Potential for Coal Power Plants to 

Co-Fire with Woody Biomass in 

the U.S. North, 2010–2030

A Technical Document Supporting the 

Northern Forest Futures Project

Michael E. Goerndt

Francisco X. Aguilar

Kenneth E. Skog

General Technical Report

FPL–GTR–237

United States Department of Agriculture

Forest 

Service

Forest Products

Laboratory

June

2015
June 2015

Goerndt, Michael E.; Aguilar, Francisco X.; Skog, Kenneth E. 2015. Potential for coal power plants to co-fire with woody biomass in the U.S. north, 

2010–2030: A technical document supporting the northern forest futures 

project. General Technical Report FPL-GTR-237. Madison, WI: U.S. 

Department of Agriculture, Forest Service, Forest Products Laboratory. 

19 p.

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Abstract

Future use of woody biomass to produce electric power in 

the U.S. North can have an important influence on timber 

production, carbon storage in forests, and net carbon emissions from producing electric power. The Northern Forest 

Futures Project (NFFP) has provided regional- and statelevel projections of standing forest biomass, land-use change, 

and timber harvest, which all influence forest contributions 

to global carbon cycles. This study supports the NFFP study 

of global carbon cycles by estimating potential local woody 

biomass supply under alternate procurement regimes and associated delivered costs to coal power plants for co-firing for 

2010 and 2030. We estimated supply and delivered costs for 

2010 and 2030 with concentric supply circles around individual power plant locations by using county-level estimates 

of woody biomass availability, harvesting and haul costs, and 

two different procurement regimes—one to remove logging 

residue and one to obtain woody biomass from forest thinning. Results of this analysis indicate that an average power 

plant in the U.S. North with the appropriate feedstock feeding technology has the potential to replace up to 10% of coal 

electricity generation with woody biomass, accounting for 

feedstock competition from adjacent power plants. We did 

not find that there would be a significant increase or decrease 

in wood co-firing potential between 2010 and 2030.

Cover images by www.shutterstock.com photographers 

clockwise, top to bottom: Claudia Otte, nostal6ie, 

Martin Capek, and Taina Sohlman. 

Keywords: renewable energy, electricity generation, 

feedstock, supply radius, future scenarios

Contents

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

2. Conceptual Background and Study Area ........................ 2

 2.1 Econometric Analysis ............................................... 2

 2.2 Description of Future Scenarios................................ 2

3. Empirical Methods.......................................................... 3

 3.1 Econometric Analysis ............................................... 3

 3.2 Availability of Woody Biomass ................................ 4

 3.3 Woody Biomass Supply for a Procurement Area...... 6

 3.4 Delivered Costs......................................................... 6

 3.5 Power Plants Suited to Co-Fire with Woody 

 Biomass..................................................................... 7

4. Results and Discussion ................................................... 7

 4.1 Spatial Distribution of Woody Biomass.................... 7

 4.2 Potential of Co-Firing and County Selection............ 7

 4.3 Net Electricity Generation Potential ......................... 9

 4.4 Costs of Woody Biomass Procurement................... 10

 4.5 Power Plants Suited to Co-Fire with Woody 

 Biomass................................................................... 10

 4.6 Implications for Public Policy ................................ 12

5. Conclusions................................................................... 14

Literature Cited ................................................................. 14

Appendix—Supplementary Information for 

Projection Scenarios, Econometric Analysis, Cost 

Assumptions, and Projected Biomass Availability ........... 17

Conversion table

English unit Conversion factor SI unit

acre 4.0469 square meter (m2)

mile 1.6093 kilometer (km)

inch (in.) 25.4 millimeter (mm)
Potential for Coal Power Plants to Co-Fire 

with Woody Biomass in the U.S. North, 

2010–2030

A Technical Document Supporting the Northern Forest Futures Project

Michael E. Goerndt, Assistant Professor

William H. Darr School of Agriculture, Missouri State University, Springfield, Missouri

Francisco X. Aguilar, Associate Professor

Department of Forestry, University of Missouri, Columbia, Missouri

Kenneth E. Skog, Supervisory Research Forester

Forest Products Laboratory, Madison, Wisconsin

1. Introduction

The Northern Forest Futures Project (NFFP) is a joint venture between the USDA Forest Service Northern Research 

Station, the USDA Forest Products Laboratory, and several 

regional- and state-level stakeholders intended to indicate 

how current trends and choices can change forests and forest uses in the 20 states of the northeastern quadrant of the 

United States. As such, the NFFP focuses on future changes 

in factors such as tree and forest health, water and soil conservation, carbon sequestration, forest products, land-use 

change, and forest fragmentation, among others. To anticipate these forest changes, NFFP has focused on assessing 

potential impacts of several drivers of forest change including population, economy, public policy, invasive species, 

energy production/consumption, climate change, forest 

growth, and species succession. Because of future uncertainty for several of these drivers, NFFP made projections for 

several alternative future scenarios that are driven by alternate values of external drivers. NFFP has provided information about estimated future conditions under seven broad 

criteria categories for the conservation and sustainable management of temperate and boreal forests as identified by the 

Montréal Process Working Group (2013). One criterion is 

“maintenance of forest contribution to global carbon cycles” 

(FS, In press) chapter 7; Montréal Process Working Group 

(2013). The three indicators for this criterion are (1) total 

forest ecosystem biomass and carbon pool, (2) contribution of forest ecosystems to the total global carbon budget, 

including absorption and release of carbon, and (3) contribution of forest products to the global carbon budget.

Estimating future potential for woody biomass bioenergy 

generation is one indicator of the potential to reduce overall 

release of carbon by using woody biomass and associated 

forest carbon recovery in place of coal carbon emissions. 

The most recent NFFP report, Future Forests of the Northern United States, provides regional- and state-level projections for forest biomass use for energy but does not indicate 

local feedstock supply potential for individual power plants 

(FS 2012b). This report provides methods and findings for 

an analysis of current and future potential to co-fire woody 

biomass with coal to produce electric power. 

Co-firing with woody biomass is one of the most common 

forms of renewable energy generation in the United States, 

building on the widespread use of biomass combustion to 

provide heat and power in the forest sector. Wood and wood 

waste currently account for about 53% of non-hydroelectric 

renewable electricity consumption in the United States (EIA 

2012). Renewable energy markets are beginning to provide 

incentives for removal of logging by-products (logging 

residue), small-diameter trees, and other non-timber woody 

material during silvicultural treatments such as sawlog harvesting, precommercial thinning, hazardous fuel reduction, 

and forestland restoration. Forest biomass from logging 

by-products and thinning operations accounts for the largest 

share of biomass feedstocks used by U.S. power plants that 

are co-firing or have co-fired with biomass in the past 

(Goerndt et al. 2013b).

A number of studies have estimated woody biomass resource supply to help determine feasibility of renewable 

energy from biomass (DOE 2011). Others have estimated 

local biomass availability and costs for individual power 

plants co-firing with biomass (Goerndt et al. 2012; Nicholls 

et al. 2006; Goerndt et al. 2013a). Goerndt et al. (2013a) 

estimated the supply potential and costs of harvesting, processing, and transporting woody biomass and unused mill 

by-products for co-firing in selected coal-fired power plants 

in the U.S. North. But that study did not include the effect of 

changes in woody biomass availability and costs over time 

resulting from changes in forest management, tree species 

composition, and operational costs of woody biomass 

procurement. 

