Bio-energy and CO2emission reductions: an integrated
land-use and energy sector perspective
Nico Bauer, et al. [full author details at the end of the article]
Received: 26 July 2019 /Accepted: 12 October 2020/
#The Author(s) 2020
Abstract
Biomass feedstocks can be used to substitute fossil fuels and effectively remove carbon from
the atmosphere to offset residual CO2emissions from fossil fuel combustion and other
sectors. Both features make biomass valuable for climate change mitigation; therefore, CO2
emission mitigation leads to complex and dynamic interactions between the energy and the
land-use sector via emission pricing policies and bioenergy markets. Projected bioenergy
deployment depends on climate target stringency as well as assumptions about context
variables such as technology development, energy and land markets as well as policies. This
study investigates the intra- and intersectorial effects on physical quantities and prices by
coupling models of the energy (REMIND) and land-use sector (MAgPIE) using an iterative
soft-link approach. The model framework is used to investigate variations of a broad set of
context variables, including the harmonized variations on bioenergy technologies of the 33rd
model comparison study of the Stanford Energy Modeling Forum (EMF-33) on climate
change mitigation and large scale bioenergy deployment. Results indicate that CO2emission
mitigation triggers strong decline of fossil fuel use and rapid growth of bioenergy deploy-
ment around midcentury (~ 150 EJ/year) reaching saturation towards end-of-century. Vary-
ing context variables leads to diverse changes on mid-century bioenergy markets and carbon
pricing. For example, reducing the ability to exploit the carbon value of bioenergy increases
bioenergy use to substitute fossil fuels, whereas limitations on bioenergy supply shift
bioenergy use to conversion alternatives featuring higher carbon capture rates. Radical
variations, like fully excluding all technologies that combine bioenergy use with carbon
removal, lead to substantial intersectorial effects by increasing bioenergy demand and
increased economic pressure on both sectors. More gradual variations like selective exclu-
sion of advanced bioliquid technologies in the energy sector or changes in diets mostly lead
to substantial intrasectorial reallocation effects. The results deepen our understanding of the
land-energy nexus, and we discuss the importance of carefully choosing variations in
sensitivity analyses to provide a balanced assessment.
Keywords Bioenergy .BECCS .Integrated assessment modeling .Land-energy nexus .Climate
change mitigation
Climatic Change
https://doi.org/10.1007/s10584-020-02895-z
This article is part of the Special Issue on “Assessing Large-scale Global Bioenergy Deployment for Managing
Climate Change (EMF-33)”edited by Steven Rose, John Weyant, Nico Bauer, Shinichiro Fuminori, Petr Havlik,
Alexander Popp, Detlef van Vuuren, and Marshall Wise.
1 Introduction
Large scale deployment of bioenergy has been identified as a key long-term option to keep
CO2emissions limited over the twenty-first century (Popp et al. 2014b;Roseetal.2013).
Bioenergy is particularly valuable in the context of deep emission reductions because it
substitutes fossil fuels directly and enables carbon dioxide removal (CDR) from the atmo-
sphere to offset residual emissions by combining bioenergy use with carbon capture and
storage (i.e., BECCS) (Klein et al. 2014b; Luderer et al. 2018). The importance of bioenergy
for the energy sector is confronted with major uncertainties about the availability, performance,
and maturity of advanced bioenergy technologies ABTs (Lomax et al. 2015; Scott and Geden
2018), potential limitations of biomass feedstock supply, and implications on the land-use
sector as well as broader issues of socio-economic development and policy implementation
(Creutzig 2014). To better inform the debate on bioenergy and climate change mitigation, the
EMF-33 study of the Stanford Energy Modeling Forum applies a series of IAMs and performs
a broad sensitivity analysis (Bauer et al. 2018b).
This study presents the land-energy coupling approach of the integrated assessment
modeling framework REMIND-MAgPIE and applies it to the EMF-33 scenario protocol
augmented by additional, more specific sensitivity analysis of context variables covering
variations in the land-use sector, socio-economic drivers, and policy implementation. The
aim is to enhance the understanding of the coupled land-energy transformation and to study
crucial factors and drivers regarding inter- and intrasectorial effects when stringent mitigation
targets should be met. We address the question how climate change mitigation increases the
intersectoral linkage and which variations lead to (i) intrasectorial effects limited to either the
energy or the land-use sector or (ii) to intersectorial effects in and between both sectors. For
this purpose, we study quantity and price information derived with the coupled REMIND-
MAgPIE model.
The REMIND-MAgPIE model integrates macroeconomy, land-use, and energy systems
and computes consistent scenarios implementing varying degrees of climate policies and
varying policy frameworks (e.g., evaluation of Nationally Determined Contributions NDCs
or optimization of policies to achieve a carbon budget). It participated in various international
IAM comparison studies since 2010 (e.g., Kriegler et al. 2014) and contributed integrated
economy, energy, land, and climate scenarios to the Shared Socioeconomic Pathways (SSPs)
(Riahi et al. 2017). The integrated modeling framework allows investigating a broad range of
uncertainties of these assumptions by means of sensitivity analysis to study physical and
economic impacts on bioenergy markets and climate policies and thereby study the land-
energy nexus in climate change mitigation scenarios.
The integrated assessment part of the EMF-33 study mainly focuses on uncertainties related
to ABTs (Bauer et al. 2018b) in the context of energy sector CO2emission limitations. ABTs
include options to convert ligno-cellulosic biomass into modern energy carriers such as
electricity, liquids, and hydrogen (incl BECCS variants). The supply side part (Rose et al.,
this issue) studies the cost and potentials of biomass feedstocks and related GHG emissions.
The present study extends the analysis by varying a broader set of context variables regarding
demand and supply side drivers of bioenergy use and use as well as socioeconomic drivers and
policy implementation.
We start from a baseline scenario without climate policies applying default assumptions
based on the middle-of-the-road narrative and quantification (SSP2). This is compared with
mitigation scenarios that implement carbon pricing policies to comply with a prescribed
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carbon budget. The carbon pricing system covers all emissions using CO2-equivalent emission
factors. The carbon budget definition follows the EMF-33 scenario protocol and accounts for
emissions from the energy sector, including industry. Land-use change emissions and affor-
estation removals develop according to the carbon price but do not enter the carbon budget.
