Question:
You are given the full text of a scientific research paper.

    Summarize the study’s **policy implications**—how the research findings can inform policy or decision-making. Your response should:
    - Interpret what the results suggest for governments, regulators, or institutions
    - Highlight potential actions, strategies, or decisions informed by the study
    - Use policy-appropriate language without speculation beyond the study’s conclusions
    - Be clear, practical, and informative

    Write a short paragraph for the “Policy Implications” section of a policy brief.

    Scientific Research Paper:
    Nature Climate Change | Volume 13 | July 2023 | 685–692
685
nature climate change
https://doi.org/10.1038/s41558-023-01697-2
Article
Bioenergy-induced land-use-change
emissions with sectorally fragmented
policies
Leon Merfort
  1
, Nico Bauer
  1, Florian Humpenöder
  1, David Klein1,
Jessica Strefler
  1, Alexander Popp
  1,2, Gunnar Luderer
  1,3
& Elmar Kriegler
  1,4
Controlling bioenergy-induced land-use-change emissions is key to
exploiting bioenergy for climate change mitigation. However, the effect
of different land-use and energy sector policies on specific bioenergy
emissions has not been studied so far. Using the global integrated
assessment model REMIND-MAgPIE, we derive a biofuel emission factor (EF)
for different policy frameworks. We find that a uniform price on emissions
from both sectors keeps biofuel emissions at 12 kg CO2 GJ−1. However,
without land-use regulation, the EF increases substantially (64 kg CO2 GJ−1
over 80 years, 92 kg CO2 GJ−1 over 30 years). We also find that comprehensive
coverage (>90%) of carbon-rich land areas worldwide is key to containing
land-use emissions. Pricing emissions indirectly on the level of bioenergy
consumption reduces total emissions by cutting bioenergy demand but
fails to reduce the average EF. In the absence of comprehensive and timely
land-use regulation, bioenergy thus may contribute less to climate change
mitigation than assumed previously.
To limit global warming and achieve the Paris climate targets, society
needs to bring down global carbon emissions to net zero and strongly
reduce non-CO2 emissions1. Future cost-effective climate change miti-
gation strategies often deploy bioenergy at large scales2. The use of
biofuels promises a low-carbon alternative to fossil-fuel-based liquids
as well as the possibility to enable carbon dioxide removal from the
atmosphere using bioenergy with carbon capture and storage (BECCS)3.
The combined ability of biofuels to overcome and compensate for
decarbonization bottlenecks is a major driver of its future large-scale
deployment4–6. However, the relevance of bioenergy as a means to
climate change mitigation is also controversially discussed7,8, since its
production will be in competition with other land-use (LU) activities
and will thus increase the already existing pressure on land systems9,10,
affecting biosphere integrity, biogeochemical flows, freshwater use
and food prices9,11–13. Compared with other renewable energy sources,
bioenergy has substantially higher specific land requirements14,15,
and there is the threat that CO2 emissions from direct and indirect
LU change16,17 (LUC/ILUC) associated with bioenergy production can
largely offset abated emissions18.
A broad range of studies have investigated LUC and ILUC emis-
sions induced by bioenergy production at different locations. They
have identified vastly different emission factors (EFs) ranging from
0 to 100 kg CO2 GJbiofuel
−1 (refs. 19–22), potentially exceeding the EF of
fossil diesel (74 kg CO2 GJ−1; ref. 23). This broad uncertainty reflects
the heterogeneity of land types22, characterized by stored carbon
content and crop yield rates24,25. Yet, these studies do not reflect the
interplay between future global climate policies and the allocation of
land areas for bioenergy and food production. Given that stringent
climate policies are projected to be the main driver for bioenergy
demand4, however, it is crucial to link the assessment of EFs to the
Received: 9 April 2021
Accepted: 12 May 2023
Published online: 26 June 2023
 Check for updates
1Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany. 2Faculty of Organic Agricultural Sciences,
University of Kassel, Witzenhausen, Germany. 3Global Energy Systems Analysis, Technische Universität Berlin, Berlin, Germany. 4Faculty of Economics
and Social Sciences, University of Potsdam, Potsdam, Germany.
 e-mail: leon.merfort@pik-potsdam.de

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for 2020–2100 into account; and second, a time-dependent marginal
30-year EF (EFmar30(t)) that accounts for incremental CO2 emissions in
t and relates it to increases of bioenergy over the following 30 years.
EFmar30 varies over time; therefore, we also report the average over time
(EFav.mar30), weighted by bioenergy production increment (Methods).
Policy design
All scenarios reach the same climate target by imposing a uniform car-
bon price applied on all GHGs in the energy sector at the level necessary
to comply with the carbon budget. However, they differ with respect
to assumptions on the coordination of additional LU and energy poli-
cies (Table 1), implying different carbon price levels. A highly idealized
scenario with a globally uniform carbon price (UCP) in both sectors
serves as an aspirational benchmark, representing a world in which
climate policies are internationally harmonized and implemented
in the energy and LU sectors without delay. This policy is contrasted
with a second benchmark scenario, where the LU sector lacks any regu-
latory scheme for controlling emissions (noLUreg)—that is, there is
neither a price instrument on any type of LU-based GHG emissions
nor any widespread land-protection scheme. Both policies assume
that bioenergy is carbon neutral in the energy sector. However, while
LU emissions are controlled in the UCP case, there is a regulation gap
in the noLUreg scenario.
To close the regulation gap, as a first set of alternative policies
we explored the effect of different LU regulatory schemes to serve
as substitutes for full GHG emission regulations. First, we assumed
reduced GHG price levels on LU emissions of 10–50% compared with
the energy sector emission prices (LUprice10–50%). Despite the tax
discount, all LU-emission sources are covered by the regulation. These
sectorally differentiated prices can be motivated by concerns over food
security or high food prices induced by high emission prices. Second,
we implemented different direct land protection policies, protecting
all or 90% of all forests, or only focus areas—namely, primary forests,
biodiversity hotspots or last of the wild areas (protX; see ‘Protected
areas’ in the Supplementary Information). Third, to assess regionally
fragmented policies, we assumed that only Organisation for Economic
Co-operation and Development (OECD) countries implement full
GHG emission pricing in the LU sector (LUprice-OECD), and to avoid
potential carbon leakage effects via the bioenergy channel, which can
be considerable46,47, we also excluded bioenergy trade (LUprice-OECD_
noTrd). In particular, exports from tropical regions with high carbon
stocks might promote additional LUC.
Contrasted to broad LU policies that are difficult to implement on
a global scale, we examined how much a tax on bioenergy consump-
tion can reduce LUC emissions. We analysed the effects of different
bioenergy tax levels overcoming the carbon neutrality assumption by
attributing EFs to bioenergy (bioTax10–50) and also excluded bioen-
ergy trade (bioTax10–50_noTrd).
Ex-post EFs
In the absence of restrictions on LU emissions (noLUreg), cumulative
(2020 to 2100) bioenergy-induced LUC emissions increase more than
tenfold from 44 GtCO2 in the UCP case to 493 GtCO2 (Fig. 1a). This is
qualitatively similar to but quantitatively more muted than the results
of Wise et al.18. Since global bioenergy production only doubles to
236 EJ yr−1, EFav80 increases from 12 to 64 kg CO2 GJbiofuel
−1, which is only
slightly smaller than the EF of conventional diesel (Fig. 1b).
Between these benchmark scenarios, LU and energy policies lead
to different consequences for bioenergy and LUC emissions. Applying
20% of the energy systems’ carbon price to terrestrial GHG emissions
reduces EFav80 to 33 kg CO2 GJbiofuel
−1. In contrast, a tax on bioenergy
consumption fails to lower specific emissions, and total emissions
decline only as a consequence of reduced bioenergy demand. Prohib-
iting bioenergy imports is not effective at all, since the largest part of
the biomass is consumed domestically in most regions.
future transformation pathways of the energy system and the climate
policy framework. Our study assesses bioenergy EFs under a range of
alternative climate change mitigation policies and thereby closes this
gap in the literature.
Most studies analysing global climate change mitigation pathways
using integrated assessment models (IAMs) consider an idealized
climate policy framework putting a uniform carbon-equivalent price
on GHG emissions from all sectors, sources and countries8. This policy
mitigates carbon emissions from the LU sector, including those caused
by bioenergy production. Under such a highly idealized policy frame-
work, bioenergy can be treated as carbon neutral in the energy sector,
since the associated emissions are regulated in the LU sector. However,
in the current real-world situation, energy and LU policies are region-
ally and sectorally fragmented. While many countries have already
started to implement GHG emission pricing in the energy sector, insti-
tutional capacity building is much less developed in the agricultural
and forestry sectors26, leaving a regulatory gap of emissions in the LU
sector27. The implicit assumption in IAMs of institutional feasibility of
LU mitigation has been criticized28, because the regulatory gap can
lead to substantial emission leakage from regulated to unregulated
regions29,30 or sectors18,31,32 involving excess bioenergy production.
To date, potential bioenergy-induced LUC/ILUC emissions are
regulated via energy sector policies (if at all)19, such as by renewable
fuel mandates (for example, the US Renewable Fuel Standard)33, fuel
carbon intensity standards (for example, California’s Low Carbon
Fuel Standard)34 or setting lower bounds on GHG emission savings
for biofuels (for example, as part of the European Union’s renewable
energy directive35). These approaches attribute highly uncertain EFs
to different types of biofuels on the basis of representative feedstocks
and conversion processes. Only few studies have analysed policy frame-
works apart from a uniform carbon price36,37. None of them systemati-
cally compared LU-based regulation schemes (which may be weak or
fragmented) with energy sector policies regarding their effectiveness
in reducing total and specific emissions related to bioenergy produc-
tion and its LU displacement effects. In this study, we investigate the
effectiveness of differently shaped energy and LU policies on bioenergy
EFs and ILUC, which is crucial to explore policy options for avoiding
unsustainable bioenergy use.
Estimating future bioenergy emissions
We applied the IAM framework REMIND-MAgPIE4,38,39 to derive cli-
mate change mitigation pathways for different policy frameworks.
All scenarios are compatible with limiting warming well below 2 °C
by setting a carbon budget of 1,000 GtCO2 to total energy-based and
LU-based CO2 emissions from 2018 to 210040. Key socio-economic
assumptions on population, gross domestic product, dietary choices
and energy demand projections that drive the model results reflect
a middle-of-the-road scenario (Shared Socioeconomic Pathway 2)41.
The coupling of the energy-system model REMIND42,43 with the LU
model MAgPIE44,45 allows for the analysis of feedback effects between
bioenergy demand, production and associated LUC emissions, while
accounting for the latest energy sector developments such as cheap
renewables and the increasing viability of electrified transportation,
which determine the substitutability of biomass.
For each policy framework, we derived bioenergy EFs by compar-
ing these pathways with a counterfactual future without bioenergy
available for decarbonizing the energy sector. We thus isolated LUC
emissions changes and attributed them to bioenergy production,
and we derived ex-post EFs (EFex-post) in units of kg CO2 emitted per
GJ of biofuel produced. Biofuels are the most prominent secondary
energy carrier produced from biomass2 (Extended Data Fig. 1). We
thus focused our analysis on EFs for liquid fuels. Since the choices of
both timing and evaluation period have a large influence on the EF,
we applied different metrics to determine EFex-post: first, an average
80-year EF (EFav80) taking cumulative bioenergy and CO2 emissions