This study builds on methods used by Aguilar et al. (2012) 

and Goerndt et al. (2013a) to project woody biomass 
General Technical Report FPL–GTR–237

2

supply and costs. We estimate costs for obtaining biomass 

at two points in time from three sources in the U.S. North: 

biomass from two forest harvesting and procurement regimes and biomass in the form of unused wood residue 

from wood products mills. Costs for biomass from forest 

thinning include costs for stumpage, harvest, chipping, and 

hauling. Costs for biomass from mills include the price at 

the mill and hauling cost. For each coal-fired power plant 

considered, we defined woody biomass procurement areas, 

maximum transport distances, and for those areas we estimated delivered fuel costs for two different forest procurement regimes including (1) removal of logging by-products 

(slash) from traditional harvest operations and (2) removal, 

in integrated operations, of logging by-products and smalldiameter trees for bioenergy use as well as sawtimber trees 

for solidwood product production. For each coal-fired power 

plant and procurement regime, biomass supply potential and 

delivered costs were estimated for a sequence of land areas 

defined by successively larger concentric circles around 

each power plant. Note that this study does not directly address competing resources such as natural gas, which has 

experienced an increase in use in recent years caused by low 

prices. Results of this analysis were directly compared to 

the 2010 estimates of supply potential and costs for power 

plants based on the two procurement regimes (Goerndt et al. 

2013a; Wear et al. 2013). 

2. Conceptual Background and 

Study Area 

The study area includes 20 states: Connecticut, Delaware, 

Illinois, Indiana, Iowa, Maine, Maryland, Massachusetts, 

Michigan, Minnesota, Missouri, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, 

West Virginia, and Wisconsin. This region, as shown in 

Figure 1, is hereafter referred to as the U.S. North. 

The U.S. North has states located in five of the electricity 

supply subregions as defined by the EIA including West 

North Central, East North Central, Middle Atlantic, New 

England, and South Atlantic (Fig. 2). There are several important reasons for focusing on this region. Biomass was 

identified as a major potential source of renewable energy 

in the region (EPA 2012; Aguilar and Garrett 2009). This 

region also hosts a large concentration of coal power plants 

(EPA 2012). Figure 2 shows EIA sub-regions and locations 

of coal-fired power plants and co-firing status in 2007 for 

the U.S. North (EPA 2012).

2.1 Econometric Analysis

Aguilar et al. (2012) developed econometric models to estimate the current probability of co-firing coal and biomass at 

the county level in the U.S. North using drivers (variables) 

such as electricity demand, technical feasibility, coal price, 

availability of wood mill by-products, state renewable portfolio standards (RPS), and transportation infrastructure. We 

estimated the probability of co-firing at the county level in 

2030 by applying the model from Aguilar et al. (2012) to 

projected values for drivers. For a description of variables 

used to estimate county-level probability of co-firing and 

which variables were projected to 2030, see Appendix 

Table A1. 

Econometric models were used to estimate which counties 

in the U.S. North have a high probability of co-firing in 

2010 and how much the probability may change by 2030. 

The econometric analysis was used to provide a “coarse 

screen” to identify counties well suited to host co-firing 

facilities. Specifically, local woody biomass resource availability and costs were estimated for counties that were in the 

top 25% in estimated probability of co-firing in 2010. 

2.2 Description of Future Scenarios

Woody biomass availability in 2030 will be influenced by 

drivers of forest growth and wood harvest. These drivers 

include future population change, land use change, greenhouse gas emissions, and climate change. We used projections of forest growth and harvest from the USDA Resources Planning Act (RPA) assessment (FS 2012a; FS 2012b; 

Wear et al. 2013). The RPA projections used scenarios that 

were defined by a socioeconomic storyline, and use of a particular global circulation model (GCM) to project changes 

in temperature and precipitation worldwide. A GCM models 

the operation of the global climate system over time based 

on physical, chemical, and biological processes and can 

project future changes in patterns of temperature and precipitation by geographic region under varying scenarios for 

greenhouse gas emissions. Each storyline is comprised of a 

set of assumptions about future population change, economic activity, land use, quantity of wood used for energy, and 

greenhouse gas emissions. The RPA assessment (and this 

study) used three of the storylines described by the 

Figure 1. Delineation of major U.S. regions, with region of 

interest (North) in dark blue.
Potential for Coal Power Plants to Co-Fire with Woody Biomass in the U.S. North, 2010–2030

3

Intergovernmental Panel on Climate Change (IPCC) (Nakicenovic et al. 2000; IPCC 2014; Solomon et al. 2007). 

•	 Storyline A1—a future where the rest of the world approaches the U.S. per capita wealth, technology use, and 

population growth. It is characterized by rapid economic 

growth and increasing global trade. Global population peaks in mid-century and then declines. It assumes 

balanced use of fossil and renewable fuels with initial 

dependence on fossil fuel but a relatively rapid increase 

in renewable energy sources. A variation of storyline 

A1, storyline A1B, predicts a balanced use of fossil and 

renewable fuels, with an early dependence on fossil fuel 

followed by a relatively rapid increase in renewable energy sources.

•	 Storyline A2—a future where the world is not converging on the U.S. experience, but rather is much more 

regionally focused. It assumes a continuously increasing 

global population and more regionally centered economic 

growth. Among these storylines, it has the highest total 

global population growth but the lowest long-term economic growth.

•	 Storyline B2—a future of global sustainable development, with some regional economic convergence. The 

B2 storyline is similar to A2, in that regional and local 

institutions as well as economies are emphasized over 

global integration. Economic growth is intermediate, but 

population growth is significantly lower than for the other 

two scenarios. Thus, per capita income for scenario B2 is 

closer to A2 than to A1. Scenario B2 also has the lowest 

projected growth in biomass energy for the global region 

that encompasses the United States.

For the remainder of this paper, we use forest growth outcomes projected by using these three storylines where each 

is paired with a single GCM. These scenarios include the 

effects of climate change and land-use change on forest area 

and forest growth. However, future timber harvest was projected based on an assumed continuation of recent harvest 

levels (FS, In press). The scenarios are labeled A1B-C, 

A2-C, and B2-C as used for the NFFP. These scenarios 

include the effects of climate change on forest growth and 

composition and include projected land-use change, but they 

also project future harvesting rates, based on the recent past 

probability of harvesting. Thus, they use a continuation of 

recent harvest rates as a baseline for projections of harvest 

rates into the future. Full details of storylines and GCM are 

provided in Appendix Tables A2 and A3. 

3. Empirical Methods 

This study uses empirical methods (1) to identify counties 

with high potential of co-firing both now and in the future 

based on the econometric analysis, (2) to estimate woody 

biomass resource availability and costs for the selected 

counties in 2010 and in 2030 under three alternate scenarios. 

All biomass quantities and costs per ton are for oven-dry 

tons of wood. The methods section is organized as follows: 

(3.1) econometric analysis, (3.2) available woody biomass, 

(3.3) biomass cost assumptions, (3.4) woody biomass 

availability for procurement areas, (3.5) delivered costs, 

and (3.6) power plants well suited to co-fire with woody 

biomass. 

3.1 Econometric Analysis

Not all econometric drivers of co-firing used in Aguilar et al. 

(2012) were projected to 2030 for this analysis. For example, the presence of roadways, railways, and waterways was 

considered fixed between 2012 and 2030. Also, state-level 

renewable portfolio standards were considered constant at 

2001 levels for 2010 and 2030 because of this variable’s 

representation of state-level desire to increase bioenergy 

generation (as of 2010, only one state in the Northern United States has not adopted a RPS). Median house value will 

most likely change between 2010 and 2030, but we did not 

find regional projection data for this variable. Likewise, it is 

possible, though unlikely, that some states currently producing coal would stop producing coal by 2030, but there are 

no reliable sources to project this possibility between now 

and 2030. Variables projected to 2030 include percentage 

of urban area (by county) (U.S. Census Bureau 2013), average electricity price (national level), and average coal price 

(national level) (EIA 2012). Total annual wood mill byproducts were projected at the state level by increasing 2010 

levels by the percentage change in growing stock removals 

between 2010 and 2030 by state under scenario A2-C 

(Fig. 3) (FS 2012a; Wear 2011). 