The carbon budget accounts for CO2removal via BECCS which offsets CO2emissions from
energy and industry and, eventually, balances the carbon budget over time. Moreover, to take
into account near-term climate policies, we restrict the model up until 2020 to implement only
weak climate policies consistent with conditional NDCs.
For the sensitivity analysis, we keep the carbon budget constant and adjust the carbon
pricing accordingly. The fourteen variations are sorted in three groups. We vary assumptions
on (i) bioenergy conversion technologies and the injectivity of CO2into geological formations,
(ii) potential and cost in the land-use sector influencing biomass feedstock supplies, and (iii)
socioeconomic drivers and climate policies implementation. These variations cover a broad set
of factors and drivers affecting the availability of biomass-based mitigation technologies, the
supply of biomass, and broader socioeconomic drivers and policy implementation, which
influence the energy and land-use sector and their interactions. The integrated scenarios are
used to investigate the land-energy nexus in the context of transition pathways with limitations
on global energy and industry emissions.
2 Methodology
2.1 The REMIND and the MAgPIE model
The REMIND and the MAgPIE model are both equilibrium models that apply optimization
methods to derive market equilibria. Also, both models cover the time horizon up until 2100
and are global in scope differentiating the world into macro-regions that form markets for food
and energy for which market equilibria are computed. Both models rely on the SSPs for
assumptions on socioeconomic drivers for demographic, economic, and technological devel-
opment (Dellink et al. 2017; KC and Lutz 2017). In this study, both models use the SSP2, a
middle-of-the-road narrative, to derive a baseline scenario that is used to assess the impacts of
climate policies. Assumptions on technological development, resource potentials, etc. in
REMIND and MAgPIE follow the SSP2 narrative and documented in the literature
(Kriegler et al. 2017; Riahi et al. 2017).
REMIND 1.7 is a Ramsey-type general equilibrium model of economic growth with a
hard-coupled detailed energy system model that applies optimization methods to find a general
market equilibrium (Bauer et al. 2018a; Bauer et al. 2012; Luderer et al. 2013). Economic
agents are assumed to have perfect foresight on future prices, which implies a rational
expectations equilibrium on all markets. The macroeconomic system demands labor, capital,
and final energy. Final energy use gradually modernizes with growing shares of electricity and
gases, whereas shares of liquids and solids decrease. The energy system is a detailed
representation of all relevant energy flows from primary to final energy as well as associated
GHG emissions. The energy system transformation is consistent with basic energy economic
principles of cost competition, but also represents inertia of infrastructures and rigidities to
ramp-up new capacities as well as endogenous technological learning. Regions are endowed
with fossil fuels, uranium, and biomass that are traded internationally subject to transportation
costs and infrastructure expansion rigidities as well as balance of payments constraints. Coal,
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gas, and biomass conversion capacities that produce electricity, liquids, and hydrogen can be
equipped with CCS. An injectivity constraint limits the annual injection of CO2into geological
formations to 0.5% of the total potential in each region.
MAgPIE 3.0 is a partial equilibrium model of the land-use sector that finds market
equilibrium by minimizing the global costs of production given price irresponsive demands
for agricultural products (Humpenöder et al. 2018;Poppetal.2014a). The model assumes
adaptative expectations for evaluation of long-term investment decisions implemented by
recursive dynamic model structure. The model is driven by demands for agricultural com-
modities, which are calculated based on population and income projections for the twenty-first
century. For meeting the demand, the model endogenously decides, based on cost-effective-
ness, about the level of intensification (yield-increasing technological change), extensification
(land-use change), and production relocation (intraregionally and interregionally through
international trade). CO2emissions from land-use change are calculated based on differences
in carbon stocks for different land types. The calculation of N2OandCH
4emissions from
agricultural production is based on IPCC 2006 emission guideline factors. The optimization
process is subject to various spatially explicit biophysical conditions such as yields, water
availability, and carbon stocks, which are derived by the global crop growth, vegetation, and
hydrology model LPJmL. Due to computational constraints, spatially explicit input (0.5-
degree resolution) is aggregated to 700 simulation units for the optimization process based
on a k-means clustering algorithm.
MAgPIE simulates two types of biomass feedstock production: 1st- and 2nd-generation
biomass feedstocks. First-generation biomass relies on conventional food crops such as maize
and sugarcane. Supplies and demands of 1st-generation feedstocks are prescribed by exoge-
nous policies. The largest potential is offered by dedicated herbaceous and woody lignocel-
lulosic bioenergy crops (such as miscanthus, poplar, and eucalyptus), which feature
significantly higher energy-specific yields per hectare than 1st-generation crops. Lignocellu-
losic feedstocks can be converted into electricity, hydrogen, and liquid fuels also in combina-
tion with CCS. Conversion into heat, solid energy carriers, and synthetic gases are available,
but cannot be combined with CCS. Residues are scaled with production volumes in forestry
and agriculture reaching a long-term maximum global potential of 70 EJ/year.
2.2 Coupling approach
The integrated assessment of climate change mitigation policies regarding the interdepen-
dencies between the energy and the land-use sector requires the explicit representation and
consistent solution of both, quantities of bioenergy and GHG emissions and their associated
prices. These variables are key features in transformation pathways of energy and land-use
systems. Since the REMIND and the MAgPIE model are each numerically heavy a full
integration of both models using a hard-link is not achievable. Therefore, a soft-link approach
is implemented to derive consistent scenarios with REMIND and MAgPIE (Bauer et al. 2008;
Messner and Schrattenholzer 2000). Basically, the soft-link approach feeds REMIND results
iteratively through MAgPIE to deliver information for updating assumptions in REMIND;
moreover, both models use harmonized assumptions on narratives and quantitative drivers
consistent with SSP2 (see Fig. S1). The iteration is repeated until changes between iterations
become negligible. The resulting scenarios of REMIND and MAgPIE are consistent regarding
price and quantity of bioenergy and GHG emissions. Variations in climate policies or drivers
shift these prices and quantities and the underlying energy and land-use scenarios.
