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BIOENERGY
Forests: Carbon sequestration, biomass energy,
or both?
Alice Favero1, Adam Daigneault2*, Brent Sohngen3
There is a continuing debate over the role that woody bioenergy plays in climate mitigation. This paper clarifies
this controversy and illustrates the impacts of woody biomass demand on forest harvests, prices, timber management
investments and intensity, forest area, and the resulting carbon balance under different climate mitigation policies.
Increased bioenergy demand increases forest carbon stocks thanks to afforestation activities and more intensive
management relative to a no-bioenergy case. Some natural forests, however, are converted to more intensive
management, with potential biodiversity losses. Incentivizing both wood-based bioenergy and forest sequestration
could increase carbon sequestration and conserve natural forests simultaneously. We conclude that the expanded
use of wood for bioenergy will result in net carbon benefits, but an efficient policy also needs to regulate forest
carbon sequestration.
INTRODUCTION
A long literature establishes that forests are an efficient carbon sink
through actions such as afforestation, forest management, and reduced
deforestation (1–5). In recent years, forest-based actions have gained
additional policy relevance as many countries have included forest
sequestration activities in their nationally determined contributions
toward reducing net carbon emissions as part of the Paris Agree-
ment (6,7). At the same time, there have been increasing efforts in
the United States and Europe to use wood as a source of biomass
energy, under the assumption that wood-based energy is “carbon
neutral” (8,9). Last, most pathways that aim to restrict global average
temperature to 1.5°C by 2100 rely on large-scale deployment of
afforestation actions and biomass used in power plants with carbon
capture and storage (BECCS) (10).
The role that forests should play in mitigating climate change is
still widely debated. Some researchers have argued that forest-based
biomass energy is not carbon neutral (11–13) and, thus, that forest-
based bioenergy should not be allowed to offset other energy sources
under renewable energy standards. These assessments have suggested
that when forest biomass energy is generated, it creates a carbon
emission to the atmosphere that may, or may not, be taken back up
by trees that grow in the future. Even if regrowth does occur, it occurs
slowly over time, increasing global climate damages during the
period when the released carbon is in the atmosphere. Given these
concerns, some have argued that society should focus on enhancing
carbon sinks rather than encouraging biomass energy (11,13–15).
Assessing whether forest biomass energy is carbon neutral is complex
and sensitive to assumptions about the spatial and temporal scale of the
analysis, feedstocks used, and supply chain emissions. Buchholz etal.’s
(16) meta-analysis found that the payback period (i.e., the time re-
quired by the forest to recover through sequestration the carbon
dioxide from biomass combusted for energy) ranged from zero to
more than 1000 years. Birdsey etal. (17) conclude that increasing
bioenergy production and pellet exports often increases net emis-
sions of greenhouse gases (GHGs) for decades or longer, depending
on source of feedstock and its alternate fate, time horizon of analysis,
energy emissions associated with the supply chain and fuel substitution ,
and impacts on carbon cycling of forest ecosystems. The studies
examined in these two reviews, however, ignore economic analysis
that accounts for the interactions between demand and supply and
forest management (18–23). These interactions include assessment
of the opportunity costs of intensified harvesting and management,
as well as expansion of intensive timber harvesting activities into
extensive (e.g., unmanaged) regions.
This paper examines key issues related to the consequences of
bioenergy policies on forests and carbon emissions, namely, manage-
ment response, the efficiency of the policy, and the impacts of
increasing biomass demand on forest ecosystem services. To analyze
these issues, we use a dynamic global forest model that accounts for
the biological and economic responses to various policy incentives
on forest management and carbon fluxes in all regions of the world
(22,23). Such integrated assessment modeling of the forest sector
provides important insights into the temporal and spatial scale of
forest management efforts that must be undertaken to increase wood
flow in the face of large, new biomass energy demands, as well as their
interactions with carbon policies. The analysis clarifies how various
policy incentives for biomass energy and carbon sequestration in-
fluence the net exchange of carbon between the atmosphere and forested
ecosystems. Last, a sensitivity analysis tests the results under different
assumptions on land constraints and economic parameters.
RESULTS
The debate behind woody biomass for energy
Three critical issues have arisen in the literature related to the use of
woody biomass for energy. First, the impact of biomass energy policies
on carbon depends on the supply response, which include investments
in new forests, increased management of existing forests, harvests
of inaccessible and unmanaged forests in the extensive margin, con-
version of those forests to more intensive uses, and substitution across
product uses. Studies that assume there is little to no management
response, or consider only use of the extensive margin, predict that
bioenergy demand will increase carbon emissions (16,17). Studies
that allow efficient investments in forestry management find that
bioenergy policies lead to a net increase in forest sequestration (18–22).
A key determinant rests on the response of land use and management
1Georgia Institute of Technology, Atlanta, GA, USA. 2University of Maine, Orono, ME,
USA. 3Ohio State University, Columbus, OH, USA.
*Corresponding author. Email: adam.daigneault@maine.edu
Copyright © 2020
The Authors, some
rights reserved;
exclusive licensee
American Association
for the Advancement
of Science. No claim to
original U.S. Government
Works. Distributed
under a Creative
Commons Attribution
NonCommercial
License 4.0 (CC BY-NC).
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to the price changes induced by bioenergy policy. If models include
management responses, higher prices invariably encourage more
management and forest area, and thus, biomass energy policies reduce
net emissions over time. Alternatively, if forest land area and forest
management do not change or decline with biomass energy policies,
and there is additional harvesting of unmanaged stocks, then the
policies typically result in net emissions. It is, thus, important to
isolate the role of management on forest carbon stocks.
