An Assessment of Natural Assets in the 

Appalachian Region: Forest Resources

Prepared for:

Appalachian Regional Commission

1666 Connecticut Avenue, NW

Suite 700

Washington, DC 20009-1068

September, 1 2014

Contract #CO-16812-2010

Prepared by:

Downstream Strategies

Fritz Boettner (Project Manager), Jason Clingerman, Rory McIlmoil, 

Evan Hansen, Laura Hartz, and Anne Hereford

West Virginia University 

Michael Vanderberg, Kathryn Arano, Jinyang Deng, Jacquelyn Strager, 

and Michael Strager

The National Network of Forest Practitioners

Colin Donohue
Assessing Appalachian Natural Assets: Forests

Table of Contents

EXECUTIVE SUMMARY AND KEY FINDINGS............................................................................................................ V

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

1.1 ABOUT THIS STUDY AND REPORT ........................................................................................................................... 1

1.2 IMPLICATIONS FOR POLICY AND RESEARCH............................................................................................................... 1

1.3 HOW TO USE THE REPORT AND DATA ..................................................................................................................... 2

1.4 DATA CAVEATS .................................................................................................................................................. 3

1.5 BACKGROUND ................................................................................................................................................... 4

1.6 LITERATURE REVIEW ........................................................................................................................................... 5

1.7 STAKEHOLDER INVOLVEMENT ............................................................................................................................... 7

2. FOREST QUANTITY......................................................................................................................................... 9

2.1 COMPONENTS AND FRAMEWORK ........................................................................................................................10

2.1.1 Standing timber volume..........................................................................................................................12

2.1.2 Woody biomass volume..........................................................................................................................12

2.1.3 Wood product volume ............................................................................................................................14

2.1.4 Forest carbon mass................................................................................................................................. 15

2.1.5 Percent of county forested......................................................................................................................16

2.2 DISCUSSION....................................................................................................................................................16

2.2.1 Supply for a biomass energy facility .......................................................................................................18

3. FOREST QUALITY.......................................................................................................................................... 23

3.1 INTRODUCTION................................................................................................................................................23

3.2 COMPONENTS AND FRAMEWORK ........................................................................................................................24

3.2.1 Stand quality...........................................................................................................................................26

3.2.2 Growth ratio ...........................................................................................................................................26

3.2.3 Stand origin.............................................................................................................................................28

3.2.4 Projected forest loss................................................................................................................................28

3.2.5 Forest disturbance ..................................................................................................................................31

3.3 DISCUSSION....................................................................................................................................................33

3.3.1 Forest disturbance, population, and road density ..................................................................................33

4. FOREST VALUE ............................................................................................................................................. 35

4.1 INTRODUCTION................................................................................................................................................35

4.2 COMPONENTS AND FRAMEWORK ........................................................................................................................36

4.3 MARKET VALUE ...............................................................................................................................................36

4.3.1 Wood value.............................................................................................................................................38

4.3.2 Wood product value ...............................................................................................................................38

4.4 NON-MARKET VALUE ........................................................................................................................................40

4.4.1 Wildlife habitat value..............................................................................................................................40

4.4.2 Watershed services value .......................................................................................................................42

4.4.3 Recreation value .....................................................................................................................................42

4.4.4 Cultural value..........................................................................................................................................42

4.5 DISCUSSION....................................................................................................................................................43

4.5.1 Poverty and the value of forests.............................................................................................................43

5. CONCLUSION ............................................................................................................................................... 45

5.1 RECOMMENDATIONS ........................................................................................................................................45

REFERENCES......................................................................................................................................................... 48

APPENDIX A: FOREST CARBON PAYMENTS .......................................................................................................... 52

APPENDIX B: CO-FIRING FOREST HARVESTING RESIDUES .................................................................................... 56

APPENDIX C: FOREST HEALTH AND BIOMASS ...................................................................................................... 66

APPENDIX D: DECISION SUPPORT SYSTEM........................................................................................................... 70

APPENDIX E: STAKEHOLDER RESPONSES.............................................................................................................. 75

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Assessing Appalachian Natural Assets: Forests

Table of Figures 

Figure 1: Forest types in the Appalachian Regional Commission region.............................................................. v

Figure 2: Forest area by county (acres) ................................................................................................................ 9

Figure 3: (A) Standing timber volume and (B) woody biomass tonnage ............................................................11

Figure 4: (A) Wood product volume and (B) forest carbon mass.......................................................................13

Figure 5: (A) Forest area by county area and (B) woody biomass supply over a 10-year timeline ....................17

Figure 6: Degraded stand biomass availability ...................................................................................................19

Figure 7: Forest harvesting residue and power plants .......................................................................................20

Figure 8: Combined heat and power ..................................................................................................................21

Figure 9: Potential site productivity (cubic feet per year, source: FIA 2009/2010)............................................23

Figure 10: (A) Stand quality and (B) growth ratio...............................................................................................25

Figure 11: (A) Forest stand origin and (B) projected forest loss.........................................................................27

Figure 12: (A) Percent risk of insect infestation and (B) housing density change ..............................................30

Figure 13: (A) Forest disturbance and (B) forest quantity case study ................................................................32

Figure 14: Habitat loss: risk and prevention.......................................................................................................34

Figure 15: Total market value per acre...............................................................................................................35

Figure 16: (A) Forest market wood value and (B) forest market wood product value.......................................37

Figure 17: (A) Non-market wildlife habitat value and (B) non-market watershed services value......................39

Figure 18: (A) Non-market recreation value and (B) non-market cultural value................................................41

Figure 19: Decision support system output - value and poverty........................................................................43

Figure 20: Forest operations in the New York City watershed...........................................................................44

Figure 21: Forest carbon in the Appalachian region...........................................................................................54

Figure 22: Available woody biomass in degraded stands...................................................................................69

Figure 23: Decision support system....................................................................................................................70

Figure 24: Screenshot of tool interface ..............................................................................................................70

Figure 25: Selecting a shapefile to be used in the ranking .................................................................................71

Figure 26: Percentage queries for the highest and lowest percentages............................................................72

Figure 27: Hot and cold spots.............................................................................................................................73

Figure 28: Stakeholder roles...............................................................................................................................75

Figure 29: Scale of work......................................................................................................................................76

Figure 30: Economic importance ........................................................................................................................76

Figure 31: Forest products utilization.................................................................................................................77

Figure 32: Management......................................................................................................................................77

Figure 33: Forest health......................................................................................................................................78

Figure 34: Biomass..............................................................................................................................................78

Figure 35: Forest threats.....................................................................................................................................80

Figure 36: Forest threats.....................................................................................................................................82

Figure 37: Forest opportunities..........................................................................................................................82

Figure 38: Forest health......................................................................................................................................83

Figure 39: Forest insects and diseases................................................................................................................84

Figure 40: Information or data needed ..............................................................................................................85

Figure 41: The most useful thing the Appalachian Regional Commission could do...........................................86

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Assessing Appalachian Natural Assets: Forests

Table of Tables

Table 1: Forest asset categories........................................................................................................................... vi

Table 2: Appalachian state forest resource assessments..................................................................................... 4

Table 3: Forest quantity assessment components.............................................................................................10

Table 4: Forest quality assessment components................................................................................................24

Table 5: Forest quality assessment components................................................................................................36

Table 6: Non-market forest values (dollars per acre).........................................................................................40

Table 7: Estimated annual forest biomass requirements for three co-firing rates............................................58

Table 8: Projected change in coal-fired generation for Appalachian EMM regions, 2020 and 2035.................59

Table 9: Harvest residues and co-firing demands under three co-firing scenarios, 2008 (dry tons)..................60

Table 10: Available volume of woody biomass via regeneration of degraded stands.......................................68

Table of Equations

Equation 1: Standing timber volume ..................................................................................................................12

Equation 2: Woody biomass volume ..................................................................................................................12

Equation 3: Wood product volume ....................................................................................................................15

Equation 4: Forest carbon mass..........................................................................................................................16

Equation 5: Stand quality....................................................................................................................................26

Equation 6: Growth ratio ....................................................................................................................................26

Equation 8: Projected forest loss........................................................................................................................28

Equation 7: Stand origin .....................................................................................................................................28

Equation 9: Forest disturbance...........................................................................................................................31

Equation 10: Wood value ...................................................................................................................................38

Equation 11: Wood product value......................................................................................................................38

Equation 12: Wildlife habitat value ....................................................................................................................40

Equation 13: Watershed services value..............................................................................................................42

Equation 14: Recreation value............................................................................................................................42

Equation 15: Cultural value................................................................................................................................. 43

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Assessing Appalachian Natural Assets: Forests

Abbreviations

ARC Appalachian Regional Commission

BMP best management practice

CP compromise programming

DSS decision support system

EIA Energy Information Administration

EMM Electricity Market Module

FIA Forest Inventory Analysis

GIS geographic information system

MACED Mountain Association for Community Economic Development

MCA multiple criteria analysis

MW megawatt

MWh megawatt-hour

NERC North American Electric Reliability Corporation

NIDRM National Insect and Disease Risk Map 

NPCC Northeast Power Coordinating Council

NPS National Park Service

NRCS Natural Resources Conservation Service

ORNL Oak Ridge National Laboratory

RFC Reliability First Corporation

RPA Resources Planning Act

SERC Southeast Reliability Corporation

SOLE Southern On Line Estimator

TPO Timber Product Output

US United States

USDA United States Department of Agriculture

USEPA United States Environmental Protection Agency

USFS United States Forest Service

USFWS United States Fish and Wildlife Service

VACAR Virginia-Carolinas

WAC Watershed Agricultural Council

WVU West Virginia University

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Assessing Appalachian Natural Assets: Forests

EXECUTIVE SUMMARY AND KEY FINDINGS

The Appalachian region consists of approximately 205,000 square miles (131 million acres), covering 420 

counties in 13 states (Figure 1). It extends more than 1,000 miles from southwestern New York to 

northeastern Mississippi and is home to 24.8 million people (ARC, 2009a). Appalachia is the largest forested 

area east of the Mississippi River; many cities and rural communities within and around the region are 

dependent upon the wise use, management, and development of Appalachian forests. 

Figure 1: Forest types in the Appalachian Regional Commission region

This project was initiated as part of the long-term research objective developed by the Appalachian Regional 

Commission (ARC) to understand Appalachia’s natural assets. The primary goal of this study is to provide 

information that will encourage the sustainable management of forest assets across the region, which 

requires developing an inventory of the forest assets, analyzing their value and usage, assessing their 

potential contribution to economic development of the region, and creating a framework to help plan their 

best use. 

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Assessing Appalachian Natural Assets: Forests

This study describes forest assets in the Appalachian region to facilitate forest management and planning 

strategies and to provide useful information and tools for decision-makers. It evaluates three aspects of 

forest resources—quantity, quality, and value—using data and information collected for a variety of metrics 

and indicators (Table 1). 

Data collected from various sources were analyzed and mapped at the county level. Many types of forest 

datasets exist across the United States (US); however, very few are consistent from region-to-region or stateto-state. The exception to this rule is the US Forest Service’s (USFS’s) Forest Inventory and Analysis (FIA) 

program, a comprehensive census of America’s forests. Forest resources do not always follow political 

boundaries or adhere to easily manageable units, but based on the FIA dataset, we examined regionally 

consistent and representative characteristics in order to understand Appalachia’s forest resources. For each 

assessment category, this study lists the specific datasets used, rationale for the methods, and overall results. 

The appendices include a discussion of forest carbon payments, an analysis of the feasibility of co-firing forest 

biomass in coal-fired power plants, an assessment of the climate and biomass resource benefits of improved 

forest stand health, and the results of a stakeholder survey.

Table 1: Forest asset categories

Quantity Quality Market value Non-market value

Standing timber volume Stand quality Wood Wildlife habitat

Woody biomass volume Growth ratio Wood product Watershed services

Wood product volume Stand origin Recreation

Forest carbon mass Insect and disease risk Cultural

Forest area Housing density change

Predicted forest loss

Forest disturbance

The Forest Quantity category examines the volume (available standing timber and annual forest products 

output), area, and carbon storage of Appalachia’s forests. The Forest Quality category is based on impacts to 

forest resources from a variety of stressors, including recent disturbances, plantation forestry, the risk of 

insects and disease, increased housing density (the number of homes per unit area) and expansion, as well as 

tree growth, mortality, and non-merchantable volume. Healthy forests can be an asset for recreational use 

and industrial growth, both of which can be significant economic drivers. The Forest Value category is 

composed of two categories: market and non-market values. Market values are those that are derived from 

market exchanges of forest resources—like selling timber. Non-market values capture other uses of forest 

resources—like providing habitat for wildlife. 

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Assessing Appalachian Natural Assets: Forests

Key findings

The following key findings are organized by asset category. 

Forest quantity

Forest resources have influenced the Appalachian region throughout its recent history. While much of the 

forestland was harvested for timber during the early twentieth century, forests cover 65% of the land base

today. 

• Forest quantity was evaluated using five indicators, which represented the relative amounts of standing 

timber, woody biomass, wood products, forest carbon, and forest area. 

• The Appalachian counties with the highest overall quantity are in the most mountainous areas of the 

region, while counties with the lowest quantity are concentrated in the southwestern region in Alabama 

and Mississippi. Many forests of the southwestern region are composed of smaller trees and other 

species when compared to the forests of the mountainous areas of Appalachia, and this can have a large 

effect on the overall volume of standing timber, woody biomass, and forest carbon stocks. 

• The counties with the highest quantity of forest resources usually contained significant areas of 

protected land, such as national forests, national parks, national rivers, state forests, or state parks.

• The counties clustered in southwestern Appalachia, despite relatively lower forest quantity scores, 

generally had the highest wood product output. This pattern suggests that the forest industry in the area 

may be impacting forest quantity.

Forest quality

Within a forested area, the quality of the resource has an impact on its potential value. Forest quality can 

impact the market value—based on selling wood fiber and solid wood products—or the non-market value, 

based on recreation or other ecosystem services.

• For this study, forest quality was evaluated by assessing five indicators: stand quality in terms of health 

and merchantability, growth ratio as a simple measure of tree growth and mortality, stand origin, 

projected forest loss via pressures from housing and insects and disease, and forest disturbance due to a 

variety of natural and artificial factors. In addition, the projected forest loss indicator is broken down into 

two components, housing density change and insect and disease risk, for a more detailed analysis.

• The counties with the highest overall forest quality were generally located in Mississippi, Alabama, New 

York, northern Pennsylvania, Ohio, and West Virginia.

• The counties with the lowest overall forest quality were generally located in Georgia, South Carolina, 

North Carolina, and Tennessee. These counties are generally near larger cities, which could be 

experiencing urban growth.

• As reflected in the projected forest loss indicator, the majority of counties are not likely to experience 

large amounts of deforestation due to housing pressure or tree mortality from insect infestation and 

disease. 

• Currently, forest disturbance does not greatly detract from forest quality, but this trend may change as 

climate change impacts on temperature and precipitation regimes shift the patterns of forest 

disturbance over time. 

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Assessing Appalachian Natural Assets: Forests

Forest value

Forests provide significant market value in the form of timber and other wood products and considerable 

non-market value for their ecosystem services and other benefits. 

Market value

More than 98% of the region’s forestland is productive timberland, and Appalachian forests greatly 

contribute to the core of the nation’s high-quality hardwood resource. These values were examined in the 

market value asset section of this report.

• Market value is based on two indicators—wood value and wood product value—which reflect observed 

market prices.

• Counties in North Carolina, Pennsylvania, Georgia, and northern West Virginia had some of the highest 

forest market values. These counties also had high values for both standing timber volume and wood 

product value in the forest quality category. This link means that market value follows some components 

of forest quantity, although this pattern usually depends on other factors as well.

• Counties in Ohio, western Alabama, and southern New York had some of the lowest market values, 

which reflects lower timber products output in Ohio and lower-density forests in western Alabama.

• High-valued counties also typically produce a greater volume of the high value wood products like 

veneer, while low-scoring counties typically produce low value products such as pulpwood.

• Notably, the distinction between the Ohio and West Virginia border is very sharp; Ohio’s low-value

counties are in stark contrast to West Virginia’s high value counties, at least on the perimeter. This 

pattern could be attributed to the significantly higher volume of high-value products coming from fine 

hardwood species in West Virginia as compared to Ohio.

Non-market value

Forests have tremendous value that is not reflected in market transactions for forest products like wood 

composites and veneer. These non-market values, such as cultural values and ecosystem services, are 

evaluated in the non-market value category. 

• Non-market value was evaluated using four categories: wildlife habitat, watershed services, recreation, 

and cultural. 

• West Virginia counties scored relatively high, compared with counties in other states. West Virginia is the 

third-most forested state in the country and the most forested in the Appalachian region. Thus, it is not 

surprising that West Virginia has a high non-market value, since two of the four components of the nonmarket value assessment category are directly driven by forest acreage

• Like the highest market values across the region, the highest non-market values also follow the forest 

quantity measurements, with a few exceptions. These results suggest that value can generally be derived 

directly from forest quantity, but other factors must also be considered in creating value from forests. 

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Assessing Appalachian Natural Assets: Forests

1. INTRODUCTION

1.1 About this study and report

This project was initiated as part of the long-term research 

objective developed by ARC to understand Appalachia’s natural 

assets. The primary goal of this study is to provide information 

that will encourage the development and sustainable 

management of forest resources across the region, which 

requires developing an inventory of forest assets, analyzing their 

value and usage, assessing their potential contribution to 

economic development of the region, and creating a discussion

to assist with planning their best use. 

The project team was comprised of several organizations, 

including Downstream Strategies—an environmental consulting 

company from Morgantown, West Virginia—and West Virginia 

University (WVU). In addition, the National Network of Forest 

Practitioners’ Appalachian Forest Resource Center was a major 

contributor to the research. Many experts in forest resources 

and economics were involved throughout this project, providing 

a well-rounded and representative team. 

Often, merging science and policy can be a tremendous 

challenge. This study attempts to summarize forest resource 

data in a way that is understandable and relevant to policy 

makers. To enhance the study and its utility, a geographic information system (GIS) geodatabase was created 

that contains all of the underlying layers and analysis results. These data can be used in other research or in 

customized analyses or mapping projects. In addition to the geodatabase, a decision support system (DSS) 

was created to assist in understanding the results, both spatially and statistically. The DSS is a customized 

ArcMap GIS software tool that analyzes spatial patterns and creates an environment where the user can 

weigh various decisions that could support or inhibit economic development. The functionality of this tool 

and technical details are presented in Appendix D. 

Other technical appendices include a discussion of carbon pricing and markets (Appendix A), an analysis of 

the feasibility of co-firing forest biomass in coal-fired power plants in the region (Appendix B), an assessment 

of the climate and biomass resource benefits of improved forest stand health (Appendix C), and the results of 

an Appalachian forest stakeholder survey completed by the Appalachian Forest Resource Center at the 

National Network of Forest Practitioners (Appendix E).

1.2 Implications for policy and research

This study assesses forest assets in the Appalachian region in order to facilitate forest management and 

planning strategies. It is not an all-encompassing analysis, but it does begin a conversation about forest 

resource management. There are many positive relationships between forest resources and economic 

development, including non-transactional or quality-of-life benefits. 

Appalachia’s economy is dependent on its natural resources, like timber, which are sold in domestic and

international markets. In addition to providing for these transactional benefits—also known as the market 

value of the forests—Appalachia’s forests provide a wealth of recreational opportunities and cultural 

importance to the region’s residents and its visitors. 

In a survey with over 130 

forestry professional 

respondents, 84% believed that 

forests are important or very 

important to the surrounding 

area’s economy.

ARC Forest Asset stakeholders

“In today's world of ‘green’ 

alternatives to all of the world's 

woes, trees are a renewable, 

biodegradable, all natural 

resource. We need to utilize 

them to our benefit.”

ARC Forest Asset stakeholder

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Assessing Appalachian Natural Assets: Forests

Design your own maps using 

forest and economic data

Create your own scenarios using 

the GIS Decision Support System

This project provides an interactive, GIS-based decision 

support tool (DSS) that allows users to prioritize areas 

for economic development opportunities. The DSS

integrates spatial data, user input, and a ranking 

algorithm.

Project GIS datasets can be downloaded from the 

Downstream Strategies Web site and used to make 

maps; examine results; navigate metrics, indicators, and 

indices; or create additional analyses.

To download data or the decision support tool, 

documentation, or the GIS-based DSS, contact the 

Appalachian Regional Commission. 

Putting the tool to work

report examples:

Throughout the document, boxes called “Putting 

the tool to work” are designed to help the reader 

identify relevant opportunities, challenges, and 

actions illuminated by the indices.

Understanding the relationship between the region’s forest resources and its economic development can 

play many important roles. By clarifying the services that abundant, healthy forests provide, local and state 

policymakers can make informed decisions on resource use, development strategies, climate policy, and 

other important issues.

It is also important for private sector leaders to fully appreciate Appalachia’s forest resources. When making 

decisions about locating new businesses, for example, leaders often consider several forest-related factors, 

including the business’s proximity to recreational opportunities and raw materials like fuelwood, timber, or 

biomass. 

This report, and the data that accompany it, can therefore be used by local and state leaders to inform their 

decisions on new policies related to forest quantity, quality, and access. It can be used by economic 

development officials to attract new businesses. And, it can be used by the private sector to inform businesslocation decisions.

1.3 How to use the report and data

This report describes the methods and data used to assess the ARC region forest resources. Throughout the 

document, boxes called “Putting the tool to work” are designed to help the reader identify relevant 

opportunities, challenges, and actions illuminated by the assessments. 

Some of the data are combined or calculated in ways chosen by the research team. However, individuals, 

agencies, and organizations may want to examine the data without the prescribed calculations. The DSS 

allows users with access to ArcMap GIS software to apply their own priorities or criteria to the forest asset 

data, thus reexamining the counties according to locally important factors. 

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Assessing Appalachian Natural Assets: Forests

1.4 Data caveats

This study and report were developed to enable practitioners across the region to understand their forest 

resources and to better plan for the future. It examines many different datasets that vary spatially, 

temporally, and in their intended application. Despite the inherent difficulties in bringing together such a 

diverse set of data, this study is the most comprehensive attempt to-date that catalogs and summarizes 

forest resource data points across Appalachia. 