Figure 2. U.S. Energy Information Administration subregions and 2007 location and co-firing status for coal-fired 

power plants in the U.S. North.
General Technical Report FPL–GTR–237

4

There is a notable difference among states in the U.S. North 

in projected growing stock removals by 2030. This is due 

in part to harvest associated with land-use change. Removals increase in states such as Rhode Island and Connecticut 

where there is a projected 4% to 5% loss (harvest) of forestland from urbanization by 2030 (FS, In press). Conversely, 

there is an increase in removals in states such as Iowa, Wisconsin, and Indiana where there is a projected increase in 

forestland greater than 100 years old and a lower projected 

loss of forestland to urban expansion. 

3.2 Availability of Woody Biomass

For 2010, estimation of biomass availability for all procurement regimes was based on county-level Timber Products 

Output (TPO) database and timber inventory and harvest 

(FIA) data reported by the USDA Forest Service (USDA 

2012; Woundenberg et al. 2010; Miles 2012). Estimates 

based on FIA data were limited to timberland to avoid 

including biomass from protected forest lands or sites 

otherwise unavailable for biomass utilization. Timberland 

is defined as forestland that is producing or is capable of 

industrial wood production and not withdrawn from timber 

utilization by statute or administrative regulation (Smith 

et al. 2009). All woody biomass projections for 2030 were 

based on projections of timber inventory and harvest for 

scenarios A1B-C, A2-C, and B2-C (FS 2012a; Wear 2011). 

The following subsections describe methods used to estimate county-level woody biomass availability by procurement regime for 2010 and for 2030 by projection scenario. 

3.2.1 Conventional Logging By-Products (Slash)

2010 Estimates—County-level estimates of logging byproducts supply associated with current commercial harvests were obtained from the USDA Forest Service Timber 

Product Output (TPO) database (USDA 2012). We assumed 

that logging by-products would be removed during the 

harvest operations used to remove commercial roundwood 

and be chipped on site prior to transport (DOE 2011). 

Projections—Logging by-products were projected to 2030 

at the county level, for each scenario by increasing the 2010 

amounts by the corresponding state-level percentage increase in growing stock harvest removals between 2010 and 

2030 estimated by scenario from the NFFP database 

(FS 2012a; Wear 2011). In doing so, we assumed that future 

logging by-products would hold a constant ratio to total harvest volumes for each scenario. 

3.2.2 Integrated Harvest

2010 Estimates—Integrated harvest operations remove both 

conventional solid hardwood products and woody biomass 

for energy use in a single operation and include chipping of 

biomass on site (Saunders et al. 2012; Bolding et al. 2009). 

Integrated harvest provides woody biomass from logging 

by-products and small-diameter trees that are not going to 

be used for commercial products. For this analysis, we used 

two different size class ranges to represent “small trees,” 

<5-in. diameter and <10-in. diameter. Whereas trees 

<10-in. diameter can be considered non-merchantable 

throughout most of the U.S. North for sawtimber, pulpwood 

harvesting often consists of removing trees between 5- and 

10-in. diameter. Therefore, the available biomass from small 

trees resulting from the following methods was adjusted to 

include only material from trees <5-in. diameter for counties 

with any output of pulpwood products based on the TPO 

database (USDA 2012). 

To estimate the amount of biomass that could be removed 

in the form of small trees as part of thinning treatments, we 

used stand density index (SDI) to guide treatments (Shaw 

2006). SDI is used by forest thinning and vegetation simulators such as the U.S. Forest Service fuel treatment evaluator 

tool and the U.S. Forest Service forest vegetation simulator (Goerndt et al. 2013a). First we estimated a maximum 

possible SDI value for softwoods, hardwoods, and mixed 

forests separately. Maximum SDI is defined for a given forest type as the maximum number of trees that can be accommodated on one acre where the quadratic mean diameter of 

trees is 10 in. (Long 1996; Doruska and Nolan 1999). The 

maximum SDI for hardwoods, mixed forests, and softwood 

forests was estimated to be 811, 470, and 496 trees per acre, 

respectively (Perez-Verdin et al. 2009; Woodall et al. 2005). 

A thinning treatment was simulated for each county where 

a forest type in the county (softwoods, hardwoods, mixed) 

had an estimated SDI greater than 30% of the maximum 

(full crown closure). We made successive simulated thinnings across all applicable diameter classes until enough 

trees were removed to leave a forest with an SDI of 30% of 

the maximum (Goerndt et al. 2012). Thinnings were done in 

such a way that the number of trees removed in a given size 

class contributed the same reduction to SDI. The simulations estimated a total amount of timber available by size 

Figure 3. Percentage change in annual growing stock 

removals from 2010 to 2030 based on scenario A2-C.
Potential for Coal Power Plants to Co-Fire with Woody Biomass in the U.S. North, 2010–2030

5

class in each county based on the <5-in. diameter size class 

or the <10-in. diameter size class depending upon assumed 

presence of pulpwood harvesting. We assumed treatments 

and amount supplied by either applicable diameter class for 

biomass would be provided over 30 years with each year 

providing one-thirtieth of the total available supply (Perlack 

et al. 2005). 

Projections—Projections to 2030 were not available for tree 

data needed to simulate thinnings and the amount of biomass that would be provided from trees <5-in. diameter or 

trees <10-in. diameter. We projected the biomass available 

from trees in these diameter classes in 2030 (by scenario) 

by increasing the county level annual amount estimated for 

2010 by the percentage increase, at the state level, in the 

volume of timber in trees less than 10 in. between 2010 and 

2030. 

3.2.3 Adjustments for Inaccessibility and 

Environmental Stability

Because certain areas are physically inaccessible and costs 

to remove biomass materials are high, not all by-products 

and small-diameter material can be assumed available for 

co-firing. Additionally, improper removal of both smalldiameter trees and logging by-products can promote soil 

erosion, as well as reduce productivity of unproductive soils 

and reduce soil nutrient levels (Sanchez et al. 2003; Scott 

and Dean 2006). The DOE (2011) stated that the recommended amount of by-products to be retained on site following harvesting varies by state, forest type, and slope, but is 

in the range of 15% to 40% of total harvest by-products. For 

this study, we assumed a retention rate of 35% of available 

biomass for both small logging by-products and biomass 

from small-diameter trees and to account for inaccessibility and maintenance of ecological functions (Goerndt et al. 

2013a). 

3.2.4 Mill By-Products (Residues)

2010 Estimates—Although included in the analysis, 

mill by-products were not considered to be a separate 

procurement regime for two reasons. First, the availability 

of unused mill by-products is usually very low compared to 

forest biomass and is nonexistent in many counties because 

of either full utilization of residues for other purposes or absence of wood processing mills. Second, aside from a possible selling price at the mill, mill by-products typically do 

not include many procurement costs in addition to transport 

costs, unlike logging by-products and small-diameter trees. 

Therefore, availability of currently unused mill by-products 

was simply added to the estimated availability of forest biomass for each of the two procurement regimes. County-level 

estimates of unused mill by-products were obtained from 

the TPO database (USDA 2012).

Projections—As with logging by-products, estimates of 

mill by-products were projected to 2030 by increasing the 

county-level estimate for 2010 by the percentage increase in 

state level annual growing stock removals for each scenario. 

County-level estimates of available woody mill by-products 

were also adjusted using the percentage increase in state 

level growing stock removals for each scenario. 

3.2.5 Biomass Cost Assumptions

Costs for woody biomass procurement were developed for 

stumpage, woody biomass harvest, field operations (e.g., 

onsite chipping) and transport. Original cost estimates for 

2010 were estimated by Goerndt et al. (2013a) (Table 1). 

