D I S S E R T A T I O N

Bioenergy Markets in aClimate Constrained World

vorgelegt vonDiplom-Ingenieur

D A V I D K L E I N,

geboren in Munster (Westf.),zur Erlangung des akademischen Grades

Doktor der IngenieurwissenschaftenDr.-Ing.

Von der Fakultat VI – Planen Bauen Umwelt der

T E C H N I S C H E N U N I V E R S I T A T B E R L I N

genehmigte Dissertation.

Gutachter:

Prof. Dr. Ottmar EdenhoferProf. Dr. Keywan Riahi

Promotionsausschuss:

Prof. Dr. Volkmar Hartje (Vorsitz)Prof. Dr. Ottmar EdenhoferProf. Dr. Keywan Riahi

Tag der wissenschaftlichen Aussprache: 28.11.2014

Berlin 2014D83

Erst glanzte das Gold, dann floss es. Schließlich wuchs es.Durch die Jahrhunderte rann ein roter Faden.Ihn nicht zu sehen, obwohl dickflussig,war eine weitverbreitete Kunst.

Arthur Dobb

Contents

Summary 7

Zusammenfassung 9

1 Introduction 111.1 The need for ambitious mitigation efforts . . . . . . . . . . . . . . . . . . 11

1.2 Options to achieve low-stabilization . . . . . . . . . . . . . . . . . . . . . 11

1.3 Properties of bioenergy making it relevant for climate change mitigation . 14

1.3.1 Potential advantages of bioenergy . . . . . . . . . . . . . . . . . . 15

1.3.2 Potential drawbacks . . . . . . . . . . . . . . . . . . . . . . . . . 18

1.4 The special case of bioenergy – or – Why we need model coupling . . . . 20

1.5 The REMIND-MAgPIE modeling framework . . . . . . . . . . . . . . . . 22

1.6 Thesis objective and outline . . . . . . . . . . . . . . . . . . . . . . . . . 25

2 Can Bioenergy Assessments Deliver? 292.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.2 How to carry out assessments . . . . . . . . . . . . . . . . . . . . . . . . 32

2.3 State of bioenergy assessment . . . . . . . . . . . . . . . . . . . . . . . . 34

2.4 Evaluating the bioenergy assessment . . . . . . . . . . . . . . . . . . . . 38

2.5 Ways forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

2.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3 The economic potential of bioenergy for climate change mitigation withspecial attention given to implications for the land system 493.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.6 Supporting Online Information . . . . . . . . . . . . . . . . . . . . . . . 60

4 Bio-IGCC with CCS as a long-term mitigation option in a coupled energy-system and land-use model 794.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.2 Modeling energy-system and land-use interactions . . . . . . . . . . . . . 82

4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.4 Conclusion and outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

4.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

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6 Contents

5 The global economic long-term potential of modern biomass in a climateconstrained world 895.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 915.2 Present bioenergy potential studies . . . . . . . . . . . . . . . . . . . . . 925.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 945.5 Discussion and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 985.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005.7 Supporting Online Material . . . . . . . . . . . . . . . . . . . . . . . . . 102

6 The value of bioenergy in low stabilization scenarios: an assessment usingREMIND-MAgPIE 1176.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1206.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1216.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1246.4 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 1296.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1306.6 Supporting Online Material . . . . . . . . . . . . . . . . . . . . . . . . . 133

7 Synthesis and Outlook 1497.1 Synthesis of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1507.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1577.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Bibliography 162

Nomenclature 177

Statement of Contribution 179

Tools and Resources 181

Acknowledgements 183

Summary

Avoiding dangerous climate change requires substantial emission reductions in the en-ergy and the land-use sector. Within the portfolio of decarbonization options bioenergyassumes a unique role because it can reduce emissions in two special ways. First, due toits versatility it can provide low-carbon energy as a substitute for fossil fuels in all en-ergy sectors. Second, due to its carbon content bioenergy combined with carbon captureand storage (CCS) can provide negative emissions that allow compensating emissionsacross sectors and time, which is of special interest for achieving low-stabilization tar-gets. However, biomass cultivation requires fertile land giving rise to concerns aboutadverse effects, such as land-use change emissions, biodiversity loss, and competitionwith food production.Scrutinizing the bioenergy assessment carried out by The IPCC Special Report on

Renewable Energy Sources and Climate Change Mitigation (IPCC 2011b) it becameapparent that the available long-term scenarios assessing bioenergy as a mitigation op-tion only cover these adverse effects to a minor degree. This thesis aims at improvingthe bioenergy assessment by addressing potential consequences of and requirements forbioenergy deployment in the energy sector and the land-use sector. Using an integratedmodel framework of energy-economy-climate and land-use this thesis investigates therole bioenergy can play in achieving ambitious long-term climate change mitigation tar-gets taking into account emissions from agriculture and land-use change. It exploresthe global biomass potential and corresponding supply prices and investigates how pric-ing land-use and land-use change emissions affects the biomass potential and resultingemissions. It analyzes how mitigation strategies and costs depend on constraints on thesupply and demand side of bioenergy. It investigates how bioenergy contributes to thetransformation and decarbonization of the energy system in general and identifies theeconomic drivers behind the choice of bioenergy conversion technologies in particular.Results show that bioenergy with CCS (BECCS) is a crucial mitigation option with

paramount importance particularly for achieving stringent climate change mitigation tar-gets. If CCS is available, bioenergy is exclusively used with CCS in mitigation scenariosand it is predominantly used to produce liquid fuels for the transportation sector. Sincebiomass is mainly supplied by lignocellulosic perennial grassy feedstock this requiresrobust gasification technologies to be available that can cope with the heterogeneousgrassy feedstock. Modern bioenergy deployment increases rapidly after 2030 to around300 EJ/yr. Mitigation costs rise sharply if bioenergy or CCS is constrained. This indi-cates that without bioenergy or CCS, it is difficult to achieve low stabilization targets.Grassy biomass feedstock can be produced at prices above 5 $/GJ. Pricing emissions

in the land-use sector increases bioenergy supply prices (by 5$/GJ in 2055 and by 10$/GJ in 2095) due to land exclusion of high-productive forest land and due to nitrogen-emissions from bioenergy production. Carbon taxes on land-use change emissions arefound to effectively prevent deforestation and thereby significantly reduce total land-usechange emissions. However, land reduction due to GHG taxes is compensated by inten-sification and expansion into land that is not under emission control, the latter of which

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increases land-use change emissions from biomass production. This indicates that bioen-ergy demand and GHG taxes at the same time (as typical for low-stabilization scenarios)put substantial pressure on the land-use sector and could induce leakage of bioenergy orfood production into land with high carbon content that is not under emission control.Average yields that would be required for large-scale bioenergy production in 2095 areroughly between 500 and 600 GJ/ha for the major producer regions. Results furtherindicate that the competition for water between agriculture, private households, and in-dustry is likely to increase heavily in many regions, particularly if forests are protectedand bioenergy is used for climate change mitigation.In climate mitigation scenarios, the value of bioenergy is found to be determined by

both its energy value and the value of potential negative emissions. Results show thatthe availability of BECCS creates a strong link between carbon prices and bioenergyprices. This causes the carbon value of biomass to exceed its pure energy value inlow stabilization scenarios with BECCS availability. Rising carbon prices thus induceinvestments in technologies that would not be built for the purpose of energy production.Furthermore, through this price link stringent climate protection targets induce a highwillingness-to-pay for bioenergy that exceeds by far bioenergy supply prices identifiedpreviously.Bioenergy is so valuable because its negative emissions increase the amount of per-

missible carbon emissions from fossil fuels and therefore allow postponing emissions re-ductions in the short-term and the preservation of some residual emissions in the longrun. For a given climate target, bioenergy thus acts as a complement to fossils ratherthan a substitute. However, this makes the prolonged short-term deployment of fossilfuels dependent on the long-term potential of biomass and the availability of CCS. Thus,uncertainties about the long-term developments of (i) conditions in the land-use sector(land availability, yields etc.) and (ii) CCS technology are highly relevant for short-termdecisions about emission reduction.

Zusammenfassung

Um gefahrlichen Klimawandel zu vermeiden, werden starke Emissionsreduktionen imEnergie- und Landnutzungs-Sektor benotigt. Zwischen verschiedenen Dekarbonisierungs-optionen sticht Bioenergie hervor, da sie zwei besondere Wege der Emissionsreduktionbietet. Erstens stellt sie wegen ihrer vielseitigen Einsetzbarkeit eine emissionsarme Al-ternative zu fossilen Brennstoffen in allen Energiesektoren dar. Zweitens kann sie auf-grund ihres Kohlenstoffgehalts in Kombination mit Kohlenstoffabscheidung (CCS) neg-ative Emissionen erzeugen und somit Emissionen anderer Quellen uber Zeit und Orthinweg kompensieren. Diese Eigenschaft ist insbesondere fur das Erreichen von Niedrig-Stabilisierungszielen interessant. Allerdings benotigt die Produktion von Biomasse frucht-bares Land, was Anlass gibt zur Sorge uber nachteilige Nebeneffekte, wie z.B. zusatzlicheLandnutzungs-Emissionen, Biodiversitats-Verluste oder Nahrungsmittel-Konkurrenz.Anahnd der im IPCC Special Report on Renewable Energy Sources and Climate Change

Mitigation (IPCC 2011b) dargestellten Literatur wird deutlich, dass die verfugbarenLangfristszenarien zur Bewertung von Bioenergie als Klimaschutzoption diese Nebenef-fekte, insbesondere die Landnutzungs-Emissionen, nur in geringemMaße berucksichtigen.Die vorliegende Arbeit mochte die Bewertung von Biomasse insbesondere im Hinblick aufnegative Nebeneffekte verbessern, indem sie Erfordernisse und Konsequenzen der Bioen-ergienutzung im Energie- und Landnutzungs-Sektor untersucht.Im Rahmen dieser Dissertation wird analysiert, welche Rolle Bioenergie bei der Erful-

lung ambitionierter, langfristiger Klimaschutzziele spielen kann. Dazu wird ein Modellentwickelt, das sowohl Komponenten des Energie-, Wirtschafts- und Klimasystems, alsauch des Landnutzungs-Systems miteinander vereint, und dabei Landnutzungs-Emissio-nen berucksichtigt. Es werden das globale Biomasse-Potential und dessen Angebotspreiseuntersucht und wie diese durch die Bepreisung der Landnutzungs-Emissionen beeinflusstwerden. Ferner wird untersucht, wie Emissions-Minderungs-Strategien und deren Kostenvon Beschrankungen auf der Angebots- und Nachfrageseite der Bioenergie abhangen.Es wird der Frage nachgegangen, wie Bioenergie zur Transformation und Dekarbon-isierung des Energiesystems beitragt, und was die okonomischen Faktoren bei der Wahlvon Bioenergie-Konversions-Routen sind.Die Ergebnisse zeigen, dass Bioenergie mit CCS (BECCS) eine entscheidende Minde-

rungs-Option mit elementarer Bedeutung insbesondere fur das Erreichen von strengenKlimaschutzzielen darstellt. Wenn CCS verfugbar ist, wird Biomasse ausschließlich mitCCS verwendet, uberwiegend zur Produktion von Flussigbrennstoffen. Da Biomassehauptsachlich aus mehrjahrigen lignozellulose-haltigen Grassorten produziert wird, setztdies die Verfugbarkeit von robusten Vergasungstechnologien voraus, die den heterogenenBrennstoff Gras verarbeiten konnen. Der Einsatz moderner Biomasse beginnt nach 2030und steigt danach rasch auf etwa 300 EJ/yr an. Beschrankungen des Bioenergiepoten-tials oder der CCS-Verfugbarkeit lassen die Vermeidungskosten erheblich ansteigen, wasdarauf hindeutet, dass ohne Biomasse oder CCS Niedrig-Stabilisierungs-Ziele schwer zuerreichen sind.Grasartige Biomasse kann zu globalen Preisen uber 5 $/GJ produziert werden. Die

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Bepreisung von Emissionen im Landwirtschaftssektor erhoht die Bioenergiepreise, dahochproduktive Waldflachen dadurch ausgeschlossen werden und Stickstoff-Emissionenaus Dungernutzung zusatzliche Kosten verursachen. Die Bepreisung von Landnutzungsan-derungs-Emissionen wirken sogar vollstandig schutzend auf bestehenden Wald, was dieglobalen Landnutzungsanderungs-Emissionen erheblich reduziert. Allerdings muss dieseReduzierung der verfugbaren Landflachen kompensiert werden durch Intensivierung undAusdehnung in Flachen, die nicht der Emissionsbepreisung unterliegen. Letzteres fuhrtwiederum zu einer Erhohung der mit der Biomasseproduktion verbundenen Landnut-zungsanderungs-Emissionen. Das ist eine Folge des hohen Drucks aus der Kombina-tion von hoher Biomasse-Nachfrage und Treibausgas-Bepreisung, wie sie typisch seinkann fur strenge Klimaschutz-Szenarien. Die fur die groß-skalige Biomasseproduktionin 2095 benotigten Durchschnittsertrage belaufen sich auf 500 bis 600 GJ/ha in denHauptproduktions-Regionen. Die Ergebnisse zeigen des Weiteren, dass die Konkurrenzum Wasser zwischen Landwirtschaft, Haushalten und Industrie in vielen Regionen starkzunehmen konnte, insbesondere falls Walder geschutzt werden und Bioenergie nachge-fragt wird.In Klimaschutzszenarien ergibt sich der Wert der Bioenergie sowohl aus ihrem En-

ergiewert als auch aus dem Wert ihrer potentiellen negativen Emissionen. Die Ana-lyse zeigt, dass die CCS-Technologie, sofern verfugbar, eine starke Bindung zwischenKohlenstoffpreisen und Bioenergiepreisen etabliert. Dadurch dominiert in Niedrig-Stabi-lisierungs-Szenarien mit hohen CO2-Preisen der Kohlenstoffwert der Biomasse ihren En-ergiewert. Somit veranlassen steigende CO2-Preise Investitionen in Technologien, dienicht aus Grunden der Energieerzeugung gebaut wurden. Diese Preiskopplung erzeugt inNiedrig-Stabilisierungs-Szenarien außerdem eine hohe Zahlungsbereitschaft fur Biomasse,welche die zuvor genannten Angebotspreise bei weitem ubersteigt.Bioenergie ist deshalb so wertvoll, weil die negativen Emissionen, die sie ermoglicht, das

Budget an noch zulassigen Emissionen aus fossilen Energietragern erhoht und es damiterlaubt, kurzfristige Emissionsreduktionen zu verzogern und langfristige Rest-Emissionenbeizubehalten. Bei einem gegebenen Klimaziel agiert Biomasse daher eher als Gegenstuckdenn als Ersatz von fossilen Brennstoffen. Jedoch macht dies den verlangerten Kurzfrist-Einsatz von fossilen Energietragern abhangig von der kombinierten Langfrist-Verfugbar-keit von Biomasse und CCS. Somit werden Unsicherheiten uber die langfristige Ent-wicklung des Landwirtschafts-Sektors und der CCS-Technologie relevant fur kurzfristigeEntscheidungen uber Emissions-Reduktionen.

1 Introduction

1.1 The need for ambitious mitigation efforts

As you read this, the atmospheric concentration of CO2 will have closely approachedor even surpassed the mark of 400 ppm1. This is an increase of 44% compared to thepreindustrial level of 280 ppm. According to Working Group I of the Fifth AssessmentReport (AR5) of the Intergovernmental Panel on Climate Change (IPCC) the globalmean temperature (GMT) increased by 0.78 � from 1850–1900 to 2003–2012 (IPCC2013). The report concludes that “warming of the climate system is unequivocal” andthat it is “extremely likely” that the observed warming was predominantly caused bythe anthropogenic increase in greenhouse gas concentrations and other anthropogenicforcings. The evidence for human influence has grown over the years as did the scientificconsensus on the need to reduce anthropogenic greenhouse gas (GHG) emissions in orderto avoid or at least mitigate dangerous climate change (Fisher et al. 2007). Based onthe awareness that with rising temperature important tipping elements in the Earthsystem could be irreversibly pushed past critical states, “implying large-scale impactson human and ecological systems” (Lenton et al. 2008), the international community of194 states recognized on the 16th Conference of the Parties in 2010 in Cancun “thatdeep cuts in global greenhouse gas emissions are required. . . with a view. . . to hold theincrease in global average temperature below 2 � above preindustrial levels” (UNFCCC2010). Meeting this temperature limit requires stabilizing the long-term atmosphericGHG concentrations at relatively low levels of 400–450 ppm. The acknowledgment ofthe 2 � target by the international community motivated a series of international multi-model studies exploring the feasibility and costs of low-stabilization targets (AMPERE,Kriegler et al. 2014a; LIMITS, Kriegler et al. 2014b; ROSE, Kriegler et al. 2013b, EMF27,Krey et al. 2013). The present thesis contributes to this research by focusing on the rolebioenergy – potentially a major mitigation option – could play in achieving those targets.Chapter 3 to 6 shed light on the potential consequences of and requirements for bioenergydeployment in the energy sector and the land use sector. Chapter 6 directly contributedto the EMF27 study.