Many types of forest datasets exist across the US; however, very few are consistent from region-to-region or 

state-to-state. The exception to this rule is the US Forest Service’s (USFS’s) FIA program. This program 

contains four key elements: 1) forest monitoring by remote sensing for stratification and field-based sampling 

of forest extent, cover, growth, mortality, removals, and overall health; 2) ownership questionnaire-based 

surveys; 3) timber product output questionnaire-based surveys; and 4) utilization studies for forest 

harvesting operations. The FIA program employs the following operational techniques to assure and improve 

the quality of data: planning, method documentation, training for data collectors, several checks for data 

quality, uncertainty analysis for survey data, peer-review of analysis products, and continuous feedback on 

data collection methods and processing. For more information on this data source, visit the national FIA Web 

site: http://www.fia.fs.fed.us.

Forest resources do not always follow political boundaries or adhere to easily manageable units, but based 

on the FIA dataset, we devised a systematic assessment method to examine regionally consistent and 

representative characteristics in order to understand Appalachia’s forest resources. The FIA program is a 

national census with a standardized regional protocol. Data are updated annually. The primary advantage of 

the FIA program is the consistent data coverage it provides across states, but its shortcoming is the density of 

field sampling. Only one sample site for every 6,000 acres of forestland is monitored, and this can lead to 

high sampling errors in areas where forests are non-contiguous or comprised of forest stands with many 

different characteristics. This should be kept in mind when viewing the maps in this report. However, the 

resource specialists on this project maintain that the FIA program is the viable dataset—at the present time—

for assessing forest resources across Appalachia. 

Most data and results were derived from two sources: (1) the FIA database (Oswalt and Turner, 2009; Straka, 

2007; USFS, 2010a); and (2) analysis of FIA Timber Product Output (TPO) data for state-level forest 

assessments compiled for the 2010 Resources Planning Act (RPA) assessment, which is part of the USFS 

Forest Inventory and Analysis program (Smith et al., 2009). States that have completed forest assessments 

and made them available online are listed in Table 2. Case study data for county-level forest residue biomass 

resources were derived from the National Renewable Energy Laboratory (Milbrandt, 2005), Oak Ridge 

National Laboratory (ORNL, 1999), and FIA databases. These data sources provide an estimate of necessary 

forest metrics for index development at the county scale, based on plot-level samples of forest resources. 

Therefore, the data carry a level of uncertainty or error associated with the estimation procedure. We 

recognize that no data source is perfect, but consider the sources used in the assessments to be the best 

available at present due to the FIA’s consistency across ARC counties. 

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Assessing Appalachian Natural Assets: Forests

Table 2: Appalachian state forest resource assessments

State Agency Web address

Alabama Alabama Forestry Commission http://216.226.177.78/PDFs/Forests_at_the_Crossroads-ALState_Assessement.pdf

Georgia Georgia Forestry Commission http://www.gfc.state.ga.us/ForestManagement/documents/

GAStateAssessment-6-17-10.pdf

Kentucky Commonwealth of Kentucky http://forestry.ky.gov/LandownerServices/Pages/

ForestlandAssessment.aspx

Maryland Maryland Department of Natural 

Resources Forest Service http://www.dnr.state.md.us/forests/sas.asp

Mississippi Mississippi Forestry Commission http://www.mfc.ms.gov/assessment-strategy.php

New York New York State Department of 

Environmental Conservation http://www.dec.ny.gov/lands/60829.html 

North Carolina North Carolina Division of Forest 

Resources http://www.ncforestassessment.com

Ohio Ohio Department of Natural 

Resources, Division of Forestry http://ohiodnr.com/tabid/22319/Default.aspx 

Pennsylvania Pennsylvania Department of 

Conservation and Natural Resources http://www.dcnr.state.pa.us/forestry/farmbill/index.html

South Carolina South Carolina Forestry Commission http://www.state.sc.us/forest/scfra.htm

Tennessee Tennessee Department of 

Agriculture, Division of Forestry http://www.state.tn.us/agriculture/forestry/sustainability.html

Virginia Virginia Department of Forestry http://www.dof.virginia.gov/info/resources/2010-State-Assessment_0

15_reduced.pdf

West Virginia West Virginia Division of Forestry http://www.wvforestry.com/events_12022K1.cfm

1.5 Background

The Appalachian region consists of approximately 205,000 square miles (131 million acres), covering 420 

counties in 13 states (Figure 1). It extends more than 1,000 miles from southwestern New York to 

northeastern Mississippi and is home to 24.8 million people (ARC, 2009a). 

Many cities and rural communities within and around the region are dependent upon the wise use, 

management, and development of Appalachia’s forests. A downsizing of critical Appalachian industries has 

led to a decline in traditional agricultural and mining jobs in many rural areas, leading to a large population 

migration to urban areas over the past 50 years. According to Freudenberg (1992), employment in traditional 

farming has dropped about 70 percent from the early 1900s and employment in other natural resource–

dependent industries, such as mining and forestry, has been cut in half.

However, these macro-level economic and social trends are not uniform across all rural areas; the major 

factors affecting migration patterns across the rural landscape have changed substantially over the last few 

decades (Nord and Cromartie, 1997). Those places rich in natural assets are more likely to experience 

substantial population growth than are areas with fewer natural assets. For instance, Johnson and Beale 

(2002), in a national study of rural counties, report a significant population rebound during the 1990s, with 

“recreation counties”—those with high tourism receipts and business activity—leading the way with a 20.2

percent population increase compared to a 10.4 percent increase for all rural counties. The economic and 

population growth patterns in Appalachia also reflect this reality (ARC, 2009a). 

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Assessing Appalachian Natural Assets: Forests

Natural assets are not only linked to population growth, but also to economic restructuring and economic 

well-being (Johnson and Beale, 2002; Shumway and

Otterstrom, 2001). For example, Shumway and Otterstrom 

(2001) report that counties rich in natural amenities 

experienced dramatic increases in employment in service 

sectors such as health care, personal services, recreation and 

entertainment, and professional services. 

Local or regional economic growth is dependent upon natural, 

social, economic, and political factors. Each factor’s 

contribution to economic growth may vary by county or region. 

This poses a challenge to researchers: to determine the relative 

importance of each factor at the county or regional level. 

1.6 Literature review 

The chosen methods, framework, data, and empirical approach 

used in this report are based on an extensive literature review.

The following section highlights regional studies on forest 

resources as well as methods used in other types of natural 

asset assessment. 

The economic value of forests has been recognized in many 

market-based forms. For example, trees and woody biomass 

are extracted for wood products like sawlogs, veneer logs, and 

pulpwood. Bioenergy production via combustion, cogeneration, and combined heat and power also has value in 

markets. In addition, terrestrial carbon storage, to a limited 

extent, has a market value. 

Forests also have non-market values by providing recreation 

opportunities, wildlife habitat, and other ecosystems services.

Forest-related economic activity plays an essential role in 

economic development and growth, both locally and regionally. 

Forests are the dominant resource on the Appalachian 

landscape, covering 86 million acres or 65percent of the region 

and have been increasing, as agricultural lands revert back to 

forestland (USFS, 2010a). More than 98percent of the region’s 

forestland is considered to be a productive timberland resource (USFS, 2010a).

Because of their importance, forests have been extensively studied in the literature. An exhaustive review of 

literature on forest resources is beyond the scope of this study; however, selected studies on natural assets 

including forests are reviewed. When necessary, additional studies on forest quantity, quality, and value are 

reviewed within their respective sections. 

“Forests are a critical natural 

resource that shaped the Region’s 

history and influenced its culture. 

Nearly all of the Region’s 

forestland experienced intensive 

timber harvesting and 

deforestation during the late 

1800s and early 1900s. These 

resilient forests today cover 86 

million acres, providing wildlife 

habitat and recreation, protecting 

the Region’s waters, and 

supporting an extensive wood 

products industry.” 

Appalachian Regional Development 

Initiative Report: Economic Assessment of 

Appalachia (2010)

“Trees play an important role in 

the world’s carbon cycle. They act 

as a sink for carbon, removing it 

from the atmosphere in terms of 

CO2 and storing it as cellulose. In 

this role, forests help mitigate the 

effect of burning fossil fuels and 

the resulting global climate change 

associated with increased levels of 

CO2 in the atmosphere.”

Forests of the Mountain State (Widmann et 

al., 2007)

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Assessing Appalachian Natural Assets: Forests

An assessment of Appalachian forest resources was recently completed by Widmann et al. (unpublished) for 

ARC. Highlights of this report include:

• Forests are the dominant resource on the landscape of the Appalachian region.

• Appalachian forests contribute to the core of the nation’s quality hardwood resource.

• Private landowners own 82percent of the forestland in the region.

• Management of private forests is the key to a sustainable resource in the Appalachian region.

• Forest productivity can be increased by better management of private land.

• The output of timber products is strong and dominated by hardwood products, but is declining in the 

face of international competition and economic uncertainty.

• Opportunities exist to increase product output with woody biomass utilization while maintaining the 

health and productivity of the region’s forests.

• Non-timber products provide emerging opportunities for sustainable harvest of traditional products.

• Forests provide benefits and services to everyone, even when economic returns are indirect or 

difficult to quantify.

An economic assessment of Appalachia was recently completed by the Appalachian Regional Development 

Initiative (2010), which provides an excellent review of economic factors in the region, including forest assets. 

The main findings from this assessment, directly applicable to forest assets, are:

• Forests in the region provide many benefits, including wildlife habitat, recreation opportunities, and 

water protection; forests also support an extensive wood products industry.

• The physical characteristics of the region result in diverse and productive forest ecosystems, 

containing approximately 100 species of hardwood trees and 25 species of softwood trees.

• Many species of trees are essential to the hardwood lumber industry, with the Allegheny Plateau of 

Pennsylvania and New York containing two-thirds of the nation’s black cherry timber volume.

• The region’s forests are maturing, with 62percent of stands dominated by large diameter trees.

• Oak, a favorable species, is lagging behind maple in terms of regeneration.

• Woody biomass is poised to be utilized for co-firing with traditional coal-fired energy generation.

• The region produces 13percent of the nation’s total TPO volume.

• Recreational, forest-based tourism is important to the region, with many rural areas attracting 

visitors from larger metropolitan areas who enjoy the outdoors. 

Several other studies focus on land-use and economic growth in Appalachia; these studies also present key 

information on the region’s forest assets. One comprehensive study—Southern Appalachian Man and the 

Biosphere Cooperative (1996)—examines the ecological conditions (i.e., atmospheric, aquatic, and terrestrial) 

and social, economic, and cultural status in several regions that include: northern Virginia, eastern West 

Virginia, northwestern South Carolina, northern Georgia, and northern Alabama. The assessment was 

accomplished through the cooperation of federal and state natural resource agencies within the region. 

In terms of the social, economic, and cultural value in the region, this study examined four aspects: (1) 

communities and human influences, (2) the timber economy, (3) outdoor recreation supply and demand, and 

(4) roadless and designated wilderness areas. To address changes in population and housing in the region, 

census data from 1970, 1980, and 1990-91 were analyzed. Other data sources included the Census of 

Agriculture for the last three decades and US Department of Agriculture (USDA) Economic Research Service 

data. Maps displayed averages for the counties in the study area as compared to averages for the seven 

states in which the southern Appalachian counties reside. In addition, surveys were conducted among 

organizations and residents to understand their attitudes toward natural resources and the environment.

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Assessing Appalachian Natural Assets: Forests

Another study examines land ownership patterns and its 

impact on the Appalachian community based on a survey of 80 

counties (Appalachian Land Ownership Task Force, 1981). The 

study found that only a few owners control most of the land 

and minerals in Appalachia: more than half (53 percent) of the 

total surface area was owned by corporations and government, 

absentee landowners, and very few of the local population. Of 

this group, absentee landowners owned the most (75 percent), 

followed by corporations and government (27percent) and 

members of the local population (1percent). Almost 40 percent

of the land in the sample, and 70percent of the mineral rights, 

were held by corporations. Additionally, the federal 

government was the single largest landowner, controlling more 

than 2 million acres at the time.. Indices were developed to 

illustrate the concentration of ownership of mineral and 

surface acres. These patterns of concentration have ripple 

effects on economic development and the wellbeing of the 

region.

1.7 Stakeholder involvement 

We solicited information and feedback from a variety of 

stakeholders in an effort to coordinate with outside 

institutions, as well as to ensure our project focus and efforts 

are aligned with regional objectives and goals. Project staff 

from the National Network of Forest Practitioners coordinated 

the stakeholder involvement through an online survey with 

closed- and open-ended questions, refer to Appendix E for 

detailed responses. 

The survey was sent to 697 recipients across the Appalachian 

region, including local development districts, conservation 

districts, resource conservation and development districts, 

private foresters, federal and state land managers, and the 

Natural Resources Conservation Service (NRCS). Of those 

surveyed, 141 participated, yielding a 20 percent response 

rate. The largest numbers of respondents were from local 

development districts, conservation districts, conservation 

organizations, NRCS, and resource conservation and 

development districts. 

In reality, respondents often self-identified with multiple roles 

such as NRCS staff and hunter/outdoorsman or soil and water 

conservation district supervisor and logger. Several selfidentified as forest landowners. Responses were shared with 

the project team during various phases of the project to direct 

and ground the project. 

Comments from 

survey respondents 

“We have more pressure from 

housing growth destroying 

forest areas than from other 

sources.”

“It seems difficult to get private 

forest landowners to 

understand the importance of 

developing a forest 

management plan and using 

sound practices in forest 

harvest operations to ensure 

sustainable forest 

management.”

“In today's world of ‘green’ 

alternatives to all of the world's 

woes, trees are a renewable, 

biodegradable, all natural 

resource. We need to utilize 

them to our benefit.”

“It [increased biomass use] 

could very well destroy them! 

Forests are more than wood 

products. Woodland plants, 

wildlife, water quality, 

recreation and aesthetics are 

but a few examples.”

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Assessing Appalachian Natural Assets: Forests

Highlights from the closed-ended questions include:

• Just over half (53 percent) of respondents felt that forests were poorly managed or very poorly managed.

• Just under half (49 percent) of respondents felt that woody biomass for energy would have a positive or 

very positive impact on their area’s forests.

• Respondents were divided on forest health: 43 percent felt forests were healthy, 36 percent felt they 

were neither healthy nor unhealthy, and 20 percent felt they were unhealthy.

• At the same time, respondents were significantly concerned about the impacts of insects and disease: 99

percent felt that these issues were somewhat or very threatening to forests. 

• The only other issue that received a similar response was invasive plants: 95 percent of respondents felt 

that invasive plants were somewhat or very threatening to forests in their area.

• 70 percent of respondents felt that over-harvest of timber was somewhat or very threatening. Yet,

according to the FIA data, there is substantially more timber growing than is harvested, so this 

perception may be at odds with the scientific data. With the current decline in harvest due to market 

depression, it is likely that the issue of over-harvest is more one of perception than fact in most places.

Highlights from the open-ended questions include:

• When asked about biomass, 27 percent of respondents specifically mentioned the importance of forest 

management plans and sustainable harvesting practices, and 31 percent of respondents highlighted the 

potential economic benefits.

• When asked about opportunities for forest in their area, 37 percent of respondents mentioned the 

importance of forest management plans.

• Respondents added numerous responses in the “Other” category, including loss of high value markets, 

impacts of strip mining, oil and gas leasing, lack of forest management plans, loss of species diversity, 

acid rain, and inadequate removal of logging roads. Problems with regeneration and deer browsing 

pressure were also mentioned by multiple respondents.

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Assessing Appalachian Natural Assets: Forests

2. FOREST QUANTITY

Forest quantity can mean many different things: trees available for wood fiber production or acres available 

for other ecosystem services and recreation, for example. Much of the region’s forestland was harvested for 

timber during the early twentieth century. Today, forests represent 65 percent of the land base and 

harvesting continues to support a forest products industry that provides raw materials and an assortment of 

wood products to both domestic and international markets. 

Figure 2: Forest area by county (acres) 

Based on the FIA data, Forest area, shown in Figure 2, is a simple measure or a “windshield” perspective of 

forest quantity in a county. Measurements of forest quantity can fluctuate annually, decreasing with the 

utilization of trees for wood fiber and growth-related mortality, and increasing with tree growth. 

Furthermore, large alterations in land-use patterns can result in substantial impacts on forest quantity, with 

afforestation (or reforestation) and deforestation positively and negatively affecting the forest asset, 

respectively. This chapter presents an assessment of forest quantity across the Appalachian region.

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Assessing Appalachian Natural Assets: Forests

2.1 Components and framework

Publicly available data were analyzed to produce five indicators of forest quantity across the Appalachian

region. For each indicator, Table 3 lists the metrics used, unit of measurement, and the data source. To 

understand these data at the county level, each forest quantity indicator—with the exception of percent of 

county forested—is divided by the forest area of the county. This step allows for better comparisons of forest 

resources from county to county, ensuring that the size of the county does not weight the results. 

1. Standing timber volume: Measures the live tree volume in each county suitable for use for 

traditional forest products, relative to the area of forestland within a county. 

2. Woody biomass volume: Measures the standing forest resource potentially available for use as 

forest biomass, relative to the total forest area within a county, whether it is for energy generation 

or other uses.

3. Wood product volume: Measures the production of the forest products industry within a county in 

terms of raw or primary material output. This indicator is not directly associated with the standing 

forest.

4. Forest carbon mass: Presents the amount of carbon stored in forests, both above- and belowground, 

relative to the total forest area within a county.

5. Percent of county forested: Presents the forested acres divided by total acres in a county, resulting 

in a ratio of forest area to county area and displayed as a percentage. 

Table 3: Forest quantity assessment components

Indicator Metric Denominator

Unit of 

measurement Data source and date

Standing timber 

volume • Volume of growing stock trees Forest acres Cubic feet of wood Forest Inventory Analysis, 2009, 2010

Woody biomass 

volume

• Aboveground live biomass in trees

• Aboveground dead biomass in trees Forest acres Volume in dry 

tons Forest Inventory Analysis, 2009, 2010

Wood product 

volume

• Sawlog volume

• Veneer volume

• Composite materials volume

• Post, poles, and pilings volume

• Fuelwood volume

• Pulpwood volume

Forest acres Cubic feet of 

wood

Forest Inventory Analysis, 2009, 2010

Timber Product Output, 2007

Forest carbon mass

• Aboveground live tree carbon

• Aboveground understory carbon

• Standing dead tree carbon

• Downed woody debris carbon

• Litter carbon

• Soil carbon

Forest acres Volume in dry 

tons Forest Inventory Analysis, 2009, 2010

Percent of county 

forested • Acres of forestland County acres

Acres of 

forest per 

county

Forest Inventory Analysis, 2009, 2010

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Assessing Appalachian Natural Assets: Forests

Figure 3: (A) Standing timber volume and (B) woody biomass tonnage

A

B

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Assessing Appalachian Natural Assets: Forests

2.1.1 Standing timber volume

Timber volume is a measure of the amount of growing stock trees1 in a county. This metric is reported in 

cubic feet per county and it is the only metric used in the standing timber volume indicator. Standing timber 

volume is a measure of growing stock trees relative to the forested area of the county.

Panel A in Figure 3 shows the standing timber volume in cubic feet per forested acre for Appalachian 

counties. Generally, there are higher standing timber volumes towards the more mountainous central 

Appalachians, while forested areas in southwestern counties (Mississippi and Alabama) show lower volumes 

per acre. Counties in western North Carolina, West Virginia, eastern Tennessee, and northwestern 

Pennsylvania generally show the highest standing timber volumes per forested acre. This suggests that the 

southwestern Appalachian forests have less timber volume than the rest of the region, per forested acre. 

Many forests in the southwestern counties are composed of smaller trees compared to counties in the more 

mountainous areas, and even though the number of trees may be similar, smaller trees result in lower overall 

volumes of sawtimber. While the trend of lower standing timber volume could be due to greater harvesting 

intensity in the southwestern and other low-scoring counties, it could also be due to differences in forest 

type and species composition (e.g., hardwood trees vs. softwood trees) where drier climates create slowergrowing and less dense structures due to limitations in growth resources. 

Equation 1: Standing timber volume

Overall, the Appalachian forests appear to be relatively well stocked, with only eight counties within the 

region showing standing timber volumes below 990 cubic feet of wood per forested acre. Tompkins County, 

New York was removed from the analysis due to irregular data, which showed a standing timber volume that 

well exceeded the range of the rest of the Appalachian region. 

2.1.2 Woody biomass volume

Woody biomass volume includes aboveground biomass in both dead and live trees and provides a measure 

of forest biomass that could be used as a resource for traditional forest products or for non-traditional uses 

such as energy production (Equation 2). Panel B in Figure 3 shows the woody biomass volume for 

Appalachian counties in tons per forested acre. Generally, there is a higher density of woody biomass volume 

towards the central Appalachians, with forested areas in southwestern counties (Mississippi and Alabama) 

showing the lowest relative density of woody biomass volume. 

Equation 2: Woody biomass volume

1 A growing stock tree is “a live tree of commercial species that meets specified standards of size, quality, and merchantability. [It] excludes rough, rotten and 

dead trees” (Wisconsin Department of Natural Resources, 2011).

Standing timber volumecounty”i” = volume of growing stock treesi/ forest areai

Woody biomass volume county”i” = (aboveground live biomassi + aboveground dead biomassi) / forest areai

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Assessing Appalachian Natural Assets: Forests

Figure 4: (A) Wood product volume and (B) forest carbon mass

A

B

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Assessing Appalachian Natural Assets: Forests

Trends in woody biomass volume are due to forest type, forest age, and forest structure. The woody biomass 

volume in Mississippi and Alabama forests may be low relative to the rest of the region because of the 

dominance of the timber industry and plantation forestry in this area. Naturally-occurring forest types of the 

central hardwood portion of the region contain species that are most valuable when they are large enough to 

meet sawlog or veneer log specifications, whereas many of the trees in the plantation forests of the 

southwestern counties are valuable enough when they meet specifications for pulpwood chips or dimension 

lumber (both are smaller than specifications for hardwood sawlog and veneer). 

Similarly, the forests in Mississippi and Alabama may be densely populated with individual trees that are 

relatively small compared to trees in the central hardwood area of the Appalachian region. Even though 

there may be a similar or even greater number of trees in Mississippi and Alabama as compared with the 

central hardwood area, smaller trees will result in a lower overall woody biomass volume. In other words, 

low-density forests with large trees can easily surpass high-density forests with small trees in terms of woody 

biomass volume.