Cost projections for 2030 were made by applying projected 

increase rates in costs for stumpage, fuel, and labor to the 

2010 cost estimates. 

Table 1—Operational and transport cost assumptions for woody biomass procurement in the U.S. North in 

2010 (Goerndt et al. 2013a) 

 Federal Non-Federal 

Mill 

Costs by-products 

Logging 

by-products 

Small-diameter 

trees 

Logging 

by-products 

Small-diameter 

trees 

Harvest ($/ton)a 5.82 14.55 5.82 14.55 n/a 

Transportation 

 Fixed ($/ton)b 6.80 6.80 6.80 6.80 6.80 

 Incremental ($/ton/mile)c

 0.23 0.23 0.23 0.23 0.23 

Stumpage value ($/ton) d 0 0 6.49 6.49 n/a 

Chipping costs ($/ton)e 4.99 4.99 4.99 4.99 n/a 

Selling ($/ton)f n/a n/a n/a n/a 4.49 

a

Estimate from hardwood and softwood values in the U.S. North (Saunders et al. 2012; DOE 2011). b

Fixed cost for operation and loading/unloading of trucks (Perlack et al. 2005; Saunders et al. 2012; Gallagher et al. 2003). c

Perlack et al. (2005); Perez-Verdin et al. (2009); DOE (2011). d

Estimated as 50% of average of hardwood and softwood pulpwood values in the U.S. North for federal and non-federal land (DOE 2011; 

Saunders et al. 2012). 

e

Average chipping costs calculated using values from Saunders et al. (2012) and Harrill 2010. f

Perez-Verdin et al. (2009).
General Technical Report FPL–GTR–237

6

2010 Estimates—Findings by the U.S. Department of Energy (DOE) (2011) suggest that logging by-product stumpage 

prices can range from about $4 per ton up to 90% of local 

pulpwood prices depending on the proportion of available 

logging by-products being harvested. The assumed stumpage values for non-federal land were set to about 50% of the 

average pulpwood stumpage values for the U.S. North as 

reported by DOE (2011). Stumpage prices for Federal land 

were assumed to be zero because biomass removal on Federal land is usually part of a storm or insect salvage 

operation, or restoration activity (DOE 2011). 

Chipping prices can vary depending on whether the biomass 

is chipped loose or bundled before chipping. Estimates from 

past studies show chipping costs ranging from $2.50 per 

ton to as high as $10 per ton (Saunders et al. 2012; Harrill 

2010). The assumed value for chipping was calculated as 

an average between maximum costs for loose material and 

bundled material to account for both possibilities. Though 

not yet widely used, slash bundling has shown promise to 

reduce costs and improve biomass logistics in some situations, particularly biomass storage and handling for base 

load power. A selling and sorting price only applies to 

mill by-products and represents costs that can be incurred 

through separating usable from non-usable material as well 

as selling prices for unused woody residues directly from 

mills and is assumed to be in the range of $4 to $6 per ton 

(Perez-Verdin et al. 2009).

Projections—The components of the original cost assumptions for 2010 were derived from several sources. Therefore, 

it was necessary to take different steps to project each cost 

component to 2030. Note that costs were projected in nominal dollars. Figure 4 shows the projected percentage change 

in cost components for woody biomass from 2010 to 2030.

The biggest challenge with projecting cost assumptions 

for delivery of woody biomass for co-firing pertained to 

the generalized nature of the base assumptions in Table 1. 

The base assumptions were typically derived by estimating 

midpoints for ranges of regional operational, transport, and 

stumpage costs for the U.S. North. Therefore, most cost 

components do not link to one specific data source that may 

have associated projections. To address this challenge, we 

identified the percentages of various cost components (in 

2010) that are for labor and fuel and used projections of 

labor and fuel prices. Most cost components include costs 

of machinery operation, which require fuel and labor for 

operation. For each cost component that relied on fuel and 

labor, we assumed that 70% of the cost was labor and 30% 

was fuel (Saunders et al. 2012). We then used projections of 

percentage change in fuel costs (EIA 2013) and wages (BLS 

2013) to project the cost components. Projected stumpage costs were available for each scenario from Ince et al. 

(2011) for non-sawtimber (small-diameter trees). Appendix 

Table A5 shows all 2030 cost estimates. 

3.3 Woody Biomass Supply for a Procurement 

Area

We used ArcGIS to estimate biomass amounts and delivered costs for procurement areas with simulated concentric 

radii (R) from 10 km (6.2 miles) to 100 km (62.1 miles) by 

10 km (6.2 mile) intervals around the selected power plant 

locations. Note that calculation of woody biomass availability within procurement areas was done four times, once 

for 2010 and for each of the three scenarios for 2030. The 

estimates for 2030 include changes from 2010 in the power 

plants selected and changes in county-level woody biomass 

availability. For larger procurement radii, the total procurement area around most power plants included several entire 

counties and county fragments. This complexity in spatial 

information needed to calculate woody biomass availability by procurement radius made it necessary to use fixed 

procurement radii that could be spatially intersected with 

county-level data rather than supply areas based directly on 

local road structure around individual power plants. Estimates were made for the annually available woody biomass 

for each power plant (B) for each procurement regime and 

procurement area from the county-level estimates as follows 

(Goerndt et al. 2013a):

(1)

where bi is the total annually available woody biomass for 

county i, ai is the percentage of the area of county i that falls 

within the procurement area (circle), and m is the number of 

counties residing in the procurement area. Equation (1) assumes that woody biomass within each county is uniformly 

distributed across the entire county (Goerndt et al. 2013a).

3.4 Delivered Costs

To estimate delivered costs of dry woody biomass for selected procurement areas around selected power plants, we 

–50

0

50

100

150

200

250

300

Harvest Transportation Chipping Stumpage Percentage change 2010 to 2030

Cost component

A1B-C

A2-C

B2-C

Figure 4. Percentage change in estimated cost components 

for woody biomass procurement by scenario from 2010 to 

2030 (EIA 2013; BLS 2013).

B = aibi

m

i–1

Σ
Potential for Coal Power Plants to Co-Fire with Woody Biomass in the U.S. North, 2010–2030

7

added together the operational costs (stumpage costs, harvest, chipping) and the transportation cost for the maximum 

transport distance. A portion of transport cost per ton is 

dependent upon maximum transport distance of woody biomass to the power plant and a portion is fixed. If we assume 

that biomass being collected is evenly distributed within a 

given radius of a plant, then the maximum transport distance 

(d) for a ton of biomass is calculated as follows (Huang et 

al. 2009; Overend 1982; Goerndt et al. 2013a):

d = Rτ (2)

where R is the procurement radius in miles, and τ is a tortuosity factor representing a ratio of road transport distance to 

line-of-sight distance and is generally assumed to be 1.2 to 

1.5 in the U.S. North (Perez-Verdin et al. 2009; Huang et al. 

2009). The tortuosity was adjusted by EIA energy sub-region (Fig. 2) with a value of 1.2 for West North Central, 1.3 

for East North Central, 1.4 for Middle Atlantic, and 1.5 for 

New England and South Atlantic to adjust for winding roads 

and hilly terrain found in the Northeast United States compared to the Central United States (Goerndt et al. 2013a). 

For each procurement radius and procurement regime, total 

delivered cost (C) was estimated as follows:

C = (Tvd) + Tf (3)

where d is the maximum transport distance for biomass, Tf 

is operational cost including loading/unloading of trucks, 

chipping, stumpage and harvest, and Tv is the transport cost 

per mile. 

