Carbon debt and carbon sequestration parity in forest

bioenergy production

STEPHEN R. MITCHELL*, MARK E. HARMON † and KAR I E. B. O’CONNELL†

*Nicholas School of the Environment, Duke University, Durham, NC 27708, USA, †Department of Forest Ecosystems and

Society, College of Forestry, Oregon State University, Corvallis, OR 97331, USA

Abstract

The capacity for forests to aid in climate change mitigation efforts is substantial but will ultimately depend on

their management. If forests remain unharvested, they can further mitigate the increases in atmospheric CO2

that result from fossil fuel combustion and deforestation. Alternatively, they can be harvested for bioenergy production and serve as a substitute for fossil fuels, though such a practice could reduce terrestrial C storage and

thereby increase atmospheric CO2 concentrations in the near-term. Here, we used an ecosystem simulation

model to ascertain the effectiveness of using forest bioenergy as a substitute for fossil fuels, drawing from a

broad range of land-use histories, harvesting regimes, ecosystem characteristics, and bioenergy conversion efficiencies. Results demonstrate that the times required for bioenergy substitutions to repay the C Debt incurred

from biomass harvest are usually much shorter (< 100 years) than the time required for bioenergy production to

substitute the amount of C that would be stored if the forest were left unharvested entirely, a point we refer to

as C Sequestration Parity. The effectiveness of substituting woody bioenergy for fossil fuels is highly dependent

on the factors that determine bioenergy conversion efficiency, such as the C emissions released during the harvest, transport, and firing of woody biomass. Consideration of the frequency and intensity of biomass harvests

should also be given; performing total harvests (clear-cutting) at high-frequency may produce more bioenergy

than less intensive harvesting regimes but may decrease C storage and thereby prolong the time required to

achieve C Sequestration Parity.

Keywords: bioenergy, biofuel, C cycle, C sequestration, forest management

Received 13 January 2012 and accepted 9 March 2012

Introduction

The search for alternatives to fossil fuel energy has

yielded several possibilities, many of which are derived

from biomass. Bioenergy has been viewed as a promising alternative to fossil fuels because of its capacity to

increase the energy security in regions that lack petroleum reserves and because their production and combustion does not require a net transfer of C from Earth’s

lithosphere to its atmosphere. While bioenergy is understandably among the most heavily promoted and generously subsidized sources of renewable energy, recent

research has brought greater attention to the environmental costs of broad-scale bioenergy production (Fargione et al., 2008; Searchinger et al., 2008, 2009) as well as

the limits of how much energy it can actually produce

(Field et al., 2008).

One alternative to crop-based biofuels is woody biomass harvested directly from forests, an avenue thought

to be more promising than harvesting non-woody species for a variety of reasons. First, woody biomass stores

more potential energy per unit mass than non-woody

biomass (Boundy et al., 2011). Second, many forms of

non-woody biomass are often utilized following a

lengthy conversion process to ethanol or biodiesel, a

process which results in a significant loss of potential

energy of the harvested biomass (Field et al., 2008) as

well as additional energy that may be expended in the

conversion process itself (Walker et al., 2010). By contrast, woody biomass is more readily utilized for energy

production without any further modifications (Richter

et al., 2009). Third, landscapes managed for bioenergy

production using woody biomass are able to store more

C per unit of land area than crop-based biofuels.

Woody biomass is already a primary source of energy

for 2 billion people; the FAO estimates that over half of

the world’s total round wood removals from forests and

trees outside forests are intended for bioenergy production (FAO; Parikka, 2004). Many of these harvests are

specifically intended to provide a C-neutral energy

source to substitute for fossil fuels (Parikka, 2004; Richter et al., 2009; Buford & Neary, 2010), yet such harvests

can arrest the C sequestration of many forests far short

of their full potential (Harmon et al., 1990; Canadell &

Raupach, 2008; Pan et al., 2011). Much of the world’s

Corresspondence: Stephen R. Mitchell, tel. + 919 491 0398,

fax + 919 684 8741, e-mail: stephen.mitchell@duke.edu

© 2012 Blackwell Publishing Ltd 1

GCB Bioenergy (2012), doi: 10.1111/j.1757-1707.2012.01173.x
forested land area stores far less C than it potentially

could (House et al., 2002; Canadell & Raupach, 2008),

and foregoing future harvest/s could provide a more

rapid amelioration of atmospheric CO2 then bioenergy

production. A recent study conducted in US West Coast

forests examined the C storage/bioenergy production

trade-offs of many ecosystems and found that the current C sink for most ecosystems is so strong that it cannot be matched or exceeded through substitution of

fossil fuels by forest bioenergy over the next 20 years.

However, due to its reliance on existing field data

instead of simulation models, it could not extrapolate

these results beyond the 20-year period (Hudiburg et al.,

2011). Another recent study that addressed these tradeoffs is the so-called ‘Manomet’ study, which modeled

bioenergy production systems for different forest types

in Massachusetts and found that utilizing forests for

bioenergy production reduces C storage without providing an equitable substitution in the near-term

(Walker et al., 2010). However, the approach taken by

the ‘Manomet’ study dealt short-term repayment in C

Debts at the stand level, while our approach focuses on

the C Debt that is incurred as a result harvesting forests

for bioenergy production over the long-term at the landscape level. We provide further description of our concept of C Debt sensu Fargione et al. (2008) by contrasting

it with what we refer to as the C Sequestration Parity,

which we outline in the discussion below.

Carbon debt

Compared to fossil fuels, woody biomass yields a lower

amount of energy per unit mass of C emitted. Since biomass harvesting reduces C storage but does not produce the same amount of energy that would be

obtained from an equal amount of C emissions from

fossil fuel combustion, recouping losses in C storage

through bioenergy production may require many years.