Counties in western North Carolina, West Virginia, eastern Tennessee, and northwestern Pennsylvania 

generally show the highest values. The southwestern Appalachian forests have less woody biomass volume 

than the rest of the region, per forested acre, which is similar to the trend seen for standing timber. This 

suggests a greater harvesting intensity and differences in forest types within the southwestern and other 

low-scoring counties compared to those counties with the highest woody biomass volume per forested acre. 

Interestingly, the northeastern counties of New York, which show low standing timber volumes, exhibit 

higher scores in woody biomass volume. This may be due to a greater density of dead trees and downed 

woody debris from natural disturbance events.

The use of woody biomass as a form of renewable energy has become increasingly popular in the last decade, 

and the most important part of siting a biomass energy facility is whether a steady and reliable supply of 

woody biomass can be procured. Example 1 on page 19 (“Degraded forest stands and carbon”) and Example 

2 on page 20 (“Forest harvesting residues in co-firing”) showcase the capabilities of the DSS that is a 

companion to this report. While Example 2 does not specifically show potential biomass energy facility sites, 

it does provide insight into which counties have the most potential for both “extra” woody biomass supply 

and current harvesting residues (also a potential source of woody biomass supply). 

2.1.3 Wood product volume

Wood product volume is derived from TPO data, which are sourced from the FIA database and the RPA 

Assessment (Smith et al., 2009). This indicator is composed of the volume of roundwood products, including 

fuelwood. Volume harvested for roundwood products includes sawlogs, veneer logs, and pulpwood from 

both industrial and nonindustrial settings. Sawlogs are usually destined for sawn products such as lumber, 

whereas pulpwood is usually destined for paper production and other wood fiber uses. Veneer logs are high 

quality sawlogs destined for use as wood veneer. Fuelwood includes roundwood logs and chips used as fuel 

in industrial, residential, or institutional situations. Composite products, like oriented strand board and 

engineered lumber, are also included, as well as post, piling, and pole production. 

Panel A in Figure 4 shows the wood product volumes in cubic feet per forested acre for Appalachian counties. 

Generally, a few Appalachian counties have high wood product volumes relative to the rest of the region. In 

contrast to standing timber volume and woody biomass volume, some southwestern counties in Mississippi 

and Alabama exhibit higher wood product volumes compared to the rest of the region, further suggesting 

that the lower scores for in-forest timber and biomass volumes could be due to increased harvesting 

intensity in those counties. 

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Assessing Appalachian Natural Assets: Forests

Equation 3: Wood product volume

The relatively low scores for wood product volume throughout the central and northern Appalachian region 

contrast with the relatively higher volumes of standing timber and woody biomass, suggesting that there 

could be opportunities for forest products industry growth in these areas. However, many counties within 

the region only reported timber product output volumes for some of the TPO data classes. Many counties 

could therefore have a greater product output than suggested by the wood product volumes shown in Panel 

A of Figure 4. The data for wood products shows a strong trend towards low volumes, which suggests that a 

disproportionately large percentage of wood product production comes from relatively few counties within 

the Appalachian region.

2.1.4 Forest carbon mass

Forests store carbon terrestrially and emit carbon to the atmosphere in the forest carbon cycle. Forest 

carbon stocks increase as trees grow and accumulate carbon in woody tissue, and stocks decrease when 

trees are removed from the forest through harvesting, as well as when trees die and decay or burn during 

wildfires. Some aboveground forest carbon is transferred belowground during decomposition and can 

eventually become part of the forest soil. Belowground carbon levels fluctuate much slower than 

aboveground carbon levels; therefore, aboveground activities such as harvesting, insects, disease, fire, and 

weather can significantly impact forest carbon stocks. 

Over the past several decades, interest has grown in forest carbon, due in large part to the emerging 

evidence surrounding climate change. Carbon markets, where they exist, place economic value on 

“additional” carbon stored in tracts of forest. These markets are a new consideration in forest landowner 

decision-making.

Carbon calculations are based on the biomass equations developed by Jenkins et al. (2003), but are adjusted 

by a factor of 0.5 to account for the dry mass of carbon contained in wood (approximately 50 percent of dry 

mass). Live tree carbon consists of aboveground tree carbon and root carbon. Total forest carbon consists of 

carbon found in live trees, standing dead trees, down dead coarse woody debris, leaf litter, soil, and smaller 

vegetation (seedlings, shrubs, and bushes). This method allows for the breakout of live trees for comparison 

to total forest carbon, which is beneficial due to the propensity for live-tree management. This indicator is a 

sum of these metrics and is reported in tons of carbon per forested acre. 

Panel B in Figure 4 shows the total forest carbon mass for Appalachian counties. Southwestern Appalachian 

counties generally have lower carbon mass than northeastern counties. This could be due, in part, to a 

greater harvesting intensity in the southwestern counties and shorter-rotation plantation forestry 

management activities. Differences in forest types also persist as drier forests and lower densities contribute 

less carbon mass in the southwestern counties, compared to wetter conditions in the central and 

northeastern counties. 

Another factor to consider here is ownership: Non-industrial private landowners dominate the Appalachian 

region as a whole, while industrial forestry is more common in the southwestern counties. 

Panel B in Figure 4 also shows contrasts in carbon mass between West Virginia and Kentucky counties, with 

Kentucky counties showing lower mass than West Virginia. This trend continues south through Tennessee 

and further into Alabama and Mississippi. While the lower carbon mass in Alabama and Mississippi can be 

Wood product volumecounty”i” = (sawlog volumei + composites volumei + veneer volumei + post-piling-pole 

 volumei + fuelwood volumei + pulpwood volumei) / forest areai

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Assessing Appalachian Natural Assets: Forests

partially explained by harvest intensity and plantation forestry, the difference in carbon mass in Kentucky and 

Tennessee is due mostly to fewer changes in forest types and a drier climate, which contributes to fewer 

down and dead trees, less forest litter depth, and lower amounts of soil carbon compared to forested areas 

in West Virginia, North Carolina, and Ohio.

Equation 4: Forest carbon mass

The shift towards valuing carbon may incentivize a change in management activity in forests that show low 

forest carbon mass, but in order to store “additional” carbon, changes in management activity may be 

required in all counties. The challenge for increasing carbon stocks in Appalachian forests is developing 

management strategies that utilize the forest resource for both carbon and economic value, while 

maintaining healthy and resilient working forests.

2.1.5 Percent of county forested

Forest area is a simple measure of the quantity of forestland relative to the total land area in the county. The 

area of a forest resource is important for all forest values, including recreation and other ecosystem

measures such as wildlife habitat and watershed services. In simple terms, the higher the forest area score, 

the greater potential benefit to a county.

Panel A in Figure 5 on the following page shows the percent forest for Appalachian counties. A large cluster 

of highly forested counties are found across West Virginia and eastern Kentucky. A greater portion of these 

central Appalachian counties are forested compared with counties in the surrounding states of Ohio, 

Pennsylvania, Maryland, Virginia, and Tennessee. New York exhibits almost entirely low scores, as does South 

Carolina. The southwestern states of Mississippi, Alabama, and Georgia show a mix of high and low 

percentages, but overall these states trend towards lower percentages. The western counties of North 

Carolina show more relative forest area than eastern counties, and although Pennsylvania exhibits lower 

percentages overall compared to its southern neighbor, West Virginia, it does show a cluster of highpercentage counties in the northern part of the state. 

2.2 Discussion

In terms of quantity, Appalachian forests are generally in good condition. Standing timber volume and woody 

biomass tonnage are many times higher in counties near national forests and other public lands. For 

example, both standing timber volume and woody biomass tonnage are high in counties that contain the 

Monongahela, George Washington, and Jefferson National Forests in West Virginia and Virginia, as well as in 

the Pisgah, Cherokee, Nantahala, and Chattahoochee National Forests in Tennessee, North Carolina, and 

Georgia. To the north, Pennsylvania shows similar trends, with higher standing timber and woody biomass 

volumes in counties containing the Allegheny National Forest and the Elk, Moshannon, and Susquehannock 

State Forests. New York continues the trend in counties containing Allegheny State Park and Catskill Park. 

Lower levels of standing timber volume and woody biomass tonnage are found in the southwesternmost 

counties of Alabama and Mississippi, but that is to be expected given the differences in forest type and 

climate. 

Carboncounty”x” = (aboveground live tree Cx + aboveground understory Cx + standing dead tree Cx + down 

woody debris Cx + litter Cx + soil Cx + root Cx + belowground understory Cx) / forest areax

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Assessing Appalachian Natural Assets: Forests

Figure 5: (A) Forest area by county area and (B) woody biomass supply over a 10-year timeline

A

B

17 | Page
Assessing Appalachian Natural Assets: Forests

Appalachian counties generally have low wood product output, which is to be expected given the nature of 

wood processing and wood product manufacturing. Woody material is generally sourced from surrounding 

counties to supply the demand for processing facilities. The southwestern counties of the region show 

greater wood product output, and this is due to the more intensive forestry practices in the area and the type 

of wood products being produced, such as dimension lumber and plywood. Most of the Appalachian region 

does not support plantation forestry as well as the southwestern states. Non-plantation forests are more 

likely to have longer rotations and partial harvests to produce larger logs for fine hardwood lumber and 

veneer. 

The forestry practices employed by the southwestern Appalachian counties compared to the rest of the 

region also have some influence on forest carbon. The smaller trees and shorter rotations in the 

southwestern counties can lead to less carbon mass, while larger trees and longer rotations store more 

carbon over time. However, other important factors in forest carbon storage include forest type and climate. 

The general trend of the Appalachian region is decreasing carbon from the northeast to the southwest.

The forests of Appalachia are plentiful; however, counties vary based on the amount of forested land and the 

level of industry and infrastructure. In terms of standing timber volume, woody biomass volume, and forest 

carbon mass, the Appalachian region is doing very well. The standout indicator related to quantity is the 

amount of wood product volume produced by each county in the region. Clearly, the forest resource could 

allow for more wood product production, but the economics of doing so require innovation to be 

competitive in the current market. 

2.2.1 Supply for a biomass energy facility

Dead standing biomass (dead tree) volume in tons was used to determine a maximum potential 

weekly biomass supply over a 10-year timeline (see Panel B in Figure 5). Ten years is most likely the minimum 

supply agreement for a potential biomass energy facility, with 15 to 20-year supply being more attractive. 

This is a very simple analysis that assumes all dead standing biomass is available for use. A more detailed 

analysis of a potential supply area would have to include ownership and accessibility and would need to 

consider post-harvest woody biomass retention guidelines. However, this analysis does outline potential 

multi-county areas that may be more suitable for a biomass energy facility, if standing dead trees could be 

salvaged as a biomass supply resource. As shown in Panel B in Figure 5 areas in southwestern Alabama, 

eastern Tennessee, western North Carolina, eastern West Virginia, and north-central Pennsylvania have the 

most standing dead tree biomass. Large volumes of standing dead trees could be a sign of recent disturbance 

or a forest with its health in decline, which may also mean the forest is in need of some active management 

activity.

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Assessing Appalachian Natural Assets: Forests

PUTTING THE TOOL TO WORK

Example 1: Degraded forest stands and carbon

Degraded forest stands present opportunities for increasing the amount of carbon sequestration and climate 

benefit that a forest can provide. The areas with the most degraded stand biomass show the most room for 

improvement, and may be especially strategic areas to target for increased carbon sequestration. 

Additionally, regenerating degraded stands equates to removing the degraded material, which is ideally 

suited for biomass cofiring, which in turn presents a climate benefit.

Degraded hardwood forest stands in the Appalachian region contain trees that are of poor form, unsound, or 

diseased; are of non-merchantable species; or have residual damage from harvesting operations and 

corresponding lower growth rates compared to healthy forests. As a result of previous management or 

harvesting practices, these degraded stands do not contain adequate densities or volumes of merchantable 

growing stock trees, which presents both challenges and opportunities for future management of such forest 

resources.

The analysis presented here reports the estimated annual volume of woody biomass available from seriously 

degraded residual stands, with the assumption that all stands could benefit from a regeneration harvest to 

improve forest health (Figure 6). Volume is reported in cubic feet. Woody biomass availability is the focus of 

the analysis because Fajvan et al. (1998) suggest that the wood fiber material of degraded stands is ideally 

utilized as feedstock for biomass-related energy enterprises. This analysis reflects data from the FIA database 

(See Appendix C).

Regenerating degraded forest 

stands through active forest 

management can contribute a 

substantial amount of woody 

biomass for utilization within 

the Appalachian forest region. 

The regeneration of these 

stands will also decrease the 

susceptibility to insect and 

disease disturbances by 

improving forest health, 

therefore mitigating the risk of 

carbon loss associated with the 

increased mortality from such 

disturbances. Furthermore, a 

healthy regenerated stand will 

grow more quickly and thus 

increase the carbon uptake in

Figure 6: Degraded stand biomass availability

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Assessing Appalachian Natural Assets: Forests

PUTTING THE TOOL TO WORK 

Example 2: Forest harvesting residues in co-firing

Appalachia’s vast forest resource offers a variety of opportunities for diversifying the region’s economy. One 

such opportunity is the use of underutilized forest biomass for electricity generation. While an expansion in 

the use of forest biomass could benefit the region economically, the scale and pace of that expansion could 

also result in negative changes to the health of the forests or create competition with other wood-using 

industries. Given the uncertainty surrounding the future of biomass demand for the region and whether that 

demand will result in negative impacts on forest assets or other industries, it is vital to analyze how demand 

for these resources will change at varying co-firing rates and over time with shifts in coal-fired electricity 

generation in ARC states.

We examine three scenarios for co-firing biomass with coal in the ARC region. (See Appendix B for the full 

analysis). The scenarios are represented by co-firing rates of 2%, 5%, and 15% by heat input, as measured by 

million British thermal units (mmbtu). The 2% co-firing rate was chosen because it is less than the 3% rate at 

which Ray and Ma (2009) estimate that competing wood industries would be impacted. We chose 5% 

because it represents the co-firing rate identified by Ray and Ma (2009) as the limit for select Appalachian 

states. According to that report, exceeding this rate would result in annual removals exceeding annual 

growth. Finally, we chose a 15% co-firing rate because it represents a feasible rate that could be achieved 

with strong policy support and incentives, but does not exceed the maximum rate at which Bergman and 

Zerbe (2004) estimate that 

effective substitution of coal 

with woody biomass—by heat 

input—can be achieved. 

However, others estimate that 

co-firing at rates greater than 

10% can reduce the efficiency 

of coal-fired power plants, but 

that at the 10% rate, the 

efficiency loss is only about 1% 

(Perlack, 2011; Smith, 2011).

Figure 7 shows the estimated 

volume of existing harvesting 

residues by county, as well as 

the location and heat input 

from coal at all coal-fired 

power plants located in the 

region in 2008. The map 

provides an illustration of the 

proximity—and therefore 

availability—of harvesting 

residues to the region’s coalfired power plants.

Figure 7: Forest harvesting residue and power plants

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Assessing Appalachian Natural Assets: Forests

Source: Japan Heat Supply Business Association (2011). 

Case study

Biomass for more than just power?

As the nation looks to transition away from dependence upon foreign oil, many turn to biomass as a source 

for renewable fuel. Appalachia’s 2.4 trillion board feet of forests offer a tremendous resource, if utilized well 

and stewarded responsibly, particularly with 38.4 billion board feet per year growing above all harvests and 

removals.

The most efficient uses of wood are heating and co-generation of heat and electricity. To produce electricity 

from wood, it is burned, and roughly 33% of the energy is utilized for electricity. The majority of the energy is 

released as waste heat, which requires plants to utilize tremendous amounts of water to dispose of the heat, 

resulting in thermal pollution of waterways.

Combined heat and power uses this waste heat for some other purpose (district heating of homes or 

community facilities, industrial uses, etc.). With efficiencies as high as 80%, more than twice as much useful 

energy is recovered. A good example of this “think heat first” approach is Austria, which has transitioned 

toward renewable energy. Today, Austria meets 16.4% of its total energy demand from biomass, largely 

combined heat and power, district heating, and high-efficiency residential systems (Schilcher and Schmidl, 

2009). The district heating system in the city of Gussing, for example, meets all of the city’s electricity 

requirements and provides surplus electricity for sale as a byproduct of heat generation (Hofbauer et al., 

2011). Of particular interest to ARC, with its mandate to 

support infrastructure development, is the widespread 

use of district heating systems. These systems rely on a 

central boiler system to supply a village, neighborhood, 

or town with heat. Austria has over 1,100 district 

heating systems, which supply 21% of its 3.6 million 

households with heat (Lacher et al., 2009).

For many community facilities such as schools, 

hospitals, colleges, housing developments, and prisons, 

wood provides substantial cost savings. A 2008 

comparison by the Biomass Energy Resource Center 

found that even at 65% efficiency, wood chips could 

provide heat for 22% the price of propane per mmbtu 

at 85% efficiency (assuming $40/ton wood chips, 

$2.20/gallon propane, and institutional scale applications) (Sherman, 2008). For these types of public or 

commercial facilities, heating costs can be reduced by up to 78%. Many industrial users also would see 

benefits from converting to wood as a heat source, but most are not aware of the potential.

In a country with a population of one-third that of Appalachia, Austria has created 12,730 jobs in woody 

biomass solid fuels with an impact of $1.14 billion Euros (Biermayr et al., 2010). While some of this 

employment is in the manufacturing of biomass systems for export, the impact is still very significant.

Figure 8: Combined heat and power

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Assessing Appalachian Natural Assets: Forests

3. FOREST QUALITY

3.1 Introduction

The quality of a forest impacts its market value because higher-quality forests can produce higher-value 

wood fiber and solid wood products. Similarly, higher-quality forests can produce higher non-market values 

for recreation or other ecosystem services. 

The market value of timber is impacted by tree grade, species, and available volume, which in turn can be 

affected by site productivity, disturbance events, and forest condition. The aesthetic quality of a forest can be 

a major factor in the non-market value placed on a recreation experience, while a healthy forest ecosystem 

promotes the value of common-good services such as water transport and flood mitigation, wildlife habitat, 

and photosynthesis. This chapter provides a comprehensive regional assessment of forest quality and a 

framework to assess how forest quality factors into economic conditions and development. 

Figure 9: Potential site productivity (cubic feet per year, source: FIA 2009/2010)

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Assessing Appalachian Natural Assets: Forests

3.2 Components and framework

Assessing the quality of a county’s forest resource requires the examination of many different datasets.

Forest quality metrics were procured from publicly available sources and used to create five forest quality 

indicators. As shown in Table 4, these five indicators include: 

1. Stand quality: Measures the potential productivity of forests as the forest cull volume (i.e., unmerchantable volume due to decay or substandard grade), weighted by the percentage of naturally 

regenerated forestland within a county. 

2. Growth ratio: Provides a simple measure of forest health by dividing annual growth of standing 

timber volume by the annual volume of standing timber lost to natural mortality.

3. Stand origin: Measures how a forest stand was last regenerated. This indictor divides the acres of 

naturally regenerated forestland by the total acres of forestland.

4. Projected forest loss: Represents the forecasted loss of forest acreage due to housing pressure and 

insects and disease. 

5. Forest disturbance: Quantifies the current amount of forest affected by abiotic factors such as 

hurricanes and floods, biotic factors such as animal disturbance, and silviculture.

Table 4: Forest quality assessment components

Indicator Metric Denominator

Unit of 

measurement Data source and date

Stand quality • Volume of growing stock trees

• Cull volume None None (ratio) Forest Inventory Analysis, 2009, 2010

Growth ratio • Annual growth

• Annual mortality None None (ratio) Forest Inventory Analysis, 2009, 2010

Stand origin • Acres of naturally regenerated forest None Percent Forest Inventory Analysis, 2009, 2010

Projected forest loss

• Risk of basal area loss due to insect 

and/or disease infestation 

• Future housing density

None Percent

National Insect & Disease Risk Map 

(NIDRM) – USFS, 2011

Forests on the Edge – USFS, 2005

Forest disturbance

• Biotic disturbance 

• Abiotic disturbance

• Silviculture

None Acres Forest Inventory Analysis, 2009, 2010

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Assessing Appalachian Natural Assets: Forests

A

B

Figure 10: (A) Stand quality and (B) growth ratio

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Assessing Appalachian Natural Assets: Forests

3.2.1 Stand quality

Stand quality measures the forest condition. This indicator is the ratio of standing timber volume to the 

volume of rough and rotten cull trees (Equation 5). The result is a measure that relates the merchantable 

amount of tree volume within a county’s forest with the non-merchantable tree volume. Higher values 

suggest healthier, more favorable forest conditions, while lower values suggest the potential for 

improvement. Both metrics within the stand quality indicator originate from the FIA dataset.

Panel A in Figure 10 shows the stand quality for Appalachian counties. Generally, all counties in the 

Appalachian region showed good stand quality, with the majority of counties showing at least 1.8 times more 

standing timber volume (i.e., volume of growing stock trees) than cull volume. For stand quality, higher 

values represent a more productive forest with trees of good form and merchantability. Much of West 

Virginia and northern Pennsylvania show higher stand quality scores than the rest of the Appalachian region. 

These areas are known for their fine hardwoods (such as red oak, white oak, and black cherry) and yellowpoplar veneer, and active forest management in these areas is already a primary objective to capture the 

value of fine hardwood species. Active management helps to create a healthy forest with timber of good size 

and quality. 

The lower stand quality ratios found throughout the southwestern Appalachian region can be misleading. 

Forests in this area are still of relatively high quality; however, primary products in the region rely more on 

trees for pulp and paper, dimension lumber, and composite wood products as opposed to fine hardwood 

lumber. In addition, cull volume does not play as important a role as it does in the fine hardwood region of 

the central Appalachians because tree form and grade are less critical to producing pulp, paper, and 

composite wood products. 

3.2.2 Growth ratio

Growth ratio is the annual growth of standing timber volume divided by the annual volume of standing 

timber lost to natural mortality (Equation 6). A growth ratio greater than one means that natural mortality is 

less than natural growth, while a ratio less than one means that net growth is negative because mortality 

volume is greater than growth volume. Growth ratio is one of the simplest measures of forest health. A forest 

with a low or decreasing growth ratio may be experiencing a decline in forest health due to various factors, 

and it may be worth improving the forest stand. 