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For a convergent solution of the soft-link approach, the interdependencies need to fulfill
certain conditions:
1. Higher emissions prices decrease GHG emissions from the energy and the land-use
sectors;
2. Higher emissions prices decrease fossil fuel use and increase bioenergy demand (this is
not necessarily the case for all IAMs under all circumstances; see Bauer et al. 2018a,b);
3. Higher emissions prices increase biomass supply costs;
4. Higher bioenergy prices decrease bioenergy demand; and
5. Higher biomass supply increases marginal costs of biomass supply and land-use GHG
emissions.
The interdependencies suggest a unique solution that can be approximated using an iterative
soft-link approach (see Fig. S1). The technical implementation of the iteration process involves
(i) a reduced form model that emulates MAgPIE within REMIND and (ii) additional con-
straints to the solution space to improve the convergence speed and stability of the iterative
approach towards the solution.
The REMIND model - represented by the mapping R(·) - derives in each iteration iscenar-
ios for bioenergy use BE and emission prices pEfor 11 regions and 17 time periods; the regions
and period indices are ignored to ease readability. The reduced form model consists of the
supply function Si(·) mapping bioenergy prices pBE to quantities BEiand land-use sector
emissions Eiof CO2,CH
4,andN
2O as exogenous constraints. Given this information in each
iteration, REMIND derives a set of emission prices pEand bioenergy use BE:
pE
i;BEi
¼RS
iðÞ;Ei
ðÞ:
The output of each REMIND run is fed into the MAgPIE model denoted as the mapping M(·)
to derive updated information on bioenergy prices and GHG emissions:
pBE
iþ1;Eiþ1
¼Mp
E
i;BEi
:
In the initial iteration, REMIND starts with an initial S0(·) that is derived from a large set of
MAgPIE runs, in which the time paths for biomass feedstock production are varied. The
function’s parameters are updated in each iteration to correct for differences in pE
iþ1and BEi
between S0(·) and actual MAgPIE runs. This corrects for approximation errors of S0(·). The
main reason for updating Si(·) is that its shape in each period depends on the time path of
biomass deployment and the carbon emission price pEthat is endogenously derived in
REMIND. For enhanced consistency between REMIND and MAgPIE scenarios, approxima-
tion errors S0(·) are reduced and made consistent with the MAgPIE model.
The updated Ei+1 is used directly as input for REMIND. The updated pBE
iþ1is used to re-
calibrate Si(·) by shifting the supply curve. The updated supply curve replicates the pairs of BEi
and pBE
iþ1. Carbon price changes affect biomass feedstock production costs and, therefore, it is
necessary to adjust the supply curves to derive consistent sets of price and quantity in the
coupled REMIND-MAgPIE model.
The iterative information exchange between both models defines a sequence that
converges towards a fixed point p*
E;E*;p*
BE;BE*
that is unique for all time steps and
regions because of qualitative dependencies 1.-5. mentioned above. The fixed point
Climatic Change
characterizes the biomass market equilibrium and equality between marginal emission
reduction costs and the emission prices in the energy and the land-use sector. The
iterative approach is robust and converges for a broad range of socioeconomic assump-
tions and emission reduction policies. The Supporting Online Material provides detail on
the numerical performance of the iteration process.
Simple implementations of fixed point iterations, such as a cobweb algorithm, are
subject to numerical heaviness and computing time. The main reason is the back and
forth of relatively similar solutions that converge only after many iteration steps. Such
zig-zagging needs to be treated, if computationally heavy models like REMIND and
MAgPIE are coupled. Reducing the number of iteration steps is crucial to reduce
computation time. Moreover, zig-zag behavior means that the distances between inter-
mediate solutions in the solution space are unnecessarily large and in each iteration step,
the optimization algorithm needs to move a large distance to find the solution for the
optimum in that iteration. Large updates between iterations also increase the risk that the
convergence process becomes instable and is terminated due to infeasibility problems in
one of the models. Therefore, it is useful to use additional information that enhances the
convergence speed and stability of the iteration but does not change the fixed point’s
location.
Three features are implemented into the present soft-link approach to stabilize and accel-
erate the convergence process by constraining the solution space. First, the biomass feedstock
supply function implemented in REMIND is derived from the MAgPIE model. In the existing
literature on soft-link approaches, general functions such as quadratic supply functions are
implemented and updated (Bauer et al. 2008; Messner and Schrattenholzer 2000). Such ad hoc
assumptions work because the location of the fixed point does not depend on the shape of the
supply function around the fixed point, but convergence is slow because it delivers poor
updates of BE. It is significantly improved by estimating a supply function derived from the
MAgPIE model (Klein et al. 2014a). The investment of computation time to derive such
function pays off, because it is sufficiently robust to parameter changes such as those studied
here.
Second, for the initial REMIND run, an appropriate set of biomass supply functions and
emission trajectories are selected. Good initialization improves the iteration process with
respect to speed and robustness. This is particularly useful because the shape of the biomass
supply functions varies with the carbon price levels, which in turn vary largely across
stabilization scenarios. Therefore, for the initial REMIND run, an appropriate set of supply
functions and emission trajectories is chosen.
Finally, a penalty term is added to the supply function used in REMIND. The penalty is
quadratic in the difference BEi
−BEi−1,whereBEi−1is fixed given the previous iteration.
Such penalty dampens the tendency of zig-zagging in the convergence process. The penalty
term does not affect the location of the fixed point because the differences get smaller and,
thus, the penalty vanishes as the fixed point is approximated.
1
1
It is worth noting that such improvements of convergence processes are not only numerical and technical.
Stiglitz (1994, p.11) highlighted that algorithms based on pure market analogous, such as Cob-Web iterations, are
inefficient procedures. Based on our experience, the efficiency of iteration processes can be strongly accelerated
by initialization and improving update mechanisms. Each update should anticipate reactions of the next iteration
to avoid time-consuming zig-zag behavior. The iterative process is very efficient and converges after about five
iterations with only small changes for additional iterations.