We refer to this recoupment as the C Debt Repayment,

calculated as the change in C storage resulting from bioenergy harvests and associated C substitution, demonstrated in Fig. 1. A mathematical representation is given

below in Eqn (1), where Cm

storageðtÞ is the amount of C

stored in a managed forest at time t, Cm

storageð0Þ is the

amount of C stored in a managed forest at t = 0 (before

bioenergy harvests have begun), and Cm

harvestðtÞ is the

amount of C biomass harvested from a managed

forest at time t, which is multiplied by the bioenergy

conversion factor gbiomass:

Cm

debtðtÞ 1⁄4 CmstorageðtÞ Cmstorageð0Þ Xn

t1⁄41

Cm

harvestðtÞ  gbiomass

ð1Þ

Carbon sequestration parity

A repayment of the C Debt does not necessarily imply

that the forest has been managed for maximal amelioration of atmospheric CO2. If a forest is managed for the

production of bioenergy to substitute for traditional fossil fuel energy as part of an effort to ameliorate atmospheric CO2 concentrations, such a strategy should be

gauged by the climate change mitigation benefits that

would accrue by simply leaving the forest unharvested.

Ascertaining the point at which a given strategy provides the maximal amount of climate change mitigation

benefits requires accounting for the amount of biomass

harvested from a forest under a given management

regime, the amount of C stored under a given management regime, and the amount of C that would be stored

if the forest were to remain unharvested (Schlamadinger

& Marland, 1996a,b,c; Marland & Schlamadinger, 1997;

Marland et al., 2007). It is expected that a forest that is

continuously managed for bioenergy production will

eventually produce enough bioenergy to ‘recoup’ the

associated loss in C storage (the so-called carbon debt)

through the substitution of bioenergy for fossil fuel

energy. However, the ultimate effectiveness of this strategy should be determined by the amount of time

required for the sum of the total ecosystem C storage

and bioenergy C substitution to exceed the amount of C

that would be stored if that same forest were to remain

unharvested (Fig. 1). We refer to this difference as the C

Fig. 1 Conceptual representation of C Debt Repayment vs. the

C Sequestration Parity Point. C Debt (Gross) is the difference

between the initial C Storage and the C storage of a stand (or

landscape) managed for bioenergy production. C Debt (Net) is

C Debt (Gross) + C substitutions resulting from bioenergy

production.

© 2012 Blackwell Publishing Ltd, GCB Bioenergy, doi: 10.1111/j.1757-1707.2012.01173.x

2 S. R. MITCHELL et al.
sequestration differential ðCm

differentialðtÞ

Þ, illustrated in

Eqn (2) below:

Cm

differentialðtÞ 1⁄4 CustorageðtÞ CmstorageðtÞ Xn

t1⁄41

Cm

harvestðtÞ

 gbiomass ð2Þ

where Cu

storageðtÞ is the amount of C stored in an unmanaged forest at time t. We refer to the crossing of this

threshold as the point of C Sequestration Parity. Thus, we

make a distinction between the amount of time required

for the bioenergy production system to recoup any

reductions in C storage resulting from bioenergy production (C Debt repayment) and the amount of time

required for the bioenergy production system to equal

the C than would be stored if the forest were to remain

unharvested (C Sequestration Parity Point), as the latter

represents a more ambitious climate change mitigation

strategy (Fig. 1).

Materials and methods

We simulated the growth and harvest of woody biomass using

a significantly updated version of the ecosystem simulation

model LANDCARB (Harmon, 2012). LANDCARB is a landscape-level ecosystem process model that can simulate a full

spectrum of potential harvesting regimes while tracking the

amount of material harvested, allowing one to simulate ecosystem C storage while tracking the amount of fossil fuel C that

could be substituted by using harvested materials as biomass

fuels. LANDCARB integrates climate-driven growth and

decomposition processes with species-specific rates of senescence and mortality while incorporating the dynamics of interand intra-specific competition that characterize forest gap

dynamics. Inter- and intra-specific competition dynamics are

accounted for by modeling species-specific responses to solar

radiation as a function of each species’ light compensation

point and assuming light is delineated through foliage following a Beer-Lambert function. By incorporating these dynamics

the model simulates successional changes as one life-form

replaces another, thereby representing the associated changes

in ecosystem processes that result from species-specific rates of

growth, senescence, mortality, and decomposition.

LANDCARB represents stands on a cell-by-cell basis, with

the aggregated matrix of stand cells representing an entire

landscape. Each cell in LANDCARB simulates a number of

cohorts that represent different episodes of disturbance and colonization within a stand. Each cohort contains up to four layers

of vegetation (upper tree layer, lower tree layer, shrub, and

herb) that each have up to seven live pools, eight dead pools,

and three stable pools. For example, the upper and lower tree

layers are comprised of seven live pools: foliage, fine-roots,

branches, sapwood, heartwood, coarse-roots, and heart-rot, all

of which are transferred to the appropriate dead pool following

mortality. Dead sapwood and dead heartwood can be either

standing or downed to account for the different microclimates

of these positions. Dead pools in a cell can potentially contribute material to three, relatively decay-resistant, stable C pools:

stable foliage, stable wood, and stable soil. There are also two

pools representing charcoal (surface and buried).

