 

production. Competition between irrigation and high-valued urban uses will 

continue, although continued efficiencies in water use in combination with 

water pricing could ameliorate some of the competition. Energy portfolios 

shift to sources with lower greenhouse gas emissions. The role of forests in 

producing biomass energy is likely to increase. Highly productive energy 

crops may affect uses of agricultural lands and create competition between 

energy and food crops. Improvements in technology and livestock and crop 

breeding may ease some of these constraints.

Global forest land is likely to expand, as will forest area in the United 

States. The composition of U.S. forests will change in response to shifting 

forest market demand and climatic changes, which will interact with natural 

disturbance events. Increases in biomass for energy could increase plantation 

forest area, possibly at the expense of natural forest cover. Increasing demand 

for resource amenities, such as outdoor recreation and biodiversity, indicates 

rising pressures on public lands to provide these services. Climate effects on 

public land resources are likely to change recreation opportunities and affect 

habitats, which will, in turn, influence the diversity of fauna and flora.

Scenario HL (RCP 8.5–SSP3): High Global Warming and Low U.S. 

Socioeconomic Growth

Scenario HL (RCP 8.5–SSP3) portends larger and faster rates of climate 

change, rapid global but low U.S. population growth, and slow economic 

growth domestically and globally. World population more than triples by 

2070, although U.S. population increases slowly to 2070 and then declines. 

Domestic population pressures on natural resources will be lower compared 

to other scenarios, particularly in concert with low economic growth. For 

example, there will be less demand for development, reducing conversion 

of natural landscapes. Though high global population growth could be 

a source of increased demand for exports of agricultural commodities 

and forest products from the United States, low global economic growth 

combined with high trade barriers will constrain such trade. The combination 

of trade barriers and limited technological innovation will reduce the rate 

of conversion to renewable energy resources and also increase reliance on 

domestic energy sources. If biomass becomes a more important domestic 

energy source, demand for forest and agricultural energy biomass could 

increase, affecting the extent and use of forest and agricultural lands. 
RMRS-GTR-412 27

Forest land is projected to decline globally, as global population pressure and 

limited agricultural productivity and technological innovation combine to 

result in expansion of cropland. Urban expansion is also likely to continue 

at the expense of natural land covers such as forest land. Forest area for the 

United States will depend on factors that could move in opposite directions: 

Increased demand for crop exports could increase cropland; increased use 

of forest biomass for energy could expand forest land in plantations. Lack 

of export demand for crops or forest products would reduce economic 

incentives for either. Global warming will exacerbate water shortages in the 

United States. The limited population pressure in the United States changes 

the domestic dynamics compared to the dynamics elsewhere in the world 

where populations will be increasing rapidly and economic growth is higher. 

Resource amenities and biodiversity are likely to be negatively affected by 

the combination of global climate shifts and low economic growth that will 

limit growth in demand for these services. Environmental protection tends 

to suffer when countries face the types of financial challenges envisioned 

by this scenario. In the United States, lower to negative population growth 

could lead to increased provision of natural amenities because of the limited 

pressures for expanded development, but maintaining existing environmental 

protections will also be critical to achieving such a result. 

Scenario HM (RCP 8.5–SSP2): High Global Warming and Moderate U.S. 

Socioeconomic Growth

Scenario HM (RCP 8.5–SSP2) has a low to moderate level of global 

population growth and moderate global economic growth. U.S. population 

growth is higher than the global average and economic growth is lower. 

These trajectories are combined with high global warming. This scenario 

assumes that globalization norms of the early decades of this century are 

maintained, indicating international trade continues at levels that are lower 

than in scenarios LM and HH (discussed next), but higher than in HL. The 

level of global cooperation generally reflects the status quo of the early 2000s 

in both environmental and market governance. 