3.5 Power Plants Suited to Co-Fire with 

Woody Biomass

The cost of biomass compared to coal is a major factor in a 

decision to co-fire with biomass, and the majority of power 

plants already co-firing pay less for biomass (per unit of energy output) than for coal (Goerndt et al. 2013b). Therefore, 

for this assessment we set a maximum cost for woody biomass feedstocks based on either the 2010 and 2030 average 

cost (in nominal dollars) of coal used for U.S. power plants 

as reported (or projected) by the EIA. Conversion of woody 

biomass to electricity generation was based on a factor of 

1.7 MWh per ton as estimated by Goerndt et al. (2012). 

Maximum potential biomass that could be used for co-firing 

by a given power plant was estimated as the amount that 

could be delivered from the procurement area at a delivered 

price at or below the allowed coal equivalent cost. 

4. Results and Discussion

4.1 Spatial Distribution of Woody Biomass

Goerndt et al. (2013a) found that many U.S. North states 

currently have substantial potential supply of both logging 

by-products and small-diameter trees. Additionally, 

Goerndt et al. (2013b) determined that many states with 

high total available woody biomass do not necessarily have 

high density (tons per acre) of woody biomass because of 

a large timberland area. Wide spatial distribution of woody 

biomass (low density) can be a hindrance to co-firing because of high cost for long transport distances needed to 

obtain substantial supply for an individual power plant. We 

estimated changes in density of biomass supply between 

2010 and 2030. Figure 5 shows estimated percentage change 

in woody biomass tons per acre by state from 2010 to 2030 

based on scenario A2-C for all procurement regimes. Availability for logging by-products changes more over time and 

space than biomass availability from integrated harvesting. 

There are decreases in woody biomass availability per acre 

in the Northeast and increases in the Midwest. Availability 

of biomass from small trees and integrated harvest has less 

variability when compared to logging by-products only. 

Logging by-products availability varies widely because of 

wide variation in projected increase in growing stock removals across the region. Increase in woody biomass from 

small-diameter trees was estimated based on the increase in 

volume of small trees, which varies less by state between 

2010 and 2030 than the increases in growing stock removals. The tendency for logging by-products to fluctuate across 

the region has an effect on the change in net generation 

potential from woody biomass between 2010 and 2030 as 

described in Section 4.3. For details on projected changes 

in woody biomass spatial density, see Appendix Table 

A4.

4.2 Potential of Co-Firing and County 

Selection

Aguilar et al. (2012) found that many of the counties with 

estimated high potential for biomass and coal co-firing are 

in the Lakes States, West Virginia, and Pennsylvania. This 

is not unexpected as these states have either a large amount 

of available woody biomass, a large number of coal-fired 

power plants, or both. Of greater importance to this study 

was the contrast between 2010 and 2030 regarding the 

spatial distribution of counties with high probability of cofiring. Figure 6 shows the expected probability of co-firing 

for 2010 and 2030.

There is not a dramatic increase in total co-combustion 

probability across the region from 2010 to 2030. Because 

of projected increases in available wood mill by-products 

in the future, the states that showed increases in the number 

of counties with high probability of co-combustion were 

primarily the Lake States (Fig. 5; Aguilar et al. 2012; Goerndt et al. 2013a). There is not a substantial difference in 

the number of counties with high probability of co-firing 

from 2010 to 2030. Although increases in population and 

electricity demand in the eastern states could encourage 

increased co-firing, projected decreases in timber harvesting 

and therefore wood mill by-products in many states counteract this effect (Fig. 5). One limitation of this analysis is that 

it did not adjust for the expectation that a notable number 

of older coal-fired power plants will be retired in the next 
General Technical Report FPL–GTR–237

8

decade. The EIA projects that retiring power plants will lead 

to a 60-gigawatt decrease in coal electricity generation in 

the United States by 2020 (EIA 2014). Whereas this factor 

could have an effect on which counties have high potential 

for co-firing, the econometric analysis included significant 

variables not directly dependent upon the presence of coalfired power plants (e.g., road and rail infrastructure, electricity demand, woody biomass availability). Given these 

factors, combined with technical feasibility, it is likely that 

the counties identified as having the highest co-firing potential will continue to have operating coal-fired power plants, 

as well as the infrastructure and biomass supply needed to 

co-fire. Because of the similarity in county-level co-firing 

probability between 2010 and 2030, the list of individual 

power plants used for the projections for 2030 contained 

219 plants, which is the same number of plants used in the 

2010 analysis with only two plants being different between 

the two datasets. 

Figure 5. Percentage change in mean woody biomass 

spatial distribution density (tons/acre) by state from 2010 

to 2030 based on scenario A2-C for logging by-products 

only (A), small-diameter trees only (B), and integrated 

harvest (C).

(A)

(B)

(C)

Figure 6. Modeled probability of co-firing wood with coal for 

electricity production by county for the U.S. North based 

on the econometric analysis. Maps illustrate differences in 

the expected probability by county between (A) 2010 and 

(B) 2030.

(A)

(B)
Potential for Coal Power Plants to Co-Fire with Woody Biomass in the U.S. North, 2010–2030

9

4.3 Net Electricity Generation Potential

Annual electricity generation from woody biomass was 

calculated by multiplying the factor of 1.7 MWh per ton 

times the estimated amount of available woody biomass 

for each procurement radius (Goerndt et al. 2013a). We 

compare power generation from wood to the power generation from coal for 2007 (3,007,000 MWh) for all selected 

power plants and calculated the percentage of possible coal 

power displacement (EPA 2012). These estimates assume 

that each plant was co-firing individually without resource 

competition (overlap) from nearby power plants. The effects 

of procurement area overlap on wood power potential will 

be discussed later in the context of counties suited to host 

co-firing facilities now and in the future. Figure 7 shows the 

estimated 2010 mean annual electricity generation among 

selected power plants by procurement regime and transport 

distance. 

Integrated harvest (forest thinning plus logging by-products) 

could provide wood for a relatively moderate proportion of 

coal power generation potential (>15% of coal generation 

at 60 miles). Notice that unlike integrated harvest, logging 

by-products could not supply enough woody biomass to 

achieve even a 10% replacement of coal within a 60-mile 

procurement radius. Figure 8 shows projected mean annual 

electricity generation for selected power plants in 2030 by 

scenario and procurement radius.

For integrated harvest, potential annual electricity generation from woody biomass changes very little from 2010 to 

2030 for all scenarios. As suggested by the discussion of 

changes in biomass density in Section 4.1, projected net 

generation from logging by-products has a greater variability among the scenarios. Also, 2030 estimates of availability 

of logging by-products differ more from the 2010 estimate 

than a similar comparison of 2030 and 2010 supply estimates for amounts from integrated harvesting. Whereas 

20%

10%

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

5 10 15 20 25 30 35 40 45 50 55 60 65

Annual electricity

generation (T

Wh)

Procurement radius (miles)

Logging byproducts

Integrated harvest

Figure 7. Estimated 2010 mean annual wood power generation for selected power plants by procurement regime and 

procurement radius. Horizontal lines indicate percentages 

of coal electricity generation in 2007 from selected power 

plants.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

5 10 15 20 25 30 35 40 45 50 55 60 65

Annual electricity

generation (T

Wh)

A1B-C

A2-C

B2-C

2010

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

5 10 15 20 25 30 35 40 45 50 55 60 65

Procurement radius (miles)

A1B-C

A2-C

B2-C

2010

(A)

(B)

Figure 8. Projected mean annual electricity generation 

(TWh) from woody biomass for selected power plants in 

2030 by scenario and procurement radius for logging byproducts (A), and integrated harvest (B).
General Technical Report FPL–GTR–237

10

integrated harvest shows all three scenarios as yielding 

slightly lower electricity generation than 2010, for logging 

by-products scenario A1B-C is the only scenario where 

wood power generation is lower than the 2010 estimate. 

Even though electricity generation from logging by-products in 2030 for scenarios A2-C and B2-C are greater than 

the 2010 estimates, most power plants would still not be 

able to replace more than 10% of coal generation using only 

this feedstock.