As shown in Panel B in Figure 10, forests in most Appalachian counties have a relatively low growth ratio. The 

lowest category ranges from about 25 times more growth than mortality down to a point where growth is 

less than mortality (a rare occurrence). If counties have a growth ratio exceeding one, growth is exceeding 

mortality. Only a few counties in the region have higher growth ratios; most of these counties are in 

Mississippi. These forest stands typically undergo intensive plantation forestry practices and can exhibit large 

growth ratios with virtually no mortality. As for the rest of the region, although most growth ratio scores are 

relatively low, all but approximately 7 percent are greater than one. Exploring options to increase the growth 

ratio may be worthwhile in low-scoring forests, such as management activities geared towards improving 

forest health.

Growth ratiocounty“i” = annual growthi / annual mortalityi 

Equation 6: Growth ratio

Stand qualitycounty“i” = volume of growing stock treesi / cull volumei

Equation 5: Stand quality

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A

B

Figure 11: (A) Forest stand origin and (B) projected forest loss

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Assessing Appalachian Natural Assets: Forests

Stand origincounty“i” = acres of natural regenerated standsi / forest acresi

3.2.3 Stand origin

Stand origin is a measure of how a forest stand was last regenerated. There are two main types of 

regeneration methods employed in the Appalachian forest region: natural and artificial regeneration. Stand 

origin was calculated by dividing the acres of naturally regenerated forestland by the total acres of 

forestland, and then multiplying by 100 to get a percentage of naturally regenerated forestland. 

The natural regeneration method allows for the seeds of trees once found on the site to become established 

and counts on forest succession and silvicultural techniques to develop the stand into a healthy, desired 

forest structure and composition. Artificial regeneration can be as simple as planting desired native species 

on sites to speed up or bypass natural succession, or as intensive as planting non-native species at very high 

initial densities, pre-commercial thinning, and using fertilizer and pesticides to control for competing 

vegetation and resources. Natural regeneration is the preferred method for the long-term, as it is usually 

much less intensive in terms of site preparation and chemical fertilizer and pesticide application, while 

favoring the maintenance of species composition, structure, and diversity. However, there are benefits to 

both types of forest stand origins.

Panel A in Figure 11 shows that most of the Appalachian region is highly dependent on natural regeneration. 

Some counties in Mississippi and Alabama rely more on artificial regeneration compared to the rest of the 

region. However, even in these counties, no more than approximately 40 percent of the forest area is 

artificially regenerated. This suggests that the forests of the Appalachians, in general, rely heavily on natural 

regeneration. 

3.2.4 Projected forest loss

Projected forest loss is composed of two metrics: the projected percent of forest loss due to change in 

housing density and the projected percent of basal area loss due to insect and disease damage. These 

percentages are combined to form the risk of total projected forest loss (Equation 8).

Nationally, forests are at risk of encroachment, fragmentation, and forest area loss due to increasing 

pressures from housing developments. Projections of future housing density increases were used to 

approximate future development within the ARC region. The Forests on the Edge study by USFS (Stein et al., 

2005) produced maps of predicted housing density changes on private lands (including private forestlands), 

available by decade from 1940-2030. Housing density categories mapped include undeveloped, rural (16 or 

fewer housing units/square mile), exurban (16-64 units/square mile), urban (64 units or more/square mile), 

and commercial/industrial. Comparisons between decades allow for assessment of conversion from less 

developed to more developed land uses by county. Public lands, which include local-, state-, and federally 

owned land from the Protected Areas Database (DellaSala et al., 2001), were excluded from the analysis. 

Equation 8: Projected forest loss

Following Stein et al. (2005), a change in housing density was defined as a change from rural to exurban, or 

from rural or exurban to urban. Changes in housing density were calculated using Forests on the Edge data 

from 2000 (based on the 2000 Census) and projections for 2030. Data for 2000 and 2030 were used to 

Projected forest losscounty“x” = housing density riskx + insect and disease riskx

Equation 7: Stand origin

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Assessing Appalachian Natural Assets: Forests

determine the areas with housing density changes. The overall housing density change for each county was 

found by determining the change in total percentage for each county between 2000 and 2030.

To account for risks to forests from insect damage and disease, we summarized data from the National Insect 

and Disease Risk Map (NIDRM) project (USFS, 2011). Specifically, we compiled average percent basal area 

loss predictions by county from the NIDRM maps. The NIDRM maps are composites of 188 individual models 

designed to predict responses to common insect and disease tree mortality agents (including gypsy moth, 

emerald ash borer, and southern pine beetle) for 57 tree species, with the final results being maps of percent 

basal area loss over the next 15 years (Krist et al., 2007). Percent basal area loss was modeled for one-square 

kilometer cells by calculating the total loss from all agents and host species as a percentage of total standing 

live volume of trees greater than one inch in diameter (USFS, 2011).

Panel B in Figure 11 shows the percentage of projected forest loss for Appalachian counties. Counties with 

the greatest projected loss dominate Georgia, South Carolina, and western North Carolina near the greater 

Atlanta metropolitan area. In these three states, the housing metric drives the high percentage of projected 

forest loss, meaning that forests are at greater risk from housing pressure than insects and disease; this is not 

generally the case in the rest of the region. Other counties with high percentages of projected forest loss are 

found near the eastern panhandle of West Virginia and Maryland near the fast-growing city of Martinsburg, 

West Virginia, and also in central and eastern Pennsylvania areas closer to larger cities. Low percentages of 

projected forest loss dominate the counties of Mississippi and New York, whereas most other states exhibit a 

mix of high and low percentages for projected forest loss, depending on the proximity of larger, fastergrowing cities or metropolitan areas. 

Across the Appalachian region, the trend suggests that the majority of counties are not likely to experience 

large amounts of deforestation due to housing pressure or tree mortality from insect infestation and disease. 

Furthermore, in order to show the impact, discussed above, of either potential future housing density 

changes or insect and disease infestation on the forests of Appalachia, the projected forest loss indicator can 

be broken down into two base maps (Panels A and B in Figure 12). These figures clarify that the projected 

housing density metric drives the potential forest loss values in the states of Georgia, Alabama, and South 

Carolina, while the combination of the risk of insect and disease and projected housing density impacts 

potential forest loss in North Carolina. 

It is interesting to note the potential impact of insect and disease risk on the future of forests in the state of 

West Virginia (Panel A in Figure 12). The impact is lost when combined in the overall projected forest loss 

map (Panel B in Figure 12) due to the magnitude of percent change in housing density across the region. 

However, the risk of insect and disease infestation does appear to be an indicator worth tracking in the 

future in West Virginia, as well as in other states such as central Pennsylvania, southern Ohio, western 

Maryland, and eastern Kentucky.

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Assessing Appalachian Natural Assets: Forests

A

B

Figure 12: (A) Percent risk of insect infestation and (B) housing density change

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Assessing Appalachian Natural Assets: Forests

3.2.5 Forest disturbance

Forest disturbance is a measure of recent forest impacts. Three metrics are used to calculate this indicator: 

biotic processes, abiotic processes, and silviculture. Biotic factors include invasive vegetation and insect 

species, as well as other animal and disease disturbances. Abiotic factors include fire- and weather-related 

disturbances. Silvicultural activities (e.g., forest harvesting and management implementation) are the only 

human-caused disturbances measured with this indicator. Surface mining activities are included under 

silviculture acres as a “land-clearing activity” (FIA disturbance code 80). However, no distinction is made 

between forest management-derived disturbance and surface mining-derived disturbance within code 80. 

Therefore, the surface-mined acres within this report are not distinguishable from, but included as, forest 

disturbance. The indicator represents the total acres of disturbed forestland, as a percentage of the total 

forest area. 

Equation 9: Forest disturbance

Forest disturbancecounty“i” = (biotic acresi + abiotic acresi + silviculture acresi ) / forest areai

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A

B

Figure 13: (A) Forest disturbance and (B) forest quantity case study

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Assessing Appalachian Natural Assets: Forests

Panel A in Figure 13 shows the forest disturbance acres for Appalachian counties. In general, Mississippi, 

Georgia, South Carolina, North Carolina, Tennessee, Virginia, Kentucky, Maryland, and Ohio exhibit the 

lowest amount of disturbed acres within the region. Alabama, West Virginia, Pennsylvania, and New York 

show a range of disturbed acres within counties from high to low. The higher percentage of disturbed acres in 

these counties could be due to the increased presence of invasive plant species such as kudzu (Pueraria 

Montana var. lobata) or insects such as the hemlock woolly adelgid (Adelges tsugae), weather, a greater 

number of recently-harvested areas for forest management operations, or land clearing for other purposes.

3.3 Discussion 

Overall, the Appalachian region is comprised of forests with good quality, and most of the Appalachian region 

is highly dependent on natural regeneration. The majority of counties show at least 1.8 times more standing 

timber volume than cull volume, which represents a productive forest with trees of good form and 

merchantability. Forests in most Appalachian counties have a relatively low growth ratio. However, the 

lowest category ranges from about 25 times more growth than mortality down to a point where growth is 

less than mortality. Only a few counties in the region exhibit mortality greater than growth. Exploring options 

to increase the growth ratio may be worthwhile in some of these forests, including management activities 

geared towards improving overall forest health. 

Projected forest loss is low for most Appalachian counties; counties with higher projected forest loss are 

generally at greater risk from housing pressure than insects and disease. This is not generally the case in the 

rest of the region, where the risk of insect and disease infestation tends to have a greater influence on 

projected forest loss. Across the Appalachian region, the majority of counties are not likely to experience 

large amounts of deforestation due to housing pressure or tree mortality from insect infestation and disease. 

Higher areas of forest disturbance in Appalachian counties tend to be found in Alabama, West Virginia, 

Pennsylvania, and New York, which could be due to the increased presence of invasive plant species, extreme 

weather events, active management, or land clearing for other purposes.

3.3.1 Forest disturbance, population, and road density

Panel B in Figure 13 compares forest disturbance with county population and road density. Road density is 

the ratio of the length of the county's total road network to the county's land area. The road network 

includes all roads in the county: motorways, highways, main or national roads, secondary or regional roads, 

and other urban and rural roads. Using the DSS, high importance was placed on forest disturbance and road 

density, while a moderate importance weight was applied to population. The map shows counties that best 

fit the query, with the top third having more roads, more forest disturbance and greater population. These 

counties in the top third are mostly in a few clusters across the region, and can be crosschecked with the 

stand quality map (Panel A in Figure 10) to determine counties in the top third that are also of below-average 

stand quality. These counties may have the most potential for increased active management for healthier 

forests because disturbance could be contributing to a lower stand quality, and the road infrastructure and 

potential workforce to successfully manage the forests may already be in place within the county.

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PUTTING THE TOOL TO WORK

Example 3: Forest habitat loss: risk and prevention

Managing forests for multiple, competing uses can create an ongoing struggle for resources. In this example, 

we focus on two competing uses of the forest: timber production and wildlife habitat. Habitat loss and 

fragmentation is a great threat to many eastern forest species (Murdock, 1994), and many species in the 

region have become rare, threatened, or endangered (Figure 14). Managing forests for these species of 

concern, while still providing wood products for timber harvest, can be a difficult balancing act. 

We have outlined counties 

with red boundaries where 

the market value of wood 

and wood products is low 

(lowest 33 percent), the 

non-market wildlife habitat 

value is high (highest 33

percent), and projected 

forest loss is high (highest 

33 percent). These 

highlighted counties should 

prove to be the “lowhanging fruit” where the 

management of forests to 

protect or restore sensitive 

species would result in the 

least amount of potential 

market value lost. 

Additionally, these counties 

are areas that could be 

targeted for protection 

from forest loss, potentially 

aiding in protecting species 

that could be on the brink 

of designation as rare or threatened.

Examples of threatened species in the Appalachian region include the Indiana Bat, Eastern Cougar, Cheat 

Mountain Salamander, James Spiny Mussel, and Running Buffalo Clover (USFWS, 2011). Alabama has the 

highest concentration of federally listed species in the region and a strong history of silviculture. A strategic 

zone of preservation that balances both of these interests could be the northern portion of its western 

border, where five neighboring counties meet the criteria for somewhat low forest market value and high 

scores on forest loss and habitat values. Additionally, the region contains seven to ten federally listed species.

Figure 14: Habitat loss: risk and prevention

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4. FOREST VALUE

4.1 Introduction

In this chapter, we consider the value of Appalachia’s forests. Economists use a variety of methods to place 

monetary values on natural resources like forests. These methods include information derived from markets 

(such as observed market prices) and information estimated from survey data or observed behaviors. This 

second approach is called non-market valuation and includes techniques such as contingent valuation, 

property value hedonics, and the travel cost method. It is important to understand and attempt to quantify 

this value in order to account for the non-transactional economy and to place value on people’s perceptions 

of the worth of forested landscapes.

In this section, the monetary values for forest resources are based on market information for wood and wood 

products. Non-market values are derived from a meta-analysis conducted by Kreiger (2001), which 

establishes a value per acre for wildlife habitat, water storage and preservation, recreation, and cultural 

values. Both the market and non-market values reflect a willingness to pay for the forest resource. 

Figure 15: Total market value per acre

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Assessing Appalachian Natural Assets: Forests

4.2 Components and framework

In this chapter, six indicators were developed to compare forest values across the region. These indicators, 

and the metrics from which they were derived, are directly associated with forest quantity and quality. For 

example, quantity metrics are used as inputs in estimating values.

The market and non-market categories are left separate, rather than combining them into a single forest 

value. This allows readers to discern between these two types of values. 

Market values are entirely determined by prices and the quantity of products sold; these data were sourced 

from FIA and TPO. However, some forest-derived goods demonstrate transactional value on markets but 

their price and production data are not included in the FIA. For example, non-timber forest products such as 

mushrooms, ginseng, edible nuts and berries, medicinal herbs, and Christmas trees are becoming an 

increasingly important source of additional income for rural communities. However, there is a shortage of 

information with regards to production or harvest data on non-timber forest products, so they were excluded 

from this report.2

Table 5: Forest quality assessment components

Indicator Metric Denominator

Unit of 

measurement Data source and date

Market value

Wood

• Volume of sawtimber

• Value of sawtimber

• Tree species

Forest acres Dollars per 

acre

Forest Inventory Analysis, 2009, 2010

Timber Market Report, 2011

Wood product • Wood product volume

• Wood product value

Forest acres Dollars per 

acre

Forest Inventory Analysis, 2009, 2010

Timber Market Report, 2011

Non-market value

Recreation • Value of recreation None Dollars Kreiger, 2001

Wildlife habitat • Value of wildlife habitat benefits None Dollars Kreiger, 2001

Cultural value • Value of cultural benefits None Dollars Kreiger, 2001

Watershed services • Value of watershed services None Dollars Kreiger, 2001

4.3 Market value

In order to calculate the market value of the forest assets in Appalachia, the project team first calculated the 

market value of wood—notably saw timber. A second indicator captured the value of wood products, 

including hardwood and softwood veneers, sawlogs, pulp, and fuelwood. The indicators included here are 

based on FIA and other datasets. 

2 The Appalachian Center for Ethnobotanical Studies provides many resources for the use and cultivation of non-timber forest products. See 

www.frostburg.edu/aces/. 

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A

B

Figure 16: (A) Forest market wood value and (B) forest market wood product value

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4.3.1 Wood value

The value of sawtimber for each county in the Appalachian region was used as a measure of wood value, or 

value of merchantable standing trees. To calculate the value of sawtimber, the net volume of sawtimber in 

board feet3 was collected for each county within the region from the FIA database. Because sawtimber prices 

were not available for every species, some sawtimber species were grouped into major species categories.

Panel A in Figure 16 shows the value of standing timber for Appalachian counties. Generally, there is a 

greater timber value in New York, Pennsylvania, Maryland, West Virginia, North Carolina, and Georgia. Other 

Appalachian counties show a mix of values. 

Equation 10: Wood value

Many wood values illustrated in Panel A of Figure 16 contrast with standing timber volumes in the quantity 

section of this report. Generally, it would be expected that counties with greater value have greater volume. 

However, some counties with high timber volume and lower wood value may have a greater density of lower 

quality or non-merchantable tree species. It follows that counties with low timber volume and higher wood 

value may have a greater density of higher quality, merchantable tree species. 

4.3.2 Wood product value

Wood product values for each county were determined by assessing the same wood products from the TPO 

database used in wood product volume (Equation 11). Panel B in Figure 16 shows the value of wood products 

for Appalachian counties. Generally, more counties exhibit higher wood product values in the central and 

southern Appalachian states. Counties in New York, Pennsylvania, Ohio, and Maryland show almost entirely 

low wood product values, as compared with other Appalachian counties. 

The low values in Ohio may be due to relatively young forests that dominate the region. The low values in 

Pennsylvania, Maryland, and New York are due to low quantities of specific types of wood products that 

command high market prices, such as veneer. The TPO data show that counties in New York and Ohio have 

low veneer production compared to the higher value counties. These differences in volume of production by 

product category (i.e., low-value versus high-value products) can have a significant impact on the final wood 

product value.

The Appalachian portions of Mississippi, Georgia, and South Carolina show the greatest density of high wood 

product values, while Alabama, North Carolina, Tennessee, Kentucky, Virginia, and West Virginia all exhibit a 

mix of high to low wood product values. However, no state in the region has many counties with high to veryhigh values.

Equation 11: Wood product value

3 The volume of sawtimber was calculated in board feet by using the International 1⁄4 inch log rule. A board foot is most easily visualized as a piece of wood 

measuring 12”x12”x1”, which equals 144 cubic inches and is the most commonly used volume measurement for sawlogs. The International 1⁄4 inch log rule is the 

standard log rule used by USFS, and estimates the available board footage in trees based on diameter of the trunk and merchantable tree height. 

Wood valuecounty“i” = Sawtimber valuei / Forested areai

Wood product valuecounty“x” = Sum wood product valuex / Forested areax

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A

B

Figure 17: (A) Non-market wildlife habitat value and (B) non-market watershed services value

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Assessing Appalachian Natural Assets: Forests

The map of wood product value in Panel B in Figure 16 shows many counties with values that contrast with 

the map of wood product volume shown in Panel A in Figure 4 although in general, counties with greater 

volume also have greater value. Counties with higher output and lower value may be producing products of a 

lower grade or value (as in the example of veneer, above), while counties with lower output and higher value 

may be producing products of a higher grade or value. Panel B in Figure 16 suggests that wood product value 

in the Appalachians is skewed towards lower values. As with wood value, this pattern occurs because a small 

number of counties have particularly high values in comparison to the majority of counties.

4.4 Non-market value

In order to evaluate non-monetary benefits from forests on a county-by-county basis, we estimated the 

wildlife habitat, watershed services, recreation, and cultural values provided by the forest.4 All of these value 

estimates are linked to the area of forest in each county. Wildlife habitat values were estimated using the 

Wildlife Habitat Benefits Estimation Toolkit developed by the Defenders of Wildlife (2006). The other three 

indicators used values from Krieger (2001), who established rates of environmental service value per acre 

(Table 6). 

Table 6: Non-market forest values (dollars per acre)

Use sector 2009 Forest value

Watershed services $50.96

Recreation $15.52

Cultural $1.16

Note: Values per acre were extracted from Krieger (2001) and adjusted to 2009 dollars. The wildlife 

habitat value index was derived from Defenders of Wildlife (2006).

4.4.1 Wildlife habitat value

To estimate the dollar value associated with terrestrial habitat in Appalachian forests, the Wildlife Habitat 

Benefits Estimation Toolkit developed by the Defenders of Wildlife (2006) was used. This toolkit uses a model 

that was developed from a meta-analysis of several studies on the different benefits associated with wildlife 

and habitat. The model lets the user enter values for acres of terrestrial habitat and assumes that the habitat 

provides for multiple species in addition to open space. Forested acres per county were entered.

Panel A in Figure 17 shows the value of wildlife habitat for Appalachian counties. While wildlife habitat values 

were generally skewed toward the low side, high values exist in certain counties in New York, Pennsylvania, 

West Virginia, and Alabama. This pattern is consistent with the distribution of scores for the other nonmarket value indicators, because they all use forest area per county as a base parameter.

Equation 12: Wildlife habitat value

4 Carbon sequestration as an additional form of ecosystem services is described in Appendix A. The amount of carbon in Appalachia is quantified through the 

forest carbon mass indicator, which is described in the forest quantity chapter. 

Wildlife habitat valuecounty“i” = County forested acresi i Wildlife habitat value per forested acre

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Assessing Appalachian Natural Assets: Forests

Figure 18: (A) Non-market recreation value and (B) non-market cultural value

A

B

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Assessing Appalachian Natural Assets: Forests

4.4.2 Watershed services value

Forested landscapes store and purify water, improving both the quantity and quality of water in the 

Appalachian region. Forests contribute to water quality by reducing soil erosion and filtering pollutants from 

water. Clean water from forest ecosystems is particularly important to many municipalities that obtain their 

water from forested watersheds (Krieger, 2001). Studies have shown a direct linkage between water quality 

and forest cover (e.g., Freeman et al., 2008). For example, decreased forest cover was significantly related to 

decreased water quality, and low water quality was related to higher treatment costs. It has also been shown 

that larger forested areas are linked with better quality of untreated water and lower drinking water prices. 

To assess the value of watershed services provided by forests, the project team applied a per-acre value from 

Krieger (2001) to the amount of forested acres within the county (Equation 13). Panel B in Figure 17 shows 

the value of watershed services for Appalachian counties. Values were highest in certain counties in New 

York, Pennsylvania, West Virginia, and Alabama. This pattern is consistent with the distribution of scores for 

the other non-market value indicators, because they all use forest area per county as a base parameter for 

the calculation.

Equation 13: Watershed services value

4.4.3 Recreation value

Forestlands in the US, both public and private, play an essential role in providing recreational opportunities 

for the public; over 173 million recreation visits were made to national forests annually since 2005 (USFS, 

2010b). Approximately 86 million acres, or 65 percent of the Appalachian region, is forested (Miles, 2010; as 

cited in Widmann et al., unpublished). Appalachian forests provide substantial opportunities for recreation 

and tourism for the region’s residents and others. Millions of residents participate in outdoor activities such 

as hunting and fishing (Widmann et al., unpublished).

The value of recreation for each county in the Appalachian region was calculated by multiplying the per-acre 

estimate from Krieger (2001) for temperate or boreal forests by the number of forested acres. 

Equation 14: Recreation value

Panel A in Figure 18 shows the value of recreation for Appalachian counties. Recreation values were highest 

in certain counties in New York, Pennsylvania, West Virginia, and Alabama counties. This pattern is consistent 

with the distribution of scores for the other non-market value indicators, because they all use forest area per 

county as a base parameter.