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2.3 The carbon budget framework and sensitivity analysis
The EMF33 study applies a carbon budget framework considering three carbon budgets that
cover the time horizon 2011–2100 that are indicative for long-term targets of the Paris
agreement (Allen 2018;Rogeljetal.2015). This study focuses on the set of scenarios that
complies with the 1000GtCO2budget for energy and industry (land-use change emissions
from MAgPIE add another 160GtCO2). This scenario is indicative for well-below 2 °C of
global warming in 2100 with 66% likelihood in the IPCC’s 5th Assessment Report. The most
recent IPCC assessment estimates that the corresponding budget at 1300GtCO2, and hence, the
present study applies a more stringent budget. A very low carbon budget of 400GtCO2and a
high budget of 1600GtCO2also investigate the response of bioenergy markets. The long-term
carbon pricing policy is subject to short-term policies that freeze the scenarios in 2020 given
the expected policy impact of NDCs (see Kriegler et al. 2018).
The carbon budgets are implemented by carbon pricing policies implementing a uniform
carbon price across regions (emission taxes or cap-and-trade systems). The carbon price
trajectory grows endogenously until 2100 to keep cumulative emissions at the limit defined
by the budget. The intertemporal carbon budget framework allows to freely allocate emissions
over time, including the possibility to offset a temporary budget overshoot by CDR using
BECCS. In the EMF33 set-up, the carbon price penalizes all GHG from all sectors and regions
based on 100 year Global Warming Potentials, whereas the carbon budget only accounts for
the CO2emissions from the energy and industry sector. The consistent pricing of land-use
GHG emissions drives the production costs of biomass feedstocks depending on direct and
indirect land-use change emissions due to expanding biomass feedstock production (Klein
et al. 2014a).
The carbon budget framework sets the main policy context for the sensitivity analysis
testing the context variables. Such variations require adjustments of carbon prices to comply
with the carbon budget. The carbon price adjustments feed back into the energy and the land-
use sector and, thus, the demand and supply for bioenergy and ultimately the shape of the CO2
emission pathway change.
Table 1provides an overview of the overall set of variations of context variables considered
in this study. It is based on the harmonized EMF33 scenario protocols, but also goes beyond
that to provide a broader analysis with the REMIND-MAgPIE model. Within the energy
conversion sector, five variations of the harmonized EMF33 sensitivities are to test the
sensitivity of techno-economic uncertainty of the costs, performance, and maturity of ABTs.
This includes the exclusion of (i) all BECCS technologies and (ii) bioliquids production from
ligno-cellulosic feedstocks as well as the (iii) delayed availability of all ABTs and (iv) the
doubling of their investment costs. The exclusion (ii) is important to consider because of the
high competitiveness between alternative bioenergy uses for a limited biomass feedstock
supply in climate change mitigation scenarios (see Bauer et al. 2018a,b). Moreover, to test
the sensitivity of the annual availability of carbon removal using BECCS, we reduce the CO2
injection rate into geological storage sites from 0.5 to 0.25% per year of the total available
storage capacity. The full exclusion of BECCS technologies is the most significant variation,
whereas the other scenarios represent more gradual changes that address specific links along
the value chain of bioenergy and CCS deployment.
In the land-use sector, we impose a hard bound that limits modern bioenergy use to 100 EJ/
year globally. This scenario is also part of the harmonized EMF33 and the EMF27 study protocol
to emulate land-use sector constraints related to broader sustainability targets and food security.
Climatic Change
Table 1 Overview on variations of context variables in the sensitivity analysis. The cases “No BECCS”and “Max. 100 EJ/year modern bioenergy”are mentioned as radical
sensitivities
Short Description Comment
Default Full Default assumptions on socioeconomic drivers (SSP2)
and ABT availability
Kriegler et al. (2017)
Bioenergy conversion
and CCS
No BECCS BECCS technologies are fully excluded. EMF33 standard
No ABT fuels Bio-liquid production from ligno-cellulosic biomass feedstocks is
excluded to reflect limitations on large scale bioenergy
technology facilities
EMF33 standard
Delayed Readiness ABT technologies are only available from 2050 onwards reflecting
delayed technological maturity
EMF33 standard
High investment cost ABT investment costs and Operation & Maintenance costs are
doubled to reflect pessimistic economics of ABTs
EMF33 standard
Low injectivity Maximum annual CO2injection rate is reduced from 0.5% to 0.25%
of the remaining geological potential. Reflects barriers in geological
storage. Default value based on unpublished expert opinion.
Not tested before
Land-use sector Max 100 EJ/year
modern bioenergy
Maximum annual potential of modern bioenergy use is limited to
100 EJ/year; includes residues, if used in modern modes. In default
case, bioenergy use is subject to land-use constraints rather than
a direct upper limit.
EMF33 standard, also
Kriegler et al. (2014)
High TC cost Land-use intensification more challenging due to higher cost to
improve yield rates reflecting pessimistic assumptions on technological
change.
Humpenöder et al. (2018)
studied higher yields.
Ag-trade fragmented Trade for agricultural products is fragmented by trade barriers to consider
increased food security concerns and national sovereignty (compare SSP3)
Schmitz et al. (2012)
studied trade liberalization
and GHG emissions. Popp
et al. (2017) for SSPs.
NoGrass Grassy biomass feedstocks are excluded (e.g., miscanthus). Reflects
technological issues to use low-cost, high-yield biomass feedstock.
Similar to Klein et al. (2011)
Socioeconomic
drivers
and climate policy
SSP1 energy demand Energy demand as in SSP1. Particularly, lower demand for transportation
fuels; electricity in some developing regions even higher. Significant
effect on baseline emissions.
Kriegler et al. (2017)introduced
SSP scenarios
Climatic Change
Table 1 (continued)
Short Description Comment
SSP1 fossil fuel
(endowment)
Fossil fuel endowments are more pessimistic and correspond to SSP1.
Significant effect on baseline emissions and bioenergy use.
Bauer et al. (2016) presents fossil
fuel assumptions
SSP1 food demand Lower food demand reflecting dietary changes towards more
sustainable development in accordance with SSP1.
Popp et al. (2010) studies GHG emissions
depending on food demand
No LU emission pricing Carbon pricing is not applied to GHG emissions from the land-use
sector. Hence, lower bioenergy production costs.