Greater variation in projected electricity generation potential 

from logging by-products may have substantial economic 

and policy implications as logging by-products are the 

most common form of woody biomass feedstock used by 

co-firing power plants (DOE 2011; Goerndt et al. 2013a; 

Goerndt et al. 2013b) This is primarily from estimated lower 

delivered costs for logging by-products. 

4.4 Costs of Woody Biomass Procurement

Costs of biomass procurement and transport limit co-firing 

potential for plants residing in areas of low biomass per 

unit area. This is particularly true for integrated harvesting 

that includes removal of small-diameter trees which incur 

higher operational costs than simply removing logging byproducts. Although electricity generation potential of woody 

biomass from an area is driven by spatial density of available biomass (Fig. 5), the delivered biomass cost is driven 

primarily by maximum transport distance (Goerndt et al. 

2013b; Table 1). Figure 9 shows the 2010 average (across 

power plants) of marginal delivered cost of woody biomass 

by procurement radius for selected power plants for both 

procurement regimes (Goerndt et al. 2013a). 

The estimates of delivered biomass costs for 2010 by 

Goerndt et al. (2013a) show that the woody biomass cost 

break-even point (equal to coal cost) for integrated harvest 

occurs at around a 40- to 45-mile procurement radius or 

50- to 60-mile maximum transport distance. This coincides 

with findings by Sami et al. (2001) and Nicholls et al. 

(2006) indicating that on average 55 to 60 miles is the maximum economically feasible transport distance for woody 

biomass to power plants.

As indicated by cost assumptions in Table 1, the procurement regime including removal of small-diameter trees (integrated harvest) had the highest delivered costs for woody 

biomass from high harvest costs. Woody biomass from 

logging by-products could conceivably be obtained from a 

greater transport distance than biomass from integrated harvests due to lower overall marginal delivered cost. However, 

the market cost of coal and many of the cost components 

for woody biomass are expected to be variable over time. 

Changes in the breakeven transport distance because of increases or decreases in marginal delivered costs for woody 

biomass could be offset by changes in coal market value or 

changes in coal costs could be offset by changes in biomass 

costs. Figure 10 compares estimates of projected 2030 marginal delivered costs of woody biomass with projected coal 

market cost (EIA 2012). 

As for 2010, marginal delivered cost for logging byproducts in 2030 is projected to be lower than for biomass 

from integrated harvest. Scenario A1B-C has a breakeven 

procurement radius for logging by-products at about 

60 miles, which is about the same as for 2010 (Fig. 9). 

Scenarios A2-C and B2-C have a breakeven procurement 

radius greater than 65 miles. 

For 2010, the breakeven procurement distance for biomass 

from integrated harvesting is 35–45 miles. For 2030, the 

breakeven point for scenario A1B-C is 30–35 miles and 

breakeven points for A2-C and B2-C are 60–65 miles. The 

breakeven procurement radius is less for A1B-C because of 

higher projected stumpage costs (Ince et al. 2011). 

4.5 Power Plants Suited to Co-Fire with 

Woody Biomass

Counties with the potential for co-firing the most biomass 

with coal are those in which a power plant can obtain the 

greatest amount of woody biomass at marginal delivered 

fuel costs that are competitive with the cost of coal (Goerndt 

et al. 2013a; Goerndt et al. 2013b; FS, In press).To estimate 

the amounts of biomass plants could obtain we used a 40-

mile maximum because the 2010 delivered fuel cost analysis 

revealed that it is the approximate break-even distance for 

the integrated harvesting regime that could provide the most 

biomass. Using these plant-level biomass supply estimates, 

10

15

20

25

30

35

5 10 15 20 25 30 35 40 45 50 55 60 65

Fuel cost ($ M

Wh–1

)

Procurement radius (miles)

Integrated harvest

Market cost (coal)

Logging by-products

Figure 9. Estimated 2010 averages of marginal delivered 

cost of woody biomass and coal by procurement radius 

(Goerndt et al. 2013a). 
Potential for Coal Power Plants to Co-Fire with Woody Biomass in the U.S. North, 2010–2030

11

Figure 11 shows the percentage of coal power that could be 

offset at each selected plant in 2010. 

Aside from the higher levels of co-firing generation potential from integrated harvest compared to logging by-products, there are noticeable patterns in spatial distribution of 

power plants (Fig. 11). Many plants in the Lake States and 

the Northeast sub-region have a relatively high co-firing 

potential for both integrated harvest and logging byproducts. Areas that are intensively harvested, and therefore 

have high availability of logging by-products, can also have 

low SDI values for forests because of current harvest removals. Therefore, many plants with high co-firing potential 

for logging by-products (e.g., Minnesota) do not experience 

as great an increase in co-firing potential with integrated 

harvest as many plants in areas with low availability of 

logging by-products. However, the highest concentration 

of power plants with high co-firing potential (under both 

regimes) occur in eastern states: Ohio, Pennsylvania, and 

West Virginia. This is logical as these states have high concentrations of timberland combined with well-established 

timber-based industries providing logging by-products and 

mill by-products (Goerndt et al. 2012). These states also 

have relatively high coal-firing infrastructure, as well as a 

strong tradition of coal mining, which may favor coal use 

0

10

20

30

40

50

60

5 10 15 20 25 30 35 40 45 50 55 60 65

A1B-C

A2-C

B2-C

2010

Market cost (coal)

–5

5

15

25

35

45

55

65

5 10 15 20 25 30 35 40 45 50 55 60 65

Procurement radius (miles)

A1B-C

A2-C

B2-C

2010

Market cost (coal)

Fuel cost ($ M

Wh–1

) Figure 10. Projected average marginal cost for woody biomass and coal for selected power plants in 2030 by scenario and procurement radius for logging by-products (A), and 

integrated harvest (B).

(A) (A)

(B)

(B)

Figure 11. Estimated percentage of annual coal electricity generation that could be replaced by woody biomass 

for selected power plant at a 40-mile circular procurement 

radius assuming (A) logging by-products regime 2010 and 

(B) integrated harvest regime 2010.
General Technical Report FPL–GTR–237

12

over biomass use for electricity generation. Figures 12 and 

13 show power plants suited to host co-firing operations in 

2030 based on percentage of annual coal net generation that 

could be generated using biomass. 

Plants with low- to mid-range co-firing potential in 2010 

generally did not experience significant increase in the 2030 

projections (Fig. 12). However, there is a noticeable shift 

in spatial distribution for plants with high potential for cofiring. There is an increase in co-firing potential in midwest 

states Iowa, Ohio, and Minnesota, and a decrease in eastern 

states New York and West Virginia. This is because midwestern states are expected to have an increase in logging 

by-products availability between 2010 and 2030, whereas 

the eastern states are expected to have a decrease (Fig. 5). 

The shift in spatial distribution of plants with high co-firing 

potential between 2010 and 2030 is also affected by changes 

in available mill by-products. Mill by-products are the sole 

alternative biomass source for power plants that are considering logging by-products as the primary procurement 

regime. As for logging by-products, 2030 estimates of mill 

residue availability were made using the projected change 

in timber harvest between 2010 and 2030 to adjust the 2010 

estimates (Section 3.2.5). Many of the power plants with 

high estimated potential for co-firing in 2010 are spatially 

clustered in areas with a high number of wood processing 

mills, which is associated with high local harvest rates (high 

availability of logging by-products) and relatively high local 

availability of mill by-products. The average percentage of 

coal net generation that could be replaced by logging byproducts in 2030 for scenarios A2-C and B2-C was 1.1 and 

1.3 times that of scenario A1B-C, respectively. 