4.4.4 Cultural value

Forests provide a wealth of cultural values, such as aesthetic value, existence value, and cultural heritage 

value (Krieger, 2001). The cultural value for each county in the Appalachian region was calculated by 

multiplying the per-acre estimate from Krieger (2001) for temperate or boreal forests by the forested acreage 

in each county. Panel B Figure 18 shows the cultural value for Appalachian counties. Values were highest in 

certain counties in New York, Pennsylvania, West Virginia, and Alabama. This pattern closely resembles the 

other non-market value indicators because this indicator is also based on forest area per county.

Watershed services valuecounty“i” = County forested acresi i watershed services value per forested acre

Recreation valuecounty“x” = County forested acresx x recreation value per forested acre

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Assessing Appalachian Natural Assets: Forests

Equation 15: Cultural value

4.5 Discussion

In terms of market value, counties with greater volumes of standing timber and wood product output 

generally have greater values. However, counties with higher timber volume and lower wood value may have 

a greater density of lower quality or non-merchantable tree species, while counties with lower timber 

volume and higher wood value may have a greater density of high quality, merchantable tree species. Also, 

counties with higher wood product output and lower value may be producing products of a lower grade or 

value, while counties with lower output and higher value may be producing products of a higher grade or 

value. A small number of Appalachian counties have particularly high values in comparison to the majority of 

counties; therefore, the market value maps show a large number of counties in the categories with relatively 

lower market values. 

In terms of non-market value, counties with higher value are often located in areas with publicly owned 

forestlands. Examples include the Monongahela National Forest in West Virginia, George Washington 

National Forest in Virginia, Allegheny National Forest in northern Pennsylvania, and Daniel Boone National 

Forest in eastern Kentucky.

4.5.1 Poverty and the value of forests

Using the DSS, a map was created 

(Figure 19) that compares standing 

timber value, wood product value, nonmarket forest value, and poverty levels. 

High importance—or weight—was

placed on poverty and wood product 

value, while more moderate weights 

were applied to standing timber volume 

and non-market value. The map shows 

counties that best fit the criteria, with 

the top third generally having greater 

poverty and forest value. These counties 

are mostly found in a few clusters across 

the central and southern Appalachian 

states. 

Figure 19: Decision support system output - value and poverty

Cultural valuecounty“x” = County forested acresx x cultural value per forested acre

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Assessing Appalachian Natural Assets: Forests

Photo credit: Pavlesich (2011).

CASE STUDY

Wood products and ecosystem services 

Roughly 80 percent of the nation’s drinking water originates from forested lands (Sedell et al., 2000). Given 

its position at the headwaters of many important watersheds, urban centers outside the region benefit from 

the services Appalachia’s forests provide without remunerating the region. The watersheds that supply New 

York City with drinking water (part of which is in Appalachia) provide a model for communities to receive 

benefits from their ecosystem services. 

From 1997-2006, and continuing to this day with the 2007 renewal, the City of New York provides funds to 

encourage and support sustainable forestry and farming activities in the City’s upstate watersheds. The City 

entered into a Memorandum of 

Agreement to support the Watershed 

Agricultural Council (WAC) with funding 

to implement conservation and water 

quality protection activities in the City 

watershed areas.

WAC has conducted outreach, education, 

and training to protect water quality 

throughout the region. Some of its 

primary efforts include conservation 

easements, best management practices 

(BMPs) on farms and forests, forest 

management plans, and whole farm or 

nutrient management plans. In 2011 

alone, WAC developed or updated more 

than 70 forest management plans, which 

covered more than 10,000 acres in the 

region (WAC, 2011a).

Currently, the New York Department of 

Environmental Protection provides $11 

million per year to WAC for a variety of projects, from conservation easements to farm nutrient and erosion 

control activities (WAC, 2011a). In addition to $3 million for conservation easements, some of which apply to 

forests, $1.1 million goes directly to activities related to sustainable forests. Since 2001, the City had provided 

over $10 million in direct support for sustainable forestry education, cost-sharing, and related activities 

aimed at protecting water quality in the short- and long-term (WAC, 2002-2011a). In addition, this 

investment has leveraged significant federal resources in rural economic development to help keep forestrelated firms in business and to keep working forests forested. This type of incentivized stewardship is called 

“payment for eco-services,” in which a market value is applied to ecosystem services (WAC, 2011b).

Similar to much of Appalachia, the Catskill/Delaware subregion of the city’s watershed is predominantly 

under private ownership: 70 percent is privately owned (Mass, 2011); and there are over 20,000 forest 

landowners in the City’s upstate watersheds in total (Brunette and Germain, 2003). New York City’s efforts to 

protect its clean drinking water model a form of incentivized stewardship practices that could be especially 

fruitful in Appalachia. 

Figure 20: Forest operations in the New York City watershed

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Assessing Appalachian Natural Assets: Forests

5. CONCLUSION

Based on a literature review, data compilation, and analysis, this study quantified numerous metrics and 

indicators that describe forest quality, quantity, and value across all Appalachian counties. These assessments 

demonstrate one way that forest resources can be quantified and understood—one we hope will help guide 

conversation and future research, and begin to place an emphasis on determining the true value of our 

natural resources, both economically and culturally. 

In addition, this project provides a GIS Decision Support System, which allows users to capture this dynamic 

and complicated resource in the context of their understanding of local forests’ relationships with economic 

development or distress.

5.1 Recommendations

The forests of Appalachia have long been the fuel for one of the region’s premiere natural resource 

extraction industries; the history and the future of the forests in the region are inextricably tied to the 

region’s economic development. Wise use of the forest resource can aid economic development, especially 

in the most rural regions. Evaluating the existing forest asset in terms of its quality, quantity, and value is the 

first step in effectively linking economic development and sustainable use of the resource. The following 

policy recommendations are possible next steps in this process. 

Promote forest management

One of the greatest needs identified in the stakeholder surveys was improved forest management. Generally, 

much timber harvest follows the adage: “cut the best, leave the rest.” Forest operators are often paid by 

timber value and have an incentive to select the most valuable trees to cut. Without a forester involved, 

timber may be cut without regard to forest reproduction, and stands may be degraded rather than 

stewarded. Most forestlands cut in Appalachia do not have a forester involved and have no forest 

management plan. If investment decisions by ARC are going to enhance forest value through time, it is 

important to prioritize timber harvests from managed lands. In addition to any direct support for 

communities undertaking forest management promotion initiatives, ARC could score forest-related project 

proposals higher if they include forest management plans. This would create incentives for partners in local 

communities to collaborate to improve management in their areas. 

Any public dollars that are invested in biomass facilities (be it direct project support, feasibility work, or tax 

incentives) should be made in ways supportive of improved forest management. Schools applying for support 

for transition to wood heat should indicate how they plan to source wood from managed lands. Large 

facilities, such as power plants and biorefineries, should source only from managed lands as their impacts 

could greatly enhance or degrade forest resources in an area. 

Work towards real-time forest information

Forest-use project areas should also consider the other competing demands for wood. Future ARC forest 

indicators work would be well to track the current demands on wood, ideally in real time. “Sourcing circles” 

for facilities could give project developers an indication of the overlapping demand for forest products, 

locations of likely over-harvest, and “gaps” where projects would be most useful. Projects in areas of low 

demand could receive additional points, and projects in high-demand areas would require sustainable 

sourcing plans. 

It would be advisable for future assessments to include process indicators around forest management. As 

states begin to digitize maps of parcels with forest stewardship plans, those maps could be requested from 

state foresters. Alternatively, per-county acreages could be used to create an indicator of the percentage of 

land under forest management plans. Information on forest certification could be added from the Forest 

Stewardship Council, American Tree Farm System, and Sustainable Forestry Initiative. 

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Assessing Appalachian Natural Assets: Forests

Prioritize future forest data sources

• Social indicators. The National Woodland Owner Survey conducts landowner surveys that include 

attitudinal questions that investigate why private landowners own their land, their intentions with 

harvest, and their history of harvest and management practices. While not compiled county-bycounty, multi-county clusters could be evaluated to determine the suitability of development 

approaches to local landowner dynamics. For example, in some areas, forest improvement may be a 

priority while in others it may not “catch on” with landowners.

• Timber-related employment. Currently the TPO is the primary data source available for wood 

production and employment. This survey only measures primary mills, despite the fact that 

secondary and tertiary processors provide more employment. Also, the survey may only be 

conducted every five years. The economic picture for mills can change rapidly. With the current 

economic decline, a significant portion of the mills in Appalachia have ceased or slowed production 

relative to the last Timber Production Output (TPO) study. 

• Growth-to-removal ratio. Growth-to-removal ratio is a fundamental measure of forest health that 

was excluded from the project because of the lack of consistent data across states. It is imperative to 

know where more timber is being cut than is growing, or vice versa. It is also important to know the 

species and grade of timber stands. For example, an annual harvest rate of 30% might be 

sustainable, but this rate might not be spread equally over all species and grades; an annual harvest 

rate of 150% for red oak sawlogs is not sustainable. Growth-to-removal ratios would aid decisionmakers and prospective investors in identifying “hot spots” of over-harvest for certain species or 

grades and ideal locations for new timber processing or biomass facilities. 

Link sustainability and employment

Given the need for employment across Appalachia and the great need for forest improvement practices, ARC 

could undertake a Forest and Communities Restoration Initiative. Such an initiative could identify important 

forest areas for ecosystem restoration projects, as well as areas of high unemployment where forests need 

improvement such as thinning, grape vine removal, or invasive species control. Partnering with USDA and 

non-governmental organizations could help to develop a collaborative pilot project on job creation through 

“green forest jobs.”

Appalachia has a tremendous forest resource, which provides monetary and societal benefits to millions of 

people from the region and beyond. The general approach to forests, in the past, has been to treat them as a 

raw material for extraction and export. Transitioning to a value-added system, and monetizing ecosystem 

services, has the potential to increase prosperity in the region. Stewardship of the resource needs to be 

improved and monitoring systems developed to track the extent of forest management and secondary valueadded activities, among others. 

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Assessing Appalachian Natural Assets: Forests

Proactively address biomass

Biomass harvest for energy production has the potential to improve forest management, but unmanaged 

extraction of biomass has the potential to threaten ecosystem health and diminish forest resources if 

demand is excessive and standards for biomass harvesting not followed. The scale of US energy demand is 

such that every stick of wood in the US could be used to generate energy and it would not put a dent in our 

long-term energy demand. Specific strategies for addressing biomass include:

• Map biomass facilities’ demand and sourcing areas, and those of existing wood users, maintaining a 

map that communities and investors can use to determine demand on a county’s forests.

• Commission research and a stakeholder process to develop biomass utilization guidelines for the 

subregions of Appalachia that maintain ecological functionality, forest health, and raw material 

supply for traditional markets other than bioenergy generation. 

• Tie public funding to procurement of wood from managed lands. 

• Partner with state foresters and USFS to increase management of Appalachian forestlands and 

landowner awareness of options and issues in forest management.

A forester in southern Ohio reminds us of the fate of Vinton County when the iron furnaces’ demand for 

wood for energy cleared the land of trees and fostered widespread erosion: “Just remember what happened 

last time we used the forests for energy.” The challenge of our time is to base our decisions on how best to 

utilize what the forests have to give over the long term, rather than demanding of them what we want at the 

present. This forest project is a meaningful step in that direction and should be followed by initiatives to 

integrate this information into the day-to-day decisions made across Appalachia.

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APPENDIX A: FOREST CARBON PAYMENTS

Discussion

Appalachian forests provide a valuable ecosystem service as an active carbon sink. Currently, the volume of 

annual forest growth in Appalachia exceeds tree removal volume (Widmann, unpublished), suggesting that 

the region’s forests are removing more carbon dioxide from the atmosphere than they release through 

natural decomposition processes. While much of this trend is the result of a decline in demand for wood 

products, the fact that the region’s forests are largely under-stocked and immature means there is a 

substantial opportunity for future carbon management aimed at reducing atmospheric carbon dioxide, 

regardless of whether demand for Appalachian hardwood recovers in the future. Managing the region’s 

forests for carbon storage could provide a new and significant source of income for private landowners, 

contributing to the diversification of the regional economy.

As described by the creators of the forest carbon index (Deveny et al., 2009), three approaches exist for 

improving the carbon sequestration potential of forests: (1) avoiding deforestation, (2) growing new forests, 

and (3) managing existing forests to increase carbon stocks. According to these authors:

“Avoiding deforestation and forest degradation stops emissions before they happen and maintains 

the carbon stocks at their existing levels. Growing forests through afforestation (growing new forest) 

and reforestation (regrowing a cleared forest) helps to recapture carbon dioxide and store it in 

forests.” (Deveny et al., 2009)

Markets for wood products and forest carbon storage occur on opposite cycles; therefore, a declining or 

struggling market for wood products serves as a perfect time to begin building a market for forest carbon. 

This is because when forest products markets are strong, there is little incentive for private timber owners to 

invest in forest carbon management without a strong carbon price. Conversely, when there is little 

competition for the wood from traditional industries, the carbon price needs only to exceed the cost of 

implementing and maintaining a forest carbon management program. 

In both cases, forest carbon management as an economic opportunity in Appalachia requires a price on 

carbon or a payment for storing carbon over a 25-year period, at a minimum. Other beneficial components 

include a strong voluntary carbon market; assistance in designing and implementing forest carbon 

management programs; the implementation of a cap-and trade program on a state, regional, or national 

level; or other factors:

“participation (in voluntary forest carbon management programs) is likely to be determined by 

factors such as the trading price of carbon dioxide, transaction costs, acceptance of forest carbon 

credits as equally tradeable with credits from emissions reduction, and the accuracy with which 

additional forest carbon storage can be estimated and reported.” (Richards et al., 2006)

As noted by the Pew Center on Climate Change:

“Several factors affect estimates of cost: forest species and practices; the value of land for 

alternative uses; the disposition of biomass, forest and agricultural product prices; methods used to 

account for carbon flows over time; the discount rate employed; and the policy instruments used.” 

(Stavins and Richards, 2005, p. v)

In its review of eleven previous studies on forest carbon storage, the Center notes that a program size of 300 

million tons of annual carbon storage would cost $50 per short ton of carbon. Additionally, a 2007 report by 

the Congressional Budget Office reported that:

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Assessing Appalachian Natural Assets: Forests

• A carbon dioxide price of $5 per metric ton would bring about changes in forest management that 

would sequester about 5-250 million metric tons per year, which is up to 4 percent of the nation’s 

2005 carbon dioxide emissions from human activity; and

• A carbon dioxide price of $50 per metric ton would bring about the full exploitation forest carbon 

strategies, sequestering more than 60 billion metric tons of carbon dioxide over a century 

(Congressional Budget Office, 2007).

Other studies have also investigated the impacts of various carbon prices on forest management: “If 

countries are able to sell credits at the market price of $20 per ton carbon dioxide–equivalent in 2020, they 

could maximize revenues from forest carbon" (Deveny et al., 2009, p. 51). Also, existing carbon offset 

projects initiated by the Mountain Association for Community Economic Development (MACED) paid 

Kentucky landowners a total of $65,000 for the carbon their forests stored in 2007. These landowners 

represent 5,006 acres of forestland that took in and stored 14,500 metric tons of carbon during 2007. That 

amounts to an average carbon payment of approximately $4.50 per ton, and an average land payment of 

approximately $13 per acre (MACED, 2009). Examples such as these provide prices and analytical results that 

may be useful in making projections for the Appalachian region. 

Yet, under a strong voluntary carbon market or a cap-and-trade program, Appalachian policymakers would 

have to account for the existence of competing uses for forests and how all uses would interact in such a 

market. In other words:

“Forest carbon credits will ultimately have to compete with credits generated from all other sectors 

of climate mitigation in the carbon markets. If offsets generated through energy efficiency projects 

or methane recapture projects are cheaper than forest carbon offsets, these competing offsets will 

be preferred in the market." (Deveny et al., 2009, p. 12) 

It is imperative to address this issue during the consideration of an Appalachian forest carbon market. 

Figure 21 depicts the volume of forest carbon presently stored in forests in the ARC study area. This 

illustration is a snapshot in time. The map includes carbon that is presently in aboveground live trees, 

aboveground dead trees, aboveground understory, down woody debris, forest litter, soil, belowground 

understory, and roots. 

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Assessing Appalachian Natural Assets: Forests

Figure 21: Forest carbon in the Appalachian region

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Assessing Appalachian Natural Assets: Forests

References

Congressional Budget Office. 2007. The potential for carbon sequestration in the United States. 

http://www.cbo.gov/ftpdocs/86xx/doc8624/09-12-CarbonSequestration.pdf 

Deveny A, Nackoney J,Purvis N. 2009. Forest Carbon Index: the geography of forests in climate solutions. 

Resources for the Future and Climate Advisers. http://www.forestcarbonindex.org/RFF-Rpt-FCI_small.pdf

Accessed Jan 31, 2011.

Mountain Association for Community Economic Development (MACED). 2009. Seven Kentucky landowners 

get paid $65,000 for managing their forests. Berea, Kentucky. http://www.maced.org/carbon-release2009.htm Accessed Jan 30, 2011.

Richards KR, Sampson RN, Brown S. 2006. Agriculture & forestlands: U.S. carbon policy strategies. Indiana 

University School of Public and Environmental Affairs, The Sampson Group, Inc., and Winrock International, 

Ecosystem Services Unit. Prepared for the Pew Center on Global Climate Change. 

http://www.pewclimate.org/docUploads/Agricultural%20and%20ForestlandsU.S.%20Carbon%20Policy%20Strategies.pdf Accessed Jan 28, 2011.

Stavins R, Richards K. 2005. The cost of U.S. forest-based carbon sequestration. Harvard University and 

Indiana University. Prepared for the Pew Center on Global Climate Change. 

http://www.pewclimate.org/docUploads/Sequest_Final.pdf Accessed Feb 1, 2011. 

Widmann R, Oswalt CM, Moser WK, Gormanson D, Johnson TG, Piva R, Nelson MD, Miles PD, Abt RC, Brown 

TC, Chamberlain JL, Cordell K, Cesa E, Emery M, Flather CH, Mercer E, Prestemon J, Smith WB, Wiedenbeck J. 

Unpublished. Appalachian Forest Assessment, Chapter 3. 

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Assessing Appalachian Natural Assets: Forests

APPENDIX B: CO-FIRING FOREST HARVESTING RESIDUES

Discussion

Appalachia’s vast forest resource offers a variety of opportunities for diversifying the region’s economy. One 

such opportunity that should be better understood for its potential impacts, both positive and negative, is 

the co-firing of under-utilized forest biomass in coal-fired electric power plants. While an expansion in the 

use of forest biomass could benefit the region economically, the scale and pace of that expansion could also 

result in negative changes to the health of the forests or create competition with other wood-using 

industries. Therefore, it is vital to analyze how demand for these resources will change at varying co-firing 

rates and over time.

Widmann et al. (unpublished) estimate that Appalachian forests contain approximately 874 million dry tons 

of potential harvest residues such as tree tops and limbs.5 The same authors also estimate that co-firing 

biomass with coal for electricity generation in the ARC region’s 246 coal-fired boilers would require 25.5 

million dry tons of forest biomass each year. Therefore, according to these estimates, the potential harvest 

residue resource is far greater than what would be required to achieve the region’s potential for co-firing 

with biomass in a single year. The analysis presented below contains a more nuanced analysis, calculating the 

annual demand for three co-firing scenarios, and comparing these demands against annual—as opposed to 

total—harvest residues.

The question of whether an expansion in biomass co-firing in Appalachia would impact other wood-using 

industries must also be addressed. For the purpose of promoting economic diversification and analyzing the 

degree to which biomass co-firing could contribute to that goal, we aim to determine whether co-firing at 

different rates would place pressure on existing demand for forest biomass resources, or whether such cofiring rates could be met by using existing and unutilized resources. In this regard, Ray and Ma (2009) 

estimate that achieving a level where biomass provides 3% of total electricity generation in select 

Appalachian states would negatively impact other wood-using industries, and that “policies and incentives 

that promote large-scale wood co-firing of coal-fired power plants (greater than 5 percent) could raise the 

annual roundwood harvest in the Appalachian states of Maryland, Ohio, Pennsylvania, and West Virginia such 

that the growth to removals ratio approaches or dips below one” (Ray and Ma, 2009). 

On the other hand, “[the] annual positive change in standing inventory coupled with biomass growing in 

understocked stands and unused harvest and mill residues offers important resources to an emerging woody 

biomass industry while maintaining current traditional forest products markets” (Widmann et al., 

unpublished). In other words, because annual forest growth in Appalachia exceeds removals, a substantial 

percentage of stands are understocked and can be improved with better forest management, and there is a 

large yet shrinking (Wang et al., 2007) resource in unused harvest and mill residues, the region can support 

some growth in co-fired generation without competing for resources with other traditional industries. 

To determine whether future demand for forest biomass as a feedstock for co-firing with coal can be met 

with existing and unutilized forest harvest residues, or whether such demand would negatively impact other 

wood-using industries, we compare estimates of future annual demand with annual harvest residues.6

However, it should be noted that full utilization of existing residues may also result in negative impacts to 

forest health due to the ecological benefits the residues provide. We draw no conclusions about what an 

optimal utilization rate would be for existing residues, but this is an important consideration when 

5 This estimate does not reflect actual harvesting residue volumes generated each year. It only represents the total volume of tree tops and limbs that exist in 

Appalachian forests.

6 This analysis focuses only on the use of biomass for co-fired electricity generation. This is due to the substantial impacts that a rapid expansion of co-firing may 

have on the region’s forest resources. However, equally significant impacts may arise from an expanded use of forest biomass for biofuels production and 

heating purposes.

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developing policy that could lead to greater demand for forest biomass resources.

Data and methodology

We examine three scenarios for the co-firing of biomass with coal in the ARC region. The scenarios are 

represented by co-firing rates of 2 percent, 5 percent, and 15 percent by heat input, as measured by million 

British thermal units (mmbtu). The 2 percent co-firing rate was chosen because it is less than the 3 percent

rate at which Ray and Ma (2009) estimate that competing wood industries would be impacted. We chose 5

percent because it represents the co-firing rate identified by Ray and Ma (2009) as the limit for select 

Appalachian states; exceeding this rate would result in annual removals exceeding annual growth. Finally, we 

chose a 15 percent co-firing rate because it represents a feasible rate that could be achieved with strong 

policy supports and incentives, but does not exceed the maximum rate at which Bergman and Zerbe (2004) 

estimate that effective substitution of coal with woody biomass—by heat input—can be achieved. However, 

others estimate that co-firing at rates greater than 10 percent can reduce the overall efficiency of the coalfired power plant, but that at the 10 percent rate, the efficiency loss is only about 1 percent (Perlack, 2011; 

Smith, 2011a).