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The impact of bioenergy on LUC emissions in the presence of
land-protection schemes depends on the precise areas that are removed
from the available land pool. A policy protecting all forests resembles
the UCP case to a large extent (EFav80 = 24 kg CO2 GJbiofuel
−1 and emissions
of 107 GtCO2 for protForest) but leads to much stronger conversion of
other natural lands (for example, savannahs and shrubland; Extended
Data Fig. 3). Exempting 10% of the forest areas from the protection
scheme (protForest90%) already doubles EFav80 to 49 kg CO2 GJbiofuel
−1,
and targeting only focus areas performs even worse (‘Sensitivity analy-
sis’ in the Supplementary Information). LU emission pricing only in
OECD countries is ineffective at reducing EFav80 because of the small
share of biomass production and carbon leakage via international
agricultural markets (Supplementary Fig. 16 and ‘Regional perspective’
in the Supplementary Information).
Timing and evaluation period have a large impact on the EF. The
30-year EF strongly varies over time (Fig. 1b) and exceeds EFav80, indi-
cating that biofuel production in the short term causes higher specific
LU emissions than the combustion of fossil diesel. Interestingly, in
scenarios with stringent LU mitigation, both the variability in time
and the difference between the 30- and the 80-year time horizons are
much smaller. Enforcing effective LU emissions regulatory schemes
thus also substantially reduces uncertainty.
Spatial allocation of bioenergy production and
emissions
There is a considerable disconnect between the spatial patterns of
bioenergy production and additional LUC emissions. We find that—
irrespective of the policy design—a large fraction of LUC emissions
does not originate from the sites of bioenergy cultivation but occurs
indirectly at formerly forested areas or pasture, where agricultural
activity displaced by bioenergy production is moved (Fig. 2). Those
ILUC emissions as well as bioenergy plantations directly replacing
carbon-rich ecosystems contribute to high EFs (for example, in the
northern regions of South America for noLUreg; Fig. 2a,c,d). Without
LU policies globally, more than 85% of the additional emissions induced
by bioenergy production originate from territories that together gen-
erate less than 16% of total biomass production (the red and dark red
wedges in Fig. 2b). By contrast, the main part of the bioenergy (more
than three quarters across all policy settings) is being produced with
a direct 80-year EF of less than 37 kg CO2 GJbiofuel
−1 (half the EF of diesel;
the blue wedges in Fig. 2), directly causing less than 10% of the total
bioenergy-induced emissions if LU regulation policies are absent.
Therefore, by only accounting for direct LUC emissions within major
bioenergy-producing regions, only a small fraction of attached emis-
sions can be traced. Accordingly, the total ILUC emissions related to
the total bioenergy production are considerable and vary strongly with
the regulatory framework.
This leads to two conclusions. First, the high flexibility of the LU
sector in reallocating agricultural uses makes ILUC emissions hard to
avoid, although the absolute level depends on the underlying global LU
or energy sector regulatory framework. Second, previous studies ana-
lysing the direct LUC EF of bioenergy often suggested that increasing
bioenergy production is linked to increasing EF, implying that limiting
bioenergy production can also effectively limit EF (for example, Daio-
glou et al.22). This rests on the assumption that expanding agricultural
area due to bioenergy use proceeds along the lines of least marginal EF
of land conversion. However, while such an allocation would be optimal
from a sustainability perspective, it is not the allocation that emerges
in the LU sector, as the EF is not the main criterion for allocating crop
land by means of economic choices. Consequentially, a bioenergy tax
reducing the overall consumption of energy crops does not automati-
cally lead to sparing areas with high carbon stocks, as the allocation of
emissions by EFs is not affected by the tax on bioenergy use (compare
noLUreg and bioTax10 in Fig. 2b).
Components of CO2 emissions and the role of BECCS
Regarding the composition of total cumulated CO2 emissions, we
observed vastly different allocations of the carbon budget for the
varying policy assumptions (Fig. 3a). We found that a large fraction of
Table 1 | Policy design
Scenario
LU sector policies
Energy sector
policiesa,b
Policy type
Benchmark policies
UCP
Carbon price on all
GHGsb at the energy
sector level
Bioenergy
is treated as
carbon neutral
Harmonized
energy and LU
sector policies
noLUreg
None
Bioenergy
is treated as
carbon neutral
None (no LU
regulation)
Fragmented policies
LUprice10–50%
Carbon price on all
GHGsb, lower than
the energy sector
levelc
Bioenergy
is treated as
carbon neutral
Broad land
protection
protForest
Primary and
secondary forests
are protected
protForest90%
90% of all primary
and secondary
forests are protected
in each grid cell
Bioenergy
is treated as
carbon neutral
Narrow land
protection
protPrimforest
Primary forests are
protected
protBH
Biodiversity hotspots
are protected
protLW
Last of the wild areas
are protected
LUprice-OECD
Carbon price on all
GHGsb at the energy
sector level, only in
OECD countries
Bioenergy
is treated as
carbon neutral
Regionally
fragmented
policies
LUprice-OECD_
noTrd
Carbon price on all
GHGsb at the energy
sector level, only in
OECD countries
Bioenergy
is treated as
carbon neutral;
no bioenergy
trade in OECD
countries
bioTax10–50
None
Bioenergy taxd
Bioenergy
policies
bioTax10–50_
noTrd
None
Bioenergy taxd;
no bioenergy
trade globally
Sensitivity analysise
UCP_
bioYield75%
Same as UCP; reduction of bioenergy
crop yields to 75%
Low yields
UCP_
highcostTC
Same as UCP; increased costs for
technological change in the LU sector
The scenarios are divided into benchmark scenarios and scenarios with fragmented energy
and LU policies. They are grouped into different policy types. For this study, we classified
LU policies based on a global GHG price or with full forest protection as ‘broad’ land
protection policies, while all others are defined as ‘narrow’, since they miss substantial parts
of carbon-rich areas (‘Protected areas’ in the Supplementary Information). aIn all scenarios,
a uniform carbon price is applied to all GHGs in the energy sector. bWhile carbon prices
are globally uniform from 2050 on, they differ between regions before 2050 for reasons of
interregional equity (Methods). cThe carbon price within the LU sector is at a level of 10–50%
of the price in the energy system for scenarios LUprice10–50%, respectively. dThe tax level
is determined by the carbon price multiplied by a predefined, fixed factor given in kg CO2
per GJ of primary energy (PE) dry matter (DM) biomass. For example, bioTax20 represents
a policy charging bioenergy as if it had an EF of 20 kg CO2 GJPE
−1. Due to conversion losses,
10–50 kg CO2 GJPE
−1 correspond to 24–122 kg CO2 GJbiofuel
−1, and while the tax level is expressed
in units of PE, ex-post EFs are expressed in units of biofuel for a better comparability with
values from the literature. eSee ‘Sensitivity analysis’ in the Supplementary Information for
details and more scenarios.