1.2 Options to achieve low-stabilization

Since our fossil fuel based energy systems are the predominant source of anthropogenicGHG emissions (Figure 1.1), accounting for 66% of total GHG emissions (57% CO2),(IPCC (2007)), emission reduction in this sector plays a central role for climate changemitigation. Emissions in the energy system are caused by burning fossil fuels, whichcontributed more than 81% to global primary energy supply in 2011 (IEA 2013). Thus,reducing anthropogenic emissions requires a fundamental restructuring of global energy

1http://www.esrl.noaa.gov/gmd/ccgg/trends/mlo.html

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12 1 Introduction

CO2Gfossil57%

CO2Gother3%

CO2Gdeforestation17%

CH4Gagriculture6%

N2OGagriculture7%

CH4Gother8%

N2OGother1% F-Gases

1%

Figure 1.1: Shares in global CO2eq emis-sions in 2004. Total: 49 GtCO2eq. Source:IPCC (2007).

supply towards low-carbon energy carriers. IPCC’s Fourth Assessment Report (AR4)reports that more than 65% of total emission reduction would occur in the energy sectorfor a wide range of emission reduction targets (Fisher et al. 2007). However, since agri-culture and land use change account for about one third of total global GHG emissions(17% from deforestation, c.f. Figure 1.1) the reduction of land use related CO2 emissionsand other GHG emissions and the enhancement of sinks in the forestry and agriculturesectors are also considered important. Especially avoided deforestation in tropical coun-tries could complement emission reductions in the energy system and significantly reduceoverall mitigation costs (Fisher et al. 2007).

Since policies aiming at climate change mitigation potentially have far-reaching conse-quences for society, economy, and environment there is a need for a public debate aboutends and means of different climate policies (Edenhofer and Kowarsch 2012). Policymaking is a complex task due to multiple interrelated direct and indirect effects includ-ing adverse unintended side-effects of means, which may require a revision of the policyends. Edenhofer and Kowarsch (2012) suggest that “the main task of sciences should beto provide a helpful, enlightening contribution to public debate, particularly in terms ofrevealing practical means-consequences of different policy options, in order to enhancepublic decision-making” and to enable the revision of policy goals. Amongst others sci-ence can contribute multi-scenario analyses that investigate different plausible stories oftransformation pathways into the future in terms of consistent, long-term, model-based,quantitative scenarios. This allows exploring possible consequences and trade-offs ofavailable policy options.

Climate policies potentially interfere with many components of the complex and in-terlinked system of energy, economy, land use, and climate. Therefore, Integrated As-sessment Models (IAMs) of global climate change have been developed that integrateknowledge from several academic domains and cover the complex relations between en-vironmental, social and economic factors (Helm 2005; Schneider 1997; Weyant et al.1996). Typically IAMs comprise representations of the energy system, the climate sys-tem, the economy, and the land use system. The models are applied to investigate abroad range of possible futures, which are defined in scenarios by combining a set ofassumptions on the future development of important factors and constraints, such aspopulation growth, food demand, technological learning, technology cost and availabil-ity, resource endowments, and climate protection target. Within these constraints IAMsdetermine a transformation pathway that is optimal with respect to a given objective,such as total cost or welfare.

1.2 Options to achieve low-stabilization 13

2020 2040 2060 2080 21000

200

400

600

800

1000E

J/yr

Coal.w/o.CCSCoal.w/.CCSOil.w/o.CCSGas.w/o.CCSGas.w/.CCSNuclearBiomass.(trad.)BECCSHydroWindSolarGeothermal

Figure 1.2: Transformation pathway of the global primary energy mix for a low-stabilization sce-nario (450 ppm CO2 equivalent in 2100 with overshooting allowed) from the REMIND model (c.f.Section 1.5) produced for the EMF27 project.

IAMs aim at determining global and regional costs of reducing GHG emissions, identifycost-effective transformation pathways to reach particular mitigation targets, as well asto investigate the type of mitigation measures required for achieving those targets. Theyare particularly useful to investigate trade-offs that arise from contrary impacts acrossregions, sectors and time (c.f. Section 1.4). A broad portfolio of potential decarbonizationmeasures is being assessed with IAMs. Regarding the energy system these measuresrange from energy conservation, fuel switching, and efficiency to various renewable energytechnologies, nuclear power, and carbon capture and sequestration (CCS). Regarding theland use sector major mitigation options are avoided deforestation, afforestation, efficientfertilization and livestock management.

Stimulated by policies in many countries the deployment of renewable energy hasevolved rapidly in recent years and is expected to increase further under mitigationpolicy. For an illustration of how the decarbonization of future energy supply couldbe performed, Figure 1.2 depicts the global primary energy mix for a low-stabilizationscenario calculated by the integrated assessment model REMIND (c.f. Section 1.5 andChapter 6), one of 18 energy-economy and integrated assessment models employed inthe Stanford Energy Modeling (EMF) Study 27 (Kriegler et al. 2013c). The resultsshow a rapidly decreasing share of fossil fuels after 2030 substituted with renewableenergy sources, of which modern bioenergy with CCS (BECCS) contributes a significantpart of at least 30% in the second half of the century. The high relevance bioenergygains in the portfolio of mitigation options, particularly in low-stabilization scenarios,is a robust finding across all models participating in the EMF 27 study (Kriegler et al.2013c; Luderer et al. 2013c; Rose et al. 2013).

Mitigating climate change is not only a question of how, but also a question of whento reduce GHG emissions. Based on a scenario review of future emissions pathwaysthe Global Energy Assessment (GEA) report concludes: “Limiting global temperatureincrease to less than 2 � above preindustrial levels (with a probability of greater than50%) requires rapid reductions of global CO2 emissions from the energy sector with apeak around 2020 and a decline thereafter to 30–70% below 2000 emissions levels by2050, finally reaching almost zero or even negative CO2 emissions in the second half ofthe century”, (Riahi et al. 2012), and further: “In particular, the later the emissions

14 1 Introduction

peak, the higher the need for net ’negative’ emissions in the second half of the century,for example, by using biomass together with carbon capture and storage”. Thus, theanswer to both, the question of how and the question of when to reduce GHG emissions,suggests that bioenergy could significantly contribute to emission reduction and its cost-effectiveness in the long run.

For a long time bioenergy was the major energy source for humans, before the indus-trial revolution launched the large-scale exploitation of fossil fuels. In the face of risingenergy demand and before the growing interest to use bioenergy for reducing GHG emis-sions there was and still is the incentive to diminish the dependency on fossil fuels withbioenergy for reasons of security of energy supply, national sovereignty, and lower vul-nerability to volatile energy prices, as pursued with bio-fuel mandates by Brazil and theUSA since the 1970s as a response to the oil shocks (EISA 2007; Harvey and McMeekin2010). Only in recent years the growing concern about climate change and the expectancethat bioenergy could help lowering emissions actuated new policies promoting bioenergy,especially in the European Union.

Today bioenergy still is the biggest contributor among renewable energy sources. In2008 bioenergy provided 50.3 EJ (10.2%) to the total primary energy supply of 492 EJ(renewable energies total 13% (63.5 EJ)), (Chum et al. 2011). Roughly 60% (30 EJ)of the biomass was used traditionally for cooking and heating in developing countriesand relies on often unsustainable use of wood, charcoal, agricultural residues and animaldung that is mainly combusted with low efficiency and causes serious health problems.Modern bioenergy that is used with higher efficiency than traditional biomass for powergeneration, heat, and transport fuels accounted for 22% (11.3 EJ) of total primary energysupply from biomass. Today modern bioenergy production mainly utilizes wood, cropand forestry residues, and waste for heat and power production and agricultural crops(such as sugarcane, maize, rapeseed, soybean, or palm oil) for producing liquid biofuels.Often the latter are classified 1st generation biofuels whereas liquid fuels from herbaceousand woody non-edible lignocellulosic plants (such as poplar, eucalyptus, switchgrass, orMiscanthus) are labeled 2nd generation biofuels. However, 2nd generation biofuels arein a pre-commercial stage yet.

1.3 Properties of bioenergy making it relevant forclimate change mitigation

Biomass has a decisive property that distinguishes it from other renewable energies: itcontains carbon. Cause and effect of this crucial property has implications not only forthe role bioenergy can play as a mitigation option but also for the way bioenergy has tobe treated in integrated assessment modeling.

First of all the carbon embedded in biomass makes it an energy carrier and a suit-able feedstock for energy conversion processes. Due to the chemical versatility of carbon,biomass can be converted into several types of secondary energy (see next subsection). Inaddition the carbon content results in an energy density that justifies long distance trans-port. Thus, bioenergy - in contrast to other renewable energies - can be traded globallysimilar to fossil fuels (Heinimo and Junginger 2009). The same property allows bioen-ergy to be stored making it dispatchable compared to the intermittent energy sourcessolar and wind. The carbon originates from plant growth and makes bioenergy part of

1.3 Properties of bioenergy making it relevant for climate change mitigation 15

the terrestrial carbon cycle. This is both a blessing and a curse at the same time, sincethe deployment of bioenergy may cause changes in the terrestrial carbon stocks in twocontrary ways. The beneficial effect is that the carbon fixed in bioenergy plants directlywithdraws carbon from the atmosphere and could be persistently stored via BECCS tech-nology reducing the carbon stock in the atmosphere. However, biomass plants are partof the land use system and cultivating biomass requires fertile land. Bioenergy thus maycause adverse changes in land-related carbon stocks by inducing land use change, suchas deforestation, which would increase atmospheric carbon stocks. Among renewableenergies bioenergy requires by far the largest land area per unit of energy (Coelho et al.2012, p. 1508) and further agronomic inputs such as labor, fertilizer, water, and energy.Bioenergy cultivation thus may interfere with the production of other agricultural goods.

Therefore, the use of biomass for producing energy and reducing emissions not onlytouches the domain of the energy-economy-climate system, but it additionally dependson and affects the evolution of the land use sector. The integration of these sectorsand their interactions constitutes challenges for modeling bioenergy that are discussed inSection 1.4. The modeling approach applied in this study was designed to accommodatethese interactions and is introduced in Section 1.5. The following sections take a closerlook at the potential advantages and disadvantages of bioenergy as a mitigation optionthat were adumbrated above.

1.3.1 Potential advantages of bioenergy

Low GHG intensity Deploying bioenergy to combat climate change is based on therationale that replacing fossil fuels with bioenergy lowers net emissions, since burningbiomass only releases the same amount of carbon to the atmosphere that was accumu-lated before by growing the plant, so no carbon would be added from combustion tothe atmosphere. However, this simple “carbon-neutrality” is hard to achieve in real pro-duction systems mainly due to emissions that accrue from land use (N2O from fertilizerinput) and land use change (CO2 from deforestation). In particular 1st generation fuels,which are promoted by current policies, are assumed to cause substantial direct and in-direct land use and land use change emissions, in some cases sufficient to neutralize thenet GHG savings (Crutzen et al. 2007; Fargione et al. 2008; Searchinger et al. 2008). Thestrongest concern, however, refers to the potential competition of 1st generation bioen-ergy crops with food crops for land, water and other agronomic inputs. On the whole,considering the projected high bioenergy demand in climate change mitigation scenarios(Figure 1.2), 1st generation crops are not supposed to be the appropriate measure forsubstantial GHG savings. Recent research therefore turned to the use of lignocellulosicfeedstocks (Melillo et al. 2009; Schmer et al. 2008). In contrast to 1st generation biomasslignocellulosic feedstock such as perennial grasses and short-rotation woody crops can beentirely converted to energy and thus feature two to five times higher yields per hectarein terms of primary energy content (Hill 2009). They are additionally advantageous over1st generation crops since they require far fewer synthetic inputs when managed care-fully (Hill 2009; Schmer et al. 2008). Consequently they are ecologically less demandingthan food crops in terms of impacts on soils, soil erosion, biodiversity, nutrient leaching,pesticide application, etc. (Cherubini et al. 2009). Therefore, this thesis focuses on theassessment of bioenergy based on lignocellulosic purpose-grown biomass.

16 1 Introduction

coal oil gas bio−100

−50

0

50

100kgCO2/GJ

Figure 1.3: Emission factors for different primary energycarriers defined as net emissions per unit of primary energyinput (kgCO2/GJ). Blue bars indicate the carbon intensityof the fuel, black lines indicate emission factors that canbe achieved by the application of different capture technolo-gies. The red bar indicates the emission factor for biomassif the total carbon content would be captured. Bioenergywithout CCS is assumed to be carbon neutral. Values arebased on the carbon content only, emissions from produc-tion processes, such as methane from mining or CO2 fromtransport, are not included. Based on IEA (2007).

Negative emissions Climate change mitigation scenarios aiming at low GHG concen-tration levels show a strong need for options to remove carbon from the atmosphere(c.f. Chapter 6). Capturing the emissions that accrue form the combustion of biomassoffers one option to generate negative emissions if more carbon is captured than previ-ously emitted for bioenergy production. There are other ways for carbon removal, suchas direct air capture technologies or afforestation. However, among these and amongall fossil and renewable energy carriers bioenergy with CCS uniquely allows generat-ing energy and negative emissions at the same time. Therefore, BECCS is considereda particularly attractive mitigation option. “Bioenergy technologies coupled with CCScould substantially increase the role of biomass-based GHG mitigation if the geologicaltechnologies of CCS can be developed, demonstrated and verified to maintain the storedCO2 over time”, (Chum et al. 2011). The paramount importance of bioenergy for thedeployment with CCS arises from its ability to offset emissions across space and timeand to prolong the use of fossil fuels in sectors that are expensive to decarbonize di-rectly. This makes BECCS a cost-effective, indirect mitigation measure particularly inlong-term low-stabilization scenarios (Azar et al. 2013; Edenhofer et al. 2010; Klein et al.2013; Kriegler et al. 2013a; Metz et al. 2005; Rhodes and Keith 2008; van Vuuren et al.2010a). Combining fossil fuels and CCS can lower net emissions but it can not generatenegative emissions. Some residual emissions remain. To illustrate the range of achievablenet emissions, Figure 1.3 shows rough estimates of emission factors for different primaryenergy carriers with and without CCS (similar values were applied in parts of this study).If CCS is applied bioenergy offers by far the lowest emissions.

Versatility In contrast to other renewable energies bioenergy is versatile with respectto primary energy carriers, conversion processes, and end products. Particularly it isthe only renewable primary energy carrier that can be converted into different types ofsecondary energy. As Figure 1.4 illustrates, diverse biomass feedstocks can feed differentconversion processes to produce a variety of energy products. This makes it a flexiblelow-carbon energy carrier that can be applied for emission reduction in different sectors,particularly in the transport sector, which in contrast to the stationary sector lacks abroad portfolio of mitigation options. Technologies that are commercial today (solidlines) refer to 1st generation biofuels only and heat and power production from wood,residues, and waste. The adverse effects of large-scale production of 1st generation

1.3 Properties of bioenergy making it relevant for climate change mitigation 17

Figure 1.4: The versatility of bioenergy: schematic view of the variety of commercial (solid lines)and developing bioenergy routes (dotted lines) from biomass feedstocks. Commercial products aremarked with an asterisk. Elements that are in the focus of this thesis are marked with red frames.Source: Chum et al. (2011), Figure 2.2.

Notes (referring to the numbers in the figure): 1. Parts of each feedstock, for example, crop residues,could also be used in other routes. 2. Each route also gives coproducts. 3. Biomass upgrading in-cludes any one of the densification processes (pelletization, pyrolysis, torrefaction, etc.). 4. Anaero-bic digestion processes release methane and CO2 and removal of CO2 provides essentially methane,the major component of natural gas; the upgraded gas is called biomethane. 5. Could be otherthermal processing routes such as hydrothermal, liquefaction, etc. DME = dimethyl ether.

bioenergy contradict the societal goal of sustainable and climate friendly production.Due to the advantages of lignocellulosic over conventional bioenergy crops concerningtheir GHG balance and sustainability (see above), this study focuses on conversion routesthat use lignocellulosic feedstock (framed in red). This set of technologies preserves theheterogeneity of the end products (heat, power, petrol, diesel, hydrogen). However,the conversion of lignocellulosic feedstock requires technologies that are different fromavailable 1st generation bioenergy technology and that are mainly in the developingstage (indicated by dashed red frames). There are mainly two reasons why lignocellulosictechnologies are in a premature state yet. First, the adverse effects of large-scale firstgeneration bioenergy production became clearer in recent years only. Second, the initialsocietal goal of energy security that triggered the deployment of 1st generation bioenergywas joined by the increasing importance of climate protection and sustainability.

With technologies that are commercial today, lignocellulosic feedstocks are only pro-viding heat and power without CCS. Most long-term mitigation scenarios, however,assume bioenergy conversion technologies with CCS to be available that are currentlyin the pre-commercial stage. These technologies can produce various types of secondaryenergy from lignocellulosic feedstock (see next paragraph). Two technologies are of spe-cial interest because they have been used commercially without CCS on the basis of coal

18 1 Introduction

so far and in contrast to current bioenergy technologies both technologies can relativelyeasily be combined with CCS (Metz et al. 2005; Mollersten et al. 2003; Obersteiner 2001;Rhodes and Keith 2008; Yamashita and L. Barreto 2004). The Fischer-Tropsch processproduces liquid fuels, while the BioIGCC technology (bioenergy integrated gasificationcombined cycle, c.f. Chapter 4) generates electricity.