Second, studies suggesting that biomass energy should be taxed
have ignored the literature showing the efficient economic treatment
of carbon fluxes between the atmosphere and forests in the broader
carbon cycle (4). From the perspective of the atmosphere, a ton of
CO2 in forests is a ton not in the atmosphere, suggesting that the
social cost of carbon can be used to value carbon exchanges, or fluxes,
between the atmosphere and ecosystems (24). Two equally efficient
schemes have been proposed in the literature to credit carbon fluxes
between the atmosphere and forests: a carbon rental approach (4)
and a carbon tax and subsidy approach (25). Under both approaches,
the efficient climate policy counts emissions when forests are used
for energy or wood products as an increase in atmospheric carbon,
but they also count sequestration that occurs when forests grow.
Bioenergy emissions can be taxed like other GHGs under an effi-
cient approach to credit carbon fluxes, but only if the benefits of
storing carbon in forested ecosystem are also recognized and sub-
sidized (25). That is, a policy that taxes forest-based bioenergy with-
out recognizing that forests also sequester carbon through growth
[the Schlesinger proposition (26)] is inefficient and will lead to too
little carbon in forests and too much carbon in the atmosphere.
Third, there are concerns that ecosystem services and biodiversity
provided by primary forests could be affected if harvests are diverted
from traditional wood products to bioenergy (13,14) or the level
and quality of standing biomass on managed forests are diminished
due to higher levels of residue removals (27), shorter rotations (28,29),
or more land moved from no/low management to more intensive
management. Concerns with ecosystem services, however, tend to
focus on the effects of biomass harvests at individual sites (13,14).
While more intensive harvesting will be a consequence of any bio-
mass energy policy that increases the demand for wood, intensification
will not occur at a few selected sites, but across forests, especially
under widespread biomass energy or carbon neutrality mandates. To
gauge the full impacts of policies, models must be able to analyze the
full spatial and temporal consequences on the global forest ecosystem.
In numbers: pro and cons of woody biomass demand
We use the dynamic global timber model (GTM) to assess how bio-
energy demand affects the forestry sector, forestland, and carbon
sequestration (4,30). GTM compares timber harvesting and manage-
ment in more than 200 managed and natural forest ecosystems
across 16 world regions (fig. S1) under different bioenergy demand
scenarios to a no-bioenergy demand scenario (22). The model used
the Shared Socioeconomic Pathway (SSP) 2 marker scenario projec-
tions of gross domestic product (GDP) and population (31–33)
to simulate forest product demand, while bioenergy demand projec-
tions [following the assumptions presented in Lauri etal. (34) that
9% to 12% of total bioenergy demand is sourced by dedicated forest
plantation; bioenergy is converted from gigajoules (GJ) to cubic meters
(m3) of forest biomass using constant conversion factors of 7.2 GJ/m3]
and carbon price paths under the same SSP2 scenario for each Inter-
governmental Panel on Climate Change (IPCC) Representative Con-
centration Pathway (RCP) are used to simulate the future demand
for woody biomass and the value of CO2 (fig. S2).
In general, bioenergy consumption is expected to increase with
the stringency of the RCP target. For reference, under RCP 1.9—the
scenario most consistent with a 1.5°C target—about 30% of the total
energy supply through 2100 is estimated to be sourced from bioenergy
with carbon capture and storage (BECCS) (33). Because bioenergy
demand is expected to come from a variety of sources, we assume
about one-third of the supply is provided by forests, which is con-
sistent with other global analyses (34). The model assumes a homo-
genous form of woody biomass energy demand without distinguishing
types of woody biomass production. From the perspective of our
model, this means that each type of wood that could be used as an
input into biomass energy production is paid the same price.
Increasing demand for woody biomass will have noticeable impacts
on the global forest sector compared with a no-policy baseline case
(tables S2 to S4). Timber prices could more than triple if woody biom ass
consumption reaches 4.3 billion m3/year by the end of this century,
as in the RCP 1.9 scenario (Fig.1A). The model does account for the
substitution between wood products and biomass, and the increasing
demand for woody bioenergy negatively affects the industrial timber
market. That is, there is a decline of between 30 and 80% in the pro-
duction of industrial timber over the century projected across the
RCPs, with the largest reduction under RCP 1.9.
Higher timber prices incentivize afforestation across the globe
(Fig.1B). However, higher timber prices also encourage harvesting
of natural forest areas (forest types are defined as follows: “plantation” =
intensively managed plantations; “managed” = extensively managed,
often naturally regenerating forests; “natural” = inaccessible, un-
managed natural forests) (Fig.1C), which are replanted as a mix of
low-managed forests and intensive plantations (Fig.1D). Total global
forest carbon sequestration is projected to increase only when the
demand for woody biomass is large enough to encourage consumption
greater than 1.1 billion m3/year by 2100, i.e., an RCP of less than 4.5
(Fig.1E). When biomass demand is lower than this level, it does not
generate high enough timber prices to generate enough investments
in forests or plantations to offset carbon losses that occur when forests
are harvested. The carbon losses occur primarily in regions domi-
nated by large areas of remaining inaccessible forests, namely, Latin
America, Southeast Asia, Canada, and Russia. Older forests have
substantial carbon stocks that are not compensated. Eventually, if
biomass energy demand is strong enough, investments in forests
will outweigh losses, and carbon turns positive. The expenditure on
forest management, as measured by dollars per hectare invested in
replanting, also matters, as it increases with more stringent climate
policy (Fig.1F).
The model accounts for carbon stored in four different pools.
The increase in carbon stocks is primarily driven by improvements
in aboveground carbon, while more forestland marginally increases
soil carbon due to afforestation relative to the baseline scenario. Carbon
stored in harvested wood products is projected to decline under all
policy scenarios because the increasing demand for woody biomass
reduces the quantity of noncombusted wood products (fig. S4).