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biomass is used in combination with CCS (58–95% in 2100), particularly
when total production is reduced by a bioenergy tax (Extended Data
Fig. 6). In the absence of comprehensive LU sector emission regula-
tions, however, a large fraction of carbon dioxide removals (CDR) from
BECCS is used to compensate the additional LUC emissions (Fig. 3b).
Without LU mitigation, for instance, in 2100 only 15% of cumulated
CDR from BECCS remain after subtracting bioenergy-induced LUC
emissions. Before 2050, cumulated bioenergy-induced LUC emissions
even exceed BECCS savings by far for all policy settings except UCP
(Extended Data Fig. 5). Note that this only compares LUC emissions
with direct CDR but does not account for fossil fuel substitution. While
the substitution effect plays an important role in reducing emissions
from fossil fuels, quantifying the avoided emissions attributed to the
bioenergy part of BECCS is inherently ambiguous, as there are also other
substitution options such as direct electrification in the integrated
systems perspective.
We furthermore observed that cumulated energy system emis-
sions need to be reduced in scenarios with high LUC emissions com-
pared with the UCP policy to balance the total budget. The additional
biomass is then used to accelerate the phase-out of fossil fuels, par-
ticularly oil (Extended Data Fig. 7).
If bioenergy is priced in the energy sector, LUC emissions decrease
with increasing tax level, but the reduced demand for bioenergy
enforces a stronger and faster electrification than in both the UCP
and the noLUreg scenarios (Extended Data Fig. 8). At the same time,
the share of emissions from the transport sector increases due to the
Cumulated LUC emissions (left scale)
Averaged bioenergy production (right scale)
UCP
None
Broad land protection
Narrow land
protection
Reg. frag.
Bioenergy policies
Low yields
0
100
200
300
400
500
a
b
0
100
200
LUC emissions (GtCO2)
Bioenergy production (EJ yr–1)
809
212
192
164
320
1,516
165
301
EFav80
EFav.mar30
UCP
noLUreg
LUprice10%
LUprice20%
LUprice30%
LUprice50%
protForest
protForest90%
protPrimforest
protBH
protLW
LUtax-OECD
LUtax-OECD_noTrd
bioTax10
bioTax20
bioTax30
bioTax40
bioTax50
bioTax10_noTrd
bioTax20_noTrd
bioTax30_noTrd
bioTax40_noTrd
bioTax50_noTrd
UCP_bioYield75%
UCP_highcostTC
0
50
100
150
EFex-post (kg CO2 GJbiofuel
–1)
Diesel*
Natural
gas*
Wind**
Hydro**
Solar
PV**
Fig. 1 | Bioenergy-induced LUC emissions, bioenergy production and EFs.
a, Cumulative global bioenergy-induced LUC emissions and bioenergy
production, given as the averaged annual global production, are both evaluated
for the period from 2020 to 2100 and shown for different policies. The policies
are grouped into different policy types as defined in Table 1. The white bars
indicate cumulative emissions and averaged annual bioenergy production in the
period from 2020 to 2050. b, Ex-post EFs given per unit of biofuel produced for
different policies. The box plots show the temporal variation of EFmar30(t) for years
t between 2025 and 2070 (sample size of n = 9; bioTax40 and bioTax40_noTrd are
exceptions with n = 8, since the year 2070 is excluded; see ‘Emission factors over
time’ in the Supplementary Information). The minimum and maximum of each
box confine the interquartile range, the whiskers represent the first and fourth
quartiles, and the centre line indicates the median value. *The reference EFs for
diesel and natural gas are taken from UBA23. **The reference values for electricity
produced (here in kg CO2 GJelectricity
−1) from solar photovoltaic (PV), hydro and
wind are medium values from AR5 based on life cycle analysis61. For a comparison
with 20-year marginal EFs and EFs of N2O, see Extended Data Fig. 2.