1.3.2 Potential drawbacks

Since lignocellulosic feedstock for energy purposes has not been produced on a large andcommercial scale yet, there is a lack of experience and scientific studies regarding itsenvironmental impacts. However, although it is supposed to be advantageous over 1stgeneration biomass (see Section 1.3.1) dedicated production of 2nd generation biomassis still expected to have potential adverse effects on the land use system, especially ifgrown on large-scale as it is projected by mitigation scenarios.

GHG balance It is expected that high demands for purpose-grown biomass could createan incentive for farmers to convert forest into cropland either directly or by displacingfood or feed crop production, which then would have to expand into forest. This couldcause significant upfront carbon emissions for bioenergy (Wise et al. 2009, Chapter 5 ofthis thesis). Conversely, the use of degraded land and abandoned land (suitable especiallyfor lignocellulosic feedstocks) can enhance carbon stocks (Chum et al. 2011). Historicallyagriculture and forestry are important drivers of land use changes and are a significantcontributor to global GHG emissions (Le Quere et al. 2009, Figure 1.1). Crutzen et al.(2007) found substantial N2O emissions from fertilizer use for 1st generation biofuels.Although purpose-grown lignocellulosic biomass feedstock needs less fertilizer input (seeabove), resulting N2O emissions and their negative impact on the GHG balance of 2ndgeneration biofuels remain an issue (Melillo et al. 2009; Popp et al. 2011a). It is essentialto include emissions from land use and land use change (LULUC) into the assessment ofbioenergy, especially if regarding bioenergy as a mitigation option, since any potentialGHG emission savings from replacing fossil fuels with bioenergy could be diminished byLULUC emissions.

Biodiversity Conflicts may arise with other sustainability aspects like biodiversity.Large-scale bioenergy production could cause loss of biodiversity by expanding into nat-ural ecosystems – many of which host high biodiversity – and replacing them with mono-cultures, or by reducing the use of traditional varieties in agriculture. If lignocellulosicenergy crops (especially perennial species), which require low-intensity management rel-ative to current biofuel systems, are grown on abandoned agricultural or degraded land,biodiversity could be improved as soil carbon and soil quality could increase over timeand habitats and ecosystem functions could be restored (Firbank 2008; Lindenmayer andNix 1993; Royal Society 2008; Semere and Slater 2007).

Water If irrigated, large-scale production of biomass can substantially reduce wateravailability in regions where water is already scarce (3). However, second generationbiomass such as perennial herbaceous crops and short-rotation woody crops generallycan be grown without irrigation. Rain-fed feedstock production does not require waterextraction from water bodies, but it can still reduce downstream water availability (e.g.

1.3 Properties of bioenergy making it relevant for climate change mitigation 19

for food production) by redirecting precipitation from runoff and groundwater rechargeto crop evapotranspiration (Chum et al. 2011).

Food competition As demand for biomass projected in mitigation scenarios is high,there could be significant competition for land between food and biomass (3). This mayincrease agricultural prices (Chakravorty et al. 2009; Wright 2009) and reduce affordablefood supply, in particular for the poor (Ivanic and Martin 2008; Zezza et al. 2008).Since lignocellulosic feedstock needs less input than 1st generation biomass it can begrown more easily on land that is not suitable for food production. Furthermore itfeatures higher energy yields per area. This can reduce but not eliminate competitionand potential elevating effects on food prices (Chakravorty et al. 2009). Albeit there isno consensus on the magnitude of the effect, Chum et al. (2011) report that a couple ofstudies agree that biofuel production increased food prices between 2000 and 2007. Eventhough caused by 1st generation biofuels in this case, this indicates the potential of large-scale bioenergy production to adversely affect food markets. “To the extent that domesticfood markets are linked to international food markets, even countries that do not producebioenergy may be affected by the higher prices. . . Evidence indicates that higher priceswill adversely affect poverty and food security in developing countries, even after takinginto account the benefits of higher prices for farmers”, (Chum et al. 2011). There isno consensus about whether biofuels establish a link between energy and food markets.While Chum et al. (2011) report on studies that found a close coupling (Bank 2009a;Schmidhuber 2008) Baumeister and Kilian (2013) deny that there is “evidence that cornethanol mandates have created a tight link between oil and agricultural markets.” Thisstudy highlights that increasing food demand is the fundamental driver of the corn price.

Rural development Increases in bioenergy demand and the resulting increase of pricesfor agricultural products could stimulate agricultural growth and rural development indeveloping countries (Schmidhuber 2008). However, using productive or degraded landfor bioenergy production could compromise the needs of local populations for subsis-tence farming. Moreover, large-scale cultivation and mechanized production could entailfurther concentration of land ownership, pushing small farmers aside and reducing em-ployment of rural workers (Huertas et al. 2010).

Residues Using agricultural and forestry residues for bioenergy production could avoidthe above-mentioned adverse effects since it does not require additional agricultural pro-duction activity. However, there are concerns that diverting residues to energy purposescould have negative impacts on the soil quality, since soil fertility and water-holding ca-pacity require recirculation of organic matter to the soil (Blanco-Canqui and Lal 2009;Lal and Pimentel 2007; Wilhelm et al. 2007). Overexploitation of harvest residues isone important cause of soil degradation in many places in the world (Ball et al. 2005;Blanco-Canqui et al. 2006; Lal 2008; Wilhelm et al. 2004).

Climate change impacts on agriculture The impact of climate change on agriculturalyields in general and bioenergy yields in particular are uncertain. First, the magnitudeand geographical pattern of climate change is uncertain. Second, while positive effectson plant growth may occur, such as increased CO2 concentration, other effects, suchas higher temperature or less precipitation, may counteract. “Overall, the effects of

20 1 Introduction

climate change on biomass technical potential are found to be smaller than the effectsof management, breeding and area planted, but in any particular region they can bestrong” (Chum et al. 2011).

1.4 The special case of bioenergy – or – Why we needmodel coupling

Having sketched these (sometimes opposed) properties of bioenergy it becomes evidentthat the use of biomass for energy production and emission reduction in contrast to otherrenewable energy sources not only affects the evolution of the energy-economy sector andthe climate system, but it also effects the land use sector. Figure 1.5 shows the systemsinvolved and their interactions. The economy’s demand for energy and food is a crucialdriver determining the size and the structure of the energy and the land use sectors whereemissions accrue from energy conversion and land use and land use change. Constrainingthe emissions requires transformation processes in all three subsectors: energy, economy,and land use. The energy sector and the land use sector are affected directly becauseemission reduction requires a transition towards low-emission technologies for energyconversion and low-emission land use practices. Indirectly this affects the economy sinceboth transformation processes need investments and may result in changes of prices forenergy and food. Given these links any change in one of the subsystems may haveimpacts on other systems. Using bioenergy as a low-carbon energy source establishes anadditional direct link between the energy sector and the land use sector. That means,even when neglecting the emission and food link of the land use sector, a transformationof the energy sector may propagate into the land use sector through the channel ofbioenergy alone. This link gets particularly relevant under climate policy, since it mayinduce large-scale deployment of biomass and thus it may require a transformation ofthe land use sector that in its dimensions might by similar to the energy transformation.Therefore, transformation pathways in the energy-economy sector should not be studiedalone but along with the resulting transition and impacts in the land use sector. Theseimpacts can be manifold as described in Section 1.3.2.

In terms of modeling this means that the in-depth analysis of future scenarios ofbioenergy deployment requires detailed modeling of both sectors. This thesis applies thefollowing two models: (i) the integrated assessment model REMIND (Regional Modelof Investment and technological Development) represents the energy-economy-climatesystem and covers a wide range of bioenergy and competing conversion technologies,(ii) the high-resolution land use model MAgPIE (Model of Agricultural Production andits Impact on the Environment) represents the supply side of bioenergy with a broadcoverage of agricultural commodities and natural constraints. A detailed description ofthe models can be found in Sections 5.2 and 6.2.

However, it is not sufficient to investigate the demand side of biomass apart from itsproduction side. Rather, for two reasons the comprehensive assessment of bioenergy asa mitigation option requires a model framework that integrates the demand side (i.e.the energy-economy-climate system) and the supply side (i.e. the land use system) ofbioenergy. The two reasons are: interaction of sectors and dichotomy of impacts.

The level of bioenergy deployment is not a one-way decision within the energy sector,but it emerges from the balancing of supply and demand. The same holds true for the

1.4 The special case of bioenergy – or – Why we need model coupling 21

Climate

Economy

Energy system

Land useBioenergy demand & supply

Dam

ages

REMIND MAgPIE

Figure 1.5: Sectors involved in the demand and supply of energy: interactions and representa-tions in the models. The REMIND model integrates representations of the energy-economy sectorand the climate system. The MAgPIE model represents the land use sector. The arrows indi-cate important real-world interactions between the systems. The sub-systems within the REMINDmodel (energy-economy-climate) are hard-linked (solid lines). Interactions between systems acrossmodel boundaries are realized with soft-links (dashed lines). Grey color identifies links that are notrepresented in the version of the REMIND-MAgPIE model framework applied in this thesis.

balancing of land use emissions and their cost-effective mitigation based on emissionprices and emission reduction costs in the land use sector.Dichotomy arises because benefits and adverse effects of bioenergy deployment occur

in different sectors that are usually modeled separately. While the bioenergy-consumingenergy system mainly benefits from its use as an energy source and mitigation option(c.f. Section 1.3.1) the land use sector potentially faces substantial adverse effects frombioenergy production (c.f. Section 1.3.2). Most prominently this regards emission sav-ings in the energy sector and additional emissions from bioenergy production in the landuse sector (Wise et al. (2009) and Chapter 5 of this thesis). These trade-offs reachingacross model boundaries (as indicated by the dashed arrows in Figure 1.5) constitutechallenges for modeling and assessing bioenergy, since it requires an approach that in-tegrates the models of energy-economy-climate (REMIND) and land use (MAgPIE),meaning it requires an exchange of relevant information and a solution mechanism thatallows to determine the optimal solution while considering the trade-offs. In other words,the models have to be linked.

22 1 Introduction

1.5 The REMIND-MAgPIE modeling framework

There are two ways of linking models. The hard-link integrates the models in one complexand numerically demanding model that is solved as a whole (energy-economy-climate arehard-linked within REMIND). This type of model linking limits the number and complex-ity of hard-coupled models to keep the numerical effort manageable and the numericalproblem solvable. In REMIND the energy system model is the most detailed one, whileeconomy and the climate system are represented by more aggregated reduced-form mod-els. Feedback effects and balancing processes, such as balancing supply and demandfor bioenergy, are endogenous to hard-linked models resulting in the most consistentrepresentation of the coupled system.The soft-link leaves the models separated and exchanges only certain information

derived from the optimal solutions of each model (Bauer et al. 2008). To cover feedbackeffects in a soft-link the models are solved iteratively and consecutively. The relevantparameters obtained from the solution of one model are exogenous to the other model.This process is potentially also numerically heavy and time-consuming, since the modelshave to be run consecutively several times until the equilibrium is established. However,the soft-link allows the separated models to be more complex and detailed than a hard-link does. Due to the complexity of the stand-alone models applied in this thesis itwas decided in favor of the soft-link approach. To further reduce the computation timethe once-through approach, a simplification of the iterative soft-link, is applied for theanalysis in Chapter 6. In this approach the models are only run once, which means thatthe models are not updated with the result of the other model and it can not be ensuredthat the equilibrium is fully established. The soft-link between the energy-economy-climate model REMIND and the land use model MAgPIE focuses on the two interactionsthat are crucial in the context of bioenergy deployment: (i) bioenergy demand and supply,(ii) land use/land use change emissions and GHG prices.Chapters 3 to 6 present analyses that were conducted at different stages of development

regarding the level of integration and complexity of the two models. The different stepsof model coupling and the corresponding chapters are depicted in Figure 1.6 on page 26and are described next.

1. The factor that determines the size of all other bioenergy-related impacts is theamount of bioenergy produced. The most basic information that can be harmo-nized between producers and consumers of bioenergy to determine the economicallyprofitable level of bioenergy deployment are the demand and price of bioenergy.Therefore, in the first step of model coupling the models exchange bioenergy de-mand and prices: the bioenergy demand calculated by REMIND is passed to MAg-PIE. MAgPIE calculates the corresponding bioenergy price, which is returned toREMIND. This was conducted with REMIND-G, a global single-region version ofREMIND, exchanging information on the global level, (Chapter 3).

2. Using the same models and the same technical approach the second step of cou-pling was extended by additionally exchanging information on GHG prices (fromREMIND-G to MAgPIE) and land use/land use change emissions (from MAg-PIE to REMIND). Furthermore, to complete the portfolio of bioenergy mitigationoptions the REMIND model was enhanced by the previously missing bioenergy-to-electricity conversion route using the BioIGCC technology with or without CCS,(Chapter 4).

1.5 The REMIND-MAgPIE modeling framework 23

Table 1.1: Overview of integrated assessment models that comprise an energy-economy-model anda land use model, based on Kriegler et al. (2014a), Popp et al. (2013), and Riahi et al. (2007) andpersonal communication.

Equilibrium Solution Coupling Model Land Technologicalconcept dynamics mode Interaction access change

GCAM Partial Recursive dynamic Hard-link Bioenergy demand Full exogenousBioenergy pricesLULUC emissionsGHG prices

IMAGE Partial Recursive dynamic Soft-link Bioenergy demand Abandoned exogenousonce-through Bioenergy prices and rest land

LULUC emissions

MESSAGE- General (MES) Intertemporal (MES) Soft-link Bioenergy demand Full exogenousGLOBIOM Partial (GLO) Recursive dyn.(GLO) once-through Bioenergy prices

(no iteration) LULUC emissionsGHG prices

REMIND- General (REM) Intertemporal (REM) Soft-link Bioenergy demand Full endogenousMAgPIE Partial (MAg) Recursive dyn.(MAg) once-through Bioenergy prices

or LULUC emissionsiterative GHG prices

3. The coupling with the regionally disaggregated version of REMIND (REMIND-R),comprising 11 regions with global coverage, is numerically more complex, sincethe mode of intertemporal optimization requires equilibrium on bioenergy markets(and in case of climate policy also on emission markets) in all regions and all timesteps at the same time. In the soft-coupling approach this may result in manyiterations until the equilibrium between the models is established. Therefore, thepreferably precise emulation of the biomass supply of MAgPIE within REMINDcan significantly reduce the runtime and the number of iterations, since it allowsREMIND to consider the approximate behavior of MAgPIE already during theoptimization. Emulating bioenergy supply within REMIND means providing thebioenergy price as a function of bioenergy demand. Therefore, bioenergy supplycurves for all regions and all time steps are derived from the MAgPIE model bymeasuring the price response of MAgPIE to different bioenergy demand scenarios.These bioenergy supply curves are presented in Chapter 5. The emissions accruingfrom bioenergy production are parameterized by marginal abatement cost curvesfor CO2 and an emission factor per unit of primary bioenergy for N2O. Both werealso derived from MAgPIE.

4. Chapter 6 presents results of a REMIND analysis that is based on the emula-tion of MAgPIE described in Chapter 5. The results contributed to the EMF27model-intercomparison study. The analysis was performed applying a once-throughversion of the soft-link. This approach starts with REMIND determining the bioen-ergy demand based on the bioenergy supply-curves that were formerly derived fromMAgPIE. Using the bioenergy demand and GHG prices computed by REMIND,the MAgPIE model is run subsequently to calculate resulting land use patternsand emissions. In the once-through approach there is no further iteration of themodels.

To the author’s knowledge there are only few IAMs that comprise a detailed represen-tation of the land use sector. These models and their most important features regardingthe coupling are listed in Table 1.1. However, it is the first time that with REMIND

24 1 Introduction

and MAgPIE an intertemporal energy-economy-climate model is coupled with a high-resolution land use model that features endogenous technological change. By couplingthese models this thesis provides an integrated model framework that allows to studyaspects that are of special interest when considering bioenergy a mitigation option: Theintertemporal perspective of REMIND allows to evaluate the unique feature of bioenergywith CCS to generate negative emissions and thus to compensate emissions across spaceand time. It enables the model to decide about short-term emissions while anticipat-ing that they can be compensated at a later stage2. Taking advantage of the detailedrepresentation of the energy system it also allows to investigate the impact of limitedavailability of technologies, in particular BECCS, on the short- and long-term mitiga-tion strategies and resulting costs. The fact that technological change is endogenous toMAgPIE makes the model an appropriate tool to determine growth rates of agriculturalyields that would be required to supply bioenergy at large-scale taking into account vari-ous constraints such as emission prices, nature conservation, and accommodation of fooddemand. More comprehensive model descriptions can be found in Sections 5.2 and 6.2.The most important features of the approach applied in this thesis are:

� The energy-economy-climate REMIND model computes the cost-effective emissionmitigation with full where (abatement can be performed where it is cheapest), when(optimal timing of emission reductions and investments under perfect foresight) andwhat (optimal allocation of abatement among the emission sources and greenhousegases) flexibility. It covers a wide range of bioenergy and competing conversiontechnologies with and without CCS.