Popp et al. (2014a), Wise et al. (2009)
study GHG emissions and land-use poli-
cies
(Flat) C-price path Carbon price increases with 5%/year until 2060 and linear
afterwards. Hence, price starts higher in 2025, but ends
lower in 2100 to achieve the prescribed budget.
Climatic Change
The constraint on modern biomass use includes biomass residues, if they are used in modern
modes such as biogas conversion. In addition, we study more gradual variations of specific parts
of the land-use sector. First, we increase the costs to achieve yield rate improvements for
endogenous land-use intensification, which reflects more pessimistic expectations for technical
progress in the land-use sector. Second, trade in agricultural products is globally fragmented to
reflect food security concerns. This reduces the flexibility to relocate land-use activities. Third, we
assume that of ligno-cellulosic biomass only woody feedstocks are available and grassy feed-
stocks are excluded, which reflects qualitative limitations of feedstock availability due to the
difficulty to use grassy feedstocks in energy conversion technologies.
In the broader context of socioeconomic drivers and policy implementation, we perform
five variations. First, we assume a more sustainable economic development pattern that
reduces energy demand growth consistent with SSP1. Second, we lower fossil fuel availability
by assuming slower technological progress; more restrictive regulations are also consistent
with SSP1. Third, we lower assumptions for food demand growth reflecting a more sustainable
diet and reduced meat consumption. Fourth, we assume that carbon pricing policies are not
applied to land-use sector emissions, which mimics the sectoral exemption from climate
policies. Fifth, we re-shape the carbon price trajectory assuming a 5% annual growth rate
until 2060 and linear growth thereafter. This assumption leads, for a given carbon budget, to a
higher CO2price in early periods and lower prices in the long run as compared with the default
setting. This mimics more ambitious near- to mid-term climate policy, whereas the long-term
target is kept constant.
3Results
3.1 Scenarios with default assumptions
Figure 1shows global energy and industry CO2emissions and total bioenergy use. For the
baseline scenario, REMIND-MAgPIE projects relatively high CO2emissions up until 2060
before they peak and decline due to increasing fossil fuel scarcity. In the climate change
stabilization scenario, net CO2emissions over the next decades are also at the upper end of
EMF-33 models, but in 2030, it is still lower than the most optimistic NDC estimates (Fawcett
et al. 2015;Iyeretal.2015; Luderer et al. 2016). Around 2050 the net emissions decrease
quickly and turn strongly net negative towards the end of the century. The CO2price necessary
to induce the emission reductions starts at 38USD per ton CO2in 2025 increasing at 4.5–5.5%
per year (see Fig. S2). The carbon price features a relatively low starting level but grows
quickly compared with other EMF-33 models.
In the absence of climate policies, REMIND-MAgPIE projects growing fossil fuel use that
drives CO2emissions in the baseline scenario. Around the middle of the twenty-first century,
traditional forms of biomass phase-out exogenously in the energy system of REMIND.
Towards the end of the century, most biomass serves to substitute oil. If fossil fuels are
assumed less abundant, bioenergy use increases, while slower energy demand growth reduces
its use. In the scenario with carbon budget, the CO2price depresses fossil fuel use to
significantly decrease emissions. This drives up bioenergy use particularly starting post
2040, when also emissions start to decline quickly. The net negative emissions post 2060
require BECCS deployment. For the very low budget, the bioenergy growth is moved 10 years
earlier, but the long-term saturation level is similar. Compared with other studies, the
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REMIND-MAgPIE bioenergy scenarios are in the range of policy pathways in 2030 reviewed
by IRENA (2014). Long-term bio-energy use is at the upper end of the EMF33 range. For
stricter carbon budgets, reductions in energy and industry CO2emissions are associated with
higher bioenergy use. The long-term saturation level of bioenergy use is similar for the
different carbon budgets, because the bioenergy price increases substantially, additional
bioenergy cannot be combined with CCS as the injection constraint becomes binding in most
regions and, finally, electrification is the more attractive option (e.g., light-duty vehicles),
which relies on other primary energy sources such as renewables.
In the baseline case, the global weighted average of regional bioenergy prices stagnates around
5USD/GJ throughout the century as regional prices vary between 3.5 and 7 USD/GJ (see Fig. S3).
This compares with actual prices for wood chips with 20% moisture at 5–6USD/GJintoday’s
markets. In the low budget case, the average biomass price increases to 10 USD/GJ by 2050. Hence,
N
N
D
C
e
s
s
t
i
m
a
t
e
s
s
I
E
A
(
2
2
0
1
7
)
SSP range 2100
Base
2.6W/m²
0
50
100
1980 2000 2020 2040 2060 2080 2100
CO
2
emissions [Gt CO
2
/yr]
SSP1
SSP
Scenarios
SSP2
SSP3
SSP4
SSP5
EMF33
Scenarios
Baseline
Low Budget
(thin lines)
Low Budget
Very Low Budget
High Budget
REMIND-
MAgPIE
Baseline
I
R
E
N
A
A
(
2
0
1
4
)
I
E
A
(
2
0
1
7
)
SSP range 2100
Base
2.6W/m²
100
200
300
400
1980 2000 2020 2040 2060 2080 2100
Bioenergy use [EJ/yr]
a
b
Fig. 1 Baseline and default scenario. Global pathways for CO2emissions (left hand side) and total bioenergy use
(right hand side) for the baseline and the low budget scenario
Climatic Change
the global primary bioenergy market volume grows to ~ 1500 bil. USD in 2050 or 1.1% of global
GDP. This figure changes to 0.6% and 2.6% in case of the high and the very low budget,
respectively. The market value of oil decreases instead. Without climate policy, the value is 2.5%
of global GDP in 2050. For the high budget case, this value does not change, but for the low budget
case, it decreases to 2% and even 1% in the very low budget case.