Expanding woody biomass energy could result in a 286 (RCP 6.0)
to 1931 (RCP 1.9) m3/year increase in total timber harvesting com-
pared with the baseline scenario. Increases in harvests are not equally
distributed across the globe though (Fig.2), with the largest harvest
increases expected in places where industrial wood harvesting is already
an important part of the landscape and regional economy. For instan ce,
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Fig. 1. Global impacts for increased wood-based bioenergy demand, 2010–2100. (A) Timber prices, (B) total forest area, (C) natural unmanaged forest area, (D) plan-
tation forest area, (E) total forest carbon stock (includes all the four carbon pools presented in fig S3), and (F) management investment relative to the baseline (no bioenergy
demand). Black, RCP 1.9; red, RCP 2.6; green, RCP 3.4; blue, RCP 4.5; orange, RCP 6.0.
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in the RCP 3.4 case, U.S. harvests increase by an average of 27% over
a 100-year period, while harvests in Southeast Asia and Russia only
increase by 6 and 2%, respectively.
The increased demand for woody biomass encourages changes
in forest management along several margins that are typically ignored
in traditional life cycle analyses. First, the area of intensively managed
plantations increases by up to 61 million hectares (Mha), or 60%, over
the baseline (Fig.1C). These plantations have rotation ages ranging from
10 to 30 years and, thus, can be used to ramp up timber and biomass
supply relatively quickly. We calculate that for every 1% increase in
timber price, the area of plantations increases by 0.32% globally.
Second, forests that currently have limited management become
more intensively managed via increased investment in harvesting and
replanting, thinning, fertilizing, and other actions. Every 100 million
m3 of additional woody biomass energy supply could create 5.6 Mha
of more intensively managed forests. Higher prices provide a market
signal to landowners, who will take steps to increase their forest
stocks via expanding the area in managed forests and/or improving
management activities (18,35). Expenditures on forest management
could increase by an average of $230 per hectare on managed forests
due to the increase in woody biomass demand, nearly 70% greater
than the mean baseline expenditure estimate (Fig.1F). Some of the
land that we project that will become intensively managed is cur-
rently ecologically sensitive and/or high in biodiversity. Assuming
that there are no other forest carbon sequestration incentives or forest
conservation policies in effect, we estimate that if woody biomass
demand rises to 4 billion m3/year, as under RCP 1.9, about 15% of
global natural forest area, or 250 Mha, could be converted to a more
intensive management regime (fig. S5). These results confirm that
“standard” bioenergy policy targeting woody biomass generally has
a negative impact on the world’s natural unmanaged or inaccessible
forests. However, these natural forests will not be converted to agri-
culture, as economic incentives offset losses through planting forests
managed at different degrees of intensity and avoided deforestation
of managed forests (fig. S5).
Third, other land will be converted to forests and, thus, increase
the absolute area of forests globally. The largest demand scenario
(RCP 1.9) estimates a potential increase in forest area by 1.1 billion
ha, 30% more than the current forest cover by 2100 (Fig.1B). This
is within the bounds of the recent paper by Bastin etal. (36), who
identify an area of 1.6 billion ha of additional land that could support
forests, of which 0.9 billion ha are located outside valuable cropland
and urban regions. For this study, we used the 1.6 billion ha as an
upper bound for our simulations, and in the sensitivity analysis, we
tested the results under the more stringent 0.9 billion limit. The
1.1 billion ha estimate is large but not an outlier (fig. S6). Our results
are confirmed by integrated assessment models (IAMs) with land
modules and crop prices, which suggest that global forest area could
increase by up to 1.0 billion and that about 5.4 billion ha could be
covered by forests under the RCP 1.9 stabilization scenario with
approximately 1.9 billion ha new forestland (31,33).
While we do not explicitly model crop production or prices, our
model incorporates land rental functions that require paying higher
prices to rent land as more land is used for forests. The increases in
rents that we project are consistent with other studies in the literature.
Under the RCP 1.9 scenario, our global average land rents between
now and 2100 are estimated to be four times higher than the base-
line rents. Popp etal. (37) similarly shows that agricultural land for
food and feed production declines as more land is used for bioenergy
and carbon sequestration. They do not present estimates of land rents,
but they do show that crop prices could be between two to six times
higher than the baseline under a high mitigation scenario.
An efficient approach to carbon management
This section illustrates the difference between an efficient carbon
management approach, which incentivizes both sequestration and
Fig. 2. Mean change in total regional harvests relative to the baseline, 2010–2100.
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avoidance of emissions, and an inefficient approach that only uses a
carbon tax to incentivize the avoidance of emissions from woody
biomass production. An efficient approach to manage carbon ex-
changes between the atmosphere and the biosphere can be accom-
plished by using either a carbon rental (4) or a carbon tax and subsidy
approach (25). Both of these efficient approaches recognize that
emissions from biomass energy are like all other GHG emissions and
that forest growth removes carbon from the atmosphere. On the other
hand, a tax on emissions from bioenergy demand (penalty scenario)
without an offsetting subsidy for carbon accumulation is an inefficient
approach because it creates relatively less demand for forest products,
depresses timber prices (Fig.3A) and forest area (Fig.3,B toD),
Fig. 3. Global impacts for alternative wood-based bioenergy policies, 2010–2100. (A) Timber price and changes in (B) total forest area, (C) natural forest area, (D) forest
plantation area, (E) total forest carbon stock, and (F) management investment relative to the baseline (no bioenergy demand). Dashed, carbon penalty; solid, forest carbon
rental; black, RCP 1.9; red, RCP 2.6; green, RCP 3.4; blue, RCP 4.5; orange, RCP 6.0.
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lowers investment in forest management (Fig.3F), and, thus, leads
to lower forest carbon stocks (Fig.3E) compared with the efficient
approach (carbon rental scenario). That is, under any given demand
for woody biomass, an approach that only penalizes emissions will
deliver less carbon sequestration than the efficient rental approach.
Moreover, timber prices are higher under the rental scenario because
there are additional benefits associated with holding carbon in forests,
thereby potentially reducing the annual timber supply.