Unlike the shifts in location of potential high co-firing with 

logging by-products between 2010 and 2030, the projections 

for potential high co-firing with biomass from integrated 

harvest do not show a decrease in the Lake States or northeastern states. This contrast between integrated harvest and 

logging by-products is due primarily to the dependency of 

integrated harvest on small-diameter trees in addition to 

available logging by-products from roundwood harvest. Unlike logging by-products, the availability of biomass from 

small-diameter trees is not projected to decrease in many of 

the eastern states between 2010 and 2030 (Fig. 5). Of the 

three assessed procurement regimes, availability of biomass 

from small-diameter trees is expected to change the least 

from 2010 to 2030 (Fig. 5), resulting in greater stability in 

spatial distribution surrounding power plants with high cofiring potential. This results in low variation among scenarios in estimated co-firing potential based on use of biomass 

from integrated harvesting in 2030. 

4.6 Implications for Public Policy

While scenarios A2-C and B2-C showed general decreases 

in average delivered woody biomass costs, scenario 

A1B-C consistently showed increases in delivered fuel costs 

Figure 12. Estimated 2030 percentage of annual coal electricity generation that could be replaced by logging byproducts for each selected power plant at a 40-mile 

concentric procurement radius by scenario.

(A)

(B)

(C)
Potential for Coal Power Plants to Co-Fire with Woody Biomass in the U.S. North, 2010–2030

13

resulting in much lower maximum procurement radii and 

transport distances for economical procurement of woody 

biomass. This may necessitate the use of compensatory incentives to offset additional costs associated with transporting biomass feedstocks greater distances, if increased biomass utilization for energy is considered desirable under this 

scenario. In the absence of incentives, power plants would 

either need to pass the extra cost to the consumer through 

retail price premiums or obtain more biomass within shorter 

transport distances. The latter could lead to ecological sustainability issues if co-firing power plants are pushed to use 

more biomass than can be sustainably procured within an 

economic transport distance. One type of policy that could 

push power plants to utilize more woody biomass within 

economic transport distances are state RPS.

4.6.1 State-Level Renewable Portfolio Standards 

As of 2013, only one of the 20 states included in the study 

area has not implemented an RPS. Demand for woody 

biomass from forests could be partially met by dedicated 

energy plantations using herbaceous or woody crops. However, it may be necessary in some states to set a maximum 

on the amount of electricity that can be produced from biomass to help achieve an RPS, unless conversion efficiencies 

are enhanced or some biomass is grown on plantations, or 

both. Ecological concerns may result in adoption of mandatory best management practices (BMPs) for woody biomass 

procurement, such as the woody biomass harvesting BMPs 

developed by the Missouri Department of Conservation 

(Enyart et al. 2009).

4.6.2 Best Management Practices 

Woody biomass harvest for energy that will likely be done 

in coordination with whole-tree harvesting (integrated 

harvesting) removes more total biomass per acre than the 

traditional roundwood harvests in the U.S. North. Traditional roundwood harvest can leave anywhere from 25% to 

45% of tree biomass on site (DOE 2011). Removal of more 

of the tree biomass can require longer operation times for 

machinery, decreases in ground cover of slash and litter, and 

fewer small-diameter and non-merchantable (by sawtimber 

standards) trees left on site. Reduction of material retained 

on site is one of the primary environmental policy issues 

for woody biomass procurement for energy. As of 2011, 

five U.S. states have drafted specific BMPs for removal 

of woody biomass including Maine, Minnesota, Missouri, 

Pennsylvania, and Wisconsin (Skog and Stanturf 2011). 

All these states are within our study area. The most notable 

changes for woody biomass BMPs concern woody biomass retention on site after harvesting. As procurement of 

woody biomass for energy generation increases, it is likely 

that more states will adopt BMPs for woody biomass harvest and procurement. Creation of more BMPs for woody 

biomass combined with RPS may create situations where 

power plants simply may not be able to afford procurement 

(A)

(B)

(C)

Figure 13. Estimated 2030 percentage of annual coal electricity generation that could be replaced by biomass from 

integrated harvest for each selected power plant at a 

40-mile concentric procurement radius by scenario.
General Technical Report FPL–GTR–237

14

of amounts of woody biomass greater than the estimates 

obtained in this study within economical transport distances 

without additional financial incentives or increases in available plantation biomass.

5. Conclusions

This study estimated local woody biomass resource supply 

and delivered costs for individual power plants in the U.S. 

North. This region has the potential to provide a substantial 

amount of woody biomass for co-firing based on physical 

resource availability and spatial distribution density of the 

timberland biomass resource. However, electricity generation potential is restricted in that most procurement regimes 

would not replace more than 10% of coal use within an 

economical transport distance. This percentage would be 

lower when accounting for resource competition if two 

or more adjacent power plants are co-firing with biomass 

simultaneously. Estimated electricity generation potential 

does not change substantially between 2010 and 2030, with 

the exception of moderate effects associated with changes 

in logging by-products because of changes in projected annual harvest. Projections of a decrease in marginal delivered 

costs for scenarios A2-C and B2-C suggest that economic 

woody biomass transport distances for most power plants 

could likely increase by 2030, but economic distance would 

decrease with higher costs projected for scenario A1B-C. 

For scenarios A2-C and B2-C, power plants may be able 

to obtain more woody biomass economically in the future, 

depending upon the rate of increase in coal market cost and 

increases in fuel costs for harvest operations and transport. 

The scenarios assumed that woody biomass use for energy 

would increase because of a combination of economic drivers that could include low prices relative to alternative fuels, 

regulatory mandates to use renewable fuels, or subsidies. 

Currently, the cost for natural gas is low and offers competition to wood biomass as a fuel to produce electric power. An 

increase in wood energy use may require regulatory mandates to use renewable fuels or to reduce GHG emissions 

(or both), which could be met by increased use of wood 

for energy. With significant mandates and continued use of 

some older coal boilers, co-firing with wood may be a way 

to meet those mandates. However if a mandate to reduce 

GHG emissions in coal fired power plants could be met by 

co-firing with natural gas then additional wood use for cofiring could be limited. 

State-level RPS may push some power plants to procure 

more biomass than may be ecologically and/or economically 

sustainable in the long-term. Implementation of woody biomass BMPs in several states may limit such procurement. 

The results of this research can help power plant owners, 

forest managers, and policy makers understand the biological, economic and policy factors that will influence the 

contribution woody biomass can make in producing electric 

power in specific locations the U.S. North. 

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Potential for Coal Power Plants to Co-Fire with Woody Biomass in the U.S. North, 2010–2030

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Appendix—Supplementary Information for Projection Scenarios, 

Econometric Analysis, Cost Assumptions, and Projected Biomass 

Availability

Table A1—Variables and corresponding data sources for the econometric analysis of probability of woody biomass 

co-firing for electrical generation in the U.S. North (Aguilar et al. 2012). Variables projected for 2030 estimatesa

Factors Proxy variables Units Source 

Electricity demand indicators Average electricity price (state)a Per kilowatt-hour (U.S. cents) EIA 2008 

County area km2 U.S. Census Bureau 2000 

County area urbanized (%)a U.S. Census Bureau 2000 

Land value Median house value (state) Thousands US$ (×103

) U.S. Census Bureau 2000, 

2013 

Transportation infrastructure Presence of principle highways (county) Binary (1= infrastructure) U.S. Census Bureau 2000 

Presence of principle railways (county) Present (0 = otherwise) U.S. Census Bureau 2000 

Presence of major rivers and streams (county) U.S. Census Bureau 2000 

Coal availability and price Presence of coal production (state) Binary EIA 2008 

Average coal price (state)a Per ton (US$) EIA 2008 

Implementation of state 

renewable portfolio standards 

(RPS) 

RPS adopted by 2001 (state) Binary DSIRE 2010 

Resource availability of 

non-woody biomass 

Annual corn yield Metric tons NASS 2008 

Resource availability of 

wood mill by-products 

Total annual by-products (county)a Volume (m3

) USDA 2012 

Subregion-level conditions Subregional binary variables Binary (1= county within 

subregion) 

U.S. Census Bureau 2000 

a

Variables that were projected to 2030. Subregional binary variables = indicator (0,1) variables for subregions as defined by the U.S. Census Bureau (2000). 
General Technical Report FPL–GTR–237

18

Table A2—Overview of IPCC storylines evaluated in U.S. North forest projections. Based on Nakicenovic et al. 