According to Skone (2012) there are only nine facilities in the US that currently co-fire coal and biomass. 

These are both utility-owned power plants and pulp and paper mills. Three of these are in the Appalachian 

region, although all nine are located in the eastern US:

• Stone Container Co. – South Carolina

• Cogeneration South – South Carolina

• Mobile Energy Services Co. – Alabama

Information regarding the co-firing % for the 3 facilities could not be identified, although a 10% biomass to 

90% coal rate is used throughout the examples provided in Skone (2012). The purpose of the example in the 

Forests report is to provide an estimate of what may be achievable under a range of conservative and bestcase scenarios (2, 5, and 15%), given the available resources in Appalachia. It is unclear why Skone (2012) 

chooses a 10% co-firing rate for analysis purposes

The baseline data for heat input from coal is taken from the US Environmental Protection Agency’s (USEPA’s) 

Clean Air Markets database, which provides annual boiler unit–level data by state and county for all coal-fired 

plants. We use 2008 data as our baseline, because it represents the most recent year for which data were 

available and the US economy was not in a recession. Using conversion rates provided by the federal Energy 

Information Administration (EIA) and others (Smith, 2011b; Antares Group, Inc. and Parsons Power, 1996), 

we then estimate the dry tons of forest biomass7 that would be required, by state and for the region, to 

achieve the three co-firing levels. In doing so, we present three scenarios for future demand for forest 

biomass as a feedstock for co-firing with coal for electricity generation. 

As a second layer of projection, we use projected rates of change in coal-fired electricity generation through 

2020 and 2035 for the seven Electricity Market Module (EMM) regions that cover the thirteen Appalachian 

states —as published by EIA—to illustrate how our results for biomass demand for the three co-firing 

scenarios may differ when comparing results based on 2008 coal-fired generation to projected coal-fired 

generation in 2020 and 2035. Doing so provides a rough idea of how forest biomass demand may change if 

co-firing rates (e.g. 2 percent) are applied to higher or lower levels of coal-fired generation.

Finally, after generating projections for forest biomass demand for each of our three scenarios, we assess the 

impact of meeting that demand on the sustainable use of Appalachia’s forest assets. We do so by comparing 

each projection for future demand with estimated volumes of existing harvest residues and with estimated 

7 We assume that the full biomass demand for co-fired electricity generation would be provided by forest biomass.

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volumes of total potential harvest residues. This will allow researchers and policy-makers to assess the 

impact of policies that have been proposed that would influence demand for forest biomass, and/or propose 

new policy options that would result in the combined benefits of supporting economic development while 

ensuring the sustainable use of Appalachia’s forest assets. 

Estimated forest biomass requirements for co-firing scenarios

This section presents our results for forest biomass feedstock required to achieve a 2 percent, 5 percent and 

15 percent rate of co-firing forest biomass with coal—based on heat (energy) input—at coal-fired power 

plants located within the ARC region. The results are presented in Table 7 by state and for the region. The 

results represent 228 coal-fired boiler units8 with a total gross coal-fired electricity generation of 

approximately 490 million megawatt-hours (MWh) in 2008, which accounted for 25 percent of all coal-fired 

generation in the US, and 54 percent of all coal-fired generation within the thirteen Appalachian states.9

 

Table 7: Estimated annual forest biomass requirements for three co-firing rates

2 percent co-firing 5 percent co-firing 15 percent co-firing

State

Total heat 

input, coal 

(mmbtu)

Biomass heat 

input 

(mmbtu)

Dry tons 

equivalent

Biomass heat 

input 

(mmbtu)

Dry tons 

equivalent

Biomass heat 

input 

(mmbtu)

Dry tons 

equivalent

Alabama 593,408,195 11,868,164 690,010 29,670,410 1,725,024 89,011,229 5,175,071

Georgia 376,737,254 7,534,745 438,067 18,836,863 1,095,166 56,510,588 3,285,499

Kentucky 126,866,780 2,537,336 147,520 6,343,339 368,799 19,030,017 1,106,396

Maryland 5,142,538 102,851 5,980 257,127 14,949 771,381 44,848

Mississippi 35,689,277 713,786 41,499 1,784,464 103,748 5,353,391 311,244

N. Carolina 219,952,337 4,399,047 255,759 10,997,617 639,396 32,992,851 1,918,189

New York 67,114,559 1,342,291 78,040 3,355,728 195,100 10,067,184 585,301

Ohio 1,052,107,362 21,042,147 1,223,381 52,605,368 3,058,452 157,816,104 9,175,355

Pennsylvania 918,990,481 18,379,810 1,068,594 45,949,524 2,671,484 137,848,572 8,014,452

S. Carolina 13,479,071 269,581 15,673 673,954 39,183 2,021,861 117,550

Tennessee 199,784,667 3,995,693 232,308 9,989,233 580,769 29,967,700 1,742,308

Virginia 49,666,024 993,320 57,751 2,483,301 144,378 7,449,904 433,134

W. Virginia 851,599,413 17,031,988 990,232 42,579,971 2,475,580 127,739,912 7,426,739

Total 4,510,537,958 90,210,759 5,244,812 225,526,898 13,112,029 676,580,694 39,336,087

Source: Heat input by state calculated using data from USEPA (2011), and represents the aggregate of heat inputs for all coal-fired boiler units in ARC counties 

in 2008. Notes: Dry tons equivalent were calculated using conversion factors provided by Smith (2011b) and reported by Antares Group, Inc. and Parsons Power 

(1996). According to Ma (2011), actual wood demand will be larger than the estimates reported in this table because a fraction of wood energy input would be 

used for evaporating the moisture remaining in the wood.

As shown in the table, replacing 2 percent of the heat input from coal in the ARC region’s coal-fired boilers, at 

2008 generation levels, would require an annual forest biomass feedstock (harvesting residues) of 5.2 million 

dry tons annually, while co-firing rates of 5 percent and 15 percent would require 13.1 million and 39.3 

million dry tons per year, respectively. 

While the focus of this report is restricted to ARC counties, it is notable that when the same analysis is 

conducted for all coal-fired boilers located within all counties that make up the thirteen Appalachian states, 

the required feedstock for each co-firing scenario is approximately 88 percent greater. 

Impact of projected changes in coal-fired generation on future biomass demand

Table 7 presents estimates of the forest biomass requirements for achieving the three co-firing scenarios 

based on 2008 data. However, significant changes in the relative prices of coal and other fuels for electricity 

generation, as well as the implementation of more stringent regulation of sulfur dioxide and nitrogen oxide 

emissions, are expected to alter the fuel mix for US electricity generation. This will have a disproportionate 

8 This number of coal-fired boiler units is slight smaller than the number of units reported by Widmann, et al. (unpublished). 9 To clarify, the ARC region comprises all or part of thirteen states, but only West Virginia actually lies completely within the ARC region. For the other states, the 

number of counties lying within the ARC region varies widely.

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impact on eastern states, which rely more heavily on coal. Consequently, future demand for forest biomass 

under the three co-firing scenarios may be higher or lower than estimated in Table 7. 

EIA publishes future projections of electricity generation by fuel for various EMM regions,10 which—for the 

purpose of conducting more refined projections—reflect subdivisions of the North American Electric 

Reliability Corporation (NERC) regions. Appalachian states fall wholly or partially within seven EMM regions 

(see Table 8). Projected electricity generation from coal for each region varies, with some regions—and 

therefore some Appalachian states—experiencing greater or lesser changes in coal-fired generation over 

time than others. Table 8 presents the projected rate of change in coal-fired generation, relative to 2008 

generation levels, through 2020 and 2035 for each of the seven Appalachian EMM regions.

Table 8: Projected change in coal-fired generation for Appalachian EMM regions, 2020 and 2035

Percent change in coal-fired electricity generation

Electricity Market Module region Appalachian states in region 2008-2020 2008-2035

NPCC Upstate New York -56.7% -52.4%

RFC East Pennsylvania, Maryland -2.0% 6.6%

RFC West Ohio, West Virginia -3.7% 13.7%

SERC Central Kentucky, Tennessee -13.2% 4.1%

SERC VACAR Virginia, N. Carolina, S. Carolina -1.1% 13.8%

SERC Southeast Georgia, Alabama -9.4% 7.6%

SERC Delta Mississippi 16.8% 15.7%

Total -5.4% 9.5%

Source: EIA (2011). Notes: Some states fall within two different EMM regions. For instance, roughly a quarter of the land area of both Maryland and 

Pennsylvania fall within the RFC West EMM region, while the remaining and greater portion of the two states falls within the RFC East region. For this analysis, 

we place each state into the dominant EMM region according to proportion of land area, and apply the rate of change in coal-fired generation for that region to 

the respective state. NPCC = Northeast Power Coordinating Council, RFC = Reliability First Corporation, SERC = Southeast Reliability Corporation, VACAR = 

Virginia-Carolinas.

As shown in the table, coal-fired electricity generation through 2020 is projected to decline for each of the 

seven Appalachian EMM regions except SERC Delta. However, as the result of growth in electricity demand—

and a projected resurgence in demand for coal-fired electricity—all EMM regions except for Northeast Power 

Coordinating Council (NPCC) Upstate are expected to experience a net increase in coal-fired generation 

through 2035 (EIA, 2011). This is also true for the Appalachian region as a whole, and illustrates how demand 

for Appalachian forest biomass resources may change over time at set co-firing rates.11 In other words, at 

each of the three co-firing rates chosen for this study, total Appalachian demand for forest biomass as a 

feedstock for co-firing with coal may be 5.4% percent lower than estimated in Table 7 based on projected 

declines in co-fired generation through 2020, and 9.5 percent higher in 2035. 

These rates will be different on the state level. For example, as Kentucky falls within the SERC Central region, 

it can be estimated that forest biomass demand in Kentucky for co-firing may be 13.2% lower in 2020 than 

estimated in Table 7, and 4.1 percent higher in 2035. However, it is important to state that significant 

regulatory changes are likely to occur that have yet to be modeled by EIA, because new regulations were still 

being drafted when the most recent EIA projections were published. The new regulations are expected to 

have a significant impact on coal-fired electricity generation (Nelson, 2011), resulting in the retirement of a 

10 The calculations underlying the results in Table 7 use heat input as the basis for calculation, while EIA’s projections are reported in billion kilowatt-hours. This 

is notable because it results in a slight discrepancy when applying projected changes in coal-fired electricity generation over time (measured in kilowatt-hours) to 

2008 results for forest biomass demand that were calculated using heat input (measured in btus). Depending on the efficiency of the boiler unit and the quality of 

the coal being burned, the amount of output—electricity generation—will vary slightly from boiler to boiler and from year to year. However, this would have a 

negligible impact on our results.

11 It should also be noted that the projected changes in coal-fired electricity generation represent projected changes for a broad region consisting of more than 

one state in most cases, and we are applying these regional projections to the ARC portions of states that make up each respective EMM region. This will result 

in a certain level of error in the analysis, but the EMM projections provide a strong proxy from which to project future changes in coal-fired generation for the 

boilers located within the ARC portion of the states.

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significant amount of coal-fired capacity. Assuming that any new coal-fired capacity developed would fail to 

equal the retired capacity, this would have a negative impact on coal-fired generation in the region, and

therefore the volume of forest biomass required to achieve the three co-firing rates. 

Achievable co-firing rates based on available forest biomass resources 

This section analyzes whether existing harvesting residues in Appalachian forests—in other words, tree tops 

and limbs left behind in the forest following a timber harvest—are sufficient for meeting projected demand 

for co-firing with coal under each of the three scenarios. To do so, we compare our demand projections with 

our estimate for current annual volume of harvesting residues as presented in Section 0. Table 9 presents the 

results of this comparison for each of the three co-firing scenarios based on 2008 levels of co-fired electricity 

generation. Table 9 also presents the estimated rates of co-firing that are achievable for each state and for 

the ARC region given existing volumes of harvest residues.

Table 9: Harvest residues and co-firing demands under three co-firing scenarios, 2008 (dry tons)

Co-firing demand

State 

Annual harvest 

residues 2% co-fire 5% co-fire 15% co-fire

Achievable cofiring rate

Alabama 1,788,917 690,010 1,725,024 5,175,071 5.2%

Georgia 754,246 438,067 1,095,166 3,285,499 3.4%

Kentucky 1,286,753 147,520 368,799 1,106,396 17.4%

Maryland 86,555 5,980 14,949 44,848 28.9%

Mississippi 863,913 41,499 103,748 311,244 41.6%

North Carolina 887,532 255,759 639,396 1,918,189 6.9%

New York 800,735 78,040 195,100 585,301 20.5%

Ohio 919,536 1,223,381 3,058,452 9,175,355 1.5%

Pennsylvania 2,507,563 1,068,594 2,671,484 8,014,452 4.7%

South Carolina 221,965 15,673 39,183 117,550 28.3%

Tennessee 1,275,330 232,308 580,769 1,742,308 11.0%

Virginia 812,849 57,751 144,378 433,134 28.2%

West Virginia 1,981,332 990,232 2,475,580 7,426,739 4.0%

Total 14,187,226 5,244,812 13,112,029 39,336,087 5.4%

Source: Annual harvest are calculated using sources and methods described below in the technical notes to this appendix. Estimates for existing

forest harvest residues are conservative as they do not include residues—tree tops and limbs—less than four inches in diameter or less than eight 

feet long. 

As shown in the table, annual forest harvesting residues that exist in the ARC counties within the thirteen 

Appalachian states are sufficient for achieving at least a 2 percent average rate of co-firing with coal—at coalfired boilers located within ARC counties—in every state except Ohio (1.5 percent). State-level results vary 

widely depending on the number and size (heat input capacity) of coal-fired boilers in ARC counties, and on 

the available harvest residues. On average, however, we conclude that the region as a whole can achieve in 

excess of a 5 percent rate of co-firing forest biomass with coal using only the existing, unutilized harvest 

residues, which amount to an estimated 14.2 million dry tons. Any additional demand for co-firing forest 

biomass with coal would require either additional timber and residue harvesting, or a transfer of woody 

biomass resources from other wood-using industries.

While the calculations are not shown here, annual harvest residues would be sufficient to achieve exactly a 5

percent rate of co-firing in 2035, using EIA’s projections and our calculations of coal-fired electricity 

generation in ARC states, and assuming that the annual volume of available harvest residues does not change 

over time. While these assumptions may not occur, the analysis does provide a sense of the volume of forest 

biomass resources that may be required for co-fired electricity generation in the future.

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Figure 7, on page 20 in the main body of the report shows the estimated volume of existing harvesting 

residues by ARC county, as well as the location and heat input from coal at all coal-fired power plants located 

in the ARC region in 2008. The map provides an illustration of the proximity—and therefore availability—of 

forest harvesting residues to the region’s coal-fired power plants.

Conclusions

The US Department of Energy states that the best opportunities for co-firing biomass with coal for electricity 

generation occur when coal prices are high and annual coal usage is significant (Hayter et al., 2004). 

Appalachian coal prices, and subsequently the delivered price of coal to power plants across the region, have 

increased significantly since 2000, resulting in rising electricity costs and greater investment in alternative 

sources of coal, fuel, and electricity generation (McIlmoil and Hansen, 2010). Additionally, many Appalachian 

states rely heavily on coal for electricity generation. Therefore, while numerous alternatives to coal exist, 

greater attention has been paid to biomass in recent years, particularly when the implementation of a 

national renewable electricity standard appeared likely. Recent political developments have quieted those 

discussions; however, rising prices for traditional fuels and the prospect of stronger regulation of air 

emissions from coal-fired power plants may reignite interest in biomass co-firing. 

While it is still uncertain whether forest biomass will be used for electricity generation in Appalachia, it is 

important to understand the potential impacts of expanding the co-firing of forest biomass resources with 

coal. An expansion in any use of forest biomass could benefit the region economically, but the scale and pace 

of that expansion could result in negative impacts on forest health and create competition with existing 

wood-using industries. 

This study estimates the volume of forest harvesting residues that would be required to achieve three 

possible co-firing levels at coal-fired boilers in the ARC region. These volumes are useful for comparing with 

estimates of existing harvest residues in order to determine the co-firing rates at which existing residues are 

sufficient for meeting the required demand, both now and in the future, or whether additional harvesting will 

be required to achieve a target co-firing rate. In doing so, we are able to inform public policy aimed at 

supporting the expansion of co-firing biomass with coal for meeting renewable energy standards or for 

achieving targeted emissions reductions. We are also able to determine whether expanding biomass co-firing 

to certain levels will result in competition for resources with existing wood-using industries—which could 

hinder rather than promote economic diversification.

Our analysis concludes that achieving a 2 percent co-firing rate at all coal-fired power plants across the ARC 

region would require 5.2 million dry tons of forest harvest residues annually, while achieving 5 percent and 

15percent co-firing rates would require 13.1 and 39.3 million dry tons per year, respectively. With an 

estimated 14.2 million dry tons of harvest residues available each year, the 2 percent scenario appears to be 

feasible. The 5 percent scenario is marginal: demand is very close to the estimated annual harvest residues. 

The 15 percent scenario is not feasible, because it would place demands on the region’s forests that clearly 

exceed their annual production of harvest residues.

Ray and Ma (2009) reached a consistent conclusion; they note that a 5 percent rate of co-firing “could raise 

the annual roundwood harvest in the Appalachian states of Maryland, Ohio, Pennsylvania, and West Virginia 

such that the growth to removals ratio approaches or dips below one.” 

It should be noted that Ray and Ma use a different methodology and a broader area of analysis, focusing on 

all coal-fired generation and forest biomass for each Appalachian state they studied rather than just the ARC 

portion of those states. Therefore, we cannot make a direct comparison of the two studies. Despite this, the 

results of both studies support an important conclusion: that any expansion of co-firing forest biomass with 

coal beyond the level where unutilized resources are sufficient for meeting the increased demand will have 

undesirable outcomes, namely increased harvesting beyond a sustainable level or competition for resources 

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with existing wood-using industries. Equally important is the fact that utilizing all existing forest harvesting 

residues may have undesirable ecological impacts, as harvesting residue provides refuge and foraging habitat 

for many species of wildlife, is a source of soil nutrients, and supports forest regeneration following natural 

or man-made disturbances (Grushecky et al., 1998). 

These conclusions raise some important questions, particularly in considering how energy policy is structured 

with the goal of supporting an increase in renewable energy, a reduction of coal-related air emissions, or 

both. Sample enumerates these questions appropriately, in asking:

“What is the optimal combination of these technologies that will maximize the contribution of 

woody biomass to the nation’s renewable energy goals, most efficiently utilize wood resources, and 

ensure that forests continue to be sustainably managed for a range of public values? How will we 

realign the framework of federal, state, and local policies, mandates, and incentives such that 

rational economic decisions will result in private capital investment that will produce this outcome?” 

(Sample, 2010, p. 1)

Sample further points out that “The useful life of a power plant may be 30+ yrs, so the decisions made today 

will affect the region’s communities and forests, and potentially limit other options, for many decades into 

the future” (Sample, 2010, p. 21). Given these considerations, any expansion of co-firing forest biomass with 

coal in the ARC region or elsewhere must be limited to a level where growth continues to exceed removals, 

where existing wood-using industries are not negatively impacted, and where resource demand and 

consumption do not negatively impact the health and regeneration of forests. As noted by Widmann et al. 

(unpublished), these goals may be achieved through maximizing the use of available underutilized resources 

and by improving the productivity of forests and the health of forest stands through improved forest 

management practices. These approaches should be pursued before promoting the use of forest resources 

such as forest harvesting residues as a feedstock for co-firing with coal at levels that negatively impact the 

health of Appalachia’s forests or traditional wood-using industries.

Technical notes

Conversion of million btus of heat input to dry tons of forest biomass required to achieve a 

given co-firing rate

Estimates of the heat content of forest biomass vary. However, a conversion rate of 17.2 mmbtu of heating 

potential per dry ton of forest biomass was provided by EIA (Smith, 2011b) and reported by Antares Group, 

Inc. and Parsons Power (1996). This is the conversion rate chosen for this study. 

The calculation for estimating the dry tons of biomass required to achieve a given co-firing rate is structured 

as follows:

Dry tons of forest biomass = mmbtu of heat input x target co-firing rate / 17.2 mmbtu per dry ton

The heating value depends on the species of tree; however, we use an average heating value of woody 

biomass and apply that value to our calculations for the Appalachian region. A more refined analysis would 

estimate potential forest biomass demand by using state- or forest-specific average heating values.

Estimating changes in coal-fired electricity generation in Appalachian states through 2020 and 

2035

EIA publishes annual reports titled Annual Energy Outlook that, among other things, provide projections of 

annual coal-fired electricity generation through 2035 for each EIA-defined EMM region. The EMM regions 

represent sub-divisions of the larger NERC regions. A map of the EMM regions modeled by EIA can be found 

here: www.eia.gov/forecasts/aeo/pdf/f2.pdf. 

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Assessing Appalachian Natural Assets: Forests

There are seven EMM regions that include or are wholly comprised of Appalachian states. Some states fall 

within two different EMM regions. For instance, less than half of the land area of both Maryland and 

Pennsylvania fall within the Reliability First Corporation (RFC) West EMM region, while the greater portion of 

the two states falls within the RFC East region. For our analysis, we place each state into the dominant EMM 

region according to proportion of land area, and apply the rate of change in coal-fired generation for that 

region to the respective state. This provides an estimate of how coal-fired electricity generation may grow or 

decline for each of the thirteen Appalachian states.

To estimate future demand for forest harvesting residue under our three co-firing scenarios, we apply the 

projected rates of change in coal-fired electricity generation for each state to total 2008 heat input from coal 

in the ARC region’s coal-fired boilers and produce aggregated values for coal-based heat input. These values 

are used in the analysis to estimate future demand for forest biomass by state. Doing so relies on a few 

assumptions that are important to note:

1. that the EMM region chosen for states that fall within the boundaries of more than one EMM 

region is appropriate, meaning the chosen EMM region overlaps with the ARC portion of the 

state;

2. that each state that falls within a certain EMM region will experience the same rate of change in 

coal-fired electricity generation over time as the EMM region does on average;

3. that the state-level estimates for changes in coal-fired electricity generation over time apply to 

the ARC portions of the respective state—in other words, we apply the state-level rate of change 

to the coal-fired boilers located within the boundary of the ARC region; and that the average 

heat content of coal burned at coal-fired boilers within each state will not change over time.