Our modeling approach with LANDCARB was designed to

account for a broad range of ecosystem characteristics and initial landscape conditions of a forest, both of which are influential in determining rate of C debt repayment and the time

required for C sequestration parity. Forests with high productivity can generate fossil fuel substitutions more rapidly than

forests with low productivity. Conversely, forests with highlongevity biomass raise the C storage of the ecosystem (Olson,

1963), which has implications for C debt and C sequestration

parity. Furthermore, forests can contain a wide range of C

stores even within a fixed range of productivity and C longevity (i.e., lower rates of mortality and decomposition; Smithwick

et al., 2007), yet we know of no study to date that has examined

the impact of forest productivity and biomass longevity on C

Debt repayment or C Sequestration Parity. Furthermore, we

know of no previous study that examines a sufficiently large

range of forest management strategies and land-use histories to

ascertain exactly what sort of situation/s might provide for an

efficient utilization of forest biomass for bioenergy production.

To provide a more comprehensive evaluation of the effectiveness of utilizing forest bioenergy as a substitute for fossil

fuels, we performed our analysis across a wide range of ecosystem properties by simulating three levels of forest growth and

three levels of biomass longevity, resulting in nine distinct ecosystems (Table 1). Levels of longevity were drawn from published rates of bole growth efficiency, mortality, and

decomposition (growth and biomass Harmon et al., 2005). The

upper and lower bounds of these parameters were intended to

cover the range of these processes for most of the world’s temperate forests. Our parameters are largely drawn from forests

of the US Pacific Northwest, but the extreme values of bole

growth efficiency, mortality, and decomposition could be considered extreme values of other forests as well, thereby giving

our results maximal applicability.

We ran each of our nine simulated ecosystems under four

sets of initial landscape conditions: afforesting post-agricultural

land (age = 0), forest recovering from a severe disturbance

(age = 0), old-growth forest (age > 200 years), and a forest harvested on a 50-year rotation (mean age ~25 years). Each combination of ecosystem characteristics and land-use history was

simulated with seven different management strategies

(Table 2), which included one unharvested control group as

well as three biomass harvest frequencies (25, 50, 100 years)

applied at two different harvest intensities (50% harvest of live

stems, 100% harvest of live stems). We assumed that our postagricultural landscape did not have any legacy C storage apart

from a small amount of soil C, thus our post-agricultural simulation did not have any spin-up simulation. However, simulations of the other land-use histories all had a 500-year spin-up

simulation were run to establish initial live, dead, and soil C

stores. Additionally, for the two simulations that were recovering from harvests and prior disturbance (recently disturbed

and rotation forest) we tracked the respective C stores from

these events. To simulate a landscape that had previously been

harvested on a 50-year rotation, we simulated an annual clearcut on 2% of the landscape throughout the 50 years prior to the

© 2012 Blackwell Publishing Ltd, GCB Bioenergy, doi: 10.1111/j.1757-1707.2012.01173.x

FOREST BIOENERGY PRODUCTION AND CO2 EMISSIONS 3
completion of the spin-up. In accordance with a prior framework for harvested C decomposition, we assumed that 60% of

the harvested C would go directly into long-term C storage

mediums (i.e., houses, buildings) that decayed at the rate of 1%

per year (Harmon & Marks, 2002). The remaining 40% of the

harvested C was assumed to be lost to the atmosphere during

manufacturing (Harmon & Marks, 2002). Landscapes were first

harvested for bioenergy production in the year following the

completion of the spin-up.

Initial conditions of our disturbed forest were analogous to

those of a severe pine beetle outbreak. To simulate this condition, we initiated a total mortality of all trees at the end of the

spin-up, prior to the biomass harvests. We then simulated an

annual salvage logging on 5% of the landscape for each of the

5 years following the simulated pine-beetle disturbance (25%

of the landscape was salvage logged). We assumed that 75% of

all salvageable biomass was removed in each salvage logging.

Salvageable materials harvested in the first 5 years following

disturbance were assumed to be stored in wood products and

subject to the same decomposition scheme outlined above

for the 50-year Rotation Harvest. Such conditions are fairly

similar to those in a landscape subject to a high-severity, standreplacing wildfire, though a landscape subject to a pine beetle

infestation will initially have more C storage than one experiencing a high-severity wildfire. However, this difference is

temporary and would have a minimal effect on the long-term

effects of biomass harvesting, thus this set of initial conditions

could also be considered as a proxy for the initial conditions

that would follow a high-severity wildfire.

Wildfire

Our analysis also incorporates wildfires in all simulations, not

only because they are naturally occurring phenomena in many

forest ecosystems, but also because amount of harvestable biomass in an ecosystem can be altered by the event of wildfire,

which needs to be accounted for. In the LANDCARB model,

fire severity controls the amount of live vegetation killed and

the amount of combustion from the various C pools, and is

influenced by the amount and type of fuel present. Fires can

increase (or decrease) in severity depending on how much the

weighted fuel index a given cell exceeds (or falls short of) the

fuel level thresholds for each fire severity class (Tlight, Tmedium,

Thigh, and Tmax) and the probability values for the increase or

decrease in fire severity (Pi and Pd). For example, a low-severity fire may increase to a medium-severity fire if the fuel index

Table 2 List of all bioenergy production system characteristics simulated. We incorporated four land-use histories, three levels of

biomass accumulation, three levels of biomass longevity, three different harvest frequencies and two levels of harvest intensity

Land-use histories Growth rates Biomass longevities Harvest frequencies Harvest intensities

Post-agricultural (age = 0) G1* L1* 100 (100Y) 50% (050H)

Recently disturbed (age = 0) G2* L2* 50 (50Y) 100% (100H)

Rotation forest (age ~25) G3* L3* 25 (25Y)

Old-growth (age > 200)

*See Table 1 for details.

Table 1 Table of selected growth, mortality, and decomposition characterstics for each of our nine ecosystems. Classifications G1,

G2, and G3 represent increasing growth rates, represented by the Site Index. L1, L2, and L3 represent increasing biomass longevities.