The biomass feedstock prices are partly driven by the carbon content of the feedstock that
becomes valuable as carbon prices grow. In this context, it is worth to note that a price of 100
USD per ton CO2implies that the embodied carbon of primary biomass feedstock is equivalent
to nearly 9 USD/GJ. Assume a capture rate of ~ 50% for the biomass-to-liquids technology
equipped with CCS. Hence, for a carbon price of 164 USD/tCO2reached in 2050 in the low
budget scenario ~ 7 USD/GJ of the total biomass feedstock price is covered by the remuner-
ation for carbon removal. Hence, climate policies appreciate biomass feedstocks for the
embodied carbon, if carbon removal is feasible.
3.2 Sensitivity analysis
Figures 2and 3summarize sensitivity analysis results for the low budget case. The impacts on
cumulative gross energy emissions and annual bioenergy use are shown in Fig. 2, whereas
Fig. 3shows changes of carbon and bioenergy prices. The scenario with default assumptions is
highlighted as a black marker serving as the reference for comparisons. Additional information
is provided in the Supporting Material.
Energy conversion Land−use Policy & Drivers
2050 2100
1250 1500 1750 1250 1500 1750 1250 1500 1750
100
150
200
100
200
300
400
Cumulative gross energy CO2 emissions [Gton CO2]
Bioenergy [EJ/yr]
Scenarios
No BECCS Max. 100EJ/yr C−price path
Low injectivity No grassy feedstocks SSP1 energy demand
No ABT fuel High TC cost SSP1 fossil fuel
Delayed readiness Ag−trade fragmented SSP1 food demand
High investment cost No LU emission pricing
Fig. 2 Sensitivity cumulative gross CO2emissions and bioenergy use for the low carbon budget. The large black
dots represent the default case that serves as reference case. The emissions exceeding the low carbon budget of
1000 GtCO2in 2100 equal the amount of CDR. The same does not hold true for the 2050 due to the
intertemporal flexibility. Bioenergy use is total global bioenergy
Climatic Change
Energy conversion sector The parameter variations considered in the energy conversion
sector tend to weaken ABTs and thus their deployment. Generally, this results in lower gross
CO2emissions by 2050 and higher carbon prices. Reactions on bioenergy markets are
ambiguous, but generally, the effects are larger in 2050 than in 2100, whereas cumulative
gross CO2emissions react more strongly in the long-term. The largest impact is realized by
excluding BECCS technologies, in which by 2050 more bioenergy is used at higher prices,
because fossil fuel use needs to decrease to a larger extend and bioenergy serves as a substitute.
A similar, though more gradual, sensitivity test is provided by the reduced CO2injectivity
case. The qualitative effect remains the same but it is quantitatively muted: bioenergy use and
prices in 2050 only increase by + 43% and + 10%, respectively. By 2100, bioenergy use is
lower in both sensitivity cases, but prices are still higher than in the default case because higher
carbon prices are necessary to limit CO2emissions in the energy sector, which feed back into
the land-use sector increasing biomass feedstock supply costs (see, e.g., Klein et al. 2014a).
The full exclusion of BECCS or its gradual limitation by reducing the CO2injectivity
reduces the amount of CDR. Halving the injectivity rate from 0.5 to 0.25%/year increases the
value of removed carbon in 2050 because the carbon price increases more strongly than the
CDR amount decreases. This indicates that the energy sector becomes economically more
dependent on carbon embodied in bioenergy, since the expenditures for permanent carbon
removal increases although the quantity decreases. Moreover, the accelerated use and increas-
ing price of bioenergy in the midterm is due to the immediate need to substitute more fossil
fuels. If the capability to remove and store the carbon embodied in bioenergy is reduced, a
higher carbon price is needed to comply with the carbon budget. The higher carbon price feeds
back into the energy system. It signals further reduction of fossil fuel use and, thus, bioenergy
Energy conversion Land−use Policy & Drivers
2050 2100
100 300 1000 3000 100 300 1000 3000 100 300 1000 3000
10
20
30
30
50
100
Carbon price [USD per ton CO2, log−scale]
Bioenergy price [USD per GJ, log−scale]
Scenarios
No BECCS Max. 100EJ/yr C−price path
Low injectivity No grassy feedstocks SSP1 energy demand
No ABT fuel High TC cost SSP1 fossil fuel
Delayed readiness Ag−trade fragmented SSP1 food demand
High investment cost No LU emission pricing
Fig. 3 Sensitivity bioenergy and carbon prices for the low carbon budget. The large black dots represent the
default case that serve as reference cases. Note the log-scale for both axes
Climatic Change
use increases to substitute fossil fuels. This policy feedback effect on bioenergy use is
observed in many EMF33 models for the case of full BECCS exclusion (Bauer et al.
2018b). The effect is muted if the CO2injectivity is reduced, but qualitatively the same.
The other three energy technology variations lead to diverse sensitivity patterns. By 2050,
the diminished technology performance leads to smaller cumulative emissions at higher carbon
prices and lower bioenergy use and prices. In particular, the delayed readiness of ABTs shows
the most notable impact on bioenergy markets (−47% price and −35% quantity) followed by
the variation of investment costs (−29% and −12%, respectively). In 2100, the sensitivity
patterns differ. Only the case with higher investment costs still shows more relaxed bioenergy
markets, but the impact is relatively small because fuel costs rather than capital costs are the
dominant cost driver in future bioenergy markets (Daioglou et al. 2020). In case of delayed
technology readiness, bioenergy use and prices exceed the default case in 2100 to partially
make up the delay implying a stronger sector interaction. Different to that, the exclusion of
ABTs for liquid fuel production relaxes bioenergy markets, but at the same time, cumulative
emissions and carbon prices are higher (+ 5% and + 15%, respectively), which is an exception
to the general result of decreasing gross emissions. This is due to within sector flexibility to
restructure the energy sector. If bioenergy cannot be used for liquid-fuel production anymore,
it is reallocated to electricity and hydrogen production with CCS, which both feature relatively
high carbon capture rates. The additional CDR is used to offset CO2emissions from increased
fossil fuel use to overcome the shortfall in liquid fuel production. Under default assumptions,
liquid biofuel production with CCS is the most competitive option to utilize the energy and the
carbon value of biomass feedstocks. It is not allocated to other conversion routes because
liquid fuel production increases the bioenergy price. At these high bioenergy prices, other
alternatives (e.g., renewables in the power sector) are the relatively more competitive
alternative.