Critically, the penalty approach, as suggested by Searchinger etal.
(13), could cause a large reduction in the area of natural inaccessible
forests of up to 200 Mha under the strong biomass energy demand
of RCP 1.9 or 2.6 (Figs.3C and 4). Without valuing standing stocks
of forests through a subsidy for forest carbon sequestration, higher
timber prices (Fig.3A) provide a substantial incentive to convert
natural forests to other forest types (Fig.3B). The tax cannot avoid
this outcome. The largest declines in natural forest areas under a
penalty approach will occur under biomass demand higher than
1.5 billion m3/year, mainly in the tropics, followed by the temperate
zone (Fig.4). In contrast, a carbon rental approach encourages pro-
tection of standing natural forests by valuing the standing stock, such
as old growth harvests in boreal regions and unmanaged forests in
the tropics. In particular, a combination of the high carbon prices
and bioenergy demands (RCPs 2.6 and 1.9) will avoid future de-
forestation of tropical natural forests relative to the baseline. That
is, opportunity costs of land are relatively low in the tropics, so there
is a reduction in forestland conversion to cropland. Second, the carbon
gains in the tropics are relatively fast and large, such that the rental
values encourage substantial carbon sequestration comparing to tem-
perate and boreal forests.
Each of the policy approaches leads to more forestland globally
over the next century, but estimates vary at the regional level (Fig.5).
In the cases where there is a penalty imposed on harvesting biomass,
even with the relatively low carbon prices of RCPs 6.0 and 4.5, there
could be 10 to 25% less forest cover in parts of the tropics by 2100
compared with the forest carbon rent policy. The forest carbon
rental payment approach is expected to increase forest cover across
the globe, with the more stringent mitigation scenarios leading to
increases of 50% or more in many parts of the world.
A forest carbon rental approach has sometimes been considered
an infeasible climate change mitigation policy option because of the
complexities with measuring and verifying changes in carbon stocks
and possible governance issues in developing countries (35,38).
However, placing a tax on woody bioenergy could exacerbate one of
the issues the policy is intended to prevent, namely, the loss of natural
forests and associated ecosystem services. Despite this, we find that
increasing the demand for wood-based bioenergy, regardless of the
policy approach, will increase the total forest carbon in nearly all
scenarios (Fig.3E). Under the penalty scenario, average stocks could
increase by 34 gigatonnes carbon dioxide eqiuvalent (GtCO2e) or
about 1% compared with the baseline. This is equivalent to 0.4 GtCO2e/
year or 1.1% of 2018 global CO2 emissions (39). Including payments for
forest carbon sequestration has additional benefits. Forest area could
expand by 500 Mha or more, and total carbon stocks could increase by an
average of 2.3 GtCO2e/year, offsetting 7.1% of current annual emissions
(Fig.3E). In nearly all scenarios, a majority of this net increase in car-
bon s tocks i s due to increases in aboveground carbon, while the substitu-
tion of some timber to bioenergy has a somewhat negative effect (fig. S7).
Growing wood-based bioenergy demand will positively affect forest
management investment regardless of the policy approach, averaging
about 80% above baseline investment. Demand for wood products
in general will stimulate investment as landowners are expected to
accrue a higher return. However, a forest carbon rental and increased
biomass demand policy could double investment compared with the
biomass penalty approach (Fig.3F). Management decisions affect
the amount of carbon stored in forests in different ways. First, GTM
controls rotation ages, which influences carbon stocks on sites, with
higher rotation ages leading to greater timber supply and more carbon,
and vice versa. Second, under high bioenergy demand and/or carbon
prices, the model projects more replanting, which, compared with
natural regeneration, will increase average carbon on a site over a
rotation. Third, GTM shifts species types over time in response to
prices and land rental values. Fourth, the model shifts species from
no management to modest management (harvesting with natural
regeneration or harvesting with replanting in some cases, depending
on value). In nearly all cases, this conversion of older forests to
younger forests leads to more timber output but less carbon. Last,
GTM includes management intensity on replanted land. This type
of management intensity, which could include genetic selection,
fertilizing, density controls, and other approaches, increases both
carbon stocks and the value of timber. This type of management
intensity is monitored in the model by the expenditure per hectare
of forestland, with higher expenditures leading to more timber and
more carbon.
Sensitivity test
We tested the effects of bioenergy demand on forestland and carbon
sequestration and the efficiency of the rental approach relative to the
penalty approach under a scenario in which forestland additions are
limited to 0.9 billion ha, following Bastin etal. (36). Even under this
constrained scenario, our findings are confirmed: Bioenergy demand
will increase total forestland and forest carbon sequestration com-
pared with a no-bioenergy (baseline) scenario (Fig.6,AandC), and
the carbon rental policy provides an efficient approach to regulate
the increasing demand for bioenergy by protecting natural forest-
land while providing woody biomass for energy at the same time
(Fig.6B). Although forest area does not expand as much with the
tighter constraint, it is around 50% lower; forest carbon storage de-
clines by only 20 to 30%. Carbon declines proportionally less than
forest area because forests are managed more intensively to increase
timber production, and this in turn enhances carbon storage. Any
time there is a conversion in natural forest areas from inaccessible
to accessible and lightly managed forests, there is lower total carbon
stored over time. Thus, the efficient carbon rental climate policy leads
to more carbon by protecting more natural forests from conversion.