The group with the lowest potential C storage had the lowest growth rate (G1) combined with the highest rates of mortality and

decomposition that yielded the lowest rates of biomass longevity (L1). The upper and lower bounds of our rates of growth and longevity were intended to cover the range of these processes for most of the world’s forests, thereby giving our results maximal applicability. Thus, the group referred to as G1-L1 is the group with the lowest potential C storage, while the group referred to as G3-L3 has

the highest potential C storage. Also note that L1 and L3 values represent extreme values of mortality and decomposition, whereas

L2 represents a median value, rather than a midpoint between L1 and L3. MortalityMAX is the maximum rate of mortality, while

kFoliage and kHeartwood are decomposition constants for foliage and heartwood. Potential C Storage is the mean amount of C storage of

an old-growth stand under these characteristics, as measured over a 500 year interval

Group

Bole growth

efficiency +DMg Stem

C/+DMg Leaf C)

MortalityMAX

(yr1) kFoliage (yr1) kHeartwood (yr1)

Potential C storage

(Mg C ha1)

G1-L1 0.35 0.03 0.25 0.1 212

G1-L2 0.35 0.02 0.2 0.02 230

G1-L3 0.35 0.01 0.15 0.01 296

G2-L1 0.54 0.03 0.25 0.1 359

G2-L2 0.54 0.02 0.2 0.02 492

G2-L3 0.54 0.01 0.15 0.01 621

G3-L1 0.84 0.03 0.25 0.1 645

G3-L2 0.84 0.02 0.2 0.02 757

G3-L3 0.84 0.01 0.15 0.01 954

© 2012 Blackwell Publishing Ltd, GCB Bioenergy, doi: 10.1111/j.1757-1707.2012.01173.x

4 S. R. MITCHELL et al.
sufficiently exceeds the threshold for a medium-severity fire.

Fuel level thresholds were set by monitoring fuel levels in a

large series of simulation runs where fires were set at very

short intervals to see how low fuel levels needed to be to create

a significant decrease in expected fire severity.

The fire regime for low-growth forests (G1) is characterized

by a low-severity, high frequency fire regime, with a mean fire

return interval (MFRI) of 16 years (Bork, 1985), similar to the

fire regime in a Ponderosa pine forest, also a low-growth rate

forest. Fire regimes for the medium and high-growth forests

(G2, G3) consisted of high-severity, low frequency

(MFRI = 250 years) fire regimes, similar to that of a Douglas-fir

or Sitka spruce forest (Cissel et al., 1999). We generated exponential random variables to assign the years of fire occurrence

(Van Wagner, 1978) based on literature estimates (Bork, 1985)

for mean fire return intervals (MFRI) for each ecosystem. The

cumulative distribution for our negative exponential function

is given in Eqn (1) where X is a continuous random variable

defined for all possible numbers x in the probability function P

and k represents the inverse of the expected time for a fire

return interval given in Eqn (2).

P Xf g  x 1⁄4

Zx

0

ke

kxdx ð1Þ

where

E1⁄2X 1⁄4 1

k ð2Þ

Fire severities in each year generated by this function are

cell-specific, as each cell is assigned a weighted fuel index

calculated from fuel accumulation within that cell and the

respective flammability of each fuel component, the latter of

which is derived from estimates of wildfire-caused biomass

consumption.

Bioenergy conversion factors

Previous studies on the mitigation potential of bioenergy have

yielded conflicting conclusions about the potential for bioenergy production from woody biomass (Schlamadinger & Marland, 1996a,b,c; Marland & Schlamadinger, 1997; Marland et al.,

2007; Walker et al., 2010). Differences in these conclusions are

due, in part, to the different assumptions regarding the efficiency of bioenergy utilization. Energy is required for transporting biomass and powering bioenergy conversion facilities,

and some is lost due to inefficiencies in the conversion process

(Hamelinck et al., 2005; Walker et al., 2010). Thus, it is difficult

to provide a one-size-fits-all estimate of bioenergy conversion

efficiency. Rather than using one value, we will evaluate a

range of bioenergy conversion efficiencies, ranging from 0.2 to

0.8, to ascertain the sensitivity of C offsetting schemes to the

range in variability in the energy conversion process. We estimate the average bioenergy conversion factor for woody biomass (gbiomass) to be 0.51, meaning that harvesting 1 Mg of

biomass C for bioenergy production will substitute for 0.51 Mg

fossil fuel C since less energy per unit C emissions is obtainable

from biomass compared to fossil fuel. Calculations for this conversion factor (gbiomass) are in the Supporting Information. A

conversion factor of 0.8 represents a highly efficient utilization

of bioenergy, though such a conversion efficiency is likely not

realistic. Conversely, a conversion factor of 0.2 represents a

highly inefficient method of energy utilization, though some

bioenergy facilities and conversion processes do operate at this

low level of efficiency (Walker et al., 2010).

We ran our analysis across 252 distinct scenarios, as we had

nine distinct ecosystems (based on three levels of forest growth

for three levels of biomass longevity), four initial types of initial

landscape conditions, and seven treatment groups (one control,

plus three treatment frequencies applied at two levels of intensity). Output from the 252 distinct modeling scenarios was analyzed using seven different bioenergy conversion factors,

meaning that our analysis had 1764 combinations of ecosystem

properties, initial landscape conditions, harvest frequencies, and

bioenergy conversion factors. Our analysis quantifies the degree

to which the harvesting and utilization of forest-derived bioenergy alters the landscape-level C storage and bioenergy production in order to calculate (1) the time required for the C

mitigation benefits accrued by forests managed for bioenergy

production to repay the C Debt incurred from the harvest, and

(2) the time required for the C mitigation benefits accrued by

forests managed for bioenergy production to achieve C Sequestration Parity, the point at which the sum of forest C storage and

bioenergy C substitution equals or exceeds the C mitigation

benefits of a comparable forest that remained unharvested.