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lack of biofuels. The dwindling availability of biomass even leads to
higher CO2 prices (Fig. 3c) compared with the already high prices in
noLUreg, which makes direct air capture with carbon storage (DACCS)
competitive as a CDR option.
It is also worth noting that even a comparatively small carbon price
on LU-sector-based emissions (LUprice10%) is sufficient to abate most
of the non-bioenergy-related LUC CO2 emissions (from 235 in noLUreg
to 56 GtCO2). At the same time, the required carbon price is reduced by
35% from US$291 to US$193 per tCO2 in 2050 (Fig. 3c).
Discussion and conclusion
In our study, we analysed the impact of bioenergy on LUC emissions in
different climate policy settings, comparing LU policies controlling land
allocation with energy policies controlling bioenergy use. We showed
that a uniform carbon price across energy and LU sectors substan-
tially lowers the specific emissions attributed to a unit of bioenergy in
comparison with scenarios without broad LU regulations. Regulations
of bioenergy demand reduce deployment level but fail to induce a
reduction of the EF, keeping it virtually at a high level and thus resulting
in substantially higher overall mitigation effort and carbon prices to
reach climate targets. A bioenergy consumption tax fails to steer LUC
decisions towards low-EF areas and cannot prevent the conversion of
higher-carbon land; hence, it is not suitable to emulate the uniform car-
bon price regime across all sectors. Our analysis thus shows that global
regulation of LUC emissions—while being extremely aspirational—is
essential for bioenergy to be beneficial for cost-effective mitigation.
The comprehensive coverage of all carbon-rich land areas worldwide
is of the utmost importance, as even a 90% forest protection has only
little effect on the EF. Since the conditions for such an exhaustive policy
framework to materialize are highly idealized, involving close collabo-
ration between and functioning governance within all countries, this
finding implies climate policy sequencing: first, global LU regulation
needs to be in place, and only then should large-scale bioenergy be
considered. Pushing ambitious land-based mitigation is compelling
44
493
277
107
276
350
62
111
236
169
138
190
167
23
25
100
225
400
12
48
108
191
Global cumulative emissions (GtCO2)
Global averaged bioenergy production (EJ yr–1)
GtCO2
EJ yr–1
a
UCP
noLUreg
LUprice10%
protForest
protPrimforest
bioTax10
bioTax50
noLUreg
noLUreg
noLUreg
b
d
c
Cumulative bioenergy-induced
LUC emissions (GtCO2 Mha–1)
Cumulative bioenergy production
(EJ Mha–1)
25
20
15
10
5
0
1.0
0.8
0.6
0.4
0.2
0
Spatial LU characteristic
EFloc    (kg CO2 GJbiofuel
–1)
ILUC
EFloc       > 74
37 < EFloc        < 74
EFloc      < 37
No emissions
av80
ex-post
ex-post
ex-post
Fig. 2 | Spatial allocation of LUC CO2 emissions and bioenergy production.
a, Spatially disaggregated bioenergy EFs for the noLUreg scenario. In bright blue
areas, bioenergy is produced without additional LUC emissions at the place of
production; in dark red areas, only ILUC emissions occur. Other territories are
classified by the ratio of bioenergy-induced emissions to bioenergy production
(EFav80
loc ). The country map is based on the object wrld_simpl in the R package
maptools. b, Total global bioenergy-induced LUC emissions and the global
averaged annual bioenergy production, colour-coded according to the
associated EFs as described in a, for selected scenarios (the other scenarios are
shown in Extended Data Fig. 4). c,d, The spatial distribution of bioenergy
production (c) and bioenergy-induced emissions (d) for the example of South
America. In all four panels, the quantities are cumulated over the period between
2020 and 2100. See the Methods for a description of the analysis of EFav80
loc  and the
Supplementary Information for figures of the other policy assumptions,
including maps of baseline (not bioenergy-related) emissions.

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because GHG emissions from food production will need to be sub-
stantially reduced regardless to meet the 1.5° or well below 2° target48.
Even though a bioenergy tax is an inferior substitute for LU pol-
icies, it is still a crucial element in energy policies, since an EF can
be directly applied to bioenergy use. To value fossil emissions and
bioenergy-induced LUC equally in the absence of LU regulations, our
results indicate that the required EF (64 kg CO2 GJbiofuel
−1 over 80 years
and 92 kg CO2 GJbiofuel
−1 over 30 years) is at the upper end of values
identified in dedicated studies, which range between 0 and 100 kg
CO2 GJbiofuel
−1 on a 30-year time horizon20. Interestingly, uniform regula-
tion of LU sector emissions reduces not only the EF but also the uncer-
tainty due to choices of operationalization, such as the time horizon.
It is important to point out that the bioenergy crop yields
reported in our study reflect average yields based on modelling results
(Methods). Present-day yields from field experiments reported by
Li et al.49 (11.5 and 8.1 t DM per ha per yr for Miscanthus and switchgrass,
respectively) exceed current practice50. Globally averaged bioenergy
crop yields in the UCP scenario are even higher in 2020 (14.1 t DM per
ha per yr), reaching 19.4 t DM per ha per yr in 2040 and 25.7 t DM per ha
per yr in 2100 due to technological progress induced by research and
development (R&D) (Supplementary Fig. 13). However, bioenergy pro-
duction is low in our scenarios in 2020 and can be realized in a few highly
productive areas (LU optimization in MAgPIE), which explains why the
bioenergy yields reported here are higher than in field experiments.
Current and future yields are important for the amount of bioenergy
that can be realized. We observe that 25% lower initial bioenergy yields
(UCP_bioYield75%) increase EFav80 from 12 to 14 kg CO2 GJbiofuel
−1, and less
optimistic assumptions on R&D efficiency (UCP_highcostTC, affecting
future bioenergy and food crop yields) increase it to 16 kg CO2 GJbiofuel
−1.
Furthermore, massive investments in technological progress are still
required to facilitate large-scale bioenergy production even in an ide-
alized policy framework (Supplementary Figs. 14 and 15). For details
regarding crop yield assumptions, refer to ‘Sensitivity analysis’ in the
Supplementary Information.
Another note is that the carbon price only internalizes the carbon
value, omitting other adverse side effects, such as unsustainable fresh-
water use, biodiversity loss51 or LUC in low-carbon ecosystems (such
as savannahs). For instance, about 300 Mha of non-forest natural land
is converted into cropland by 2100 in UCP, one third of that due to bio-
energy (Extended Data Fig. 3). The EF does not capture N2O emissions
from fertilization of bioenergy crops52 or the impact on food prices53 and
also ignores benefits from fossil fuel substitution. For example, we find
that within the UCP policy framework, agricultural commodity prices
increase sharply by 50–120% from 2010 to 2100 (higher values for pes-
simistic crop yield assumptions), with half of that being attributed to
bioenergy (Extended Data Fig. 9). Furthermore, our results depend on
techno-economic assumptions of BECCS and DACCS technologies. In par-
ticular, more optimistic assumptions on DACCS might reduce the demand
for bioenergy. Lastly, it is noteworthy that the carbon price as an indicator
of mitigation effort is not equivalent to total mitigation costs of a policy.
Our study confirms that LU emission pricing is an effective and
efficient instrument to regulate LUC emissions even under large-scale
UCP
None
Broad land prot.
Narrow land
prot.
Reg. f.
Bioenergy policies
L. yields
UCP
noLUreg
LUprice10%
LUprice20%
LUprice30%
LUprice50%
protForest
protForest90%
protPrimforest
protBH
protLW
LUtax-OECD
LUtax-OECD_noTrd
bioTax10
bioTax20
bioTax30
bioTax40
bioTax50
bioTax10_noTrd
bioTax20_noTrd
bioTax30_noTrd
bioTax40_noTrd
bioTax50_noTrd
UCP_bioYield75%
UCP_highcostTC
–500
0
500
1,000
1,500
CO2 emissions (GtCO2)
0
25
50
75
100
ηBECCS (%)
150
200
250
300
350
UCP
noLUreg
LUprice10%
LUprice20%
LUprice30%
LUprice50%
protForest
protForest90%
protPrimforest
protBH
protLW
LUtax-OECD
LUtax-OECD_noTrd
bioTax10
bioTax20
bioTax30
bioTax40
bioTax50
bioTax10_noTrd
bioTax20_noTrd
bioTax30_noTrd
bioTax40_noTrd
bioTax50_noTrd
UCP_bioYield75%
UCP_highcostTC
Carbon price (US$2005 per tCO2)
Sector
Non-transport
Transport
LUC from food production
LUC/ILUC from bioenergy
Direct air capture
Removal BECCS residues
Removal BECCS energy crops
Policy type
UCP
None
Broad land protection
Narrow land protection
Regionally fragmented policies
Bioenergy policies
Low yields
a
c
b
Net emissions
Fig. 3 | Composition of emissions, BECCS efficiency and carbon prices.
a, Composition of total cumulative (2020–2100) anthropogenic CO2 emissions
for different policy assumptions. Energy sector emissions are split into demand-
side emissions from the transport sector and non-transport-sector emissions
comprising supply-side emissions (for example, for electricity production)
and demand-side emissions from buildings and industry, including process
emissions. LUC emissions from food production are CO2 LUC emissions not
related to bioenergy. Bioenergy from residues is assumed to be carbon neutral.
b, The 80-year BECCS efficiency factor ηBECCS indicates how much of the stored
carbon is net removal from the atmosphere if bioenergy-induced LUC emissions
are subtracted. For instance, ηBECCS = 15% for noLUreg implies that only 15% of
the CDR savings are effectively removed from the atmosphere, as the remaining
85% are offset by LUC emissions. The 30-year BECCS efficiency factor is shown in
Extended Data Fig. 5. c, Energy system GHG prices in the year 2050. After a phase-
in period, prices are equal across regions from 2050 on.