Additional sensitivity analysis that adjusts key model parameters
such as the land supply elasticity, management response to forest
investment, and the cost of accessing and clearing natural forests to
half and double their original values indicates that our general find-
ings still hold (fig. S8). That is, higher biomass demand will increase
the value of timberland, incentivize additional investment in forest
management and afforestation, and result in greater forest carbon
stocks over time, even if our model includes more pessimistic (i.e.,
less responsive) parameter values. However, the relative impact that
each parameter has on the estimates varies. For example, a low land
supply elasticity results in about half the afforestation rate of the
high land supply elasticity case, simulating the potential effect if
landowners are more resistant to converting their agricultural lands
to forests. As a result, forest carbon stocks would still increase over
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Fig. 4. Changes in global forest area by major ecosystem relative to baseline case under alternative wood-based bioenergy policies. Square, carbon penalty; circle, for est
carbon rental; black, RCP 1.9; red, RCP 2.6; green, RCP 3.4; blue, RCP 4.5; orange, RCP 6.0.
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the next century, but at a rate that is about 40% less than most of the
other sensitivity cases. Furthermore, our sensitivity analysis indi-
cated that estimates were most sensitive to the forest carbon rental
scenarios, especially in the cases with high carbon prices that in-
centivize more competition between carbon, bioenergy, and timber
production.
Fig. 5. Regional changes in forest area relative to 2010 for forest carbon rental and carbon penalty scenarios.
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DISCUSSION
Projections using IAMs show that bioenergy demand is very likely
to dominate under the 1.5° to 2°C target scenarios (10). Our study
provides a comprehensive outlook of how this bioenergy future will
affect forest harvests, prices, timber management investments, the
area of forest, and forest carbon balance when market interactions
and management responses are considered. Dynamic market and
management responses are usually ignored in many environ-
mental studies that provide only a partial view of the ongoing
debate about benefits and risks of increasing global bioenergy de-
mand (12–17,36).
The results show that lower levels of bioenergy demand, consistent
with RCPs 4.5 and 6.0, can lead to net carbon emissions if the higher
prices encourage more harvesting of natural forests but not enough
of an increase in investments in forest regeneration. For RCPs more
stringent than 4.5, bioenergy demand is sufficiently high that it
encourages strong enough investments in forest management to offset
the negative effects of harvesting inaccessible and natural forests. Thus,
for these higher levels of bioenergy demand, there are net positive
effects on the global carbon balance, although there are notable
impacts on natural forests. Across all ranges of bioenergy demand,
efficient forest carbon sequestration policies can be deployed to ensure
that forests are carbon neutral and that forest carbon stocks are main-
tained. Further, these efficient policies can significantly reduce the
loss of inaccessible and natural forests. In contrast, inefficient policy
proposals, such as taxing carbon emissions from biofuels without also
accounting for the gains that accrue to forest growth, cause poten-
tially large losses of natural and inaccessible forests and lead to carbon
emissions in some circumstances.
There are two important reasons why stocks are enhanced in the
face of strongly growing demand. First, when demand grows, prices
rise and landowners with growing forests will typically hold trees to
take advantage of the rising prices, as there is a higher opportunity
cost of felling them prematurely. If the demand for biomass energy
turns out to be short-lived, lasting only a couple years, then land-
owners would be encouraged to harvest trees earlier than otherwise,
which would reduce carbon stocks and lead to net emissions. However,
biomass energy projections associated with long-run phenomenon
like climate change suggest that the demand for wood-based biomass
energy will grow over time.
Second, rising prices incentivize foresters to increase regeneration
and management expenditures. These include replanting, fertilizing,
managing for competition, and other practices aimed at increasing
the value of and size of the growing stock. Expansion of biomass
energy production would increase management over a wide swath
of forests around the world, but most intensification would occur in
places that are already intensively managed. For context, the stock
of forests has increased steadily in the southern United States and
has stabilized in the Pacific Northwest since 1950, despite old growth
harvesting that continued up to the 1990s (40).
These findings advance the policy discussion by capturing several
dynamics of how landowners respond to incentives, namely, that
economic incentives promote more forest management. This out-
come is different than that of Schlesinger etal. (26) and others, who
do acknowledge regrowth of forests but argue that emissions in the
near term are particularly harmful because they cause damages during
the entire time it takes for forests to regrow. This stance ignores the
benefits of the past accumulation of carbon embodied in current forest
stocks, which is an important component of the global carbon budget.
The argument can also be extended to the future: Is it fair to hold
landowners accountable for today’s emissions without considering
the benefits of their future regrowth, especially given that the future
carbon storage is more valuable than even today’s emission?
In addition to increasing the stock of forests, higher prices make
forests more resilient to land use change. The real price of forest
products has increased since the 1940s, while the real price of crops
has fallen. As a result, the area of land in forests in the many parts of
the United States and Europe has increased over the same time frame
due to afforestation and the abandonment of low-productivity agri-
cultural land (41,42). Rising prices, however, can encourage land-
owners with unmanaged, or natural forests, to liquidate their stock
sooner than otherwise. If a policy that accounts for forest sequestration,
such as the carbon rental policy, is not implemented, bioenergy demand
could harm natural forests and the ecosystem and the biodiversity
services that they provide. Further, as long as policies are implemented
efficiently, large areas of natural forests would remain intact.
This study provides an improved understanding of the benefits
and risks of increasing global bioenergy demand on forest area and
forest carbon mitigation potential under alternative policy scenarios.
However, there are at least two other factors that could be integrated
Fig. 6. Estimated impacts for alternative land constraint scenarios under all RCPs. (A) Total forest area; (B) natural forestland and (C) forest carbon sequestration
versus woody biomass production relative to the baseline (no bioenergy demand) for alternative land constrained scenarios and policy scenarios under all the RCPs.
Diamond, 1.6 billion ha additional forestland limit; plus, 0.9 billion ha additional forestland limit; gray, carbon penalty; pink, forest carbon rental. Trend lines: dashed,
1.6 billion ha; solid, 0.9 billion ha additional forestland limit.
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in future research to provide a more complete analysis of these issues
in a dynamic framework: first, estimating the effects of climate change
on forest growth, merchantable yields, dieback, and biome shifts on
regional biomass supply; and second, analyzing how emerging tech-
nological and/or social transformation processes may affect the pro-
jected demand for woody biomass, especially over a long time horizon.