Estimating annual forest harvesting residues by county

FIA does not report harvesting residues; however, given the growth in wood-using industries and the 

expansion in demand for historically underutilized forest biomass resources, there is a strong need for better 

data on county-level harvesting residues. For this study, we generate our own estimates for the average 

volume of harvest residues that remained in Appalachian forests following timber harvest in 2009 and 2010. 

To estimate the existing harvest residues by county and state in the ARC region, we:

1. determined the average acres of timberland in the Appalachian region in 2009 and 2010, using data 

obtained from FIA;

2. applied a 2 percent annual harvest rate to the total area of timberland to estimate the acreage of 

harvested timberland for 2009 and 2010; and

3. applied an average forest harvesting residual per acre harvested of 8.2 tons per acre. Multiplying 8.2 

tons per acre by the estimated acreage of timber harvest resulted in an estimated volume for annual 

harvest residual. This calculation was performed for each ARC county, and the results are illustrated 

in Figure 7. 

The 2 percent harvest rate used in the calculation represents an average harvest rate as presented by 

Grushecky (2009) for West Virginia. As other similar studies are not available for determining a state-specific 

average harvest rate for all ARC states, we apply this rate to all ARC counties. 

Similarly, the lack of available research on forest harvesting residuals in other ARC states required us to use 

the best available information. Therefore, the multiplier used in this study of 8.2 tons of harvest residue per 

acre harvested was drawn from the same analysis, which was conducted for West Virginia forests in 2007 

(Grushecky, 2009). This is slightly smaller than a previous estimate of 8.4 tons per acre reported for West 

Virginia based on harvests in 1993 and 1994 (Grushecky et al., 1998). A later study focusing only on the 

southern counties of West Virginia found an even higher density of harvest residues at 10.4 tons per acre 

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Assessing Appalachian Natural Assets: Forests

(Grushecky et al., 2006). Each of these estimates are considered to be conservative since they were 

generated without measuring residues less than 4 inches in diameter or less than 4 feet in length. 

Grushecky et al. (2006) report wide variability in forest harvesting residue density among different species—

ranging from 0.1 (cherry and softwoods) to 5.0 (oak) dry tons per acre—as well as among individual counties. 

For instance, the average density of harvest residues in Boone County, West Virginia is reported as 12.7 tons 

per acre, while that for Logan County is reported as 7.1 tons per acre. These examples of variability in postharvest residue density illustrate the need for better county-level data for harvest residues, as harvesting 

rate, harvest practices, species composition, and other factors may result in under- or overestimates of 

residues. However, for this study we used the best available research and information. 

References

Antares Group, Inc. and Parsons Power. 1996. Utility coal-biomass co-firing plant opportunities and 

conceptual assessments. Prepared for the Northeast Regional Biomass Program and United States 

Department of Energy. Final Report. http://www.nrbp.org/pdfs/pub12.pdf Accessed Jan 27, 2011.

Bergman R, Zerbe J. 2004. Primer on wood biomass for energy. USDA Forest Service, State and Private 

Forestry Technology Marketing Unit, and Forest Products Laboratory, Madison, Wisc. 

http://www.fpl.fs.fed.us/documnts/tmu/biomass_energy/primer_on_wood_biomass_for_energy.pdf

Accessed Jan 27, 2011.

Energy Information Administration (EIA). 2011. Annual Energy Outlook 2011. Supplemental tables for 

regional detail. Table 96: Electricity generation by Electricity Market Module Region and Source (Reference 

case). http://www.eia.gov/oiaf/aeo/supplement/suptab_96.xls Accessed Jul 16, 2011.

Grushecky ST, Hassler CC, Fajvan MA. 1998. Logging residue yield equations from West Virginia timber 

harvests. West Virginia University, Division of Forestry, Morgantown, West Virginia.

Grushecky ST, McGill DW, Anderson RB. 2006. Inventory of wood residues in southern West Virginia. West 

Virginia University, Appalachian Hardwood Center and Division of Forestry. 

http://ahc.caf.wvu.edu/joomla/index.php?option=com_remository&Itemid=148&func=fileinfo&id=132

Accessed Aug 9, 2011.

Grushecky ST. 2009. Detailing biomass availability in Appalachia – a case study from West Virginia (WV) (with 

“special” estimates for Ohio). Presentation to the 2009 meeting of the Ohio Forestry Association. West 

Virginia University, Appalachian Hardwood Center. 

http://wvbiomass.org/index.php?option=com_jdownloads&Itemid=0&task=view.download&cid=3 Mar 7-8. 

Hayter S, Tanner S, Comer K, Demeter C. 2004. Biomass co-firing in coal-fired boilers. US Department of 

Energy, Energy Efficiency and Renewable Energy, Federal Energy Management Program. 

http://www1.eere.energy.gov/femp/pdfs/fta_biomass_cofiring.pdf Accessed Jan 27, 2011.

McIlmoil R, Hansen E. 2010. The decline of Central Appalachian coal and the need for economic 

diversification. Downstream Strategies, Morgantown, W.Va. 

http://www.downstreamstrategies.com/documents/reports_publication/DownstreamStrategiesDeclineOfCentralAppalachianCoal-FINAL-1-19-10.pdf Accessed Jun 10, 2011. 

Ma L. 2011. Email correspondence with author McIlmoil. Aug 11.

Nelson G. 2011. Greenwire. Fate of old coal plants may hinge on new toxic-cutting technology. New York 

Times. http://www.nytimes.com/gwire/2011/04/13/13greenwire-fate-of-old-coal-plants-may-hinge-on-newtoxic-91053.html?scp=1&sq=coal%20plant%20gigawatt&st=Search Apr. 13.

64 | Page
Assessing Appalachian Natural Assets: Forests

Perlack RD. 2011. Senior Research Economist, Bioenergy Resource & Engineering Systems Environmental 

Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee. Email correspondence with author 

McIlmoil. Jun 28.

Ray C, Ma L. 2009. On the issue of wood co-firing of coal-fired power plants. Penn State University College of 

Agricultural Sciences, School of Forest Resources. WoodPro TechNote 2009-4. 

http://woodpro.cas.psu.edu/WoodPro%20Word%20HTML%20files/WoodPro%20TechNote%202009-4.htm

Accessed Jan 31, 2011.

Sample VA. 2010. The future of wood bioenergy in the United States: defining sustainability, status, trends 

and outlooks for regional development. Section 1.1: What role will forests play in America’s long-term energy 

future? Pinchot Institute for Conservation. http://www.pinchot.org/pubs/304 Accessed Oct 10, 2011.

Skone, T.J. 2012. Role of Alternative Energy Sources: Pulverized Coal and Biomass Co-firing Technology 

Assessment. DOE/NETL-2012/1537. 91 pp.

Smith R. 2011a. Renewable energy analyst, Energy Information Administration, Washington, DC. Email 

correspondence with author McIlmoil. Jul 15.

_______________. 2011b. Renewable energy analyst, Energy Information Administration, Washington, DC. 

Email correspondence with author McIlmoil. Jun 24.

US Environmental Protection Agency (USEPA). 2011. Clean air markets – data and maps. Query: unit-level 

emissions, annual, all units. Output: state, facility name, facility ID, gross load (MWh), heat input (mmbtu), 

county, facility latitude, facility longitude, operating status, fuel type(s) (primary). 

http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=emissions.wizard Accessed Jul 22, 2011.

Wang J, Grushecky S, McNeel J. 2007. Biomass resources, uses, and opportunities in West Virginia. West 

Virginia University, Biomaterials and Wood Utilization Research Center, Division of Forestry and Natural 

Resources. http://www.wdscapps.caf.wvu.edu/biomatwurctr/files/wvbiomass09102007.pdf Accessed Jan 29, 

2011.

Widmann R, Oswalt CM, Moser WK, Gormanson D, Johnson TG, Piva R, Nelson MD, Miles PD, Abt RC, Brown 

TC, Chamberlain JL, Cordell K, Cesa E, Emery M, Flather CH, Mercer E, Prestemon J, Smith WB, Wiedenbeck J. 

Unpublished. Appalachian Forest Assessment, Chapter 3. 

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Assessing Appalachian Natural Assets: Forests

APPENDIX C: FOREST HEALTH AND BIOMASS

Evaluating degraded stands and management opportunities for improving forest health and 

increasing woody biomass availability in the Appalachian region

Discussion

Degraded hardwood forest stands in Appalachia contain trees that are of poor form, unsound, or diseased; 

are of non-merchantable species; or have residual damage from harvesting operations and corresponding 

lower growth rates compared to healthy forests. Degraded stands may also have low stocking densities or 

patches of low stocking, and may lack advanced regeneration of desirable species. As a result of previous 

management or harvesting practices, these degraded stands do not contain adequate densities or volumes of 

merchantable growing stock trees, which presents both challenges and opportunities for future management 

of such forest resources.

A study on the effects of harvesting practices on West Virginia’s wood supply (Fajvan et al., 1998) showed 

that 25 percent of the sites harvested during the study year had the majority of residual trees classified as 

ungraded growing stock, but still carried a basal area density of 70 square feet per acre. Stands are usually 

not considered to be seriously degraded unless they contain less than 50 square feet per acre of acceptable 

growing stock trees (Clatterbuck, 2006). Therefore, the stands considered in this analysis do not contain 

sufficient numbers of acceptable growing stock trees, and can be considered to be seriously degraded 

residual stands that can benefit from some type of management activity for improving forest health. 

Seriously degraded stands should be regenerated, as they simply do not contain enough acceptable growing 

stock to successfully be improved through rehabilitation practices. Regeneration methods usually require the 

removal of most trees, while leaving the understory seedlings and saplings intact and void of their larger 

competition. Depending on the species composition and size of the understory, the desired forest structure 

and composition can be influenced by proper understory management techniques in the years immediately 

following a regeneration harvest. 

The immediate benefits of degraded stand management to the forest are minimal and the trees removed are 

usually of low quality. However, even low quality trees can be utilized as a raw woody material. The longterm benefits to the forest are greater, and include the regeneration of merchantable tree species of good 

form and vigor, improved aesthetics, greater resiliency to disturbances such as insects and fire, and greater 

potential for terrestrial carbon storage by decreasing the risk of carbon loss and increasing forest growth 

(Ryan et al., 2010). 

Data and methods

This analysis reports the estimated annual volume of woody biomass available from seriously degraded 

residual stands, with the assumption that all stands could benefit from a regeneration harvest to improve 

forest health. Woody biomass availability is the focus of the analysis because the research of Fajvan et al. 

(1998) classified degraded stands as having wood fiber potential, which suggests a low quality material 

ideally utilized as feedstock for biomass-related energy enterprises. 

FIA data were compiled for Appalachian counties. To calculate the annual potential volume of available 

woody biomass from degraded residual stands, the net volume of live trees and the annual harvest rate in 

terms of cubic feet of wood volume removed were queried from the FIA database. The forestland area 

subject to annual harvest was assumed to be 2 percent of the total forestland area within each county, and 

harvested areas with the majority of residual trees classified as ungraded growing stock were assumed to be 

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Assessing Appalachian Natural Assets: Forests

25 percent of the total annual harvest area (Fajvan et al., 1998). The calculation of the volume of woody 

biomass available from degraded stands was formulated as: 

Vdegraded_stands = (vaverage * aharvest) * %degraded

 Where: 

vaverage = average cubic foot per acre of county forested area minus annual 

removal volume

 aharvest = acres of forested area subject to harvest within county

%degraded = percentage of harvested forest area classified as degraded 

 stands

Results

Applying the results from the Fajvan et al. (1998) study suggests that upwards of 788 million cubic feet of 

post-harvest residual live tree volume could be available for utilization from approximately 430,000 acres of 

degraded stands in the Appalachian forest region. Degraded stand areas ranged from 140 acres in Hancock 

County, West Virginia to greater than 3,400 acres in Delaware County, New York. The average area of 

degraded stands across the Appalachian forest region was approximately 1,000 acres per county.

Because it was concluded that all degraded stands in this region could benefit from a regeneration harvest, 

and that the focus was to utilize the harvested trees as woody biomass feedstock, a conversion to dry mass is 

necessary. We assume an average specific gravity of 0.50 for green wood equates to an average dry wood 

density of 31.2 pounds per cubic foot (Forest Products Laboratory, 1999). This yields approximately 12 million 

dry tons of woody biomass at 100 percent utilization. Hardwood species account for 90 percent of the 

biomass potential, whereas softwood species comprise the rest. However, complete utilization of the woody 

material is virtually impossible to achieve even with recent forest harvesting and chipping technology, and 

therefore a more realistic utilization level would be less than 12 million dry tons. Results of potential woody 

biomass volume ranged from approximately 27,000 tons in the western counties of Maryland to 2.4 million 

tons in the Appalachian counties of Pennsylvania (Table 10). County estimates appear to be greatest in the 

central Appalachian forest region and generally taper in the counties closer to the regional edge (Figure 22). 

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Assessing Appalachian Natural Assets: Forests

Table 10: Available volume of woody biomass via regeneration of degraded stands 

Hardwood Softwood

State Tons Percent Tons Percent Total

Alabama 862,580 66% 444,871 34% 1,307,451

Georgia 549,936 77% 163,372 23% 713,308

Kentucky 1,112,175 98% 26,428 2% 1,138,603

Maryland 25,884 95% 1,261 5% 27,145

Mississippi 312,996 59% 216,337 41% 529,333

New York 715,711 88% 95,448 12% 811,159

North Carolina 1,015,009 92% 90,661 8% 1,105,670

Ohio 725,809 97% 18,827 3% 744,636

Pennsylvania 2,368,637 96% 94,817 4% 2,463,454

South Carolina 168,889 79% 44,909 21% 213,798

Tennessee 1,173,047 93% 83,917 7% 1,256,964

Virginia 758,246 93% 53,302 7% 811,548

West Virginia 1,153,200 98% 17,749 2% 1,170,949

Total 10,942,119 89% 1,351,899 11% 12,294,018

Conclusion

Regenerating degraded forest stands through active forest management can contribute a substantial amount 

of woody biomass for utilization within the Appalachian forest region. The regeneration of these stands will 

also decrease the susceptibility to insect and disease disturbances by improving forest health, therefore 

mitigating the risk of carbon loss associated with the increased mortality from such disturbances. 

Furthermore, a healthy regenerated stand will grow more quickly and thus increase the carbon uptake in 

trees. Increasing carbon uptake and decreasing the risk of carbon loss will yield greater terrestrial carbon 

stocks in the future, as well as provide a healthier forest overall.

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Assessing Appalachian Natural Assets: Forests

References

Clatterbuck WK. 2006. Treatments for improving degraded hardwood stands. Professional Hardwood Notes 

#6. Knoxville, TN: University of Tennessee Extension SP680, 12 p. 

Fajvan MA, Grushecky ST, Hassler CC. 1998. The effects of harvesting practices on West Virginia’s wood 

supply. Journal of Forestry 96(5): 33-39.

Forest Products Laboratory. 1999. Wood handbook—wood as an engineering material. USDA Forest Service, 

Forest Products Laboratory. FPL–GTR–113. Madison, Wisc. 463 p.

Ryan MG, Harmon ME, Birdsey RA, Giardina CP, Heath LS, Houghton RA, Jackson RB, McKinley DC, Morrison 

JF, Murray BC, Pataki DE, Skog KE. 2010. A synthesis of the science on forests and carbon for US forests. 

Issues in Ecology Report No. 3. 16p.

Figure 22: Available woody biomass in degraded stands

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APPENDIX D: DECISION SUPPORT SYSTEM

Integrated GIS tool

One of the important aspects of any 

applied research project is the method 

of communicating results to decisionmakers. The output of our study 

includes an interactive GIS-based DSS 

(Figure 23) that allows resource 

managers to evaluate inputs and 

results of this study, understanding the 

true spatial nature and relationships of 

these natural assets. This system 

integrates spatial data, user input, and 

a ranking algorithm within a multiple 

criteria analysis (MCA) framework. The 

goal of this framework is to provide a 

tool to integrate spatial data with a 

MCA-solving algorithm called 

compromise programming (CP), which 

allows users to quickly and 

interactively explore and analyze county-level data.

MCA is an alternative approach to traditional economic evaluation techniques. The basic idea behind MCA is 

to provide a framework for analyzing choices with multiple criteria and conflicting objectives (Malczewski, 

1999). A spatial MCA approach aids in the identification of the most suitable management solution for a 

given purpose. The approach also allows users to examine the effects of alternative options and presents 

options in a variety of forms such as monetary units, physical units, and qualitative judgments. This makes it 

possible to analyze tradeoffs between different objectives and address potential conflicts at an early stage, 

thereby providing the ability to analyze the 

sensitivity and robustness of different choices. 

The CP ranking algorithm was chosen because

it allows a more theoretically significant 

ranking of alternatives as compared to a linear 

weighted model. It also allows the user to 

integrate sensitivity analysis by altering 

weights and parameter values to highlight the 

concern of the decision-maker over the degree 

of separation or difference from the ideal 

criteria score. The highest ranked results are 

those that are closest to the ideal or furthest 

from the least preferred alternatives. CP

algorithms have been used in many different 

MCA applications including ranking of irrigation 

technologies (Tecle and Yitayew, 1990), 

planning water resource systems (Duckstein 

Figure 23: Decision support system

Figure 24: Screenshot of tool interface

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Assessing Appalachian Natural Assets: Forests

and Opricovic, 1980; Gershon and Duckstein, 1983), developing forest watershed management schemes 

(Tecle et al., 1988a), selecting wastewater management alternatives (Tecle et al., 1988b), defining 

hydropower operations (Duckstein et al., 1989), and performing river basin planning (Hobbs, 1983). 

The tool compares allows the decision-maker to assign a weight (or importance value) to each individual 

criterion and then combine all criteria together for a comprehensive overall result. End users can combine 

and map the various factors for ranking economic development and forest resources in the ARC region. The 

final spatial model uses an extension to ESRI’s ArcGIS software. The extension consists of a graphical interface 

designed to guide the user through the process of interactively specifying weights and viewing results Figure 

24. This model also provides the ability to display the top- and bottom-ranked counties and to map spatial 

clusters. 

The CP ranking model requires that the user first highlight or make active a shapefile in the table of contents 

that contains attributes the user wishes to use for the ranking (Figure 25). It is assumed that the user already 

calculated or added the needed fields to the table in order to use the ranking model. All of the criteria are 

normalized by the program, so the user does not have to worry about non-commensurate data. All that is 

required is the direction of value influence. For example, if a higher value for an attribute is desired, then 

nothing has to be 

altered in the CP 

interface; this is 

the default.

However, if the 

user feels that a 

lower value is 

preferred, the 

inverse button 

should be 

selected. 

The parameter 

values of P

indicate the 

concern of the 

decision-maker 

over the 

deviation from 

the ideal values.

These values 

represent the 

concern of the decision-maker over the maximum deviation (Tecle and Yitayew, 1990; Duckstein and 

Opricovic, 1980). The larger the value of P, the greater the concern. For P = 1, all weighted deviations are 

assumed to compensate each other perfectly. For P = 2, each weighted deviation is accounted for in direct 

proportion to its size. As P approaches infinity, the alternative with the largest deviation receives more 

weight and importance (the largest of the deviations completely dominates) (Zeleny, 1982). To solve the 

multi-criteria problem using the CP algorithm, the vectors of ideal point values and worst values are 

determined and then used to compute the values distances from the ideal points. The preferred alternative 

has the minimum Lp distance value for each P and weight set that may be used. Thus, the alternative (county, 

in our example) with the lowest value for the metric will be the best compromise solution because it is the 

nearest solution with respect to the ideal point. The parameter P acts as a weight attached to the deviations 

Figure 25: Selecting a shapefile to be used in the ranking

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Assessing Appalachian Natural Assets: Forests

according to their magnitudes. Similar weights for various deviations signify the relative importance of each 

criterion (Romero and Rehman, 1989). 

The result of the model run is the addition of a new field to the shapefile—the CP metric. Lower values are 

preferred and a legend is produced automatically for the user. This legend can always be altered to show a 

different display of the ranked counties. The true utility of the tool is in the ability to quickly run different 

scenarios and test the spatial sensitivity of results.

Finding the top or bottom percentages

Some of the other tools available for the user include the ability to find the top or bottom percentage of 

ranked features. This was designed to highlight the counties that meet certain threshold requirements in 

regard to the rankings (Figure 26). 

Spatial clusters

The Spatial Clusters Tool is based on a hot spot analysis. This is a spatial statistical calculation that takes into 

account the spatial position of features and their attributes. The purpose of using the tool is to find areas 

with high values surrounded by other high values (hot spot) or low values surrounded by other areas with 

low values (cold spot) that are statistically significant. Figure 27 shows spatial clusters for a sample indicator, 

which include both hot and cold spots. 

Figure 26: Percentage queries for the highest and lowest percentages

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Assessing Appalachian Natural Assets: Forests

It is important to carefully select the analysis field. The Z-scores and P-values are measures of statistical 

significance, which tell users whether or not to reject the null hypothesis, feature by feature. In effect, they 

indicate whether the observed spatial clustering of high or low values is more pronounced than one would 

expect in a random distribution of those same values. 

References

Duckstein L, Opricovic S. 1980. Multiobjective optimization in river basin development. Water Resources 

Research 16(1):14-20.

Duckstein L, Tecle A, Nachnebel HP, Hobbs BF. 1989. Multicriterion analysis of hydropower operation. Journal 

of Energy Engineering 115(3):132-152.

Gershon M, Duckstein L. 1983. Multiobjective approaches to river basin planning. Journal of Water Resources 

Planning and Management 109(1):13-28.

Hobbs BF. 1983. Discussion of multiobjective approaches to river basin planning. Journal of Water Resources 

Planning and Management 109(1):13-28.

Malczewski J. 1999. GIS and Multicriteria Decision Analysis. New York, NY: John Wiley and Sons. 

Romero C, Rehman T. 1989. Multiple Criteria Analysis for Agricultural Decisions. New York, New York: Elsevier 

Science Publishing Company Inc.