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691
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deployment of bioenergy. However, we also show that those policies
cannot be emulated by regulation of bioenergy use, raising the ques-
tion to what extent the LU sector can be effectively regulated to make
large-scale bioenergy use sustainable. The literature points to numer-
ous challenges for regulating LU, ranging from monitoring, reporting
and verification to the need for huge institutional capacity54–56. Effective
regulation may also be undermined by high bioenergy and food prices
creating political pressure to clear land. Moreover, the distributional
implications of regulating LU emissions affect land tenure and liveli-
hoods (for example, small-scale landowners that need to clear land to
eat), raising strong equity and political economy concerns57–59.
Hence, the policy challenge is to either close the regulatory gap by
comprehensively regulating the LU sector (on a global level, as our analy-
sis on regional fragmentation shows) and produce biomass at scale, or
substantially reduce bioenergy demand by increasing its price, as bioen-
ergy cannot be treated carbon neutral otherwise. Given the difficulties in
regulating the LU sector described above, this implies that bioenergy may
play a much smaller role in climate change mitigation than suggested by
most IAM scenarios2. The main driving force behind this challenge is the
huge demand for non-electric energy, particularly transport fuels. Broad
and deep electrification of end uses would thus lower the pressure on the
land system and bypass the regulatory gaps in the LU sector60.
Online content
Any methods, additional references, Nature Portfolio reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author con-
tributions and competing interests; and statements of data and code
availability are available at https://doi.org/10.1038/s41558-023-01697-2.
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Article
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Methods
General
To assess the impact of bioenergy and to fully cover feedback effects
between LUC CO2 emissions and bioenergy demand, we used the cou-
pled integrated assessment modelling framework REMIND-MAgPIE.
REMIND v.2.1.2 is an open-source global multi-regional
Ramsey-type general equilibrium model of economic growth with
a detailed representation of the energy sector, hard-coupled to the
macro-economic core42,43. Using optimization methods, it finds a mar-
ket equilibrium while maximizing intertemporal global welfare. Via
different conversion routes (see Supplementary Table 1 for a list of
technologies converting biomass feedstocks into secondary energy
carriers, including conversion efficiencies and carbon capture rates
for BECCS technologies), REMIND represents the supply, trade and
conversion of biomass feedstocks along the value chain to final energy
carriers along with relevant GHG emissions and removals. The REMIND
model therefore values the energy and the carbon content of biomass
feedstocks given the market conditions and the regulatory framework.
In climate change mitigation scenarios, most of the biomass (primarily
DM lignocellulosic biomass; Extended Data Fig. 10) is converted into
bio-liquids (Extended Data Fig. 1).
MAgPIE v.4.2.1 is an open-source global multi-regional partial
equilibrium model of the LU sector that models LU dynamics spatially
explicitly using recursive dynamic optimization44,45. The model covers
two types of modern purpose-grown bioenergy feedstocks—namely,
grassy and woody biomass—as well as residues from forestry and agri-
culture. Both types are lignocellulosic biomass feedstocks that can be
used for second-generation bioenergy technologies (Supplementary
Table 1). First-generation feedstocks from sugar/starch or oil crops
are also available, but we assumed that production may be expanded
only until 2030 and is limited afterwards, due to higher environmental
impacts than second-generation feedstocks. Since the irrigation of bio-
energy crops leads to unsustainable freshwater use9, we only allowed
for rain-fed production.
Both models are soft-coupled, balancing prices and quantities of
purpose-grown lignocellulosic bioenergy feedstocks and GHGs4. For
residues and first-generation feedstocks, prices and quantities are not
directly coupled but instead exogenously prescribed and harmonized
between REMIND and MAgPIE42. Since we focused our analysis on
second-generation biofuels from purpose-grown feedstocks (residues
are assumed to be carbon neutral), a potential bioenergy tax is imposed
only on and the ex-post evaluation of EFs refers only to purpose-grown
lignocellulosic biomass. Therefore, in the remainder of the study,
the term ‘bioenergy’ always refers to purpose-grown lignocellulosic
biomass, if not stated otherwise. The main policy instrument to meet
a given climate target is a pricing of GHG emissions. GHG prices that are
by default applied to all types of GHGs from all sectors and sources are
derived in REMIND and passed to MAgPIE to meet the predefined GHG
budget in 2100 of total energy- and LU-sector-based CO2 emissions. All
scenarios in this study were derived with middle-of-the-road assump-
tions on socio-economic drivers (Shared Socioeconomic Pathway 2)
and meet a global CO2 emissions budget of 1,000 GtCO2 from 2018 to
2100, allowing for a temporary overshoot. This budget is derived by
subtracting 100 GtCO2 emissions due to Earth system feedback from
the remaining carbon budget of 1,170 given in Rogelj et al.40 (67th per-
centile for the 2 °C target), arriving at 1,070 GtCO2. As safety margin,
this value is rounded down to 1,000 GtCO2.
Carbon prices
In the UCP scenario, all types of GHG emissions from the energy and LU
sectors are charged with a uniform carbon equivalent price PGHG(t, r)
in US$ per tCO2 that increases with time t. Prices can differ between
modelling regions r before 2050 for reasons of inter-regional equity,
but they will eventually converge to globally harmonized prices by
205038. In the LU sector, the GHG price incentivizes avoiding emissions
from LUC and may trigger afforestation for CDR. A detailed descrip-
tion of the techno‐economic data on reforestation and afforestation
can be found in Strefler et al.62. In the energy sector, the GHG price
amplifies the demand for bioenergy as it substitutes fossil fuels and
generates carbon revenues from BECCS technologies. These revenues
for negative emissions in combination with biofuel revenues incentiv-
ize bioenergy even in the presence of high feedstock prices or high
bioenergy tax levels.
In scenarios with a partial LU price (LUprice10–50%), the price on
GHG emissions in the LU sector is reduced in every time step and every
modelling region to the corresponding fraction of the respective price
level on energy-system-related GHG emissions (for example, to 10%).
To be consistent with the narrative of a largely unregulated agri-
culture, forestry and other LU sector, we assumed that in scenarios
without a price on CO2 emissions from LUC (noLUreg, protForest,
protForest90%, protPrimforest, protBH, protCPD, protFF, protLW,
bioTax and partially LUtax-OECD scenarios), non-CO2 GHGs are also
exempted from the GHG price in the LU sector. This has the side effect
that these scenarios also involve substantially higher non-CO2 GHG
emissions from agricultural activities than scenarios with a carbon
price, in particular CH4 and N2O. As a result, radiative forcing levels
and the resulting global mean temperature responses differ between
scenarios, even though cumulative CO2 emissions coincide. How-
ever, since agricultural CH4 emissions are not related to bioenergy
production, and N2O emissions from grassy bioenergy production
are negligible compared with LUC CO2 emissions (Extended Data
Fig. 2), we omitted the effect of non-CO2 GHG emissions for assessing
the impact of bioenergy. Nevertheless, differences between scenarios
in global mean temperature in 2100 as a result of varying LU-related CH4
and N2O emissions (derived with MAGICC v.6; ref. 63) are in the range
of less than 0.2 K (Supplementary Fig. 18).
Land protection
In scenarios with explicit land-protection schemes (protForest, pro-
tForest90%, protPrimforest, protBH, protCPD, protFF and protLW),
we removed the respective areas from the land pool that is potentially
available for any agricultural activities (Supplementary Figs. 3–9). In
protForest, all primary and secondary forests are protected (3,683 Mha
in total); in protForest90%, only 90% of the forests are protected in each
simulation unit (3,315 Mha in total; Supplementary Fig. 10 shows the
MAgPIE simulation units); and in protPrimforest, only primary forests
are protected (1,339 Mha in total). The other land-protection policies
affect only some focus areas. In protBH, biodiversity hotspots are pro-
tected (909 Mha); in protCPD, centres of plant diversity are protected
(651 Mha); in protFF, frontier forests are protected (1,084 Mha); and in
protLW, last of the wild areas are protected (3,635 Mha)64.
Additionally, in all scenarios, specific land areas are protected or
dedicated for afforestation according to the Nationally Determined
Contributions targets of the nations that are participating in the Paris
climate agreement (Supplementary Fig. 11).
Crop yields and technological change
Yields of agricultural production (including bioenergy crops) are
growing endogenously, driven by investments into R&D triggering
technological change. The yield rate improvements depend on R&D
and infrastructure spending according to an elasticity derived from
empirical estimates65 with the investment-to-yield ratio:
IY (τr) = 1.9 × 10
3τ2.4
r
(US$ ha
−1)
as a function of the LU intensity indicator τr (developed by Dietrich
et al.66) for each region r. In this way, yield improvements become more
expensive with higher LU intensity. For instance, in a region r1 with
τr1 = 1, increasing yields by about 1% requires investment costs of
1.9 × 103 × 12.4 × 0.01 (US$ ha−1) = US$19 ha−1, while in a region r2 with