Taking this more complex and integrated approach might not change
our overall findings but rather provide more insight into additional
risks that could be considered when designing efficient bioenergy
and forest carbon sequestration policies.
MATERIALS AND METHODS
The forestry model used in this analysis is the GTM, which was ini-
tially developed to study dynamic forest markets and policies (4,30).
GTM combines the spatially detailed data on forests with an eco-
nomic model that weighs optimal forest management alternatives.
This version of the model does not include climate change impacts,
but the land classes in the model can be linked to vegetation types
represented in ecosystem models such as BIOME/LPX-Bern (43,44)
or MC2 (45,46). The baseline scenario used in this study is consistent
with current climatic conditions. Moreover, GTM incorporates
overall constraints on land areas derived from the ecological models,
such that only land that is capable of naturally supporting forests
can be converted to forestland (44). For this specific study, we
included more restricted limitations on regional land that can be
converted to forest following the estimates presented in Bastin etal.
(36) of 1.6 billion ha and 0.9 billion ha.
GTM was recently used in a validation exercise to provide a his-
torical assessment of global and regional timber harvesting, timber
management, and carbon stocks from 1900 to 2010in Mendelsohn
and Sohngen (47). Thus, our first simulation period (a decade)
overlaps with the historical decade. Furthermore, Sohngen etal. (48)
conducted a Monte Carlo analysis with the model to assess how un-
certainty in the land supply elasticity and forest biomass (yield)
parameters would affect timber supply and carbon outcomes. The
historical validation illustrates that the model can reproduce forestry
management, land areas, timber prices, and timber stocks. The Monte
Carlo illustrates that the model is most sensitive to the yield function
parameters in terms of the carbon outcomes and timber supply out-
comes. Land supply elasticity is uncertain, but given that the elasticity
parameter is likely not to vary systematically, uncertainty in the land
supply elasticity does not have strong effects on carbon or timber supply.
GTM contains 200 forest types i in 16 regions. Figure S1 shows
the regional disaggregation. Forest resources are differentiated by
ecological productivity and by management and cost characteristics.
To account for differences in ecological productivity, different land
classes in different regions have different yield functions for timber,
derived from the underlying inventory data. Moreover, forests are
broken into different types of management classes. The first type is
moderately valued forests; these are forests managed in rotations and
located primarily in temperate regions. The second type is natural
inaccessible forests, located in regions that are costly to access. To
be conservative, inaccessible forests are assumed to be in equilibrium,
such that they are neither accumulating nor releasing carbon. Inac-
cessible forests are unmanaged and located in places that are costly
to access for timber market reasons. Over time, some of them in our
model become accessible due to economic reasons, e.g., timber prices
rise, making additional hectares economically efficient to harvest. If
they become accessible, they are harvested, and when regrown, they
are subject to applicable forest growth functions. The third type is
low-value forests located in temperate and boreal areas that are lightly
managed, if they are managed at all. The fourth category includes
low-value timberland in inaccessible and semi-accessible regions of
the tropical zones. The fifth type includes the high-valued timber
plantation that is managed intensively; these forests can principally
be found in subtropical regions of the United States, South America,
southern Africa, the Iberian Peninsula, Indonesia, and Oceania.
GTM is an economic model of forests that maximizes the net
present value of consumers’ and producers’ surplus in the forestry
sector. By maximizing the net present value, the model optimizes the
age of harvesting timber a and the intensity of regenerating and
managing forests m
t
i . It is an optimal control problem, given the
aggregate demand function, starting stock, costs, and growth func-
tions of forest stocks.
GTM relies on forward-looking behavior and solves all time pe-
riods at the same time; this means that when land owners make de-
cisions today about forest management, they do so by considering
the implications of their actions today on forests in the future with
complete information. The result is a forecast of what a competitive
market would also do with forestland.
Mathematically, this optimization problem is written as
max ∑
0
∞ ρ
t
{
∫0
Q
t
tot
{
D( Q
t
ind , Z
t,RCP ) + D( Q
t,RCP
wbio ) − C( Q
t
tot )
}
dQ
t
tot −
∑
i C
G
i ( m
t
i , G
t
i ) − ∑
i C
N
i ( m
t
i , N
t
i ) − ∑
i R
t
i ( ∑
a X
a,t
i ) + CC
t,RCP,policy
}
(1)
In Eq. 1, t is a discount factor, D( Q
t
ind , Z t,RCP ) is a global demand
function for industrial wood products Q
t
ind and average global con-
sumption per capita Zt,RCP from the International Institute of
Applied Systems Analysis (IIASA) SSP database (33). In particular,
we use the SSP2 IAM marker scenario from MESSAGE GLOBIOM
under each of the five IPCC RCPs [according to the IIASA SSP data-
base (33), the global consumption per capita under the SSP2 IAM
marker scenario does not change across RCPs].
Industrial timber demand follows the general functional form
Q
t
ind = A t ( Z t,RCP ) P
t
, where At is a constant, is the income elas-
ticity, Pt is the timber price, and is the price elasticity. The global
demand function is for industrial round wood, which is itself an input
into products like lumber, paper, plywood, and other manufactured
wood products.
Wood demand for bioenergy production Q
t,RCP
wbio is estimated by
adjusting the total bioenergy consumption in the IIASA SSP data-
base Q
t,RCP
bio with the proportion of global biomass energy produced
from wood by following similar assumption as in Lauri etal. (34).
Figure S2 (A and B) shows total bioenergy consumption and total
woody biomass supply under each RCP for the SSP2.
We assume there is an international market for timber that leads to a
global market clearing price. As the price of wood for bioenergy rises to
compete with industrial timber, both timber and bioenergy will be traded
internationally (49). Competition for supply will equilibrate their prices.