� The high-resolution land use model MAgPIE endogenously treats the trade-offbetween land expansion (causing costs for land conversion and for resulting car-bon emissions) and intensification (requiring investments for research and develop-ment). It considers spatial explicit, biophysical (land, water, potential yields) andbiodiversity constraints (forest protection, non-irrigated biomass).

� By coupling the models, bioenergy demand and supply are balanced and GHGemissions from the land use sector are taken into account and can be equally pricedwith emissions from energy conversion. Resulting emission costs in MAgPIE arereflected in bioenergy prices. Therefore, mitigation efforts can be allocated cost-effectively across energy and land use.

� Both models feature a global and long-term perspective required, since GHGs ac-cumulate in the atmosphere worldwide and transitions in the inert earth, climate,and social systems take decades. The global and multi-regional perspective is alsoreasonable, because fossil fuels, biomass, and other agricultural crops can be tradedglobally.

2This implies a persistent and reliable institutional framework that guarantees intertemporal balancingof emissions. Acknowledging that this is a challenging task, this thesis assumes these institutionalpreconditions to be fulfilled.

1.6 Thesis objective and outline 25

1.6 Thesis objective and outline

The objective of this thesis is to investigate the potential contribution of bioenergy toclimate change mitigation. The overarching research questions are: how, when, andwhy is bioenergy deployed under long-term climate change mitigation policy? How aremitigation strategies and costs affected by constraints on the demand side of bioenergy(such as limited availability of conversion technologies) and the supply side (such asforest protection or GHG pricing)?

To answer these questions an integrated model-framework was developed that accountsfor the interactions between the sectors involved in bioenergy demand and supply. Inparticular the model-framework balances the demand and supply of bioenergy takinginto account the trade-off between emission savings due to bioenergy deployment in theenergy sector and potential, additional emissions accruing due to bioenergy productionin the land use sector. The framework soft-links two models, each of which covers aspectsthat are of special interest when considering bioenergy a mitigation option: To evaluatethe ability of bioenergy to provide multi-sector mitigation due to its versatility and cross-sector mitigation due to negative emissions, the detailed energy system model REMINDis applied that comprises various bioenergy and competing energy conversion technologieswith and without CCS. The intertemporal optimization approach with perfect foresightaccounts for the potential of bioenergy to provide negative emissions and compensateemissions across time. The spatially explicit MAgPIE model of the land use sector isapplied to derive bioenergy prices and to account for potential adverse effects of bioenergyproduction, in particular emissions from land use and land use change.

The remainder of the thesis is structured as follows. After sketching the current state ofbioenergy assessments and suggesting routes to improve these assessments (Chapter 2),Chapter 3 introduces the coupled model framework. The analysis uses the first stage ofmodel coupling to identify the cost-effective contribution of bioenergy to a low-carbontransition. It focuses on the resulting implications for the land use sector. Using thenext stage of coupled models (enhanced by land use emissions and their pricing) Chap-ter 4 investigates techno-economic aspects of bioenergy conversion with CCS, focusingon the bio-to-electricity route via BioIGCC technology (biomass integrated gasificationcombined cycle) and its sensitivity to different biomass feedstocks. Chapter 5 exploresthe global potential and corresponding prices of lignocellulosic purpose-grown biomassunder emission constraints in the land use sector and provides regional biomass supplycurves that serve as a basis for the analysis in the next chapter, and that can also be ap-plied in other IAMs to represent the global bioenergy potential. Finally, Chapter 6 usesresults from the previous chapters (techno-economic parametrization of the BioIGCCtechnology in Chapter 4, and bioenergy supply curves in Chapter 5) to investigate thedrivers behind bioenergy deployment that determine the value of bioenergy under differ-ent climate change mitigation targets and under full and limited availability of mitigationoptions.

Each chapter of this thesis addresses a specific research question. These questions aredefined below followed by a short overview of the respective chapter. Figure 1.6 andTable 1.2 give an overview of the models and model communication used in the analysesof the chapters. Chapter 7.1 presents a synthesis of the main results of this thesis andprovides an outlook on further research.

26 1 Introduction

REMIND6R

MAgPIE

• Bioenergy7demand• GHG7prices

MAGICC

• LandDuse7emissions

• Energy7emissions

• Energy7system7results:• Technology7mix• Mitigation7costs• Energy7emissions

• Land7use7emissions• Land7use7pattern

• Temperature7and7forcing

EMF277model7runs EMF277resultsMAGICC7preDruns

• Forcing7as7a7function7of7emissions

MAGICC

• Bioenergy7supply7curves• GHG7emission7baselines7

from7agriculture• MAC7for7CO2DLUC• N2O7emission7factor7for7

bioenergy

MAgPIE

MAgPIE7emulator7runs

MAgPIE

• Bioenergy7prices• GHG7emissions

SoftDlink7REMINDDG7D MAgPIE

• Bioenergy7demand• GHG7prices

REMIND6G

SoftDlink7REMINDDG7D MAgPIE

MAgPIE

• Bioenergy7prices

• Bioenergy7demand

REMIND6G

Coupled

Chapterb3„Thebeconomicbpotentialbofbbioenergybforbclimatebchangebmitigationbwithbspecialbattentionbgivenbtobimplicationsbforbtheblandbsystemb“

Coupled

Chapterb4“Bio6IGCCbwithbCCSbasbablong6termbmitigationboptionbinbabcoupledbenergy6systembandbland6usebmodel”

Standalone

Chapterb5„Thebglobalbeconomicblong6termbpotentialbofbmodernbbiomassbinbabclimatebconstrainedbworld”

ConsecutivebS„once6through“”

Chapterb6„Thebvaluebofbbioenergybinblowbstabilizationbscenarios:banbassessmentbusingbREMIND6MAgPIEb“

Figure 1.6: Models and model communication (indicated by arrows) used for the analyses in therespective chapters. See Section 1.5 for technical and Section 1.6 for content-related background ofthe respective chapters.

1.6 Thesis objective and outline 27

Table 1.2: Overview of models used for the analyses in the following chapters

Chapter Paper REMIND-G REMIND-R MAgPIE Mode

3 Bioenergy and land use X X coupled4 BioIGCC X X coupled5 Supply curves X stand-alone6 Bioenergy value X X consecutive

What is the current state of bioenergy assessment? (Chapter 2)

Political decisions about future mitigation strategies require a comprehensive and bal-anced assessment of mitigation options that integrates analyses from diverse researchcommunities. The IPCC Special Report on Renewable Energy Sources and ClimateChange Mitigation (SRREN) aims to provide such an assessment for various types of re-newable energies (IPCC 2011b). Focusing on the bioenergy parts of the SRREN, Chap-ter 2 of this thesis first provides an overview of the current state of bioenergy assessmentby reviewing the insights from the SRREN on bioenergy. Secondly it suggests require-ments that assessments should address in order to satisfy the need of policy makers forcomprehensive analysis. In particular this regards identifying and possibly reconcilingdisparate views of different research communities. Finally, based on these suggestionsit scrutinizes the topics and perspectives explored in the different SRREN chapters andconsiders how the SRREN performs in relation to the discussed assessment requirements.It closes with suggestions on possible routes toward improved assessment making.

What is the cost-effective contribution of bioenergy to a low-carbon transition,paying special attention to implications for the land system? (Chapter 3)

Biomass supplied by lignocellulosic feedstock is expected to contribute substantially toemission reduction in future energy systems. However, the potential of second genera-tion biomass remains unclear due to large uncertainties about future agricultural yieldimprovements and land availability for biomass production. Furthermore, large-scalebioenergy production may create conflicts with sustainability aspects, like food and wa-ter security or forest protection. Therefore, this chapter explores the cost-effective con-tribution of bioenergy to low-carbon transition including its costs and trade-offs withfood and water security. In addition, it aims to assess the the impact of forest conserva-tion on bioenergy potentials based on the rationale that bioenergy is not carbon neutral.Emerging bioenergy prices are calculated including implicit costs due to biophysical con-straints on land and water availability. Since the level of bioenergy deployment dependson bioenergy prices and vice versa this analysis uses a coupled model framework thatsoft-links the energy-economy-climate model REMIND-G to the spatial explicit land usemodel MAgPIE.

What is the mitigation potential of the BioIGCC technology and how does itdepend on the techno-economic performance and the biomass feedstock?(Chapter 4)

A comprehensive assessment accounting for the versatility of bioenergy requires a detailedmodeling of different bioenergy conversion routes. This chapter explores the mitigation

28 1 Introduction

potential of the high-efficient biomass-to-electricity BioIGCC technology that can copewith heterogeneous feedstocks and that can relatively easily be extended by a carboncapture process. The analysis investigates how the deployment of the BioIGCC technol-ogy as a mitigation option depends on its techno-economic performance and the biomassfeedstock. There are mainly two types of biomass raw material, woody and grassy, withdifferent production costs and different chemical properties relevant for the gasificationstep embedded in the BioIGCC process. The techno-economic parametrization of thetechnology is based on a broad literature review. To account for different prices of thebioenergy feedstocks we use the integrated assessment model REMIND-G soft-linked tothe land use model MAgPIE.

How much biomass can by supplied under full land use competition at whatprice? How does the biomass potential depend on GHG prices? (Chapter 5)

Bioenergy competes with other mitigation options in terms of economic competitivenessfor reducing emissions and supplying energy. Since negative emissions from BECCS cansignificantly reduce stabilization costs, the biomass potential and corresponding supplyprices become crucial factors that affect mitigation strategies and mitigation costs (c.f.Chapter 6). This chapter investigates the long-term supply prices for large-scale pro-duction of purpose-grown second generation biomass on a multi-regional level under fullland use competition with other agricultural activities. Under stringent mitigation tar-gets a strong demand for bioenergy and full carbon pricing may coincide (as a result ofpricing energy and land use emissions). Therefore, this study examines how GHG pricesaffect the potential and the supply prices of biomass. The spatial explicit land use modelMAgPIE is used to derive regional supply price curves for biomass with and withoutGHG taxes in the land use sector.

Which factors determine the willingness-to-pay for bioenergy under climatechange mitigation targets? How do these factors affect the choice of bioenergyconversion routes? (Chapter 6)

This chapter investigates the use of bioenergy in long-term climate change mitigationscenarios. Due to its energy and carbon content, and on the condition that it is com-bined with CCS, bioenergy - among all fossil and renewable energy carriers - uniquelyfeatures the capability to produce negative emissions. This study analyses how theseproperties determine the value of bioenergy for climate change mitigation and how theyaffect the choice of bioenergy conversion technologies if carbon and bioenergy marketsare interlinked. Furthermore, it investigates how the impact of these drivers on thewillingness-to-pay for bioenergy depends on the bioenergy potential and the availabilityof technology. Finally, the study explores how the long-term potential of BECCS inter-feres with the short-term and long-term deployment of fossil fuels. For this analysis weuse the integrated assessment model REMIND of the energy-economy-climate systemthat embeds bioenergy supply curves and agricultural emissions derived from the landuse model MAgPIE (c.f. Chapter 5).

2 Can Bioenergy Assessments Deliver?

Felix CreutzigChristoph von Stechow

David KleinCarol Hunsberger

Nico BauerAlexander Popp

Ottmar Edenhofer

Published as Creutzig, F.; von Stechow, C.; Klein, D.; Hunsberger, C.; Bauer, N.; Popp, A., andEdenhofer, O. (2011): Can Bioenergy Assessments Deliver?, Economics of Energy and EnvironmentalPolicy, 1(2), 65–82.

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65

Can Bioenergy Assessments Deliver?

Felix Creutzig,a,b,c Christoph von Stechow,c David Klein,c Carol Hunsberger,d Nico Bauer,c

Alexander Popp,c and Ottmar Edenhoferb,c

abstract

The role of biomass as a primary energy resource is highly debated. Next generationbiofuels are suggested to be associated with low specific greenhouse gas emissions.But land consumption, demand for scarce water, competition with food productionand harmful indirect land-use effects put a question mark over the beneficial effectsof bioenergy deployment. In this paper, we investigate the current state of bioenergyassessments and scrutinize the topics and perspectives explored in the SpecialReporton Renewable Energy Sources and Climate Change. We suggest that an appropriateassessment requires a comprehensive literature review, the explicit exposition ofdisparate viewpoints, and exploration of policy-relevant content based on plausible“storylines”. We illustrate these storylines with the IPCC’s emission scenarios andpoint out routes to improve assessment making on the future role of bioenergy.

Keywords: Bioenergy, Assessment, Tradeoffs, Sustainability, Scenarios

http://dx.doi.org/10.5547/2160-5890.1.2.5

f 1. INTRODUCTION g

Bioenergy plays a crucial role in the global transition from fossil fuels to renewable energy,and possibly also for climate change mitigation. With intensive use of traditional biomass,primary energy from plant resources currently exceeds that of other renewable options, in-cluding wind energy. The benefits and impacts of bioenergy depend on what feedstocks areused for what purpose and how and where they are produced. In particular, greenhouse gas(GHG) emissions from bioenergy use are widely varying, uncertain and the subject of intensivedebates (e.g., Malca and Freire 2010; Plevin et al. 2010; Creutzig et al. 2012). One part ofthe scientific literature indicates that high direct and indirect land-use emissions compromisethe benefits of the current use of many biofuels (e.g., Crutzen et al. 2008; Hertel et al. 2009;Popp et al. 2011a). Another part of the literature highlights the potential of large-scale bio-energy deployment to mitigate climate change and to even produce negative GHG emissionsin combination with carbon capture and storage technologies (e.g., Edenhofer et al. 2010).In addition to the climate conundrum, large-scale deployment of bioenergy is influenced byenergy security concerns, is subject to industry interests, and impacts food security, biodiver-sity, water scarcity, soil quality and subsistence farming (e.g., Fargione et al. 2010).

The complexity of this system produces a high level of uncertainty about future outcomes.Policy makers therefore have a need for comprehensive analysis to help inform their decisionsabout energy, climate change and associated risks. Taking climate change mitigation as a

a Corresponding author. E-mail: felix.creutzig@tu-berlin.de; +49 30 314 78864.b Department of Economics of Climate Change, Technical University Berlin, Germany.c Potsdam Institute for Climate Impact Research, Potsdam, Germany.d Department of Geography and Environmental Studies, Carleton University, Ottawa, Canada.

Economics of Energy & Environmental Policy, Vol. 1, No. 2. Copyright � 2012 by the IAEE. All rights reserved.

2.1 Introduction 31

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framework for analysis, two questions emerge: What is the global warming impact and mit-igation potential of bioenergy deployment in various scenarios? And: how sustainable is bio-energy deployment in these scenarios? Only a comprehensive and balanced assessment, inte-grating analyses from diverse research communities, can provide at least tentative answers tothese questions and identify the main sources of uncertainty. Such an assessment is crucial toinform political decisions that intend to influence the future portfolio of mitigation options.The IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation(SRREN) aims to provide such an assessment for renewable energies in general (IPCC 2011a),and bioenergy in particular (Chum et al. 2011). Here we critically evaluate this assessmentbased on the understanding that the mitigation perspective needs to be accompanied by otherperspectives to avoid a one-dimensional analysis. Section 2 outlines the tasks of an assessment.Section 3 reviews the insights from the SRREN on bioenergy. Section 4 scrutinizes the rep-resentation of bioenergy in the different SRREN chapters based on the assessment require-ments. Section 5 suggests possible routes towards improved assessment making.

f 2. HOW TO CARRY OUT ASSESSMENTS g

Assessments are emerging as a distinct literature category in academia (Keller 2010). Promi-nent examples include the assessment reports of the IPCC (2007), the upcoming GlobalEnergy Assessment and Global Environmental Outlook, the Millenium Ecosystem Assessment(MA, 2005), and more specific reports such as the Assessment Report of the Urban ClimateChange Research Network (Rosenzweig et al. 2011), and many assessments in other areas ofscience. Unlike scientific publications, assessment reports are requested by a legal body andsubject to specific criteria and procedures, like the review process, to ensure high quality.Assessment preparation can take up to five years including scoping, author selection and severaliterations of writing and reviewing.

The IPCC reports are special in that they result from an official UN process, signed offby all 194 national governments that are members of the IPCC. The focus of the currentliterature on assessment making has primarily been on the underlying model of scientificpolicy advice (e.g., Pielke 2007; Beck 2010), on specific aspects of assessment making, suchas the treatment and communication of uncertainty (e.g.,van der Sluijs et al. 2008; Mastran-drea et al. 2011) or on the assessment process (e.g., Agrawala 1998; Farrell and Jager 2006)—focusing on the impact of assessments (Cash et al. 2002; Mitchell et al. 2006; Keller 2010).We are, however, not aware of any framework that specifies criteria for evaluating the contentof a particular assessment. For evaluation of an IPCC report, such as the SRREN, the pro-cedures of the IPCC itself will thus provide us with a point of departure.