Results

Times required for repayment of the carbon debts

Most Post-Agricultural landscapes repaid their C debts

within 1 year because their initial live C storages were

low to begin with and did not require any waiting period for the repayment of their C Debt (Fig. 2). Thus, by

undergoing a conversion from a Post-Agricultural landscape to a bioenergy production landscape, there was a

repayment of the C Debt as well as an increase in landscape C storage. Similarly, Rotation Harvest landscapes

harvested for bioenergy production every 100 years

increased their C storage, as they were previously harvested at a frequency of 50 years. Most of the Rotation

Harvest landscapes repaid their C Debt in a year due to

their initially low live C storage, as their average stand

age is ~25 years. However, some of these landscapes

that were clear-cut every 50 or 25 years required much

longer to repay their C Debt. Harvesting with greater

frequency and intensity lowers C storage and prolongs

the time needed for repayment of the C Debt; clear-cut

harvests performed on Rotation Harvest landscapes

every 25 years required 100 to over 1000 years to repay

their C Debt. Once a landscape requires several years to

repay its C Debt, it may then exhibit sensitivity to the

bioenergy conversion efficiencies used to calculate rate

at which it can substitute for C emissions from fossil

© 2012 Blackwell Publishing Ltd, GCB Bioenergy, doi: 10.1111/j.1757-1707.2012.01173.x

FOREST BIOENERGY PRODUCTION AND CO2 EMISSIONS 5
fuels. Recently Disturbed landscapes required more

time for a repayment of the C Debt and were much

more sensitive to harvest frequency, harvest intensity,

and bioenergy conversion efficiencies (Fig. 2). Following

disturbance, these landscapes can store high amounts of

dead C that can persist for decades. Due to low net primary production following disturbance, recovery to

pre-disturbance levels of C storage can take many years,

ranging from 20 to over 1000 years. Old-growth landscapes usually took the longest amount of time to repay

their C debts because their initial C storages were so

high, ranging from 19 to over 1000 years.

Times required to reach carbon sequestration parity

The amounts of time required for C Sequestration Parity were usually longer than the amounts of time

required for a repayment of the C debt. In general,

Old-Growth landscapes achieved C Sequestration

Parity at a faster rate than other categories of land-use

history since they have more initial biomass available

for bioenergy production (Fig. 3). Recently Disturbed

landscapes were the second fastest, followed by Rotation Harvest landscapes, though differences between

these two categories of land-use history are relatively

minor. Post-Agricultural landscapes took longer

than the other categories of land-use history, due to of

a lack of initial biomass available to harvest for

bioenergy production.

Times required to reach C Sequestration Parity were

longest for the low-productivity ecosystems and shortest for the high-productivity ecosystems (Fig. 3), indicating that high productivity ecosystems were able to

more quickly recoup their substantial reductions in C

storage compared to the rates at which low-productivity

ecosystems were able to recoup their considerably

smaller reductions in C storage. Within each respective

grouping of ecosystem productivity (G1, G2, G3), there

were significant effects of different biomass longevities

(L1, L2, L3) on the amount of time required for C

Sequestration Parity. Increased biomass longevity (i.e.,

lower rates of mortality and decomposition) increased

Fig. 2 Comparisons of the time required for a repayment of the C Debt Repayment among three of our nine ecosystem types, each

with six biomass harvesting regimes and four land-use histories. Note that times are represented on a log scale. Different harvesting

regimes are indicated on the x-axis, with 50% and 100% harvesting intensity represented as 50H and 100H, respectively. Harvest

frequencies of 25, 50, and 100 years are represented as 25Y, 50Y, and 100Y.

© 2012 Blackwell Publishing Ltd, GCB Bioenergy, doi: 10.1111/j.1757-1707.2012.01173.x

6 S. R. MITCHELL et al.
the times required to reach C Sequestration Parity, a

trend which was consistent across all three rates of

ecosystem productivity.

Regardless of land-use history and ecosystem characteristics, most scenarios required well over 100 years to

reach C Sequestration Parity. Simulations with total harvests performed every 25 years often required more

than 1000 years for C Sequestration Parity. Some scenarios achieved C Sequestration Parity in < 50 years, but

most of these were scenarios with relatively high bioenergy conversion efficiencies. Harvests performed at

lower frequency (50, 100 years) and intensity (50%

harvest) required less time; partial harvests (50% harvest) performed every 25 years appeared to reach C

Sequestration Parity more rapidly than any other management regime. Harvesting frequency and intensity

appeared to affect all ecosystems similarly. Without

exception, performing a clear-cut every 25 years

resulted in the greatest reduction in C storage and

required the longest periods to achieve C Sequestration

Parity, suggesting that attempts to generate bioenergy

from forests would be most effective in substituting for

fossil fuels when managed for moderate amounts of

production over a long time scale.

Discussion

Delays in the time required for a net benefit of a substitution of bioenergy for fossil fuels are caused by two

factors. First, harvesting materials for bioenergy

increases the C losses from the forest over the losses

caused by mortality and decomposition, thus, increasing

the amount of biomass harvest for bioenergy production

will increase the C Debt. Second, since there is less

potential energy per unit of C emissions in biomass

energy compared to fossil fuels, substituting biomass

for fossil fuels does not result in a 1 : 1 substitution of

energy per unit of C emission. Consequently, ecosystems that are capable of quickly repaying their C Debts

were those that had little C storage to begin with.

Our simulations demonstrated that initial landscape

conditions and land-use history were fundamental in

determining the amount of time required for forests to

repay the C Debt incurred from bioenergy production.