Nature Climate Change
Article
https://doi.org/10.1038/s41558-023-01697-2
already high LU intensity τr2 = 2, increasing yields by about 1% requires
19 × 22.4(US$ ha−1) = US$100 ha−1. For more details, please refer to
Dietrich et al.65,66.
Yields in MAgPIE are initialized with yields from the hydrology
and vegetation model LPJmL. The yield rates in LPJmL reflect values
achievable on the basis of geophysical and hydrological conditions
and plant physiology. The yields from LPJmL are recalibrated (typically
reduced) to match regional cropland in 199567, setting the starting
point for the optimization in MAgPIE. During the optimization process,
yields can increase over time as described above. Over the twenty-first
century, R&D-driven improvements can lead to yield rates that exceed
base-year values. Since yields for modern herbaceous bioenergy crops
at the commercial farm level may be lower than what has been found in
small-scale trials68, we added sensitivity scenarios, in which the initial
bioenergy yields are reduced to 75% or 50% compared with the default
values (UCP_bioYield75 and UCP_bioYield50). Since future yield rates
and the productivity of the R&D sector are subject to uncertainty, we
further analysed a scenario in which marginal costs for yield-improving
technological change are substantially increased (UCP_highcostTC).
Within the parametrization for the investment yield ratio IY(τr) for this
scenario, we used the highest estimates for the parameters (2.3 instead
of 1.9 for the pre-factor and 3.3 instead of 2.4 for the exponent/elastic-
ity). The higher exponent in particular increases the marginal costs
for technological change in regions where yields are already high. It is
important to highlight that not only bioenergy yields but also yields
of food crops and forage are increasing due to technological change.
Pasture yields are not linked to crop yield increases but to an exogenous
pasture management factor. Also, while R&D-induced technological
change is the sole driver of yield gains for a fixed physical environment
and fixed crop type, it is not the only reason why average yields within
an economic region vary over time. For instance, shifting production
of a certain crop type to more fertile land increases average yields,
while extending production to, for example, marginal land with lower
yields reduces them.
The sensitivity analysis on scenarios with lower yields can be found
in the Supplementary Information, section ‘Sensitivity analysis: Pes-
simistic yield assumptions’.
Food demand and food prices
Food demand in MAgPIE depends on the income of households. Prices
influence food demand only via the income effect of the price shock,
with increasing prices reducing the real income of households. Due
to the impact of food prices on the real income of households, higher
food prices may lead to small reductions in demand. How food demand
depends on real income (modelled via demand elasticities of income)
is described in Bodirsky et al.69 and Bodirsky et al.70. However, the effect
is very small, and the differences between food demand trajectories
are almost negligible in the scenarios (Supplementary Fig. 17). This is
thus equivalent to the assumption of a food-first policy. In particular,
the demand for livestock products is not affected by the different
policy settings. The potential (unintended) effect that bioenergy
reduces emissions indirectly due to changes in consumption71 is thus
not present in this study. Therefore, the derived EFs only account
for changes in livestock production systems rather than changes in
meat demand.
Bioenergy tax
As explained above, the default policy assumption regarding the pric-
ing of emissions is a uniform carbon price on both energy- and LU-based
GHG emissions. Emissions related to bioenergy production are thus
already penalized directly within the LU sector, so the energy system by
default treats bioenergy as a carbon-neutral energy carrier. In the sce-
narios with only energy sector policies (bioTax), we assigned an ex-ante
EF (EFex-ante) to bioenergy that should reflect potential bioenergy-related
GHG emissions. EFex-ante represents emissions on a global average and is
equal for each economic region r and time step t. It directly transforms
into a bioenergy tax Tbio(t,r) via the price on GHGs PGHG(t,r):
Tbio (t, r) = EFex-ante × PGHG (t, r) (US$ GJ
−1
PE)
which is applied to every unit of DM biomass—that is, at the level of PE.
Since the literature and the results of the present study indicate that
specific emissions attributed to a unit of bioenergy are highly uncertain
even on a global average, we explored the effect of different values
of EFex-ante ranging from 10 to 50 kg CO2 GJPE
−1, which translates to 24
to 122 kg CO2 GJbiofuel
−1 (41% energy conversion efficiency). It is worth
noting that EFex-ante is in general not equal to the actual emissions that
are eventually attributed to bioenergy, which are derived ex-post from
our scenarios (EFex-post).
In most other publications applying the REMIND model, bioenergy
is actually charged with a ‘sustainability tax’ that reduces the demand
for bioenergy irrespective of the policy design to reflect uncovered
externalities, such as unsustainable water usage, food price increase,
the loss of biodiversity and nitrogen losses to the environment38. In
the present study, however, we deactivated this tax, since we wanted
to assess the impact of bioenergy given a certain policy assumption in
an otherwise uncontrolled market.
Ex-post EF
Due to ILUC induced by bioenergy production, it is impossible to dis-
entangle LUC CO2 emissions related to bioenergy production from
LUC emissions that result from other agricultural activities such as an
expansion of cropland or pasture. For each policy setting p, we defined
and derived metrics with different time aggregation methods to com-
pare the model results. The time aggregation is crucial for the metric
because cropland expansion causes immediate emissions to increase
bioenergy production afterwards. To evaluate the effect of bioenergy
production on LU emissions, we derived EFex-post by comparing two sce-
narios for each p—first, the actual policy scenarios with the availability
of purpose-grown bioenergy (bioOn) and, second, the counterfactual
scenario, in which purpose-grown lignocellulosic bioenergy produc-
tion is not allowed (bioOff). A similar approach has been applied in, for
example, Daioglou et al.22 and Pehl et al.72 Using the full scenario output
as a basis considers feedback effects from reduced afforestation in the
presence of large-scale bioenergy production (see Supplementary
Fig. 11 for afforestation potentials in the counterfactual scenarios).
Using a counterfactual scenario also ensures that the total additional
LUC emissions are correctly attributed to total bioenergy without mak-
ing the accounting error that occurs when counterfactual regrowth
of natural vegetation is ignored in partial analysis (identified by
Haberl et al.73). Although the scenario results comprise full information
about changes in emissions and bioenergy production, choices about
the aggregation over time have substantial influence on the metric.
First, the 80-year average EF comprises all LUC CO2 emissions
attributed to bioenergy production:
EF
av80(p) =
E80
bioOn(p) −E80
bioOff(p)
B80
bioOn(p)
(kg CO2 GJ
−1)
where E80
bioOn and E80
bioOff denote the global net cumulative LUC emis-
sions over the period 2020–2100 for the scenario with bioenergy on
and off, respectively. B80
bioOn represents the global cumulative amount
of purpose-grown lignocellulosic biomass produced globally over
the same period. The 80-year period equals the period for the carbon
budget. Due to the asynchronicity of emissions and bioenergy har-
vest, a shorter time horizon would generally lead to higher EFs during
periods of bioenergy production expansion.
Second, we defined the marginal 30-year EF at time t, which relates
additional emissions in t to increments of bioenergy production in t
that are assumed constant for the coming 30 years, as