Tecle A, Yitayew M. 1990. Preference ranking of alternative irrigation technologies via a multicriterion 

decision making procedure. American Society of Agricultural Engineers 33(5):1509-1517.

Figure 27: Hot and cold spots

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Assessing Appalachian Natural Assets: Forests

Tecle A, Fogel MM, Duckstein L. 1988a. Multicriterion analysis of forest watershed management alternatives. 

Water Resources Bulletin 24(6):1169-1177.

_______________. 1988b. Multicriterion selection of wastewater management alternatives. Journal of Water 

Resources Planning and Management 114(4):383-398.

Zeleny M. 1982. Multiple Criteria Decision Making. McGraw - Hill Book Company, New York, NY.

74 | Page
Assessing Appalachian Natural Assets: Forests

APPENDIX E: STAKEHOLDER RESPONSES

Appalachian forest assessment stakeholder survey 

The Appalachian Region Forest Stakeholder Survey was conducted as an online survey gathering input for the 

Forest Assessment from several categories of stakeholders involved in forest resource management, 

community economic development, and economic and recreational uses of the forest. The survey was 

conducted and analyzed by the National Network of Forest Practitioners’ Appalachian Forest Resource 

Center. 

Respondent role and scale

The survey was sent to 697 recipients across the Appalachian region, including local development districts, 

conservation districts, resource conservation and development districts (RC&Ds), private foresters, federal 

and state land managers, and Natural Resources Conservation Service (NRCS) staff. 141 of those surveyed 

participated, yielding a 20% response rate. Of the respondents, the largest numbers were local development 

districts, conservation districts, conservation organizations, NRCS foresters, and RC&Ds. 

Respondents answered from multiple perspectives. While we included them in surveys because of their 

formal roles in the region, they often self-identified with multiple roles (NRCS staff and 

“Hunter/Outdoorsman” or soil and water conservation district supervisor and logger). Several others selfidentified as forest landowners. The results are richer for this pattern, but the ability to define differences in 

results between respondent groups is limited by their identification with multiple stakeholder roles.

Figure 28: Stakeholder roles

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Assessing Appalachian Natural Assets: Forests

Figure 29: Scale of work 

Closed-ended questions

The following questions were assigned a rating on a five-point scale. The bars on each chart are ordered from 

a rating of one (Very Unimportant) to a rating of (Very Important).

Figure 30: Economic importance

Note: The average score was 3.89.

32.4%

16.5%

1.4%

43.2%

6.5%

County

State

Community

Multi-county

area

0%

10%

20%

30%

40%

50%

60%

Very

Unimportant

Unimportant Neither

Unimportant

or Important

Important Very Important

How important are forests in your 

local area’s economy? 

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Assessing Appalachian Natural Assets: Forests

Respondents generally felt forests were economically important in their areas. 52 percent rated forests as 

“Important” to their areas economy, and 32percent characterized them as “Very Important.” 14 percent 

rated them “Very Unimportant” to their area’s economy.

Figure 31: Forest products utilization

Note: The average score was 2.58.

Figure 32: Management 

Note: The average score was 2.51.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Very

Underutilized

Underutilized Balanced Use Over Used Very Over

Used

How would you describe the current 

use of forest products in your area? 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Very Poorly

Managed

Poorly

Managed

Adequately

Managed

Well Managed Very Well

Managed

To what extent do you think your 

areas forests are well managed? 

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Assessing Appalachian Natural Assets: Forests

Figure 33: Forest health

Note: The average score was 3.17.

Figure 34: Biomass 

Note: The average score was 3.34.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Very Unhealthy Unhealthy Neither

Unhealthy or

Healthy

Healthy Very Healthy

How would you describe the 

environmental/ecological health of 

your area’s forests? 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Very Negative

Impact

Negative

Impact

No Impact Postive Impact Very Positive

Impact

What effect do you think use of 

woody biomass for energy will have 

on your area’s forests? 

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Assessing Appalachian Natural Assets: Forests

When asked to explain their answers (“In what ways will the use of woody biomass for energy impact your 

area’s forests?”), many respondents said there could be negative or positive impacts depending on whether 

harvesting was done in accordance with management plans and best management practices.

The topics brought up in the open-ended answers broke out into three primary categories with some 

individuals mentioning multiple categories:

1. Potential economic benefits of biomass utilization (despite the fact that the question focused on 

forests- this may have biased results as respondents focused on community benefit rather than 

answering about how it would impact the forests).

2. The importance of management plans and harvesting practices in influencing the impact of biomass 

(again, this was unsolicited, so a large percentage probably agree with this).

3. Comments that indicated concern about negative impacts on soils, regeneration, diversity, soil 

fertility, and biomass demand could lead to increase erosion, and conversion from diverse forests to 

less diverse stands. There was concern that the additional demand could result in over-harvest: 

• It is a poor idea that will lead to forests being stripped and land destroyed affecting the 

overall soil and water conditions.

• I am concerned that landowners would not follow BMPs on our Highly Erodible soils

• It could very well destroy them! Forests are more than wood products. Woodland plants, 

wildlife, water quality, recreation and aesthetics are but a few examples.

Interestingly, some of the strongest comments were not from environmental or conservation groups but 

from soil scientists, forestry technicians, and others actually involved in forest science. 

The first statement (perhaps the strongest) was from a conservation district member who identified his 

stakeholder role as a logger (he was answering based on his perspective as a logger more than conservation 

district member). 

These results are surprising as one might expect environmental groups to say things like “it may destroy the 

forest” but having a local logger say that is surprising. His rationale was that removal of the slash (small 

downed wood like treetops) would remove protection for the saplings and greatly damage regeneration. 

Given the general concern with regeneration and deer browse, his perspective fits with some other 

stakeholder concerns. He also mentioned concerns about soils.

Given the history of clearing and cropping on Appalachia’s soils it is probably reasonable to be concerned 

about the impact of removing the biomass that otherwise cycles into organic matter in the soil. Research has 

also shown that the lack of coarse woody debris (sticks and logs on the forest floor) can have significant 

impacts on forest ecology. 

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Assessing Appalachian Natural Assets: Forests

Figure 35: Forest threats

Insects and disease

Overwhelmingly respondents felt that insects and disease problems posed a threat to the forests in their 

areas. 

Invasive plants

The only category that participants felt was “Very Threatening” more than insects and disease was Invasive 

Plants. 

Fragmentation/Development

Likely there is geographic skew to these responses. Some areas in Appalachia are experiencing greater levels 

of land development while in other places it really is less of a threat. 

Over-harvest

Over two thirds of respondents felt timber harvest was a threat. However, in the FIA data, there is 

substantially more timber growing than is harvested, so this perception may be at odds with the scientific 

data. But in some areas certain grades or species are often harvested faster than they grow, and in certain 

areas harvest is greater. New demand for biomass will likely increase wood removals, but it is uncertain if 

how much they will drive harvest on additional acres. With the current decline in harvest due to market 

depression, it is likely that the issue of over-harvest is more one of perception than fact in most places. This 

perception is very important to be aware of and to include in regional planning and communication. 

Other

Interestingly, climate change showed up as being considered by more than half of respondents to be “Not a 

Threat.” There is significant scientific evidence that climate change will impact forests significantly, and it is 

likely that the severity of insect, disease, and invasive plant problems in US forests is exacerbated by climate 

0% 20% 40% 60% 80% 100%

Other

New demand for woody biomass...

Climate change

Loss of loggers and forest...

Loss of demand for wood in recent...

Over-harvest of timber

Fire

Fragmentation of forest land from...

Invasive plants

Insects and disease

How big of threat are the following to the health 

and sustainability of forests in your area? 

Very Threatening Somewhat a Threat Not A Threat

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Assessing Appalachian Natural Assets: Forests

change, or will be in the future. There appears to be a disconnect between the current science and 

perceptions in the region around this issue. 

The loss of forest workforce was not seen as an issue by over a third of the respondents. In many areas a 

significant percentage of the workforce (loggers, etc.) has been lost during the recent collapse of timber 

markets. In addition to the recent losses from the market disruption, the overall age of loggers in many parts 

of the region is very high, and the next generation is often not as interested in taking on that occupation. This 

is anticipated to result in a reduced ability to conduct forest management practices until a new workforce is 

developed. The knowledge and skills of the workforce will also be lost. 

Respondents added numerous responses in the “Other” category, including loss of high value markets, 

impacts of strip mining, oil and gas leasing, lack of forest management plans, loss of species diversity, acid 

rain, and inadequate removal of logging roads. Problems with regeneration and deer browsing pressure were 

also mentioned by multiple respondents.

Open-ended questions

A number of open-ended questions were posed in the second half of the survey which produced several 

thousands of responses which we coded into categories based on the number of responses. For responses 

that included more than one answer (e.g., suggesting three important threats or opportunities) we coded 

each of the answers, so the total percentages are the percentage of all participants that included a certain 

response. 

The coding of open-ended responses is not a precise as tabulation of answers where participants select from 

a defined set of choices, so the numerical values should be taken with a grain of salt. The richness of openended questions is in the range of replies produced by stakeholders. 

7. In what way will the use of woody biomass for energy impact your area’s forests?

See details in the section above, but in general respondents indicated that they believed that biomass 

markets could:

• + Contribute to better management and more awareness of forest management. (25 percent) 

• - Lead to over-harvest, and remove nutrients from already depleted forest ecosystems. (25 percent) 

• + Help create jobs and create markets for low quality wood that could help pay for forest restoration 

efforts. (31percent) 

Several respondents felt biomass harvest could be positive if the right elements were in place (27 percent) 

such as:

• Biomass harvesting guidelines utilized consistently

• Use of BMPs by landowners and loggers

• Low impact harvesting methods

• Management plans for harvested lands

Generally the sense was that if biomass is harvested as part of a management plan, following appropriate 

guidelines it could be good. If proper care is not taken biomass could have a negative impact on soils and 

forest health. Some people believed that it would be deterministically good or bad, but most seemed to feel 

like it depends. 

Several respondents indicating significant alarm and fear of the potential negative impacts of biomass 

harvest. Surprisingly, those were not the environmental respondents but included forestry professionals and 

a logger. 

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Assessing Appalachian Natural Assets: Forests

Figure 36: Forest threats 

Figure 37: Forest opportunities 

Forest management. This included increase in use stewardship/ forest management plans by landowners, 

improved management practices, certification, ecological restoration, and resources for management plans 

and practices. Harvesting on steep slopes without causing erosion was an issue of note. The general 

sentiment was that there was an opportunity in improving timber and forest resources, benefitting 

economically and ecologically. Some programs exist, but even current ones operate with a backlog “state 

agency is understaffed and often have to tell a woodland owner it may be 6-8 months before they can meet 

0% 5% 10% 15% 20% 25% 30% 35% 40%

Oil/Gas/Shale

Uncategorized

Decline in Markets

Deer/Regeneration

Over-Harvest

Fire

Development/Fragmentation

Invasive Species

Insects and Disease

Poor Management

What do you feel are the biggest threats to your 

area’s forests?

0% 5% 10% 15% 20% 25% 30% 35% 40%

Carbon Credits

Non-Timber Forest Products

Uncategorized

Timber

Recreation

Biomass

Marketing/ Value Adding

Forest management

What are the biggest opportunities you see 

relating to forests in your area?

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Assessing Appalachian Natural Assets: Forests

with them to develop a management plan” (waits are even longer in some areas, though this was not 

mentioned by survey respondents). 

Marketing/ Value adding. Increasing the secondary/value adding to wood (e.g., furniture making rather than 

just shipping out raw lumber) was a major emphasis, as was helping local wood producers capture markets 

(green building, specialty markets etc). 

Biomass. Most responses were general, though the use of biomass markets to improve forest management 

(and remove material that needs to be removed in improved management) was highlighted. This was seen as 

compatible with existing timber production (but relies upon management choices to that end)

Recreation. Most responses were general with an emphasis on hunting and tourism. Some mentioned 

infrastructure needs (horse, mountain bike, and hiking trails as well as camping) as well as increasing 

open/public lands (undoubtedly in certain areas but not others). Nature based/eco-tourism was also 

mentioned by multiple respondents. 

Timber. Timber was widely viewed as a resource. For some it was also an “opportunity” based on rebounding 

markets, “careful” harvest, Etc.

Carbon credits. It is likely that two years ago this would have been higher, but with carbon trading markets 

collapsed only two people mentioned this. If cap and trade legislation happens or carbon otherwise 

monetized in a consistent way interest will likely be much greater.

Non-timber forest products. Ginseng, mushrooms, and other products. 

Uncategorized. Urbanization was mentioned. Without more description it is unclear if this relates to markets 

for wood, recreation or something else. One respondent indicated “There just aren’t opportunities.” This may 

be a wider-spread attitude that would need to be addressed before much happens in some areas. 

Figure 38: Forest health

0% 10% 20% 30% 40% 50%

Overharvest

Development

Deer/Regeneration

Climate Change/ Drought

Disease

Invasive Species

Management

Insects and disease

What are the biggest issues related to the health 

of forests in your area?

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Assessing Appalachian Natural Assets: Forests

Insects and disease. Details in other question

Invasive species. This largely referred to invasive plants, Ailanthus in particular. 

Management. This referred both to management and landowner knowledge about management. Highgrading (harvesting of the most commercially desirable grades and species leaving stands degraded) was a 

big issue, though “lack of management,” “no management,” and “lack of active management” were at least 

as common answers.

Disease. Details in other question

Deer/Regeneration. Deer overpopulation is a big issue in some parts of the region. It is probably one of the 

biggest threats in certain areas, with “nice looking” stands but no regeneration. 

Climate change/Drought. Both drought and climate change were mentioned. These were grouped together 

as climate factors. Likely droughts and over-wintering of species because of unusually warm winters will 

exacerbate many of the other problems forests face. Interestingly pollution was not mentioned often, 

despite great impacts on high elevation forests in the region. 

Development. Development and fragmentation was clearly a big issue for many respondents but they 

seemed to focus on it less when asked about issues related to forest health. Many of the other issues 

(invasive plants etc.) are impacted by this, however.

Over-harvest. As referenced earlier, the region is not overharvested. Some places, some species, some 

grades may be over-harvested. 

Figure 39: Forest insects and diseases

Note: Answers to this question were mostly to inform the indicator development process. Different subregions experience 

different threats.

0% 10% 20% 30% 40% 50%

Thousand Canker Disease

Pine Bark Beetles

Hemlock Wooly Adelgid

Gypsy Moth

Emerald Ash Borer

What forest insects and diseases most threaten 

the health and productivity of forests in your 

area?

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Assessing Appalachian Natural Assets: Forests

Figure 40: Information or data needed 

Management Information to Landowners. This overlaps some with other information such as BMPs

BMPs. This was not the BMPs already established for harvesting operations but best practices broadly: 

management of small parcels, construction of trails, sustainable biomass production, invasive species control, 

results of new scientific research, forest’ role in stormwater runoff, reducing livestock impacts on forests, and 

decision support tool for small landowners.

Market information. This included current and historical timber pricing, where to market timber (a list of 

mills that is updated often and easily available was suggested). Market trends and projections were also 

mentioned. 

Good data but...Good scientific/technical information exists, but it needs to get to the right people (primarily 

landowners).

Invasive species control. Current information and new research.

GIS/maps. Current and historical information as well as ecologically oriented GIS layer.

Economic value. This included value of timber as well as standing trees, and economically preferable biomass 

species. 

0% 5% 10% 15% 20% 25% 30% 35% 40%

Timber Harvest Data

Economic Value

Composition and Trends

GIS/Maps

Invasive Species Control

Good Data But Needs to Get to Right...

Market information

Best Management Practices

Management Info to Landowners

What type of information or additional forest 

related data do you need to make good 

decisions?

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Assessing Appalachian Natural Assets: Forests

Figure 41: The most useful thing the Appalachian Regional Commission could do 

While many participants were not sure of ARC’s role, the following were the suggestions for what could be 

done by ARC to advance sustainable management and use of forests:

Educate landowners. This area, overlapping some with the next, focused on encouraging landowners to get 

management plans, encouraging the use of a professional forester, and providing information through 

publications and trainings. Some was routine information and some was about improved practices “Develop 

and disseminate best practice information.” Overall this cluster of answers reinforced the sentiment of 

focusing on improved forest management as a key to sustainable use of forests and broader economic 

benefit. 

Communicate the value of forests. There was a broad feeling that greater public awareness of the benefits 

of the region’s forests was important. “Outreach- people need to know how important our forest resource 

is.” “Market both tangible and non-tangible values.” “Serve as a catalyst for community engagement in 

defining the future uses of forests.” “Educate forest landowners of the benefits to implementing forest 

management plans.” Some of the emphasis was landowner awareness but the majority was broader: 

“Educate the public!” 

Provide funding. Funding has been cut in many forestry divisions across the region. Whether it is through 

advocating for more funding for forestry programs or providing direct funding for specific activities, a number 

of respondents pointed to this role for ARC. Ideas for ARC’s role focused mostly on 

reinforcing/supporting/deepening efforts to reach landowners with information on management, but also 

included incentives for restoration and improved stewardship, support for organizations “leading the way 

with sustainable forest products,” support for industrial parks, and training for loggers.

Policy. Tax issues and active management of public lands were mentioned twice each. Other suggestions 

were largely single responses. Laws mandating use of Best Management Practices (voluntary in many states) 

were suggested, as was additional funding for landowner incentives for management. Recognition of 

ecosystem services and monetization of these services was also mentioned. 

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

Planning/Research

Demonstration

Market Development

Partner with others

Policy

Provide funding

Communicate the value of forests

Educate landowners

What is the most useful thing the Appalachian 

Regional Commission could do to advance 

sustainable management and use of forests?

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Assessing Appalachian Natural Assets: Forests

Market development. Promotion of the region’s wood products was mentioned (particularly as a 

green/renewable material) as was promotion of non-timber products from the region (ginseng, mushrooms, 

etc.). Improved market information was mentioned, as was assistance in expanding woody biomass use. 

Partner with others. While the percentage of respondents who mentioned this was low, the emphasis was 

very clear and it is probably a sentiment shared by others. “Don’t recreate the wheel, it wastes money,” 

“Don’t reinvent the wheel.”

Demonstration. Suggested demonstrations included biomass pilot projects, demonstrations of the benefits 

of forest management, Low Impact Development and Green Infrastructure (mentioned twice), and a 

conference sharing best practices currently being demonstrated.

Planning/Research. Ideas included a comprehensive study of Eastern WV forests to encourage proper 

management techniques for forest health, sustainable design of highways and roadways, and greater 

understanding of the region’s soils (and their potentials and limitations)

Respondents also provided additional comments (“If there is anything else you feel we missed that you 

would like to comment on about the role of forests in your area, please do so here.”)

“Timber harvest is a once in a lifetime thing for most landowners and they need help in deciding how to do 

that harvest to ensure a good future harvest some day.”

“Forests control erosion on our steep slopes...Fisheries are suffering real damage due to lack of enforcement 

of BMPs on log roads and harvesting practices...Drive through (Webster and Randolph counties) in the winter 

and observe the number of uncovered dirt log roads silting our streams. It is saddening. Look at the number of 

cuts on one hillside. Do roads really need to be cut every 150 to 200 feet on a mountain side? Permanent 

roads that are properly drained and gravel paved can benefit logging companies along with other BMPs via 

saving future recutting, reclamation, and maintenance.” 

“Wayne NF [National Forest] can play a role of modeling landscape level oak-hickory restoration. However, 

we have low capacity for outreach.”

“We have more pressure from housing growth destroying forest areas than from other sources.”

“It seems difficult to get private forest landowners to understand the importance of developing a forest 

management plan and using sound practices in forest harvest operations to ensure sustainable forest 

management.”

“In today's world of "green" alternatives to all of the world's woes, trees are a renewable, biodegradable, all 

natural resource. We need to utilize them to our benefit.”

“WV relies on the forestscape for tourism. Maybe the State should offer incentives to maintain the forest. ARC 

should work to develop the infrastructure to establish finish use industry here in the region and not allow our 

raw material to be sent oversees for processing. Jobs in WV.”

Conclusions and recommendations

Many of the results are self-explanatory and will not be discussed. But in the interests of providing some 

highlights and insights from the report this section will provide some additional commentary. 

While much of ARC’s emphasis has been on infrastructure and entrepreneurship, there was a clear indication 

from stakeholders that they felt there were opportunities in addressing how the forest resource was 

managed.

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Assessing Appalachian Natural Assets: Forests

With as many as 90% of landowners harvesting timber without a forester involved and more than 90% in 

some areas not having any management plan for their forests, there is a great loss of current and future 

benefits. 

Timber stands in the region are generally neglected and often just treated as a “savings account” where 

landowners will liquidate valuable timber when they need cash for medical, inheritance, or other expenses. 

The economic benefits could be greatly increased if deliberate forest management takes place. Landowners 

would benefit from increased timber income, wood users such as mills would benefit from greater quantity 

and quality of merchantable lumber, and communities would benefit from increased value-adding and

greater profitability from milling of higher quality timber.

But forest management is a long-term process. ARC would have to be committed to investing in the region 

rather than looking just for immediate job creation or income benefits. Forests are an asset that will 

appreciate over time and which will generate income and wealth over the long haul if invested in. 

The benefits of improved management are not just greater timber revenue. Forests can be healthier and 

more resilient and can sequester greater quantities of carbon dioxide. 

Development strategies that create jobs and income now from “high-grading” existing timber will reduce 

long-term prosperity. Approaches that involve improved forest management will result in greater long-term 

forest health, timber abundance, and prosperity. 

Nowhere is this more true than the demand for biomass. Respondents felt good about the potential for 

biomass to create economic opportunities and maintain or improve forest management. But this is likely to 

be true only if timber is well managed. 

Highly concentrated demand (for example that of biorefineries or large scale electricity generation which 

require supply in the millions of tons) would likely have distorting impacts if not harvested from lands under 

management. Areas close to facilities would likely be over-harvested or liquidated (with some amount of 

valuable sawlog and veneer growing stock being diverted to low-value biomass use) while remote areas 

would be little impacted.

Pairing biomass development with incentives for forest management can clear out a lot of “junk” from the 

woods, improving growing stock and long-term production of valuable sawlogs and veneer. Without that 

incentive for management the opportunity for using biomass markets to improve our forest will largely be 

missed, and in some areas the harvest will have negative impacts.

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