Fig. 3 Comparisons of the time required for a repayment of the C Sequestration Parity among three of our nine ecosystem types,

each with six biomass harvesting regimes and four land-use histories. Note that times are represented on a log scale. Different harvesting regimes are indicated on the x-axis, with 50% and 100% harvesting intensity represented as 50H and 100H, respectively. Harvest frequencies of 25, 50, and 100 years are represented as 25Y, 50Y, and 100Y.

© 2012 Blackwell Publishing Ltd, GCB Bioenergy, doi: 10.1111/j.1757-1707.2012.01173.x

FOREST BIOENERGY PRODUCTION AND CO2 EMISSIONS 7
While Recently Disturbed and Old-Growth landscapes

required considerable time to repay their C Debts, PostAgricultural and Rotation Harvest landscapes were

capable of repaying their C Debt in relatively short time

periods, often within 1 year. However, a quick repayment of the C Debt and an increase in C storage does

not imply a high degree of bioenergy production; it

merely indicates that more C is being stored in a bioenergy production system. Post-Agricultural landscapes

undergoing afforestation have minimal initial C storage,

and managing them for an appreciable yield of bioenergy production would require a considerable waiting

period. Furthermore, the conversion of an agricultural

field to a forest could have short-term climatic warming

effects while the afforesting landscape is in the early

stages of succession, since a decrease in landscape

albedo resulting from afforestation could yield climatic

warming effects that would overshadow any climatic

cooling effects associated with an uptake of atmospheric

CO2 (Jackson et al., 2008; Anderson et al., 2011), as the

latter would be relatively small during the early stages

of forest succession. By contrast, a Rotation Harvest system would not undergo a significant change in albedo

during a transition to a landscape managed for bioenergy production. However, Rotation Harvests have a much

different legacy than a Post-Agricultural landscape, since

a history of harvesting on the landscape implies that

there is additional wood being stored in wood products

which are slowly decomposing (see Methods). Consequently, the ongoing decomposition of previously

harvested materials lowers terrestrial C storage.

The times required for Old-Growth landscapes to

repay C Debt were similar to the times required for

them to achieve C Sequestration Parity, since the initial

C storage of an old-growth landscape is at or near the

level of C that could be stored in the landscape if it

were to remain unharvested. Consequently, Old-Growth

landscapes required long periods of bioenergy production to achieve C Debt Repayment and C Sequestration

Parity. For the three other land-use histories, reaching

the point of C Sequestration Parity requires much more

time than a repayment of C Debt. Trends were quite

consistent among the Recently Disturbed, Rotation Harvest, and Old-Growth landscapes and most simulations

required at least 100 years to reach C Sequestration Parity (Fig. 3).

Times required for C Sequestration Parity were longest for the low-productivity ecosystems and shortest

for the high-productivity ecosystems. Similarly, the

effects of biomass longevity were quite consistent

among the Recently Disturbed, Rotation Harvest, and

Old-Growth landscapes (Fig. 3). Within each respective

grouping of ecosystem productivity (G1, G2, G3),

there were significant effects of different biomass longevity rates (L1, L2, L3) on the amount of time

required to reach a point of C Sequestration Parity.

Higher rates of biomass longevity (i.e., lower rates of

mortality and decomposition) resulted in longer times

required for C Sequestration Parity, a trend which

was consistent across all three rates of ecosystem productivity (Fig. 3). Such a result may seem counterintuitive at first, but the net effect of lowering mortality

and decomposition rates is that potential C storage is

increased. Since ecosystems with lower mortality and

slower decomposition have higher potential C storage,

more bioenergy substitutions must be produced to

exceed the amount of C stored in a forest that is

allowed to grow without harvest. Annual biomass harvest varied little among our different levels of longevity. Therefore, higher rates of biomass longevity raised

the target for C Sequestration Parity without resulting

in a comparable increase of bioenergy production. We

note that biomass longevity is largely a function of

the environmental factors that control rates of biomass

decomposition, such as temperature and moisture, and

is governed by catastrophic disturbances to a lesser

degree. Our simulations reiterate previous findings

(Mitchell et al., 2009; Campbell et al., 2012) about the

limited impact that wildfires have on biomass longevity; wildfires may temporarily lower the C storage of

the landscape but most of the losses that occur are

among unharvestable components of the forest, such

as leaf litter and fine woody debris. Most of the harvestable biomass remains unconsumed even by highseverity wildfires and can either be salvage harvested

shortly thereafter or persist on the landscape for

decades (Mitchell et al., 2009; Campbell et al., 2012).

However, C storage is not the only way that vegetation affects climate, as different levels of surface reflectance (albedo) and evapotranspiration result in different

levels of heat absorbance in the terrestrial biosphere

(Jackson et al., 2008; Anderson et al., 2011). Utilizing

degraded agricultural lands for the production of bioenergy via non-woody plant species (i.e., switchcane,

switchgrass, etc.) could both reduce heat absorbance in

the terrestrial biosphere and produce bioenergy to serve

as a substitute for fossil fuels. A recent study by Beringer et al. (2011) estimated that, by 2050, the cultivation

of bioenergy crops on degraded agricultural land could

produce 26–116 EJ yr1

, 3–12% of projected global

energy demand. Additional energy may be obtained

from secondary sources, such as residues from agriculture and forestry, municipal solid waste, and animal

manures, and the combined production potential could

potentially be around 100 EJ yr1 by then (Ifeu, 2007;

Iea, 2009; Wbgu, 2009; Haberl et al., 2010), thereby generating an additional 10% of projected global energy

demand (13–22% total). However, it is unclear what

© 2012 Blackwell Publishing Ltd, GCB Bioenergy, doi: 10.1111/j.1757-1707.2012.01173.x

8 S. R. MITCHELL et al.
proportion of degraded agricultural lands would be better utilized for climate change mitigation via reforestation, rather than by non-woody bioenergy production.