Nature Climate Change
Article
https://doi.org/10.1038/s41558-023-01697-2
EF
mar30(p, t) = EbioOn(p, t) −EbioOff(p, t)
30 × Badd
bioOn(p, t)
(kg CO2 GJ
−1)
with EbioOn(p,t) and EbioOff(p,t) being the total LUC emissions in the year
t for the scenario policy assumption p and its respective counterfactual
scenario without bioenergy. Badd
bioOn(p, t) is the bioenergy production
increment in year t—that is, the difference in total production from the
previous year. The EF is thus based on two assumptions. First, we
assume that the difference in emissions from the counterfactual sce-
nario in year t is fully attributed to the bioenergy production increment
in that same year. Second, we assume that bioenergy plantations initi-
ated in the given year will be constantly producing the same amount
of bioenergy over the next 30 years on the additional cropland.
The average EF for the 80-year time horizon matches the time
frame of the carbon budget. Due to the flexibility of the model to allo-
cate bioenergy use and emissions optimally over time, bioenergy pro-
duction is maximized towards the end of the evaluation period, while
emissions peak much earlier. Deriving an average 30-year EF would
thus be meaningful only in scenarios that meet an emissions budget
by 2050. To assess the value of bioenergy in a shorter time frame, given
our assumptions on the climate target formulation, we thus use the
marginal EF, which only depends on bioenergy production increment
and bioenergy-induced emission in a given year.
Third, since EF
mar30 (p, t) varies over time, we derived an averaged
marginal EF:
EF
av.mar30(p) =
2070
∑
t=2025
EF
mar30 (p, t) ×B
add
bioOn(p, t)
∑
2070
t=2025Badd
bioOn(p, t)
(kg CO2 GJ
−1)
applying the bioenergy production increment addition as weight to
the 30-year EF of a given year. Years with the highest increment of
bioenergy production thus receive the highest weight. Since in the
scenarios the climate target is met in 2100, due to the 30-year evalu-
ation period only EFs until 2070 are utilized to calculate the average
EF. Also, in some scenarios, bioenergy production decreases again
after 2070—calculating EFmar30 is thus not meaningful in these years.
For two scenarios with very low bioenergy production (bioTax40
and bioTax40_noTrd), bioenergy production decreases after 2060,
so EFav.mar30 here only covers the years until 2060 (see Supplementary
Figs. 1 and 2 for time series of emissions, bioenergy production incre-
ment and EFs).
EFex-post is usually expressed in terms of CO2 emissions per unit of
biofuel, to make it comparable with fossil fuels. Since the thermochemi-
cal conversion to liquid fuels is subject to substantial conversion losses
(the energy conversion efficiency for second-generation biofuels
(Fischer-Tropsch diesel) is only 41%), the EF per unit of PE biomass is
smaller, and other energy carriers derived from biomass, in particular
electricity or hydrogen, exhibit different EFs due to different energy
conversion efficiencies (Supplementary Table 1).
EFs were also evaluated in a spatially disaggregated manner. For
our study, the LU model MAgPIE was applied using 1,000 distinct
simulation units, rMAgPIE, revealing individual patterns of agricultural
activities. Each simulation unit represents a cluster of aggregated
0.5-degree-resolution grid cells with similar properties44,74 (Supple-
mentary Fig. 10), and for each of them, an 80-year average EF can be
calculated individually:
EF
av80
loc
(p, rMAgPIE) =
E80
bioOn (p, rMAgPIE) −E80
bioOff (p, rMAgPIE)
B80
bioOn (p, rMAgPIE)
(kg CO2 GJ
−1)
There are clusters of grid cells without bioenergy production
(EF
av80
loc
= ∞) and others for which the difference in emissions from the
counterfactual scenario is zero or even marginally negative—that is, a
simulation unit rMAgPIE with equal or less emissions than in the scenario
without bioenergy. Here the EF
av80
loc  is set to zero.
Since the EFs are given as the ratio of emissions and bioenergy
production, there is no information on the total volume of each of these
quantities in the different areas in Fig. 2a. For selected scenarios, the
spatial allocation of LUC emissions and bioenergy production quanti-
ties is depicted in the section ‘Spatial land-use characteristics’ of the
Supplementary Information.
Using a counterfactual scenario to derive the EF can lead to a situ-
ation in which additional LUC emissions to this counterfactual scenario
are rather small or even zero, while baseline LUC emissions from the
counterfactual scenario (in the same simulation unit) are already sub-
stantial. Bioenergy production is then associated with a relatively small
EF
av80
loc , even though the actually occurring emissions are large. However,
since these emissions also emerge in the baseline/counterfactual sce-
nario, in this study they are not attributed to bioenergy. Such areas can
be interpreted as ‘lost’ territories that will suffer LUC irrespective of
the bioenergy production, given a distinct policy setting.
The 80-year BECCS efficiency factor
We defined the efficiency of the CDR potential of BECCS as
ηBECCS = (1 −
E80
bioOn −E80
bioOff
CDR
80
BECCS,bioOn
) × 100%,
where CDR
80
BECCS,bioOn  are the total cumulative negative emissions
associated with BECCS from purpose-grown biomass. A scenario with-
out bioenergy-induced LUC would thus imply an efficiency of 100%,
while in a scenario in which bioenergy-related emissions are equal to
the CDR saving via BECCS, the efficiency is 0%. The BECCS efficiency
factor shown in Extended Data Fig. 5 uses cumulative emissions and
savings for the 30-year time horizon until 2050 instead.
Since bioenergy from residues is allowed in the counterfactual
scenarios, we excluded the BECCS emission savings related to residues
from the calculation of ηBECCS. This efficiency factor is derived to relate
bioenergy-induced LUC emissions to the CDR potential of BECCS. It
does not, however, cover other benefits of bioenergy to the energy
system, particularly the benefits of substituting biofuels for fossil
fuels. However, quantifying the avoided emissions attributed to the
bioenergy part of BECCS is inherently ambiguous, as there are also
other substitution options such as direct electrification in the inte-
grated systems perspective. Due to the fixed quantity target on total
emissions, more or less emissions from the LU sector (including the
CCS part) will be—by definition of the target—balanced with changes
in fossil fuel use. Consequently, the partial effect of the substitution
effect throughout the overall fossil fuel sector is not independent
of the net emission changes caused by LU and carbon removal of the
same quantity of bioenergy. In addition, bioenergy-related emissions
do not cover all negative effects, as described in the paragraph on the
bioenergy tax.
Data availability
The model runs and scenario data for this study are archived at Zenodo
under a CC-BY-4.0 license75.
Code availability
REMIND is open source and available on GitHub. The model version
used in this study is 2.1.2, which can be downloaded at https://github.
com/remindmodel/remind/releases/tag/v2.1.2. MAgPIE is open source
and available on GitHub. The model version used in this study is 4.2.1,
which can be downloaded at https://github.com/magpiemodel/mag-
pie/releases/tag/v4.2.1. Documentation can be found at https://rse.
pik-potsdam.de/doc/magpie/4.2.1/.
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Acknowledgements
The research leading to these results has received funding by the
DIPOL from the German Federal Ministry of Education and Research
(BMBF) under grant number 01LA1809A (L.M., N.B. and J.S.). This work
was supported by the NAVIGATE project funded by the European
Union’s Horizon 2020 research and innovation programme under
grant number 821124 (N.B., F.H. and J.S.), by the RESCUE project from
the European Union’s Horizon Europe programme under grant number
101056939 (L.M.) and by the ARIADNE project from BMBF under grant
number 03SFK5A (D.K. and G.L.).
Author contributions
L.M. performed the model experiments, analysed the scenarios,
produced the figures and led the writing of the manuscript. L.M.,
N.B., F.H., G.L. and E.K. designed the study, the scenarios and the
analysis. L.M., N.B., F.H., D.K., J.S., A.P., G.L. and E.K. contributed to the
development of the models, the presented ideas and the text.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at
https://doi.org/10.1038/s41558-023-01697-2.
Supplementary information The online version contains supplementary
material available at https://doi.org/10.1038/s41558-023-01697-2.
Correspondence and requests for materials should be addressed
to Leon Merfort.
Peer review information Nature Climate Change thanks the
anonymous reviewers for their contribution to the peer review of
this work.
Reprints and permissions information is available at
www.nature.com/reprints.