Equation 2 shows that the total quantity of wood depends on the
area of land harvested in the timber types in i for each age a and
time t ( H
a,t
i ) and the yield function ( V
a,t
i ) , which is itself a function
of ecological forest productivity
t
i and management intensity m
a,t
i .
Q
t
tot = ∑
i
(
∑
a H
a,t
i V
a,t
i ( θ
t
i , m
a,t
i )
)
(2)
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The functional form for the yield function is
V
a,t
i ( m
a,t
i ) = h ∗
[
exp
(
δ i − π i
─
a
)
]
(3)
Per equation h = φ i (1 + m
a,t
i ) i
, h is the stocking density, which
can be adjusted depending on the intensity of management, m
a=1,t
i .
We restrict stocking elasticity, , to be positive and less than 1. The
i affects the elasticity of management inputs in forestry to account
for technology change. Initial stocking is denoted by φi. Increase
in m
a=1,t
i will increase h, e.g., dh/dZ>0, but the increase diminishes
as m
a=1,t
i rises, e.g., d2h/dZ2<0. The model chooses management
intensity by optimally choosing m
a=1,t
i . Increases in management
intensity will increase yield and shift the entire yield function up-
ward. Forests are assumed to grow according to V
a,t
i ( Z
a=1,t
i ) , where
and are species-dependent growth parameters (fig. S3 shows a
represent ative yield function assuming h=1.32, =5.2, and =30).
C( Q
t
tot ) is the cost function for harvesting and transporting logs to
the center (mills or power plants) from each of timber type.
The stock of land in each forest type adjusts over time according to
X
a,t
i = X
a−1,t−1
i − H
a−1,t−1
i + G
a=0,t−1
i + N
a=0,t−1
i (4)
The initial stocks of land X
t
i are given, and all choice variables are
constrained to be greater than or equal to zero, and the area of timber
harvested H
a,t
i does not exceed the total timber area. G
t
i is the area of
timber regenerated land planted, and N
t
i is the new forest planted.
C
G
i (·) is the cost function for planting land in temperate and previ-
ously inaccessible forests, while C
N
i (·) is the cost function for planting
forests in subtropical plantation regions.
GTM takes into account the competition of forestland with crops
and livestock using a rental supply function for land (2). In Eq. 1, R
t
i (·)
is the rental cost function for the opportunity costs of holding tim-
berland X
a,t
i . For example, if timber prices rise relative to agricultural
land prices, the model predicts that timber owners will rent suitable
farmland for at least a rotation. Similarly, if timber prices fall rela-
tively to agricultural land prices, suitable forest land will be converted
back to farmland upon harvest. In addition, the model accounts for
the aggregate global effects of moving land between forests and agri-
culture by shifting all of the land supply functions for individual
forest types as a function of the aggregate global area of forestland.
As more land globally moves from agriculture to forests, all rental
functions shift inward, making it more costly to convert any land
from agriculture to forestry. This captures the effect that having less
land in agriculture would have on land prices. We have assumed that
the elasticity of land supply is 0.25, such that a 0.25% reduction in
the global area of agricultural land would cause all land rents to
rise by 1%. Note also that the rental supply function is restricted to
agricultural land that is naturally suitable for forests. It presumes
that the least productive crop and pasture land will be converted
first and that rental rates increase as more land is converted and
sendogenous.
The model is also developed to account for the global forest carbon
stocks and flows following a method first presented by Sohngen and
Sedjo (50) and updated by Daigneault etal. (18). Carbon is tracked in
four pools: aboveground carbon, soil carbon, forest product carbon,
and slash.
Aboveground carbon C
a,t
i accounts for the carbon in all compo-
nents of the living tree, including roots, as well as carbon in the forest
understory and the forest floor, but does not include dead organic
matter in slash, which is contained in a separate pool. For this analy-
sis, we assume that carbon is proportional to total biomass, such
that carbon in any forest of any age class is given as
C
a,t
i = i V
a,t
i ( m
t0
i ) (5)
where i is a species-dependent coefficient that converts biomass to
carbon. Given this, the total forest carbon pool TFCP
t
i for each timber
type is calculated as
TFCP
t
i = ∑
a C
a,t
i X
a,t
i (6)
Carbon in harvested forest products HC
t
i is estimated by track-
ing forest products over time as follows
HC
t
i = (1 − t ) i ∑
a ( i V
a,t
i H
a,t
i ) (7)
where i is the proportion of harvested timber volume that is car-
bon stored permanently, and it is estimated to be 0.30 (51), i is a
parameter that converts forest products into carbon (regional and
forest type based), while tis the portion of wood used in the energy
sector, and it is endogenously selected by the model; that is, HC
t
i
accounts only for carbon stored in wood products, not woody bio-
mass used for energy production. Carbon stored in woody biomass
used for bioenergy production is calculated as follows
BIOC
t
i = t ∑
a i ( V
a,t
i H
a,t
i ) (8)
Soil carbon SOLC t
i is measured as the stock of carbon in forest
soils of type i in time t. The value of
¯
K , the steady-state level of car-
bon in forest soils, is unique to each region and timber type. The
parameter i is the growth rate for soil carbon. In this analysis, we
capture the marginal change in carbon value associated with manage-
ment or land use changes. When land use change occurs, we track
net carbon gains or losses over time as follows
SOLC t+1
i = SOLC t
i + SOLC t
i ( i )
[
(
¯
K − SOLC t
i )
─
SOLC t
i
]
(9)
Last, we measure slash carbon AS
t
i as the carbon leftover on site
after a timber harvest
AS t
i = ∑
a (
a
i V
a,t
i H
a,t
i − i V
a,t
i H
a,t
i ) (10)
Over time, the stock of slash SP
t
i builds up through annual addi-
tions and decomposes as follows
SP
t+1
i = AS
t
i + (1 − ϑ i SP
t
i ) (11)
Decomposition rates ϑi differ, depending on whether the forest
lies in the tropics, temperate, or boreal zone.