(2000), Ince et al. (2011), IPCC (2007), and FS 2012a 

IPCC storyline characteristics Storyline A1 Storyline A2 Storyline B2 

General development themes Globalization; economic 

growth; introduction of new 

and more efficient energy 

technologies; capacity building 

More differentiated world with 

less trade compared to storyline 

A1B; uneven economic 

growth; slower technological 

change 

Sustainable development; 

diversified technology; 

Increased concern for 

environmental and social 

sustainability compared to 

the A2 storyline. 

Synopsis Rapid economic globalization. 

International mobility of 

people, ideas, and technology. 

Strong commitment to marketbased solutions. Strong 

commitment to education. 

High rates of investment and 

innovation in education, 

technology, and institutions at 

the national and international 

levels. A balanced energy 

portfolio including fossil 

intensive and non-fossil energy 

sources 

Consolidation into economic 

regions. Self-reliance in terms 

of resources and less emphasis 

on economic, social, and 

cultural interactions between 

regions. Technology diffuses 

more slowly than in the other 

scenarios. International 

disparities in productivity, and 

hence income per capita, are 

largely maintained or increased 

in absolute terms. 

A trend toward local selfreliance and stronger 

communities. Communitybased solutions to social 

problems. Energy systems 

differ from region to region, 

depending on the availability of 

natural resources. The need to 

use energy and other resources 

more efficiently spurs the 

development of less carbonintensive technology in some 

regions. 

Global GDPa

 growth Very high (6.2×) Medium (3.2×) Medium (3.5×) 

U.S. GDP growth Medium (3.3×) Low (2.6×) Low (2.2×) 

Global energy use Very high High Medium 

Oil and gas availability High Low Medium 

Energy technological pace 

and sources 

Rapid; gas, biomass, and other 

renewables 

Slow; coal and gas Medium; gas, oil, and biomass 

Global population growth Medium (1.3×) High (1.7×) Medium (1.4×) 

U.S. population growth Medium (1.5×) High (1.7×) Medium (1.3×) 

Global expansion of primary 

biomass energy production 

(2010–2060) 

High (Highest for USA) Medium Medium (lowest for USA) 

Relative increase in greenhouse 

gas emissions 

Medium High Low 

a

Gross domestic product.

Table A3—Storyline and global circulation model combinations analyzed, and 

associated text for a description of storylines. The scenario names A1B-C, 

A2-C, and B2-C used in this report refer to the combination of the primary 

global circulation model with the respective IPCC storyline 

Global circulation model IPCC storyline A1B IPCC storyline A2 IPCC storyline B2 

CGCM3.1a Primary 

(Scenario A1B-C) 

Primary 

(Scenario A2-C) 

CGCM2b Primary 

(Scenario B2-C) 

a

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

Model (CGCM3), Medium Resolution (T47). http://www.ec.gc.ca/ccmac-cccma (Accessed: July 27, 

2012). 

b

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

Modelling and Analysis http:// http://www.ec.gc.ca/ccmac-cccma (Accessed: July 27, 2012).
Potential for Coal Power Plants to Co-Fire with Woody Biomass in the U.S. North, 2010–2030

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Table A4—Estimates of woody biomass spatial distribution (tons/acre) in 2030 by state and future scenario from 

timberland in the U.S. North 

 Logging by-products Small trees (SDI)a Integrated harvest 

State na A1B-C A2-C B2-C A1B-C A2-C B2-C A1B-C A2-C B2-C 

Connecticut 8 0.004 0.004 0.004 0.308 0.326 0.317 0.308 0.326 0.317 

Rhode Island 5 0.004 0.009 0.004 0.094 0.094 0.098 0.098 0.103 0.103 

New Jersey 21 0.009 0.004 0.004 0.263 0.272 0.263 0.268 0.277 0.268 

New York 62 0.031 0.036 0.031 0.410 0.401 0.406 0.442 0.433 0.437 

Massachusetts 14 0.031 0.031 0.027 0.321 0.335 0.330 0.352 0.366 0.357 

Michigan 83 0.049 0.054 0.058 0.294 0.294 0.299 0.343 0.348 0.357 

Iowa 99 0.049 0.067 0.080 0.125 0.129 0.134 0.178 0.196 0.214 

Vermont 14 0.054 0.045 0.040 0.379 0.384 0.388 0.433 0.428 0.428 

Maryland 24 0.054 0.062 0.062 0.272 0.263 0.294 0.326 0.321 0.357 

Ohio 88 0.058 0.062 0.067 0.285 0.285 0.290 0.343 0.348 0.357 

Illinois 102 0.062 0.067 0.062 0.192 0.196 0.196 0.254 0.263 0.259 

Missouri 115 0.062 0.054 0.067 0.219 0.219 0.219 0.281 0.268 0.281 

Delaware 3 0.067 0.071 0.098 0.330 0.321 0.339 0.397 0.393 0.433 

West Virginia 55 0.071 0.076 0.089 0.303 0.308 0.308 0.379 0.384 0.397 

Pennsylvania 67 0.071 0.080 0.076 0.343 0.343 0.352 0.415 0.424 0.428 

New Hampshire 10 0.089 0.085 0.080 0.393 0.401 0.415 0.482 0.486 0.495 

Minnesota 87 0.098 0.103 0.085 0.232 0.236 0.232 0.330 0.339 0.317 

Wisconsin 72 0.120 0.120 0.116 0.236 0.232 0.236 0.357 0.352 0.352 

Indiana 93 0.152 0.178 0.143 0.214 0.214 0.219 0.366 0.393 0.361 

Maine 16 0.152 0.152 0.161 0.343 0.352 0.343 0.495 0.500 0.504 

Region total 1038 1.289 1.361 1.356 5.558 5.607 5.679 6.847 6.946 7.021 

a

Stand density index. 

Table A5—Projections of operational and transport cost assumptions for woody biomass procurement in the U.S. 

North to 2030 for all applicable scenarios 

 Logging by-products Small-diameter trees 

Costs A1B-C A2-C B2-C A1B-C A2-C B2-C Mill by-products 

Harvest ($/ton)a 10.38 10.38 10.38 25.93 25.93 25.93 na 

Transportation 

 Fixed ($/ton)b 6.80 6.80 6.80 6.80 6.80 6.80 6.80 

 Incremental ($/ton /mile)c

 0.36 0.36 0.36 0.36 0.36 0.36 0.36 

Stumpage value ($/ton) d

 24.11 10.90 5.56 24.11 10.90 5.56 na 

Chipping costs ($/ton)e 8.91 8.91 8.91 8.91 8.91 8.91 na 

Selling ($/ton)f na na na na na na 4.49 

a

Derived from percentage increases in fuel cost and labor cost (Saunders et al. 2012; BLS 2013; EIA, 2013). b

Fixed cost for operation and loading/unloading of trucks (Perlack et al. 2005; Saunders et al. 2012; Gallagher et al. 2003) c

Derived from percentage increases in fuel and labor cost (Saunders et al. 2012; BLS 2013; EIA 2013). d

Derived using percentage increase in non-timber stumpage value by IPCC scenario (Ince et al. 2011) e

Derived from percentage increases in fuel and labor cost (Saunders et al. 2012; BLS 2013; EIA 2013). f

Perez-Verdin et al. (2009).