The IPCC states that “the best possible scientific and technical advice should be includedso that the IPCC Reports represent the latest scientific, technical and socio-economic findingsand are as comprehensive as possible”; and in preparing an IPCC report, “Lead Authors shouldclearly identify disparate views for which there is significant scientific or technical support”(IPCC 2011b, p. 6). Also: “It is important that reports describe different (possibly contro-versial) scientific, technical, and socio-economic views on a subject, particularly if they arerelevant to the policy debate” (IPCC 2011b, p. 7).

Hence, an IPCC assessment needs to meet three tasks: 1) provide a comprehensive reviewof the relevant literature; 2) identify and possibly reconcile disparate views; and 3) present the

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scientific content in a manner relevant to policy makers, drawing on the outcomes of the firsttwo tasks. Let us elucidate each task in turn.

First, the review character of an assessment is different from most review articles in dis-ciplinary journals. A review article usually summarizes the results of a particular scientificcommunity on a specific topic, e.g. land modelers on biomass resource potential. An assess-ment, in contrast, has the mandate to bring different epistemic communities together, com-munities that work on the same topic but contribute different methods, perspectives, lan-guages, and assumptions.1 As a consequence, an assessment needs to be comprehensive bothin topics covered and in participation of epistemic communities.

Second, by bringing together different communities, an assessment allows for the iden-tification of disparate views and perspectives and scrutiny of the reasons for divergence. Inparticular, an exploration of the whole solution space (e.g., identifying costs, benefits and risksof mitigation options) becomes challenging when the fact-value separability cannot be takenfor granted as a precondition for the distinction between means and ends. The separationbetween facts and values collapses when indirect consequences of means have the potential toundermine the achievement of the societal ends that the means are intended to address (Dewey1988). The relevant example here is where extensive use of bioenergy (the means) to achieveclimate change mitigation (the end) causes unforeseen consequences (e.g. increased risk offamines) (Edenhofer and Seyboth forthcoming). Ideally, a communication effort betweenscientific communities helps to track down different assumptions and worldviews, to makevalue judgments transparent, and to explain the observed divergence in results and types ofanalysis. If this is achieved, it is much easier to reconcile divergent results, and also identifypossible co-benefits and trade-offs between societal goals and thus detect and possibly avoidunintended consequences and promote co-benefits. On this basis, assessments can often iden-tify research gaps and opportunities for collaboration, and can produce a closed loop bycommunicating these findings to the scientific community.

Third, an IPCC assessment is supposed to be policy-relevant without being policy-pre-scriptive. When results of different epistemic communities mismatch or when the differenttypes of analysis are difficult to reconcile, the communication of the respective sets of as-sumptions and worldviews and their corresponding results become paramount. An assessmentcan then inherit the role of an “honest broker”, communicating the divergent scientific con-clusions to policymakers in an accessible way (Pielke 2007). The use of “storylines” in assess-ments breaks down complexity and constitutes a useful tool for communication to policymakers, but also to peers and the interested public (Kriegler et al. 2010, Arnell et al. 2011).We understand a storyline to be a narrative (e.g., a rapidly globalizing and consumption-oriented world with efficient markets). Scenarios correspond to a storyline by specifying aparticular set of assumptions (e.g., population and economic growth; energy poverty; increas-ing energy demand; technological development; lifestyle changes, such as a global increase inmeat consumption). Given a specific scenario, models can then produce pathways, whichprovide numeric outcomes and impacts (e.g., bioenergy deployment). Comparison betweenscenario assumptions will then help to explain the discrepancy between different outcomesand the corresponding impacts. Varying perspectives of epistemic communities can translateinto different storylines and corresponding scenarios, but also to different emphasis on di-mensions within one storyline. Comparing different storylines with varying emphasis allows

1. According to Haas, an epistemic community is “a network of professionals with recognised expertise and competence in aparticular domain and an authoritative claim to policy relevant knowledge within that domain or issue-area” (Haas 1992, p. 3).

2.2 How to carry out assessments 33

68 Economics of Energy & Environmental Policy

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the identification and possibly quantification of risks, trade-offs and co-benefits. Feeding theseresults back into the sphere of public debate might result in substantial revisions of societalgoals and the respective policy instruments.

f 3. STATE OF BIOENERGY ASSESSMENT g

Before evaluating the SRREN bioenergy assessment, we need to summarize its main findings.We roughly follow the SRREN and discuss five key dimensions of the bioenergy assessment:1) costs; 2) life-cycle emissions; 3) resource potential and deployment; 4) socioeconomic andenvironmental impacts; and 5) governance (sections 3.1–3.5). For this, we mostly rely onSRREN Chapter 2 (“Bioenergy”: Chum et al. 2011), Chapter 9 (“Renewable Energy in theContext of Sustainable Development”: Sathaye et al. 2011), Chapter 10 (“Mitigation Potentialand Costs”: Fischedick et al. 2011), and Chapter 11 (“Policy, Financing and Implementation”:Mitchell et al. 2011).

3.1. Costs of bioenergy

The SRREN cost analysis is based on levelized cost of energy (LCOE) calculations. LCOEis calculated as the per-unit price at which energy must be generated from a specific sourceover its lifetime to break even. As a result, levelized costs of energy enable an apple-to-applecomparison of different sources of energy with widely diverging cost structure. Figure 1 dis-plays levelized costs of bioenergy for various feedstocks and purposes. Ethanol and biopowerproduction show cost reductions due to technological learning comparable to those of otherrenewable energy technologies. But estimated feedstock cost supply curves also point out thatincreased production leads to higher marginal costs, e.g. because of lower quality land (Chumet al. 2011).

Crucially, the SRREN finds that levelized costs of bioenergy are already competitive withfossil fuels for some feedstocks, purposes and countries. For example, ethanol from sugarcaneoutperforms gasoline in the Brazilian transport market. In Europe, biomass heating applica-tions in the building sector, often designed as cogeneration facilities, are cost competitive andincrease rapidly. The large amount of traditional biomass, still dominating overall biomassuse, is mostly grown locally, and is often not part of formal markets.

3.2. Life-cycle emissions

As bioenergy use is partially motivated by climate change, the carbon balance of feedstocksand production pathways is of particular interest and is frequently instrumentalized for policygoals (Creutzig & Kammen 2009). The SRREN breaks down life-cycle emissions accordingto the different life-cycle methods. Relying on attributional life-cycle analysis (LCA)—ac-counting for the direct emissions of the supply-use-disposal chain, the SRREN reveals thatbiomass used for electricity and heat always has lower CO2 life-cycle emissions than fossil fuels(SRREN Fig. 2.10). For transportation, the relative performance of biofuels compared withgasoline and diesel depends on the particular feedstock and production context. Possibly morerelevant, however, are the consequential marginal GHG emissions of bioenergy use, includinge.g. the indirect land-use emissions from deforestation. SRREN Figure 2.13 summarizes emis-sions from land-use change, differentiating between models and world regions. The figure andthe accompanying text demonstrate unambiguously that land-use emissions are potentially

34 2 Can Bioenergy Assessments Deliver?

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FIGURE 1Levelized cost of energy service from commercially available bioenergy systems at a 7% discount rateand with feedstock cost ranges differing between technologies (from Chum et al. 2011). For biofuels,the range of levelized costs represents production in a wide range of countries whereas levelized costsof electricity and heat are given only for major user markets of the technologies for which data wereavailable. The underlying cost and performance assumptions used in the calculations are summarized

in SRREN Annex III.

higher than the direct emissions of conventional fuels. A key challenge is that emissions occurup-front, contributing immediately to climate change, whereas potential carbon savings occurin the future, after paying back the initial carbon debt (Fargione et al. 2008). The SRRENconcludes that increased bioenergy deployment needs to be supplemented with better protec-tion of tropical forests and other carbon-rich ecosystems.

3.3. Resource Potential and Deployment

According to the literature review in the SRREN, the global technical potential for bio-energy, considering also demand for other land-use, ranges from less than 50 EJ to more than1000 EJ in 2050 (Fig. 2a; Dornburg et al. 2010; Haberl et al. 2010). In some of the studies,

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70 Economics of Energy & Environmental Policy

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FIGURE 2a) Bioenergy technical potential in 2050 listed according to categories. b) Expert judgment on

deployment in 2050 from SRREN Chapter 2 and deployment scenarios from SRREN Chapter 10.Source: adapted from (Creutzig et al. 2012).

the theoretical potential is even considered to exceed 1500 EJ by 2050 (e.g., Smeets et al.2007). Contrast this with current energy demand of around 500 EJ and expected energydemand of between 500 and 1000 EJ in 2050 (Fischedick et al. 2011). The huge uncertaintyis rooted in the following factors, among others: soil degradation; water scarcity; yield growth;production potential of degraded land; nature protection; and climate change feedback.

Based on this review of the available literature, the authors conclude that realistic de-ployment levels of biomass for energy could reach a range of 100 to 300 EJ/yr around 2050(Fig. 2b). But: “the inherent complexity of biomass resources makes the assessment of theircombined technical potential controversial and difficult to characterize” (Chum et al. 2011).Based on cost projections, including the opportunity cost of land, future biomass supply curvescan be derived, implicitly determining the market potential (SRREN Figure 2.5). Differentassumptions on economic and energy demand growth, the cost and availability of competinglow-carbon technologies as well as different mitigation scenarios add complexity to potentialestimates. Taking these into account, integrated assessment models (IAMs, see Box 1) obtainranges of potential deployment of bioenergy comparable to the SRREN Chapter 2 expertjudgment. In these models, deployment is estimated to be higher when mitigation targets aremore ambitious (Fig. 2b).

3.4. Socioeconomic and environmental impacts

In addition to the GHG performance of bioenergy options (see section 3.2), other socio-economic and environmental impacts are also analyzed in the SRREN (Chum et al. 2011;Sathaye et al. 2011).

First, the increased demand for agricultural inputs such as land and water influences foodcommodity prices and thus food security. The SRREN points out possibly relevant but un-certain contributions of increased biofuels production to the food price increase in the mid-2000s. This implies an overall adverse effect on food security in developing countries (WorldBank 2009).

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Second, increased biomass production may imply increased income for farmers and agro-business. But using productive and degraded lands for bioenergy purposes might compromisethe needs of local populations for subsistence farming. This is particularly important forvulnerable communities and female farmers who may have less secure land rights (FAO 2008).

Third, natural ecosystems can be destroyed to make space for bioenergy plantations,leading to biodiversity loss. For example, the rising demand for biofuels has contributed toextensive deforestation in parts of Southeast Asia; palm oil plantations support significantlyfewer species than the forest they replaced (Fitzherbert et al. 2008). Biodiversity loss may alsooccur through indirect land use change (see section 3.2). In some cases bioenergy expansioncan lead to increased biodiversity, e.g., through the establishment of perennial herbaceousplants or short-rotation woody crops in agricultural landscapes (Semere and Slater 2007).

Fourth, the impact on water resources varies greatly across feedstocks, cultivation systemsand conversion technologies. While biofuels derived from irrigated crops are water intensive,use of agricultural or forestry residues or rain-fed feedstock production does not require waterextraction from lakes, rivers or aquifers. But the latter might reduce downstream water avail-ability by redirecting precipitation to crop evapotranspiration. Aquatic ecosystems might nega-tively be affected by leaching as well as by emissions of nutrients and pesticides. In contrast,ligno-cellulosic feedstock might decrease water demand. Water impacts can be reducedthrough integration in agricultural landscapes as vegetation filters to capture nutrients inpassing water (Borjesson and Berndes 2006).

Fifth, the soil impacts of feedstock production (e.g., soil carbon oxidation, changed ratesof soil erosion, and nutrient leaching) depend heavily on agronomic techniques and the feed-stock under consideration. Similarly, the risk of soil degradation associated with using residuesfrom agriculture or forestry heavily depends on management, yield, soil type and location.While wheat, rapeseed and corn require significant tillage (FAO 2008), crops that providecontinuous cover might have a positive effect on soil outside the growing season of annualcrops by reducing erosion (Berndes 2008).

The SRREN concludes that “few universal conclusions . . . can currently be drawn, giventhe multitude of rapidly evolving bioenergy sources, the complexities of physical, chemicaland biological conversion processes, the multiple energy products, and the variability in en-vironmental conditions” (Chum et al. 2011, p. 258).

3.5. Governance

Global, regional, national and local policies shape agricultural practices and affect bio-energy resource potential, GHG performance of bioenergy deployment and other socioeco-nomic and environmental dimensions. Depending on the combination of specific policy pri-orities, such as climate change mitigation, trade, energy security, food security or ruraldevelopment, the overall policy impact can be decisive or negligible, conflicting or comple-mentary, sustainable or unsustainable. The policy chapter of SRREN concludes that biofuelmandates and blending requirements are key drivers in the development of most modernbiofuel industries (Mitchell et al. 2011). The example of Brazil is given where a combinationof tax incentives, blending mandates, regulation and infrastructure investments, starting inthe 1970s, produced a high share of biofuels in the overall fuel mix. More recent biofuelmandates in the U.S. and the EU, and high subsidies for corn ethanol, induced a surge inbiofuel demand and deployment but were non-discriminative with respect to life-cycle GHGemissions, resulting in mostly low-cost biofuel deployment with relatively high GHG emis-

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sions. In response, the updated low-carbon fuel standard (California), renewable fuel standard(U.S.) and fuel quality directive (EU) introduce rules that discriminate based on GHG emis-sions (Creutzig et al. 2011). Similarly, sustainability criteria and certification schemes forbioenergy sources aim to limit harmful impacts of bioenergy deployment. The policy reviewin chapter 11 is organized by end-use sectors (electricity, heating, and transport). All sectorsare increasingly reliant on bioenergy. As a result, the discussion of policies relevant to bioenergydeployment is fragmented over chapter 11.

f 4. EVALUATING THE BIOENERGY ASSESSMENT g

In this section, we evaluate the bioenergy assessment of the SRREN, notably its chapters 2(“Bioenergy”) and 10 (“Mitigation Potential and Costs”). In particular, we verify whether thebioenergy assessment conforms to the assessment criteria developed in section 2 of this article:

• Is the assessment comprehensive in topics and communities?

• Are diverging assumptions made transparent? Is reconciliation attempted?

• Is there a consistent set of policy-relevant storylines?

Box 1. The role and purpose of Integrated Assessment Models

Integrated Assessment Models (IAMs) are key to the SRREN and previous AssessmentReports of the IPCC. IAMs are tools for exploring long-term and global transitionpathways under various opportunities and constraints. IAM teams develop their modelsinto different directions and aim to improve the level of detail (e.g. energy conversiontechnologies, etc.) and to integrate more systems (e.g. the land-use system). In additionto research of individual teams, the international community undertakes model com-parison exercises. These consist of undertaking model runs with common assumptionsof the policy targets and other constraints, possibly also harmonizing the assumptionson population, GDP, and other drivers. The community compares the scenario resultsof different transition pathways. The modelers” attention shifted to strong emissionreduction in recent years, resulting in increased deployment of bioenergy in models. Tosystematically understand unintended side effects of land-use change, some IAMs arecoupled to global land-use models.

4.1. Comprehensiveness

Chapter 2 of SRREN collects insights on bioenergy deployment from various disciplinesand communities. Agro-economic and biophysical models of land use and availability providethe backbone for potential deployment estimates. These models also consider water availabilityand food security as constrains to different degrees. Studies from the life-cycle community arecited to estimate the GHG emissions of bioenergy. Techno-economic studies deliver costestimates of various bioenergy feedstocks and pathways. Analysis of policy instruments con-tributes to evaluating the governance of bioenergy. The results of these contributions aresummarized in section 3 of this paper.

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Social scientists, analyzing inter alia discourses, political economy, and local communities,also contribute to the huge literature on bioenergy in ways that go beyond what is capturedin the SRREN. For example, human geographers and anthropologists often observe localcommunities and the de facto implementation of bioenergy policies and programs. A commonobservation is that the intended outcomes of bioenergy initiatives diverge from their realimpacts (Borras et al. 2010). For example, research in India finds that despite a “pro-poor”discourse about the oilseed shrub Jatropha curcas, efforts to promote the crop have favoredresource-rich farmers and likely contributed to a widening of the wealth gap (Ariza-Montobbioet al. 2010). Similarly, in Brazil the spread of sugar cane for bioenergy has been linked toincreased social exclusion (Hall et al. 2009). Further case studies that examine the interactionsbetween bioenergy deployment and subsistence farming reveal circumstances that have pro-duced better or worse outcomes for local people (McCarthy 2010). Biofuel policies have alsobeen identified as a major driver of the recent increase in both the number and size of large-scale land acquisitions (Franco et al. 2010; Vermeulen and Cotula 2010), a trend with sig-nificant implications for social relations and smallholder farmers (Toulmin et al. 2011). TheSRREN makes scarce reference to these studies. The use of marginal land for subsistencefarming is noted as a constraint in 2.2.2.1 and 2.2.4.3, pointing out that subsistence farmingmay considerably, or even totally, limit the potential of marginal land for bioenergy deploy-ment. But the social science literature on local politics is not cited or used to identify successfulprograms.