The results highlight that worsening assumptions of costs, maturity, and performance of
bioenergy technologies and CO2injectivity interfere with the complex and dynamic interac-
tions between the energy and the land-use sector. It is crucial how the variations specifically
affect the energy and carbon flows and how the valuation changes. Particularly, worsening the
carbon capture capability does not imply that less bioenergy is used and, thus, the intersectorial
relations do not necessarily become weaker; for instance, in 2050, more bioenergy is used in
cases with low injectivity or full exclusion of BECCS. Also, the sector coupling can decrease
in terms of physical bioenergy use, but the overall willingness to pay for the embodied carbon
by the energy sector could be boosted, which reinforces the intersectorial coupling in eco-
nomic terms. Therefore, general statements on how assumptions about bioenergy technologies
affect the sectorial interrelationship via emissions and bioenergy markets are not possible. The
effects depend on the specific changes of assumptions as well as the complex and dynamically
changing market context.
Land use sector The variations that we tested constrain biomass production systems. These
variations lead to similar sensitivity patterns that differ in quantitative extent. Directly limiting
biomass feedstock supply for modern use to 100 EJ/year leads to the strongest impact. The
exclusion of grassy biomass feedstocks also impacts bioenergy markets (in 2050 + 70% price
and −50% quantity), which is due to lower energy yields per hectare and higher costs of
woody biomass production. In both sensitivity cases, gross cumulative emissions are reduced,
because less carbon is embodied in the bioenergy and thus less CDR is realized. Therefore,
higher carbon prices are required to reduce cumulative gross CO2emissions from energy and
Climatic Change
industry. Moreover, due to price irresponsive bioenergy demand the total revenue for
bioenergy grows, because the relative quantitative reduction of bioenergy delivered to the
energy sector is overcompensated by the relative increase of bioenergy prices. Hence, stronger
economic coupling between the sectors is the result of weaker quantitative sector interaction. If
land-use sector limitations reduce bioenergy use, this also leads to structural effects of
bioenergy use. The reduced quantity of bioenergy is reallocated from liquid fuel production
to hydrogen and electricity generation to better exhaust the value of carbon embodied in
bioenergy by choosing technologies with higher carbon capture rates.
Assuming higher costs for yield improvements hardly affects carbon and bioenergy
markets, whereas within the land-use sector total cropland is extensified (additional 125 Mio
ha in 2050 and 330 Mio ha in 2100 compared with 2000 and 2300 million hectare,
respectively, in the mitigation scenario with default parameterization). Consequently, net
cumulative land-use CO2emissions increase (60GtCO2by 2100). Similarly, stronger restric-
tions on agricultural goods trade trigger adjustments of regional production patterns within the
land-use sector that buffer effects on global bioenergy use. These two sensitivity tests lead
mostly to adjustments within the land-use sector, whereas the effects on the energy sector are
negligible.
Socioeconomic drivers and climate policy Varying socioeconomic drivers towards more
sustainable pathways also show different qualitative and quantitative reactions. Less
optimistic assumptions on fossil fuel availability reduce carbon prices (22%), but
bioenergy use is hardly affected because final energy demand remains unchanged.
Conversely, slower growth of final energy demand reduces bioenergy use and prices
substantially (−27% and −13% in 2050, respectively). The relaxed sector coupling
results from a combination of lower demand for both, bioenergy and carbon offsets.
Assumptions on dietary changes are of little importance for bioenergy and energy
CO2emissions, but the pressure within the land-use sector is substantially reduced: in
2050 total cropland decreases by 250 Mio ha while cropland for bioenergy plantations
increases by 65 Mio ha. The lower pressure on land systems decreases N2O emissions
by 19% and cumulative CO2emissions from land-use change by 2100 are 50GtCO2
lower.
The shape of the carbon price trajectory is an important factor governing the timing of CO2
emissions and removals, which in turn influences bioenergy use. Higher carbon prices in 2050
increase bioenergy use and prices (+ 11% and + 8%, respectively), whereas lower carbon
prices in 2100 relax the market situation (−17% and −25%, respectively). Hence, achieving
the long-term target with higher near-term ambition also leads to a stronger sector interaction
by mid-century and weaker sector coupling in the long-term. The exemption of land-use
change emissions from the carbon pricing regime leads to mildly higher bioenergy use at much
lowerprices(+5%and−25% in 2100, respectively). However, this exemption leads to serious
impacts on the land-use sector. By mid-century, land-use is intensified by increased fertiliza-
tion, which increases N2O emissions by 32%. Towards the end of the century, land-use is
extensified by 100 Mio ha cropland leading to additional 90GtCO2emissions.
The impact of choosing a flatter carbon tax trajectory on near-term emissions and bioenergy
markets is consistent with the finding that tighter climate policies increase bioenergy use,
because it implies lower emissions and, thus, stronger need to substitute fossil fuels with low
carbon fuels such as bioenergy. Moreover, since the carbon budget is not changed, but
reallocated over time, more fossil fuels can be used towards the end of the century which
Climatic Change
consequently levels bioenergy use off. The exemption of land-use sector GHG emissions
increases the pressure on land by first intensification and later also extensification. Hence, it is
crucial to implement the climate policy in a comprehensive way covering both sectors and all
emissions. Note that in this study, these additional land-use emissions are not balanced by
emission reductions in the energy sector, which would reinforce the pressure on the land-use
sector (Wise et al. 2009).
The impacts of the sensitivities discussed here depend on the strength of the carbon budget.
If the emission limitations are stronger, the impacts of the changes in the assumptions increase.
The supplementary material provides detailed information and discussion.
4 Concluding discussion
The energy-land nexus is interesting for researchers and policy makers in the context of
climate change mitigation because both sectors will become more closely integrated in a
complex and dynamic interaction. The interaction involves substantial physical quantities and
economic values of bioenergy and emissions that are studied by the integrated energy and
land-use sector model REMIND-MAgPIE. Large scale deployment of modern bioenergy, incl.