Development patterns of the United States and globally will reflect the 

wide variation currently seen in land use regulation and zoning across and 

within countries. Technological improvements will continue at historical 

rates, which could support technologies and energy use portfolios that 

limit greenhouse gas emissions. The change in energy use toward reducing 

greenhouse gas emissions will be relatively slow, but biomass use will 

increase in some global regions, including the United States. Therefore, there 

could be potential expansion of forest land and forest biomass exports. If 

demand for forest biomass for energy increases, there could be additional 

conversion of natural forest to plantations; overall forest land area is likely to 

decline in the United States in the long run.
28 Future Scenarios

Population growth is likely to increase demand for cropland, putting 

additional pressure on natural landscapes for conversion. Agricultural 

productivity improvements could lessen these impacts, but the effects 

of global warming on agriculture are also expected to influence both the 

location and extent of cropland. As with all high warming scenarios, water 

availability will be constrained, particularly in areas of the United States 

already experiencing water supply issues. Water shortages will also impact 

aquatic biodiversity as there may be pressure to reduce environmental 

protections such as in-stream flow levels. 

High warming will impact recreation opportunities, particularly those related 

to water and snow activities. Biodiversity will be affected in various ways; 

some species will be at greater risk of becoming endangered. 

Scenario HH (RCP 8.5–SSP5): High Global Warming and High U.S. 

Socioeconomic Growth

Scenario HH (RCP 8.5–SSP5) has the highest level of global warming 

and the highest economic growth globally and in the United States. World 

population increases slowly through 2070 and then declines, while in the 

United States population grows at the fastest rate of all the scenarios. As 

with other scenarios where the global and U.S. trends diverge, the resource 

consequences may also diverge. In the United States there will be a larger 

population with increased income to fuel increased demand for natural 

resource commodities and amenities. Worldwide there will be income-fueled 

increased demand, but the effects will be offset to some extent by the slowing 

population trend. 

This scenario includes a strong reliance on markets, with open markets 

that encourage trade. Economic globalization implies, in this case, that 

economic growth is prioritized over environmental objectives that are 

perceived as negatively affecting economic growth; less emphasis is placed 

on global governance standards and global environmental cooperation. Rapid 

technological innovation is likely to enhance productivity in agricultural and 

forest product commodities. Growing income is generally consistent with 

high demand for resource amenities such as natural areas and biodiversity. 

While this scenario should reflect such demand, the substantial population 

and economic growth is highly likely to reduce natural land cover to 

meet development for housing and associated infrastructure. There is no 

assumption of constraints on development in this scenario, so the pattern of 

development could range from intensive to extensive, and is likely to vary 

according to variations in State and local land use ordinances. 

With high levels of global warming, impacts on natural resources are also 

expected to be substantial. The implicit assumption in this scenario is that 

economic growth and technological innovation will be able to overcome 

some of the biophysical constraints imposed by climate change. Continued 

reliance on fossil fuels indicates a lesser role for agricultural or forest 

biomass for energy than other scenarios, but some expansion is likely. Effects 
RMRS-GTR-412 29

of global warming on water will increase supply and demand imbalances 

across the United States; improved technology for water use will ease 

some of the demand increases, but there will still be pressure to reduce 

high consumptive uses such as irrigation so that supplies can be diverted to 

urban uses. The interaction of warming effects on agriculture and associated 

demand for irrigation will make these tradeoffs challenging.

High economic growth indicates increasing demand for outdoor recreation, 

but opportunities are likely to be shifted or constrained (or a combination 

thereof) from both global warming and recreational use levels that exceed 

environmental capacities. Technology has often improved access for some 

recreation; technology could also create substitutes for outdoor recreation 

that ameliorate some demand pressures. Demand for biodiversity may 

be high in this scenario, reflecting increases in income, but the marketdriven growth in commodity use is likely to reduce the extent of natural 

environments critical to maintaining aquatic and terrestrial species. Coupled 

with warming effects, this contraction of the natural landscape is likely to put 

species at greater risk of becoming endangered. 
30

CONCLUSIONS 

The Resources Planning Act (RPA) Assessment scenarios provide the 

context for the resource analyses that meet the legislative mandate of the 

RPA. The global Representative Concentration Pathways (RCPs) and 

Shared Socioeconomic Pathways (SSPs) are critical to providing global 

context and coherence to the national-scale scenarios that are described in 

this document. The future of global and domestic natural resources could 

differ substantially across the four scenarios. These scenarios will be used 

in the RPA Assessment modeling framework, but they could also be used 

by other modeling systems to allow comparisons of results. As the 2020 

RPA Assessment analyses are completed, the projection of resource effects 

associated with the underlying drivers of change will allow us to provide 

more resource-specific narratives of how the different scenario drivers and 

assumptions interact.
31

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