Another gap is that governance of bioenergy deployment is discussed in a fragmented way(see section 3.5). A comprehensive review of bioenergy policies, their impact on GHG emis-sions, deforestation, biodiversity, water and food competition is missing. As noted in 3.2,uncertainties of life-cycle emissions can be very high, constraining the reliability of policiesthat rely on quantitative estimates. This fundamental problem of policy making is not dis-cussed in the SRREN.

4.2. Reconciliation and clarification of assumptions

The key dimensions of assessment of the future role of bioenergy are, as identified above,costs, GHG emissions, resource potential and deployment, socio-economic and environmentalimpacts and governance. The SRREN makes clear that projections in any of these dimensionsare highly uncertain and contingent. SRREN Chapter 2 brings together research results fromdifferent types of analysis—some of which are difficult to reconcile. Based on this review ofpartially disparate views and the underlying methods and assumptions, several key trade-offsarise with respect to the future role of bioenergy. In SRREN chapter 10, the IAMs exploremore than 150 scenarios, some of which vary bioenergy deployment constraints exogenously.Table 1 specifies these different trade-offs, and summarizes how the different chapters treatthem.

A major gap of the SRREN bioenergy assessment was identified as the missing reconcil-iation between the LCA and the IAM community, representing disparate perspectives onbioenergy-associated GHG emissions (Creutzig et al. 2012). IAMs assume first-best worldswhere so-called market failures, such as land-use emissions from bioenergy deployment, areaddressed by appropriate policy instruments. A key result of IAMs is that low-carbon bioenergycan substitute fossil fuels, emerging as the key renewable energy source in 2050 and beyond(Fischedick et al. 2011). LCA researchers observe life-cycle emissions of biofuels that can becomparable to gasoline, and are highly uncertain (Plevin et al. 2010). High deployment levels

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TABLE 1Bioenergy trade-off characterization in SRREN

Possibletrade-offs

Insights from bioenergy experts(Chapter 2, SRREN)

Insights from integrated models (Chapter 10, SRREN)

Deploymentandaffordability

Higher deployment implies highermarginal land and production costs (Fig.2.5); but higher deployment also implieseconomies of scale and technologicallearning, decreasing unit prices (Fig. 2.21)

Global bioenergy cost-supply curves are given fordifferent land use scenarios based on SRES assumptions(Fig. 10.23). Marginal costs of biomass productionincrease with increasing deployment level (Fig. 10.23)but over time they decline due to land productivityimprovements, learning of conversion technologies, andcapital-labor substitution (10.4.4, Table 10.10). Despitebeing considered in some IAMs, neither assumptions norinsights from IAMs on costs are reported.

Deploymentand wateravailability

Possible competition between bioenergydeployment and water security. Impact onwater resources varies greatly acrossfeedstocks, cultivation systems (e.g.irrigated or rain-fed) and conversiontechnologies (2.2.4.2, 2.5.5.1).

Briefly mentioned in 10.6.2.3: “RE can have impacts onwaters, land use, soil, ecosystems and biodiversity.”Neither assumptions nor insights from IAMs on wateravailability are reported, mainly due to a lack of literature(van Vuuren et al. 2009).

Deploymentand foodsecurity

Cited studies generally agree on adiscernable contribution to food priceincreases by bioenergy deploymentexpansion, but not on the size of thiscontribution. This implies an overalladverse effect on food security indeveloping countries—particularly forhigh oil price development (2.5.7.4).

Only one study (de Vries et al. 2007) addresses thistrade-off (10.4.4). For a food-first policy, it findsdeclining technical potential as a “direct consequence ofmore people, [. . .] hence more land demand for foodproduction”. The assumptions made for the bioenergysupply curves (Fig. 10.23: production on abandoned andrest lands only) also imply an underlying food-first-policy. No explicit information about food-securityassumptions in IAMs is given. The “relationship betweenbioenergy production, crop production anddeforestation” is identified as a knowledge gap in 10.2.4.

Deploymentand climatemitigation

GHG performance of bioenergy isestimated by LCA analyses showingsubstantial but hugely varying life-cycleemissions for different types of bioenergy.In some cases land-use emissions arepotentially higher than the directemissions of conventional fuels (2.5.2,2.5.3).

For stricter mitigation targets, more bioenergy isdeployed. Neither the assumptions in nor the insightsfrom IAMs concerning co-emissions are reported.10.2.2.4 says: “Some studies have indicated that it is thecombination of bioenergy with CCS that makes lowstabilization goals substantially easier through negativeemissions”

Deploymentand soil quality

Soil impacts of bioenergy feedstockproduction (e.g., soil carbon oxidation,changed rates of soil erosion, nutrientleaching) depend on agronomictechniques and feedstock. Under certainconditions, bioenergy crops can enhancecarbon sequestration in soils. Residueremoval could negatively impact soilcarbon and fertility (2.2.4.1, 2.5.5.3).

Briefly mentioned in 10.6.2.3: “RE can have impacts onwaters, land use, soil, ecosystems and biodiversity.”Neither assumptions nor insights from IAMs on wateravailability are reported, mainly due to a lack of literature(van Vuuren et al. 2009).

Deploymentand subsistencefarming

Using degraded lands for bioenergypurposes might compromise the needs oflocal populations for subsistence farming(2.2, 2.5.7.5).

Not mentioned.

(continued)

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Copyright � 2012 by the IAEE. All rights reserved.

TABLE 1Bioenergy trade-off characterization in SRREN (continued)

Possibletrade-offs

Insights from bioenergy experts(Chapter 2, SRREN)

Insights from integrated models (Chapter 10, SRREN)

Deploymentandbiodiversity

The impact of bio-crop production onbiodiversity depends on crop choice,agricultural management and previousland use. Biodiversity loss may also occurindirectly. Under certain conditions,however, the effect might be positive(2.2.4.4, 2.5.5.2).

Briefly mentioned in 10.6.2.3: “RE can have impacts onwaters, land use, soil, ecosystems and biodiversity”; andin 10.3.1.4: “As the available land for bioenergy islimited and competition with nature conservation issuesas well as food and materials production is crucial, thesectoral use for the available bioenergy significantlydepends on scenario assumptions and underlyingpriorities”.

could lead to co-emissions from land use change (e.g., Fargione et al. 2008; Melillo et al.2009; Popp et al. 2011a). and agricultural intensification (e.g., Wise et al. 2009; Popp et al.2011b). Creutzig et al. (2012) conclude that plausible scenarios of future bioenergy deploy-ment correlated with high bioenergy-induced GHG emissions are systematically underrepre-sented in the literature and in SRREN, specifically.

Another relevant gap is the absence of trade-off specification between deployment andsubsistence farming and informal markets. This seems to be related to the absence of expertson this topic. Most studies on subsistence farming emphasize the local variability of effects.The question then is under which conditions what kinds of bioenergy deployment can benefitsubsistence farmers. This kind of question needs to be given greater attention, and possiblybe comprehensively answered, in future bioenergy assessments.

While SRREN identifies disparate views on the future role of bioenergy and providesdetailed analyses from different communities, the reconciliation of insights sometimes remainsincomplete. A systematic summary, similar to table 1, linking the treatment of trade-offs inChapter 2 and 10 is not provided by SRREN. In some of the cases this is due to a lack ofliterature. But, more crucially, interdisciplinary communication across SRREN chapters andtheir respective communities is missing (for early efforts of tentative integration see (e.g., vanVuuren et al. 2009; Wise et al. 2009)). The SRREN provides little indication of how researchcould help to assess the salience of the respective trade-offs, e.g. through improved conse-quential LCA and through integrated assessment of climate, energy, economy and land use.

4.3. Consistent storylines

In this subsection, we evaluate storylines of Chapter 2 and Chapter 10 of SRREN, andtheir interaction.

Special Representative Emission Scenarios

Chum et al. develop four storylines aiming to clarify possible futures in a high-dimensionaloutput space. For this they map their expert judgment on future bioenergy deployment onthe four Special Representative Emission Scenarios (SRES), developed by the IPCC in 2000,relying on Hoogwijk et al. (2005). These scenarios represent storylines on globalization/re-gionalization and more environmentally sensitive versus more materially oriented world (IPCC2000) and form the common scenario basis for the assessment of climate change and itsmitigation for the climate modeling and integrated assessment communities in preparation of

2.4 Evaluating the bioenergy assessment 41

76 Economics of Energy & Environmental Policy

Copyright � 2012 by the IAEE. All rights reserved.

FIGURE 3Possible futures for 2050 biomass deployment for energy: Four illustrative contrasting sketches

describing key preconditions and impacts following world conditions typical of the IPCC SRESstorylines (see IPCC 2000; Hoogwijk et al. 2005), taken from Chum et al. (2011), p. 308, Fig.

2.27.

the Third and Fourth Assessment Reports. Each SRES bundles a set of assumptions, repre-senting a storyline. The SRES emphasize fossil fuel availability but hardly discuss bioenergy.

In Figure 3, the four storylines are adapted for bioenergy following Hoogwijk et al. (2005)and organized in a matrix, regional versus global orientation, and material/economic versusenvironmental/social orientation. The material/economic direction is identified with poorgovernance, the environmental/social dimension with good governance. In the global orien-tation, bioenergy deployment approaches a high number of 300EJ in 2050; in the regionalorientation, deployment is limited to 100EJ in 2050. The figure is built on the hypothesisthat “biomass and its multiple energy products can be developed alongside food, fodder, fiber,and forest products in both sustainable and unsustainable ways”. Each storyline is associatedwith key preconditions and key impacts. In these storylines, Chum et al. attempt to reconcile

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global drivers of energy demand with the detailed analysis of a particular renewable energysource in a narrative way. This attempt is very challenging, but is nonetheless a crucial exercise.Particularly, parts of their storylines could be criticized (e.g. asking: 1) Could high deploymentand poor governance imply net additional GHG emissions? 2) How do these storylines relateto the original SRES?). But the main point is to take these narrative storylines and system-atically scrutinize and analyze them, taking all relevant insights on market dynamics, LCOE,resource potential, GHG emissions, water scarcity, food security, policy options and the as-sociated trade-offs discussed in Section 4.2 into account, and then verify the plausibility ofstorylines or adapt them to consistent results of these analysis efforts in a more structured andpossibly quantitative way. IAMs are understood to be the right tool to systematically analyzetradeoffs and different storylines. The next section will thus present how storylines are usedin the SRREN analysis of bioenergy deployment levels as derived from IAMs.

Modeling storylines (Chapter 10 of SRREN)

For energy and climate change, IAMs provide a suitable infrastructure for scenario de-velopment. Each scenario, common bundles of assumptions, represents a storyline, reflectingnumerous assumptions on input parameters and model design. Specific realization and nu-meric representations constitute pathways. To some extent, one could call chapter 10 ofSRREN the storyline chapter. Two questions arise then: First, does the set of storylines onbioenergy cover the identified dimensions and trade-offs of SRREN Chapter 2? Second, dothese storylines map on the SRES storylines, as identified above?

The IAM scenario results assessed in the SRREN cover a wide range of assumptions oneconomic and energy demand growth, the cost and availability of renewable energies, andcompeting low-carbon technologies. Only scant information is given on future bioenergydeployment. Most scenarios assume a reduction in traditional biomass, and substantial growthin modern bioenergy sources (SRREN Section 10.2.2.2), not further discriminating betweendifferent types of bioenergy. Most models do not cover the land use sector explicitly but relyon an exogenous supply cost function for bioenergy. In fact, in many models future yieldimprovements, land competition or land exclusion due to food production, forest protection,biodiversity, soil quality, and water scarcity are lumped into this supply cost curve. Globalbioenergy cost-supply curves are given for 2050 and four different land use scenarios (SRRENFig. 10.23) based on the same SRES storylines presented in Chapter 2 (Fig. 3). In contrastto the potential deployment sketches in Chapter 2 also considering “poor governance” cases(A1 and A2) Chapter 10 supply cost curves assume “good governance” for all SRES storylines(A1, A2, B1, B2). This is indicated by the assumption that bioenergy is produced on aban-doned and rest land only, which implies underlying food-first or nature protection policies.The maximal potentials given with the supply-curves range from 170 EJ/a (B2) to 420 EJ/a(A1) and are sufficient to cover the deployment levels of 300 EJ/a (A1, B1) and 100 EJ/a (A2,B2) presented in Chapter 2. Section 10.3.1.4 briefly points out that “the available land forbioenergy is limited and competition with nature conservation issues as well as food produc-tion is crucial” and as a consequence “the use of bioenergy significantly depends on scenarioassumptions and underlying priorities”. However, the SRREN gives no explicit informationabout land availability and biomass costs assumptions in IAMs.

Many IAMs do not account for the GHG emissions from (indirect) land-use change andincreased land-use intensification; in effect, bioenergy is generally assumed to be carbon-neutral. Exceptions are models like POLES, IMAGE, MiniCAM and MESSAGE incorpo-

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Copyright � 2012 by the IAEE. All rights reserved.

FIGURE 4Bioenergy deployment levels (indicated by the size of the circles) of IAM scenarios along crude

proxies of the SRES dimensions.

rating more detailed land use modules. In conclusion, sustainability issues related to bioenergysupply are poorly reflected in IAMs (Sathaye et al. 2011: Section 9.4).

Hence, the space of possible bioenergy storylines explored with IAMs is very narrow.Neither is the SRES scenario space of Chapter 2 systematically covered.

Harmonization of Storylines

Figure 4 visualizes the insufficient consideration of SRES dimensions in assessment mod-els. It depicts the bioenergy deployment levels of IAMs along the same global-regional andenvironmental-material dimensions used for the deployment matrix in Fig. 3. For the repre-sentation of the material-environmental dimension, we use the growth rate of global finalenergy intensity. Energy intensity is a short-hand for the final energy use per unit of GDP.High negative growth rates of final energy intensity indicate a rapid improvement of energyefficiency corresponding to an environmentally oriented world (Grubler 2004). To identifywhether a scenario represents a globally-oriented or a regionally-oriented world, we use theconvergence over time in the levels of per-capita-GDP as an indicator. More precisely weestimate the growth rate of the gap in per-capita-GDP between regions with lower incomeand the leading region (Barro and Sala-i-Martin 2003). High negative growth rates correspondto fast convergence and represent a “globally oriented” world. The growth rates for both axesare derived for the scenarios from 2020 to 2050 with 10-year intervals using the geometricalmean. Regional values of convergence are weighted with population to obtain a global value.The chosen indicators are the only ones related to these SRES dimensions on which a sub-

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stantial number of IAMs have reported data. Other indicators would be a better fit to representSRES dimensions (Hoogwijk et al. 2005).

The graph shows some variety of convergence across models but little or no variety ofconvergence in scenarios within one model. In contrast to high projections of deploymentlevels for a globally oriented world in Chapter 2, IAM results in Fig. 4 show no clear sensitivityof deployment levels to any of the two depicted dimensions, not even within the models.Even acknowledging the limited possibilities to represent all relevant dimensions in highlydemanding models, Fig. 4 illustrates that IAMs insufficiently operationalize important di-mensions of bioenergy supply. Harmonization of assumptions with Chapter 2 is not at-tempted.

Numerous specifications need to be introduced into IAMs such that a more completescenario space, representing the trade-offs identified in SRREN Chapter 2, can be systemat-ically explored in an integrated setting. The complexity of existing IAMs suggests that this isa highly ambitious task. Creutzig et al. (2012) suggest that more specialized models with highresolution on bioenergy but coarse grained representation of other energy technologies cancomplement and soft-couple to the current model world.

f 5. WAYS FORWARD g

We have summarized the state of bioenergy assessment as performed in the IPCC’s SpecialReport on Renewable Energies. Assessments need to comprehensively present literature, rec-oncile disparate views by making assumptions transparent, and develop coherent storylinesaround varying sets of assumptions to be policy relevant. The SRREN succeeds in bringingvarious insights from different communities together—but insufficiently represents resultsfrom social sciences. The governance of bioenergy is discussed in a fragmented way. Trade-offs between bioenergy deployment and other essential land-use related dimensions of the bio-socio-sphere are identified and discussed. The key trade-off between emission savings frombioenergy and emission production by induced land-use changed is not represented in theIAMs of the SRREN. Storylines of representative scenarios representing various worldviewsare identified but—with the exception of deployment costs—not systematically explored inmodels. The report remains largely silent on possible tradeoffs and risks related to variationson induced co-emissions, and impacts on human living condition on a global scale but alsoin regional or local settings. This paper has considered how the SRREN performed in relationto the discussed assessment requirements. Understanding why it did so, and how its gaps couldrealistically be filled, would require considering a broader set of issues including the institu-tional context. The integrated assessment community is currently working on a new class ofstorylines, the so-called shared socio-economic pathways (see Kriegler et al. 2010; Arnell etal. 2011). We express the hope that this process, together with upcoming assessments, fillsthe gaps left by the SRREN and leads to further improved exploration of bioenergy futures.

f ACKNOWLEDGMENT g

We are grateful to Jan Minx, Gunnar Luderer and Jan Steckel for helpful comments on aprimary version of this manuscript.

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80 Economics of Energy & Environmental Policy

Copyright � 2012 by the IAEE. All rights reserved.