BECCS, is projected to take off around mid-century, if global mean temperature increase shall
be limited to well-below 2 °C above pre-industrial levels. For the stronger 1.5 °C target,
bioenergy use would start to increase earlier, whereas the scale of long-term deployment varies
little. Biomass feedstocks produced in the land-use sector are valued in the energy sector for
the potential to substitute fossil fuels and to remove the carbon embodied permanently from
the atmosphere by deploying BECCS. Both features make biomass feedstocks increasingly
valuable over time in climate change mitigation scenarios and lead to a stronger interlinkage of
the energy and the land-use sector. At the same time, the increasing demand for biomass
feedstocks and the pricing of land-use GHG emissions increase the pressure on land systems.
These crucial interaction channels are included in the soft-linked energy and land-use sector
model REMIND-MAgPIE, which both also rely on the same socioeconomic drivers and
derived the associated assumptions from the same narrative of a middle-of-the-road pathway
(SSP2, see Bauer et al. 2017;Poppetal.2017). The soft-link applied for the REMIND-
MAgPIE coupling covers the most essential interactions. It is known that soft-links are not
fully considering all interactions and are subject to approximation errors (Bauer et al. 2008).
However, for the energy-land-nexus, the bioenergy and the emission links are crucial and need
analysis in integrated frameworks with spatial and technological detail.
The earlier EMF27 study (Kriegler et al. 2014;Roseetal.2013) identified the
potential value of biomass feedstocks and its system-wide role in the coupled energy-
land system and the Fifth Assessment Report of the IPCC highlighted these results. The
EMF27 study tested the sensitivity of large scale bioenergy deployment strategies. The
full exclusion of CCS technologies (incl. BECCS) has been identified as the single most
important factor for the achievability and mitigation costs of the well-below 2 °C target.
Moreover, gradually limiting modern bioenergy use to 100 EJ/year ranked second for the
mitigation costs. The potential and techno-economic performance of wind and solar
energy technologies or the gradually faster improvement of economy-wide energy
intensity lead to smaller impacts on mitigation costs.
The EMF33 scenario protocol focuses on large scale bioenergy deployment and climate
change mitigation and studies radical sensitivity cases; these include the full exclusion of
Climatic Change
BECCS technologies as well as the hard limit on modern bioenergy use. On top of that,
EMF33 also includes more gradual sensitivity tests for the availability, maturity, and perfor-
mance of ABTs. The latter leads to smaller sensitivities than the full exclusion of BECCS
technologies (see also Bauer et al. 2018b). We find that the changes in prices and quantities of
bioenergy and emissions are heterogeneous depending on which characteristic of bioenergy
technologies is specifically changed. It is crucial whether the energy or the carbon-related
aspect of bioenergy technologies is changed. This means that the gradual sensitivities are not
only muted versions of the radical sensitivity of excluding BECCS technologies altogether, but
they lead to qualitatively different results. Techno-economic variations to explore the carbon
value of bioenergy increase carbon prices but temporarily increase bioenergy use, whereas
limitations on producing high-value liquid fuels reduce bioenergy use while carbon prices also
increase. Therefore, sensitivity tests of bioenergy technologies require careful design and
analysis of energy and carbon-related aspects. It is worth noting that the injection rate turned
out to be a crucial parameter. The default value applied here is based on expert opinion. The
sensitivity of this parameter suggests that improved knowledge could strongly reduce
uncertainties.
In this study, we also expanded the sensitivity analysis beyond the hard limit of 100 EJ/year
on modern bioenergy use to more gradual sensitivity tests of key assumptions such as
agricultural trade and yield improvements. Different to the sensitivity tests regarding bioenergy
technologies the sensitivity patterns for prices and quantities of bioenergy and emissions are
qualitatively the same and only differ in magnitude. The more gradual constraints on biomass
production systems lead to much smaller changes in the intersectorial interactions (particularly
bioenergy markets) than the hard bound on modern bioenergy use. The more gradual
sensitivities lead to intrasectorial adjustments that buffer the intersectorial effects. The
intrasectorial effects on intensification and extensification of land-use are, however, substan-
tial. Similar results are expected for variations of other key assumptions in the land-use sector
that indicated only small changes in bioenergy prices (see Humpenöder et al. 2018, for more
information).
The differentiated analysis presented here puts sensitivity tests into perspective. For instance, the
sensitivity with respect to energy demand growth appears relatively small when compared with the
radical variations regarding bioenergy use and BECCS availability. However, moving the focus
towards the more gradual sensitivities narrows the difference and can even reverse the ranking. The
27% lower bioenergy use in 2050 due to lower final energy demand is more substantial when
compared with gradual rather than radical sensitivities of bioenergy technologies and supply,
especially in terms of bioenergy and carbon price changes. Hence, the radical sensitivities are
without doubt interesting for diagnostic purposes and these can be implemented relatively easily
into different IAMs for the purpose of harmonized model comparisons. However, for a fair and
balanced assessment of climate change mitigation, the set of sensitivity tests has to be chosen
carefully to avoid biases in results. The present study systematically compares radical and gradual
sensitivity tests and highlights that the radical sensitivities induce substantial variations in results.
The very strong intersectorial impacts get much smaller for the gradual sensitivities. This is mostly
due to the intrasectorial adjustments that partly insulate sectors. These intrasectorial flexibilities,
however, can imply very strong effects on the transformation of energy use or land-use changes.
Nonetheless, the energy-land nexus becomes more intense for stronger climate change mitigation
targets and, thus, the intersectorial coupling becomes tighter via emissions and bioenergy markets.
Climatic Change
Supplementary Information The online version contains supplementary material available at https://doi.
org/10.1007/s10584-020-02895-z.
Funding Open Access funding enabled and organized by Projekt DEAL.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and
indicate if changes were made. The images or other third party material in this article are included in the article's
Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included
in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or
exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy
of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Affiliations
Nico Bauer
1
&David Klein
1
&Florian Humpenöder
1
&Elmar Kriegler
1
&
Gunnar Luderer
1,2
&Alexander Popp
1
&Jessica Strefler
1
*Nico Bauer
Nico.Bauer@pik-potsdam.de
1
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
2
Department of Global Energy Systems, Technische Universität Berlin, Straße des 17. Juni 135,
Berlin, 10623, Germany
Climatic Change