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Extended Data Fig. 1 | Primary energy biomass distribution. The area plots
show the share of biomass that is converted into different secondary energy
carriers. The main conversion process is the biomass to liquids route via
gasification and Fischer-Tropsch synthesis of lignocellulosic biomass (in our
scenarios, all liquids derived from lignocellulosic biomass originate from that
process). The biomass shown here originates from both purpose-grown energy
crops and residues (see Extended Data Fig. 10, showing shares of different
feedstocks). Please note that some conversion processes have a second energy
carrier type as co-product. In particular in the biomass-based Fischer-Tropsch
process electricity is co-produced, which makes up almost all of the electricity
that is derived from lignocellulosic biomass.

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Extended Data Fig. 2 | Bioenergy emission factors derived with different
metrics. In addition to EFs in Fig. 1a we here explicitly show marginal 30-year EFs
for the years 2035 and 2050 in green, 80-year average N2O EFs in bright red, as
well as the averaged marginal 20-year EF in orange. Please note that by definition
the 20-year marginal EF is simply the 30-year marginal EF multiplied with 1.5,
since the accounting period for bioenergy production is divided by that factor.
The boxplot shows the temporal variation of EFmar30(t) for years t between 2025
and 2070 (sample size of n = 9; bioTax40, bioTax40_noTrd are exceptions with
n = 8, since the year 2070 is excluded, see ‘Emission factors over time’ in the SI).
The minima and maxima of the box confine the inter-quartile range, the whiskers
represent the 1st and 4th quartile, and the center states the median value. N2O EFs
are converted to CO2-eq using a global warming potential of 26576 and are
calculated equivalently to the 80-year average EF for CO2. It can be seen that the
different policy assumptions have a rather small effect on the N2O EF with values
between 4.6 and 7.2 kg CO2/GJbiofuel. While the N2O EF can mostly be neglected
relative to the high CO2 EF in scenarios where LU policies are missing, for the UCP
scenario cumulative N2O emissions until 2100 in carbon equivalents are roughly
40% of the cumulative CO2 emissions, since the EF for CO2 is relatively small.

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Extended Data Fig. 3 | Land cover change. Shown are changes in the land
cover with respect to 1995 of the four main land types represented in MAgPIE,
‘Forest’, ‘Other Land’, ‘Pasture and Rangelands’ and ‘Cropland’. The dark shading
represents the land cover change that is attributed to bioenergy production
(‘bioen.-induced’) and the light shading represents the land cover change that
is observed in the respective counterfactual scenario (‘cf. baseline’), that is,
the fraction that is not attributed to bioenergy. Areas classified as ‘Other Land’
comprise non-forest natural vegetation (such as savannahs or shrubland),
abandoned agricultural land and deserts. Some of these areas also store
substantial amounts of carbon.

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Extended Data Fig. 4 | Allocation of LUC CO2 emissions and bioenergy production by land-use characteristic. In Fig. 2b only a selection of scenarios was
presented. Here we show the allocation of emissions and bioenergy production for all policy assumptions. For a description, please refer to Fig. 2 in the main text.

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Extended Data Fig. 5 | The BECCS efficiency factor in 2050. ηBECCS is an
indicator of how much of the sequestered carbon is effectively removed from the
atmosphere if bioenergy-induced LUC emissions are subtracted. A negative
efficiency indicates that bioenergy-induced LUC emissions exceed CDR savings
by BECCS in the year 2050. For instance, ηBECCS = −200% implies that emissions
are 200% higher than savings, that is, three times as high. For the UCP policy
setting, emissions and savings are just equal in 2050.

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Extended Data Fig. 6 | Share of biomass used with CCS and total production
of primary energy biomass. The share of biomass that is used in combination
with CCS, shown on the left-hand side, is given as a fraction of total primary
energy biomass production, that is, including traditionally used biomass and
1st generation biofuels (which cannot be combined with CCS) and residues. This
total primary energy production is shown on the right-hand side for comparison.
For clarity we highlight only a few scenarios with colors, grey lines refer to the
other scenarios.

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Extended Data Fig. 7 | Liquids by primary energy carrier. Biomass, coal and oil can be converted to liquid fuels and biomass and coal have an option to add CCS
(E-fuels are not considered in this study). Shown are shares of all liquids that are produced from these primary energy carriers, either with or without CCS included.
Shares are given in the year 2050 and 2100 for different policy settings.

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Extended Data Fig. 8 | Final energy mix. Total final energy is compound of different energy carriers. The shares are shown for different policy settings in the years
2050 and 2100.

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Extended Data Fig. 9 | Global agricultural price index relative to 2010.
Black dots denote the agricultural price index of a given scenario. Orange
bars represent the index of the respective counterfactual scenario without
bioenergy production. The green bars indicate the difference between
scenarios with and without bioenergy, that is, the fraction of the average price
change for agricultural goods induced by allowing for bioenergy production.
The agricultural price index is defined as the change of averaged prices of all
agricultural commodities relative to the year 2010 (which has the value 100).
Addítionally to the scenarios shown in the main text, we here show the
additional scenario from the sensitivity analysis on pessimistic crop yields
(UCP_bioYield50), see ‘Sensitivity analysis: Pessimistic yield assumptions’ in
the SI. Please refer to ‘Food demand and prices’ in the SI for more details on the
impact of policy assumptions on the food prices.

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Extended Data Fig. 10 | Bioenergy feedstocks to the energy system. The area
plots show the share of bioenergy from different sources. While today residues
are almost exclusively used traditionally, that is, using the solid biomass for
cooking and heating mostly in developing countries, in our scenarios this shifts
within the next 30 years towards modern usage, mostly in form of creating
second generation bio liquids from it (see also Extended Data Fig. 1).

    Policy Implications:

Answer:
• Pushing for globally comprehensive land protection is the most effective way to reduce the high indirect land-use-change emissions associated with modern biofuel production from energy crops.
• A price on all CO2 emissions from land clearing — not limited to direct bioenergy-related land conversion — is an effective and efficient instrument to protect land and reduce emissions from biofuels.
• Pricing all land carbon pools even at a substantial discount below the energy sector CO2price is more effective than a full protection scheme that covers only 90% of all forests globally.
• Given the deficiencies of current global land protection, indirect greenhouse gas emissions from biofuels need to be regulated as stringently as greenhouse gas emissions from fossil fuels.
• Combining biofuel production with carbon capture and storage can mitigate the high emissions, but even then, biofuels have a negative climate balance on a 30-year time horizon.