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48 2 Can Bioenergy Assessments Deliver?

3 The economic potential of bioenergyfor climate change mitigation withspecial attention given toimplications for the land system

Alexander PoppJan Philipp Dietrich

Hermann Lotze-CampenDavid KleinNico Bauer

Michael KrauseTim BeringerDieter Gerten

Ottmar Edenhofer

Published as Popp, A.; Dietrich, J.P.; Lotze-Campen, H.; Klein, D.; Bauer, N.; Krause, M.; Beringer,T.; Gerten, D. and Edenhofer, O. (2011): The economic potential of bioenergy for climate changemitigation with special attention given to implications for the land system, Environmental ResearchLetters, 6, 034017.

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IOP PUBLISHING ENVIRONMENTAL RESEARCH LETTERS

Environ. Res. Lett. 6 (2011) 034017 (9pp) doi:10.1088/1748-9326/6/3/034017

The economic potential of bioenergy forclimate change mitigation with specialattention given to implications for the landsystemAlexander Popp1, Jan Philipp Dietrich1, Hermann Lotze-Campen1,David Klein1, Nico Bauer1, Michael Krause1, Tim Beringer1,Dieter Gerten1 and Ottmar Edenhofer1,2

1 Potsdam Institute for Climate Impact Research (PIK), PO Box 60 12 03, 14412 Potsdam,Germany2 Department for the Economics of Climate Change, Technical University Berlin,10623 Berlin, Germany

E-mail: popp@pik-potsdam.de

Received 14 October 2010Accepted for publication 18 July 2011Published 16 August 2011Online at stacks.iop.org/ERL/6/034017

AbstractBiomass from cellulosic bioenergy crops is expected to play a substantial role in future energysystems, especially if climate policy aims at stabilizing greenhouse gas concentration at lowlevels. However, the potential of bioenergy for climate change mitigation remains unclear dueto large uncertainties about future agricultural yield improvements and land availability forbiomass plantations.

This letter, by applying a modelling framework with detailed economic representation ofthe land and energy sector, explores the cost-effective contribution of bioenergy to a low-carbontransition, paying special attention to implications for the land system. In this modellingframework, bioenergy competes directly with other energy technology options on the basis ofcosts, including implicit costs due to biophysical constraints on land and water availability.

As a result, we find that bioenergy from specialized grassy and woody bioenergy crops,such as Miscanthus or poplar, can contribute approximately 100 EJ in 2055 and up to 300 EJ ofprimary energy in 2095. Protecting natural forests decreases biomass availability for energyproduction in the medium, but not in the long run. Reducing the land available for agriculturaluse can partially be compensated for by means of higher rates of technological change inagriculture. In addition, our trade-off analysis indicates that forest protection combined withlarge-scale cultivation of dedicated bioenergy is likely to affect bioenergy potentials, but also toincrease global food prices and increase water scarcity. Therefore, integrated policies forenergy, land use and water management are needed.

Keywords: bioenergy, climate change mitigation, forest conservation, food security,biodiversity, water security

S Online supplementary data available from stacks.iop.org/ERL/6/034017/mmedia

1748-9326/11/034017+09$33.00 © 2011 IOP Publishing Ltd Printed in the UK1

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1. Introduction

In the past, burning fossil fuels, deforestation and other humanactivities have released large amounts of greenhouse gasesinto the atmosphere. Ambitious climate protection, includinga large-scale transformation of the global energy system, isneeded to prevent risks to ecosystems and human societies.Over the last few years, bioenergy has gained worldwideinterest for its potential to supply energy services at low levelsof greenhouse gas (GHG) emissions, but also to meet ruraldevelopment goals [1]. Today, almost all of the commerciallyavailable biofuels are produced from either starch- or sugar-rich crops (for bioethanol), or oilseeds (for biodiesel). Recentresearch has found that these bioenergy sources have theirdrawbacks [2, 3] and turned to the use of ligno-cellulosicfeedstocks, such as perennial grasses and short rotation woodycrops for bioenergy production (e.g. [4, 5]). A special point ofinterest is that scenarios striving for low GHG concentrationtargets may require options within the energy sector to removeCO2 from the atmosphere (‘negative emissions’). This impliesthat human-induced uptake of CO2 would have to be largerthan the amount of human-induced GHG emissions. One ofthe few technologies that may result in negative emissions isthe combination of bioenergy and carbon capture and storage(CCS) [6].

Recent estimates on the potential global ligno-cellulosicbioenergy supply range from less than 100 EJ yr−1 to over1000 EJ yr−1 for 2050 [7–9]. But besides biomass availability,future application of biomass for energy production is alsodetermined by its interaction with other energy options andrelative costs. In most energy scenarios, bioenergy use isprojected to be in the order of 150–400 EJ in the year 2100(e.g. [10]). Although these studies give first insights intofuture use of bioenergy, for assessing the potential contributionof bioenergy to the future energy mix it is indispensableto investigate the supply and demand side of bioenergy ininteraction. Furthermore, claims to the land system in termsof future yield improvements and land use expansion remainunclear.

Besides uncertainties of cost-effective use of bioenergyapplication, large-scale energy crop production may createconflicts with other sustainability aspects, like food and watersecurity or protection of forests for climate change mitigationand biodiversity conservation. First, bioenergy expansionis expected to put pressure on food and feed prices andcosts of agricultural production [11]. Second, large-scalebioenergy production and associated additional demand forirrigation may further intensify existing pressures on waterresources [12]. Third, in tropical and sub-tropical developingcountries deforestation happens due to land clearing for newcrop- and pasture land but also due to the use of biomassfor traditional heat and energy production. There wouldbe an additional pressure to convert forest into croplandfollowing the large-scale cultivation of second-generation,ligno-cellulosic bioenergy crops. Forests are a major storage ofcarbon [13], so there is an adverse impact when forest carbonis released for the purpose of bioenergy production [14]. Butdeforestation not only removes a carbon sink, it is also regarded

as the greatest threat to terrestrial biodiversity as forests arethe most biologically diverse terrestrial ecosystems [15, 16].Therefore, nature conservationists support forest conservationfor climate change mitigation [17, 18].

The main objective of this study is to investigatethe potential contribution of bioenergy to climate changemitigation, including its costs and trade-offs with food andwater security in an integrated framework. In addition, weaim to assess the impacts of forest conservation on bioenergypotentials based on the rationale that bioenergy is not carbonneutral. For this, we have linked a global dynamic vegetationand water balance model, a global land and water use model,and a global energy–economy–climate model. The vegetationmodel supplies spatially explicit (0.5◦ resolution) agriculturalyields and water fluxes. The land use model delivers cost-optimized land use patterns and rates of future yield increasesin agricultural production. Moreover, shadow prices arecalculated for irrigation water (as an indicator for waterscarcity), food commodities, and bioenergy (as an indicatorfor changes in production costs) under different land useconstraints such as forest conservation for climate changemitigation and as a contribution to biodiversity conservation.The energy–economy–climate model generates the demandfor bioenergy, taking into account the direct competition withother energy technology options for GHG mitigation, based oneconomic costs of bioenergy production.

2. Methodology

The three models used here are the global vegetation andhydrology model LPJmL [19, 20], the global land useoptimization model MAgPIE [21, 22], and the global energy–economy–climate model ReMIND [23]. A brief descriptionof each model follows, but a more detailed discussion can befound in the supporting information (SI) (available at stacks.iop.org/ERL/6/034017/mmedia).

2.1. Model descriptions

LPJmL simulates biophysical, biogeochemical and hydrologi-cal processes on the land surface on a global 0.5◦ × 0.5◦ grid.Each grid cell considers the fractional areas of both naturalecosystems (nine plant functional types, whose geographicaldistribution is simulated by the model) and agricultural systems(twelve crop functional types representing the world’s majorcrops and grazing land, whose distribution is prescribed fromexternal data sources). In addition to food and feed crops,LPJmL also explicitly accounts for three types of specializedgrassy and woody bioenergy crops, i.e. Miscanthus, poplar andeucalyptus, for bioenergy supply [24]. LPJmL simulates, at adaily time step, carbon fluxes (gross primary production, auto-and heterotrophic respiration) and pools (in leaves, sapwood,heartwood, storage organs, roots, litter and soil) as wellas water fluxes (interception, evaporation, transpiration, soilmoisture, snowmelt, runoff, discharge), in coupling with thedynamics of the vegetation. For example, carbon and waterfluxes are directly linked to vegetation patterns and dynamicsthrough the linkage of transpiration, photosynthesis and plant

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water stress. Water requirements and water consumptionof irrigated and rainfed crops are distinguished. Thephenology (sowing and harvest dates) of the different CFTsis simulated dynamically based on CFT-specific parameters,monthly precipitation and temperature averaged over 20 years(representing farmers’ past climate experience) and daily soilmoisture.

MAgPIE is a mathematical programming model coveringthe most important agricultural crop and livestock productiontypes in 10 economic regions worldwide (AFR = Sub-SaharanAfrica, CPA = Centrally Planned Asia including China,EUR = Europe including Turkey, FSU = the Newly Indepen-dent States of the Former Soviet Union, LAM = Latin Amer-ica, MEA = Middle East/North Africa, NAM = North Amer-ica, PAO = Pacific OECD including Japan, Australia, NewZealand, PAS = Pacific (or Southeast) Asia, SAS = SouthAsia including India). It takes regional economic conditions,such as demand for agricultural commodities, economicdevelopment, and production costs as well as spatially explicitdata on attainable crop yields, land and water constraints ofall terrestrial grid cells from LPJmL into account and derivesspecific land use patterns for each grid cell (resolution of0.5◦ × 0.5◦). Moreover, the model can endogenously decideto acquire yield-increasing technological change at additionalcosts. The use of technological change is either triggered bya better cost-effectiveness of technological change comparedto other investments or as an answer to production constraintssuch as land scarcity. The non-linear objective function ofthe land use model is to minimize the total cost of productionfor a given amount of regional food and bioenergy demand.Regional food energy demand is defined for an exogenouslygiven population that changes over time in 10 food categories,based on regional diets [25]. Future trends in food demandare computed as a function of income (measured in terms ofgross domestic product (GDP)) per capita based on a cross-country regression (Popp et al 2010). Food and feed forthe 10 demand categories can be produced by 20 croppingactivities and 3 livestock activities. Feed for livestock isproduced as a mixture of grain, green fodder produced oncrop land, and pasture. As we focus on cellulosic bioenergycrops, in this study bioenergy is supplied from specializedgrassy and woody bioenergy crops, i.e. Miscanthus, poplar andeucalyptus. All bioenergy products in the model are deliveredinto an aggregated demand pool. The model computes ashadow price for binding constraints in specific grid cells,e.g. related to water availability, reflecting the amount a landmanager would be willing to pay for relaxing the constraint byone unit. Furthermore, since the model minimizes productioncosts for a given demand for food and bioenergy products, theshadow prices for a given demand level show the marginalincrease in production costs for an additional unit of output.

ReMIND is an integrated modelling framework thatembeds a detailed energy system model (ESM) within a macro-economic intertemporal growth model with perfect foresightand a climate system model that computes the effect of GHGemissions on global mean temperature (GMT). The presentstudy uses a global single-region version. The energy sectorcomprises a large number of energy conversion technologies

Figure 1. Information flow within the modelling framework. In apre-processing step, the land use model MAgPIE is informed by dataon crop yields and carbon content from the biophysical modelLPJmL. The energy–economy–climate model ReMIND and theMAgPIE model are coupled by exchanging price and quantityinformation on bioenergy.

that convert primary energy carriers into final energy carriersthat are supplied to the macro-economic framework. Givenvarious economic, technological and natural constraints, theoptimal solution implies an efficient allocation of investmentsinto energy conversion technologies and macro-economiccapital accumulation. In the case of climate change mitigation,an energy mix is chosen that reduces GHG emissions andminimizes mitigation costs. Ligno-cellulosic biomass is aprimary energy carrier that can be converted into various typesof final energy: most notably the ReMIND model comprisescombined heat and power plants, heat plants, synthetic naturalgas, solid biomass, hydrogen and Fischer–Tropsch plants. Ifbiomass is combined with carbon capture and sequestration,net emissions can be negative.

2.2. Coupling

Figure 1 gives an overview on how the model componentsinteract.

First, LPJmL-simulated, spatially explicit attainableproduction levels of food, feed and bioenergy crops underexplicit consideration of water limitation are provided to theMAgPIE model at a 0.5◦ resolution. Then, MAgPIE simulatesspatially explicit land use and irrigation water use patterns,while at the same time taking effects of technological andagro-economic change into account. This flexibly integratesbiophysical constraints into an economic decision-makingmodel and provides a straightforward link between monetaryand physical processes.

Second, the ReMIND and the MAgPIE models arecoupled by exchanging price and demand information onbioenergy. However, the exchange of price and demandinformation is limited in terms of computational efficiency

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Figure 2. Bioenergy demand and prices (time series until 2095) for the scenario with bioenergy for climate change mitigation (M) (red line)and the scenario with bioenergy and avoided deforestation for climate change mitigation (M FC) (orange line).

and solvability because both models are applying optimizationmethods. Therefore, we use a soft-linked approach [26]where both models are solved in isolation and informationflows between them are brought into agreement in an iterativeprocess (meta-optimization).

The chain starts with ReMIND (R) calculating a globalbioenergy demand scenario (B) in the energy system up to 2100based on given biomass cost curves (SB).

B = R(SB). (1)

This data is delivered to MAgPIE (M) for computing newcost curves for biomass for the biomass scenario obtained fromReMIND.

SB = M(B) (2)

Since MAgPIE minimizes production costs for a givendemand for bioenergy products, these numbers show themarginal increase in production costs for an additional unitof output. This can also be interpreted as the minimum pricethat the energy sector would have to pay for one additionalunit of bioenergy from the agricultural sector. If e.g. pricesfor mineral oil would increase, e.g. due to shrinking reservesor effects of emission regulations, this would translate into ahigher demand for bioenergy, which would in turn raise theshadow price for bioenergy in our model. The shadow price ofthe global demand constraint in the model reflects the changesin the aggregate world market price for bioenergy feedstocks.

Global bioenergy demand from ReMIND is fulfilledby MAgPIE without any trade restrictions, i.e. biomasswill be produced globally according to regional comparativeadvantages (in areas with most suitable production conditionsand, hence, lowest costs of production). As an economicoptimization model, MAgPIE delivers spatially explicit landuse patterns and converts explicit restrictions on land and wateravailability into implicit costs of bioenergy production to beused in the energy system model. This iterative process isrepeated until equilibrium is established, i.e. no more changesin bioenergy demand (derived by ReMIND) and costs (derivedby MAgPIE) occur.

2.3. Scenarios

We apply our land use energy–economy–climate modellingframework under different land use scenarios to assessthe potential contribution of bioenergy to climate changemitigation, including its costs and side effects. A ‘referencescenario’ (Ref) portrays a possible future with growingpopulation, increasing food and feed demand, but withoutbioenergy demand for climate change mitigation and withall suitable land available for cropland expansion. Suitableland is defined by the Global Agro-Ecological Zone (GAEZ)methodology using the land suitability index [27, 28]. In ourapproach, productive land has to be at least marginally suitablefor rainfed crop production according to climate parameters,topography and soil type, and is not covered by built-up,grazing or forestry land. This scenario serves as a pointof reference for the assessment of technological change inagricultural production (i.e. yield increase), land use dynamicsand shadow prices for food and irrigation water. In theclimate change mitigation scenario (M), bioenergy demand iscalculated by ReMIND under climate policies that limit GHGemissions in the energy sector until 2095–1100 Gt CO2 withthe aim to stay below 2 ◦C change in global mean temperature(GMT), compared to the pre-industrial level. But especiallyin tropical and sub-tropical developing countries, bioenergyproduction is expected to increase the conversion of intact andfrontier forests into cropland. These forests represent areas ofhigh value for carbon [29] and for biodiversity [16], so there isa potentially adverse impact when forest carbon is released forthe purpose of bioenergy production [30, 31]. Therefore, in theforest conservation scenario (M FC) we additionally constrainthe land pool available for cropland expansion by excluding theshare of presently intact and frontier forests [32, 33] for eachgrid cell. By this approach, the amount of suitable land forcropland expansion is reduced by 74% globally.

3. Results

3.1. Cost-effective contribution of bioenergy

In the scenario without forest conservation (M), bioenergydemand increases up to about 300 EJ in 2095 with a demandof about 100 EJ in 2055 (figure 2(a)). This demand scenario

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Figure 3. Total agricultural land (a) and share of bioenergy cropland in total cropland (b) for the scenario with bioenergy for climate changemitigation (M) (upper row) and the scenario with bioenergy and avoided deforestation for climate change mitigation (M FC) (lower row) in2015 (red bars), 2055 (orange bars) and 2095 (yellow bars).

is a result of the economic interplay between the agriculturaland the energy sector where simulated bioenergy prices arerising to 7 US$ per GJ in 2095 (figure 2(b)). For thisspecific scenario, biomass from dedicated bioenergy crops willcontribute 25% to the total global demand for primary energycarriers (figure SI3 available at stacks.iop.org/ERL/6/034017/mmedia). 95% of the bioenergy production will be convertedinto secondary energy in combination with carbon capture andstorage (CCS). This combination of technologies enters themarket between 2020 and 2030 (figure SI4 available at stacks.iop.org/ERL/6/034017/mmedia) and has the unique feature ofproviding ‘negative emissions’. However, forest exclusion forthe purpose of biodiversity conservation and climate changemitigation affects the availability of cost-efficient biomass forenergy production significantly. The amount of bioenergysupplied is reduced to about 70 EJ in 2055 and 270 EJ in2095 in the scenario with 100% forest conservation (M FC)(figure 2(a)).