Last, in Eq. 1, the term CCt, RCP, policyrepresents the carbon payments /
penalties for forest owners according to the policy implemented. For
this study, GTM has been enhanced to capture public policy efforts
that either penalize bioenergy demand or value climate mitigation
benefits of forests sequestration. GTM assumes that the incentives
in the timber product, woody biomass, and carbon sequestration sys-
tem can be implemented efficiently. That is, GTM portrays an ideal
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world in which the carbon price and/or subsidy is implemented
simultaneously everywhere, and there are no trade barriers or other
limitations in the use of woody biomass for energy or governance
issues. This is an ideal framework: Carbon obviously has not been
traded globally, and there are widespread reservations about trading
it in the atmosphere as well as in forests. There are measurement,
monitoring, and verification problems, as well as concerns about
leakage and permanence.
Starting with the scenarios simulated in Favero etal. (22), this study
explores four possible policy scenarios:
1) Reference scenario: No bioenergy demand and carbon price
are implemented;
2) Bioenergy scenario: Exogenous bioenergy demands from SSP2
across RCPs Q
t,RCP
wbio are included;
3) Forest carbon rental scenario: Exogenous bioenergy demands
from SSP2 across RCPs are included, and forest owners are com-
pensated by annual rent for providing annual carbon sequestration
according to the carbon prices from the IIASA SSP database;
4) Carbon penalty scenario: Bioenergy demands from SSP2 across
RCPs are included, and carbon emissions upon harvests for energy
are taxed.
The policy scenarios are described in Eq. 1 with the term CCt,RCP,policy.
Moreover, in the reference and bioenergy scenarios, the term CCt,RCP,policy
is assumed to be equal to zero since no policy efforts are implemented
to value or penalize bioenergy demand.
On the other hand, in the forest carbon rental scenario, forest
owners receive carbon payments for the carbon permanently stored
in wood products and are compensated by annual rent for providing
annual carbon sequestration according to the carbon prices P
t,RCP
c from
the IIASA SSP database (fig. S2C) as follows
CC t,RCP,carbon_rental = P
t,RCP
c
[
∑
i HC t
i + ( SOLC t+1
i − SOLC t
i )
]
+
R
t,RCP
c ∑
i TFCP t
i
(12)
The first part of Eq. 11 is the carbon transferred to long-lived
wood products ( HC
t i ) from each forest i valued at the carbon price
P
t,RCP
c . The change in soil carbon ( SOLC
t
i ) when land switches be-
tween forests and agriculture is also valued at the carbon price (13,15).
The second term is the annual rent, R
t,RCP
c , whereby the total carbon
stocks in forests TFCP
t
i are rented during the time period that the
carbon is stored following Sohngen and Mendelsohn (4). The rental
value for carbon is
R
t,RCP
c = P
t,RCP
c −
P
t+1,RCP
c
⁄ (1 + r) t
(13)
where r is the interest rate. This equation accounts for potential
price increases in carbon that occur as carbon accumulates in the
atmosphere.
Last, in the carbon penalty scenario, forest owners pay a penalty
for the carbon released when timber is harvested to supply bioenergy
demand.
CC t,RCP,penalty = − P
t,RCP
c ∑
i BIOC t
i (14)
Table S1 provides the list of parameter values used to parameter-
ize equations for the simulations presented in this study. More de-
tails on the version of GTM used in this analysis are available in (18)
and (22).
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/
content/full/6/13/eaay6792/DC1
Supplementary Materials and Methods
Model estimates
Fig. S1. GTM—Regional Aggregation.
Fig. S2. Global assumptions for alternative RCP scenarios.
Fig. S3. Yield for representative species in the GTM.
Fig. S4. Estimated changes in global forest carbon stock pools in GtCO2 relative to the baseline
scenario under each RCP.
Fig. S5. Changes in global forest area by major ecosystem versus woody biomass supply under
the bioenergy demand scenario relative to the baseline scenario.
Fig. S6. IAMs’ estimates of forest areas and crop areas (2010–2100) under the RCP 1.9 and RCP
2.6 from the IIASA SSP database.
Fig. S7. Estimated changes in global forest carbon stock pools in GtCO2 relative to the baseline
scenario under each RCP and the two policy approaches.
Fig. S8. Key parameter sensitivity impacts relative to 2010 for RCP 2.6 Forest Carbon Rental
scenario.
Table S1. GTM parameter values.
Table S2. Baseline key GTM estimates, 2010–2100.
Table S3. Baseline global forest area (Mha) by major ecosystem, 2010–2100.
Table S4. Baseline global total forest carbon stocks by major ecosystem, 2010–2100.
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Acknowledgments
Funding: This project was supported by the USDA National Institute of Food and Agriculture
McIntire-Stennis project number ME0-41825 through the Maine Agricultural & Forest
Experiment Station; Maine Agricultural and Forest Experiment Station Publication number
3701. Author contributions: A.F., A.D., and B.S. contributed the central idea, and A.F. and A.D.
refined the idea. A.F., A.D., and B.S. analyzed the data. A.F., A.D., and B.S. contributed equally to
the drafting of the manuscript. Competing interests: The authors declare that they have no
competing interests. Data and materials availability: All data needed to evaluate the
conclusions in the paper are present in the paper and/or the Supplementary Materials.
Additional data related to this paper may be requested from the authors.
Submitted 9 July 2019
Accepted 2 January 2020
Published 25 March 2020
10.1126/sciadv.aay6792
Citation: A. Favero, A. Daigneault, B. Sohngen, Forests: Carbon sequestration, biomass energy,
or both? Sci. Adv. 6, eaay6792 (2020).
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Forests: Carbon sequestration, biomass energy, or both?
Alice Favero, Adam Daigneault and Brent Sohngen
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