Non-woody bioenergy crops would need a sufficiently

high surface reflectance if their climate change mitigation benefits were to exceed the mitigation benefits of

afforestation, but the studies conducted on this topic

have yielded conflicting results. Some studies have suggested that land cover types with high albedos could

yield a greater cooling to the atmosphere than temperate forests (Diffenbaugh & Sloan, 2002; Oleson et al.,

2004; Bala et al., 2007) while other studies have shown

the opposite (DeFries et al., 2002; Jackson et al., 2005;

Juang et al., 2007), indicating that further research on

these tradeoffs is needed.

Further research is also needed to ascertain the

potential conversion efficiencies of woody biomass.

Our findings indicate that an accounting of the C

emissions that are necessary for the harvest, transport,

and firing of woody biomass must be performed if

forest bioenergy is to be utilized without adding to

atmospheric CO2 concentrations in the near-term.

Many of our combinations of forest productivity, biomass longevity and harvesting regimes required more

than 100 years to achieve C Sequestration Parity, even

when the bioenergy conversion factor was set at near

maximal level. A consideration of stand characteristics

and land-use history may also prove to be imperative

for any bioenergy production system to be effective.

Competing land-use objectives make it highly unlikely

that forests will be managed purely for C mitigation

efforts, and many of the current management objectives within existing forests will undoubtedly prevent

them from reaching their full C storage potential.

Achieving the maximal C mitigation potential of what

remains becomes all the more imperative, as mean

global temperatures, sea-level rise, or the melting of

ice sheets may continue long after any future stabilization of atmospheric CO2 and other greenhouse gases

(Jones et al., 2009). Managing forests for maximal C

storage can yield appreciable, and highly predictable,

C mitigation benefits within the coming century, while

managing forests for bioenergy production will require

careful consideration if they are to provide a C neutral

source of energy without yielding a net release of C

to the atmosphere in the process.

Acknowledgements

We thank Frank Schnekenburger for his programming expertise. Research was funded by a NASA New Investigator

Program grant to K. E. B. O’Connell (NN604GR436), the H. J.

Andrews LTER (DEB-0218088), and the Kay and Ward Richardson Endowment.

References

Anderson R, Canadell J, Randerson J, Jackson R (2011) Biophysical considerations in

forestry for climate protection. Frontiers in Ecology and the Environment, 9, 174–182.

Bala G, Caldeira K, Wickett M, Phillips TJ, Lobell DB, Delire C, Mirin A. (2007)

Combined climate and carbon-cycle effects of large-scale deforestation(pg 6550).

Proceedings of the National Academy of Sciences of the United States of America, 104,

9911–9911.

Beringer TIM, Lucht W, Schaphoff S (2011) Bioenergy production potential of global

biomass plantations under environmental and agricultural constraints. GCB Bioenergy, 3, 299–312.

Bork J (1985) Fire History in Three Vegetation Types on the East Side of the Oregon Cascades. Ph.D. Dissertation. Oregon State University, Corvallis, OR.

Boundy B, David SC, Wright LL, Badger PC, Perlack R (2011) Biomass Energy Data

Book. Oak Ridge National Laboratory, Oak Ridge, TN. Available at: cta.ornl.gov/

bedb (accessed 30 April 2011).

Buford M, Neary D (2010) Sustainable biofuels from forests: meeting the challenge.

Biofuels and Sustainability Reports, Ecological Society of America. Avaliable at:

http://www.esa.org/biofuelsreports (accessed 30 April 2011).

Campbell JL, Harmon ME, Mitchell SR (2012) Can fuel reduction treatments really

increase forest carbon storage in the western US by reducing future fire emissions? Frontiers in Ecology and the Environment, 10, 83–90.

Canadell JG, Raupach MR (2008) Managing forests for climate change mitigation.

Science, 320, 1456–1457.

Cissel JH, Swanson FJ, Weisberg PJ (1999) Landscape management using historical

fire regimes: Blue River, Oregon. Ecological Applications, 9, 1217–1231.

DeFries RS, Bounoua L, Collatz GJ (2002) Human modification of the landscape and

surface climate in the next fifty years. Global Change Biology, 8, 438–458.

Diffenbaugh NS, Sloan LC (2002) Global climate sensitivity to land surface change:

the Mid Holocene revisited. Geophysical Research Letters, 29, 1-4.

Fao Wood Energy. Available at: http://www.fao.org/forestry/databases/29420/en/

(accessed 10 September 2011).

Fargione J, Hill J, Tilman D, Polasky S, Hawthorne P (2008) Land Clearing and the

Biofuel Carbon Debt. Science, 319, 1235–1238.

Field CB, Campbell JE, Lobell DB (2008) Biomass energy: the scale of the potential

resource. Trends in Ecology & Evolution, 23, 65–72.

Haberl H, Beringer T, Bhattacharya SC, Erb K-H, Hoogwijk M (2010) The global

technical potential of bio-energy in 2050 considering sustainability constraints.

Current Opinion in Environmental Sustainability, 2, 394–403.

Hamelinck CN, Suurs RaA, Faaij APC (2005) International bioenergy transport costs

and energy balance. Biomass and Bioenergy, 29, 114–134.

Harmon ME. (2012) The Forest Sector Carbon Calculator. Available at: http://landcarb.

forestry.oregonstate.edu/default.aspx (accessed 30 April 2011).

Harmon ME, Marks B (2002) Effects of silvicultural practices on carbon stores in

Douglas-fir-western hemlock forests in the Pacific Northwest, USA: results from

a simulation model. Canadian Journal of Forest Research-Revue Canadienne De

Recherche Forestiere, 32, 863–877.

Harmon ME, Ferrell WK, Franklin JF (1990) Effects on carbon storage of conversion

of old-growth forests to young forests. Science, 247, 699–702.

Harmon ME, Fasth B, Sexton J (2005) Bole decomposition rates of seventeen tree species in Western U.S.A. H.J. Andrews LTER Web Report

House JI, Prentice IC, Le Quere C (2002) Maximum impacts of future reforestation

or deforestation on atmospheric CO2. Global Change Biology, 8, 1047–1052.

Hudiburg TW, Law BE, Wirth C, Luyssaert S. (2011) Regional carbon dioxide implications of forest bioenergy production. Nature Climate Change, 1, 419–423 advance

online publication.

Iea (2009) Bioenergy – A Sustainable and Reliable Energy Source. IEA Bioenergy. Available at: http://www.ieabioenergy.com/LibItem.aspx?id=6479 (accessed 12 March

2012).

Ifeu (2007) Biomass as a Resource for the Chemical Industry. IFEU, Heidelberg.

Jackson RB, Jobbagy EG, Avissar R et al. (2005) Trading water for carbon with biological sequestration. Science, 310, 1944–1947.

Jackson RB, Randerson JT, Canadell JG et al. (2008) Protecting climate with forests.

Environmental Research Letters, 3.

Jones C, Lowe J, Liddicoat S, Betts R (2009) Committed terrestrial ecosystem changes

due to climate change. Nature Geoscience, 2, 484–487.

Juang JY, Katul G, Siqueira M, Stoy P, Novick K (2007) Separating the effects of

albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophysical Research

Letters, 34.

© 2012 Blackwell Publishing Ltd, GCB Bioenergy, doi: 10.1111/j.1757-1707.2012.01173.x

FOREST BIOENERGY PRODUCTION AND CO2 EMISSIONS 9
Marland G, Schlamadinger B (1997) Forests for carbon sequestration or fossil fuel

substitution? A sensitivity analysis. Biomass & Bioenergy, 13, 389–397.

Marland G, Obersteiner M, Schlamadinger B (2007) The carbon benefits of fuels and

forests. Science, 318, 1066–1066.

Mitchell SR, Harmon ME, O’connell KEB (2009) Forest fuel reduction alters fire

severity and long-term carbon storage in three Pacific Northwest ecosystems. Ecological Applications, 19, 643–655.

Oleson KW, Bonan GB, Levis S, Vertenstein M (2004) Effects of land use change on

North American climate: impact of surface datasets and model biogeophysics.

Climate Dynamics, 23, 117–132.

Olson JS (1963) Energy storage and the balance of producers and decomposers in

ecological systems. Ecology, 44, 322–331.

Pan Y, Birdsey RA, Fang J et al. (2011) A large and persistent carbon sink in the

world’s forests. Science, 333, 988–993.

Parikka M (2004) Global biomass fuel resources. Biomass and Bioenergy, 27, 613–620.

Richter DD, Jenkins DH, Karakash JT, Knight J, Mccreery LR, Nemestothy KP (2009)

Wood energy in America. Science, 323, 1432–1433.

Schlamadinger B, Marland G (1996a) Carbon implications of forest management strategies. Forest Ecosystems, Forest Management and the Global Carbon Cycle, 40, 217–232.

Schlamadinger B, Marland G (1996b) Full fuel cycle carbon balances of bioenergy

and forestry options. Energy Conversion and Management, 37, 813–818.

Schlamadinger B, Marland G (1996c) The role of forest and bioenergy strategies in

the global carbon cycle. Biomass and Bioenergy, 10, 275–300.

Searchinger T, Heimlich R, Houghton RA et al. (2008) Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change. Science,

319, 1238–1240.

Searchinger TD, Hamburg SP, Melillo J et al. (2009) Fixing a critical climate accounting error. Science, 326, 527–528.

Smithwick E, Harmon M, Domingo J (2007) Changing temporal patterns of forest

carbon stores and net ecosystem carbon balance: the stand to landscape transformation. Landscape Ecology, 22, 77–94.

Van Wagner CE (1978) Age-class distribution and forest fire cycle. Canadian Journal

of Forest Research-Revue Canadienne De Recherche Forestiere, 8, 220–227.

Walker T, Cardellichio P, Colnes A et al. (2010) Biomass Sustainability and Carbon Policy Study. NCI-2010-03 (2010). Manomet Center for Conservation Sciences, Massachusetts.

Wbgu (2009) Future Bioenergy and Sustainable Land Use. Earthscan, London and Sterling, VA.

Supporting Information

Additional Supporting Information may be found in the

online version of this article:

Figure S1. Times for Carbon Debt Repayment for all PostAgricultural landscapes.

Figure S2. Times for Carbon Sequestration Parity for all

Post-Agricultural landscapes.

Figure S3. Times for Carbon Debt Repayment for all Rotation Harvest landscapes.

Figure S4. Times for Carbon Sequestration Parity for all

Rotation Harvest landscapes.

Figure S5. Times for Carbon Debt Repayment for all

Recently Disturbed landscapes.

Figure S6. Times for Carbon Sequestration Parity for all

Recently Disturbed landscapes.

Figure S7. Times for Carbon Debt Repayment for all OldGrowth landscapes.

Figure S8. Times for Carbon Sequestration Parity for all

Old-Growth landscapes.

Please note: Wiley-Blackwell are not responsible for the

content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the

article.

© 2012 Blackwell Publishing Ltd, GCB Bioenergy, doi: 10.1111/j.1757-1707.2012.01173.x

10 S. R. MITCHELL et al.