3.2. Land expansion and technological change

In the reference scenario (Ref), total cropland (excludingabandoned land) increases from 1442 million ha in 2005 to1770 million ha in 2095. In the scenario without forestconservation (M), cultivation of dedicated bioenergy cropsincreases total cropland to 1830 million ha, but forest exclusion(M FC) limits total cropland to 1520 million ha in 2095(figure 3(a)). In the M scenario, highest increases in croplandarea compared to 2005 can be observed in LAM (98%), AFR(89%) and PAS (39%). In the M FC scenario, highest increasesin total cropland area until 2095 occur in PAO (30%), AFR

(26%) and LAM (24%). Our simulation results reveal thatin the scenario without forest conservation (M) up to 29 Gtof additional cumulative CO2 emissions from land use changedue to the cultivation of dedicated bioenergy crops are likelyto occur until 2095. These co-emissions are negligible in thescenario with forest conservation (M FC) (figure SI5 availableat stacks.iop.org/ERL/6/034017/mmedia).

In 2095, highest shares of cropland used for bioenergycrop cultivation (figure 3(b)) can be found in FSU (31%) andLAM (24%) in the M scenario, and in FSU (37%) and LAM(23%) in the M FC scenario.

Figure 4 indicates (i) total cropland share and (ii) bioen-ergy cropland share for each grid cell, as derived by MAgPIEfor the mitigation scenario without forest exclusion (M) andthe forest conservation scenario (M FC) in 2095. The mapsshow that, the more forest is available for agricultural landexpansion, the more cropland is shifted towards tropicalregions where today large amounts of intact tropical forests canbe found, especially in AFR, LAM and PAS.

The second mechanism that allows increasing foodand bioenergy production is through intensification andtechnological change on currently used agricultural land. Inthe ‘reference scenario’ (Ref), an average global rate of yieldincrease of 0.6% per year is projected until 2095. This isequivalent to an increase in yields by the factor 1.8 in 100years. Due to increasing bioenergy demand the global rateof yield increase would have to rise to 0.8% per year (M).The highest rate (0.9% per year until 2095) can be foundin the forest conservation scenario (M FC), due to additionalrestrictions of land availability for agricultural expansion,figure 5 shows that in the M scenario, compared to the

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Figure 4. (a) Cell-specific share of total crop land and (b) share of bioenergy cropland in total cropland for the scenario with bioenergy (M) aswell as (c) cell-specific share of total crop land and (d) share of bioenergy cropland in total cropland for the scenario with bioenergy andavoided deforestation (M FC) (right column) in 2095.

Figure 5. Required annual yield increase until 2095 for the referencescenario (Ref) (black bars), the scenario with bioenergy for climatechange mitigation (M) (light grey bar) and the scenario withbioenergy and avoided deforestation for climate change mitigation(M FC) (dark grey bar).

reference scenario (Ref), yield increases due to the enhancedcultivation of dedicated bioenergy crops rise most stronglyin FSU, CPA and LAM. If, in addition, natural forests wereconserved (M FC), highest yield increases can be found inLAM, AFR and PAS.

3.3. Food and water prices

Figure 6 shows the regional aggregated food price index,i.e. the average of all crop and livestock products weighted withtheir average share in total food demand. Compared to Ref, thefood price index rises most strongly in EUR (22%) and FSU(16%) until 2095 if climate change mitigation is taken intoaccount and all suitable land is available for land expansion(M). But if forest conservation (M FC) is considered, the foodprice index rises most prominently in AFR (82%), LAM (73%)and PAS (52%) until 2095, compared to the reference scenario.

In the scenario without forest conservation (M), strongestgrowth in the regional water price index, i.e. changes in shadowprices for irrigation water relative to the reference scenariountil 2095, can be found in LAM (210%), FSU (170%) and

Figure 6. Regional food price index, i.e. the average of all croppingand livestock activities weighted with their share in total demand.The food price index describes relative changes to the referencescenario (no bioenergy demand for climate change mitigation and allsuitable land available for cropland expansion) for each year.

PAS (130%) (figure 7). In this case, bioenergy croplandcompetes directly for irrigation water with other agriculturalactivities. The forest conservation scenario (M FC) increasesthe regional water price index most heavily in LAM (460%),AFR (390%) and PAS (330%).

4. Discussion

Our study aims at assessing the cost-effective contributionof bioenergy to climate change mitigation by coupling

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Figure 7. Regional water price index. The water price indexdescribes changes in shadow prices for irrigation water relative to thereference scenario (no bioenergy demand for climate changemitigation and all suitable land available for cropland expansion) as amean over all years (2015–95) for all regions.

and applying the vegetation and hydrology model LPJmL,simulating agricultural yields and hydrological processes,the land use optimization model MAgPIE, simulating thecosts of biomass supply, and the energy–economy–climatemodel ReMIND, simulating the demand for bioenergy. Oursimulation results reveal that the energy sector uses about100 EJ globally in 2055 and up to 300 EJ in the year2095 from dedicated energy crops, if all suitable land foragricultural production was made available for land expansion.However, cultivation of bioenergy crops has several effects:it increases crop land expansion; it takes over a hugeshare in total cropland; it is mainly located in areas thattoday are occupied by intact ecosystems; and it increasesCO2 emissions from deforestation. Thus, converting intactecosystems, such as tropical rainforests or open woodlands,which store large amounts of carbon and belong to the mostdiverse terrestrial ecosystems, counteracts global climate andbiodiversity protection goals [24]. For bioenergy to make areal net contribution to climate change mitigation, intact forestshave to be protected (e.g. [5]). Our analysis shows that inthe scenario where intact and frontier forests are excluded thereduced land pool available for agricultural use would have tobe compensated by higher rates of technological change (0.9%per year until 2095) at additional costs to fulfil the demand forbioenergy. Simulated rates of technological change have to beseen in comparison with historical yield growth of about 1.3%

annually from 1970 to 1995 averaged across all crops [25].Yield growth rates have declined in the most recent decade [34]but yield growth potential is still considerable [35].

Due to rising bioenergy prices, restrictions for landavailability decrease bioenergy use in the energy system in2055 considerably to about 70 EJ. But in the long run(until 2095) the use of biomass in the energy system iscompetitive, mainly due to the option of generating negativeemissions in the energy system by using CCS (figure SI4available at stacks.iop.org/ERL/6/034017/mmedia). Reasonis the difficulty to supply the transportation sector with low-carbon fuels. Bioenergy is partly converted to fuels and partlyto electricity, both in combination with CCS. In our model,the resulting negative emissions compensate for higher grossemissions from fossil fuels. However, with respect to theimportance of CCS for the contribution of bioenergy to climatechange mitigation one needs to consider that the availability ofthis technology is still uncertain and not yet proven on a largescale. CCS will require huge infrastructure developments,in particular a pipeline network similar to the existing gastransport infrastructure, and new storage capacities. Inaddition, hazards and risks related to this technology, such asleakage of stored and transported CO2, ground instabilities orcontamination of groundwater [36], are not considered in ourmodelling approach.

Our results are in the lower range of recent studiesof bioenergy potentials. The potential supply of bioenergyproduction from dedicated energy crops, as reported in theliterature, varies from zero to several hundreds of EJ peryear (e.g. [7, 8, 24, 33, 37–39]). These ranges differ dueto large discrepancies in assumptions about land availabilityfor biomass plantations and yield levels (including futureyield improvements) in crop production. For example, thehighest biomass potential of 1500 EJ for 2050 [38] is basedupon an extremely intensive, technologically very highlydeveloped agriculture. On the contrary, the lowest biomasspotential for 2050 [39] is based on a pessimistic scenario withhigh population growth, high food demands and extensiveagricultural production systems. Our approach for assessingbioenergy potentials has the great advantage that, on theone hand, it considers the trade-off between land expansionand yield-increasing technological change endogenously. Onthe other hand, it is based on a global biogeochemical andbioclimatic analysis of plant growth potentials in contrastto assessments that have simply extrapolated findings fromplantation field studies to the larger scale (such as [37]).Therefore, limitations of global biomass potential due tolimitations in water availability for plant transpiration [40]have been frequently underestimated or downplayed, if notignored [41].

The cost-effective and sustainable contribution of bioen-ergy from dedicated energy crops to climate change mitigation,as it has been assessed in this study, can be enhanced or reducedif other assumptions are taken into account. On the one hand,other biomass resources such as the use of agricultural andforest residues, ranging from very low estimates to around100 EJ [7], could be considered. However, competingapplications of biomass for soil improvement or animal feed

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could reduce the potential of residues for bioenergy applicationto the lower end of the range [42]. On the other hand,our assumption on global availability of cellulosic biomasswithout any trade restrictions may overestimate our calculatedbioenergy potentials. Even though the volume of biomass tradefor food, feed and fibre has grown rapidly in recent years [43],trade of biomass for bioenergy is in its initial phases, withwood pellets, ethanol, and palm oil being typical examplesto meet growing demand [44]. Transportation of cellulosicbiomass for bioenergy production could become more efficientby converting biomass into charcoal and thus increasing itsenergy content [45].

While assessing biomass production potentials, it isimportant to consider the complex linkages between large-scale cultivation and use of biomass for energy and climatechange mitigation, and conflicts with other sustainabilityaspects. First, besides co-emissions from deforestation,large-scale cultivation of perennial ligno-cellulosic bioenergycrops is expected to increase the competition for land andtherefore raise N2O emissions from agricultural soils due tointensification. Popp et al [46] showed that these co-emissionsfrom bioenergy production are only a minor factor comparedto the mitigation potential of bioenergy in the long run.Second, avoiding deforestation for biodiversity conservationby excluding intact forests (mainly in the tropics) from suitableland for cropland expansion could add additional pressure onother natural ecosystems with extraordinary biodiversity, suchas savannas.

Other studies (e.g. [47]) indicate that systems integratingbioenergy and feed production and producing synergies doexist. To some extent, these types of innovation areimplicitly considered in our additional technological changein agriculture. However, the large-scale use of biomass forbioenergy production as indicated in our study will still affectfood and water security due to increased competition for land,water and other inputs of agricultural production. We haveshown that food prices and the implicit values of irrigationwater are only slightly affected without forest conservation,but rise significantly (especially in the tropics) if forests areexcluded from available land for future cropland expansion.Higher food prices follow directly from competition withenergy crops, due to limitations in land availability andassociated needs for technological development at additionalcosts. Shadow prices of irrigation water rise with increasingforest conservation, because less land is available for rainfedagriculture, and biomass accumulation for dedicated bioenergycultivation leads to higher evapotranspiration rates, which canreduce water availability in regions where water is alreadyscarce [48]. This is a strong indication that the competitionfor water between agriculture, private households, and industryis likely to increase heavily in many regions. Furthermore,our analysis indicates that competition for water may not onlyincrease in regions where bioenergy crops are cultivated orwhere forests are conserved (i.e. Latin America). To gainspace for bioenergy crops and forest conservation, agriculturalproduction of food and livestock feed is likely to be shifted toother regions (i.e. South Asia) which are originally not directlyaffected by these activities and thereby increase shadow pricesof irrigation water there indirectly.

We conclude that bioenergy from dedicated ligno-cellulosic energy crops is likely to be a cost-efficientcontribution to the future energy mix. Without consideringco-emissions from deforestation, biodiversity issues, andimpacts on food and water security, the biomass resourcepotential could deliver a considerable amount of the world’sprimary energy demand up to 2095. Our trade-off analysisindicates, however, that restrictions on land availability,by protecting untouched tropical forests and other high-carbon ecosystems, are likely to reduce bioenergy potentialssignificantly in the medium run, but less so in the longrun. Most likely, forest conservation combined with large-scale cultivation of dedicated bioenergy for climate changemitigation will generate conflicts with respect to food supplyand water resource management. Integrated policies for energyproduction, land use and water management are thereforeneeded to steer the potential contribution of bioenergy to thefuture energy mix, without neglecting the side effects on land-use-related GHG emissions, biodiversity conservation, foodand water security.

Acknowledgments

We gratefully acknowledge financial support by the GermanBMBF Projects ‘GLUES—Global Assessment of Land UseDynamics on Greenhouse Gas Emissions and Ecosystem Ser-vices’, ‘Hydrothermal carbonization of biomass—Potential,Experiment, Pilot plant’, and the EU FP7 projects ‘Visions ofLand Use Transitions in Europe (VOLANTE)’ and ‘EnhancingRobustness and Model Integration for The Assessment ofGlobal Environmental Change (ERMITAGE)’. We wish tothank two anonymous reviewers for their valuable commentswhich substantially improved the paper.

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3.5 References 59

Supporting Online Information (SI) SI-Methods

The dynamic global vegetation and hydrology model LPJmL

LPJmL is a process-based ecosystem model which simulates dynamically the growth, production and

phenology of 9 plant functional types (representing natural vegetation at the level of biomes; Sitch et

al., 2003), 12 crop functional types (CFTs), grazing land (Bondeau et al., 2007; Fader et al., 2010). The

model version used here additionally considers three types of specialized grassy and woody bioenergy

crops such as Miscanthus or poplar for bioenergy supply. Bioenergy trees are managed as short rotation

coppice with a harvest cycle of 8 years, while biomass grasses are harvested annually at the end of the

growing season (Beringer et al., 2011). Carbon fluxes (gross primary production, autotrophic and

heterotrophic respiration) and carbon pools (in leaves, sapwood, heartwood, storage organs, roots, litter

and soil) as well as water fluxes and pools (interception, evaporation, transpiration, soil moisture,

snowmelt, runoff, discharge) are modeled in coupling with the dynamics of the vegetation. For

example, c transpiration, photosynthesis and plant water stress are explicitly coupled, and atmospheric

CO2 concentration directly affects transpiration and biomass production through physiological and

structural plant responses (Gerten et al., 2004; Gerten et al., 2007).

Water requirements and water consumption of irrigated and rainfed food and feed crops are

distinguished in the model. In case plants are irrigated (on the areas equipped with irrigation techniques

according to Portmann et al., 2010 and Fader et al., 2010), it is assumed that the gross irrigation needs –

which is determined daily according to the plants’ water stress status (derived from soil and

atmospheric moisture) and considering per-country irrigation efficiency – can always be fulfilled

(‘IPOT’ simulation as described in Rost et al., 2008). Unlimited availability of water resources is also

assumed for irrigated bioenergy plantations. The phenology (sowing dates, leaf status, harvest dates) of

1

60 3 Implications for the land system

the different plant types is simulated by LPJmL based on a set of CFT-specific parameters derived from

long-term temperature and precipitation conditions. Simulated crop yields are calibrated at country

scale against those reported in the Food and Agriculture Organization’s FAOSTAT (2008) database, in

order to ensure that yield levels and associated water use productivities and evapotranspiration fluxes

are realistically represented (Fader et al., 2010, 2011).

All processes are modeled at a daily resolution and on a global 0.5° grid.

The suitability of LPJmL for vegetation/crop and water studies has been proven in a number of studies

by validating simulated phenology and yields (Bondeau et al., 2007, Fader et al. 2010), crop water

productivities in rainfed and irrigated agriculture (Fader et al., 2011), biomass production of bioenergy

crops (Beringer et al., 2011), river discharge (Gerten et al., 2004, Biemans et al., 2009),

evapotranspiration (Sitch et al., 2003; Gerten et al., 2004) and irrigation water requirements (Rost et al.,

2008).

The land use allocation model MAgPIE

MAgPIE (Lotze-Campen et al., 2008, 2009, Popp et al. 2010) is a global land use allocation model,

which has been coupled here to the grid-based dynamic vegetation model LPJmL. It takes regional

economic conditions such as demand for agricultural commodities, level of agricultural technology,

and production costs as well as spatially explicit data on potential crop yields, land and water

constraints (from LPJmL) into account and derives specific land use patterns, yields and total costs of

agricultural production for each grid cell. The objective function of the land use optimization model is

to minimize total cost of production for a given amount of regional food and bioenergy demand. Hence,

it assures equilibrium between demand and supply, but without prices as endogenous variables.

Regional food energy demand is defined for an exogenously given population in 10 food energy

categories, based on regional diets (FAOSTAT, 2008). Future trends in food demand are computed as a

function of income (measured in terms of gross domestic product (GDP)) per capita based on a cross-

2

3.6 Supporting Online Information 61

country regression. Food and feed energy for the demand categories can be produced by 20 cropping

activities and 3 livestock activities. Feed for livestock is produced as a mixture of grain, green fodder

produced on crop land, and pasture. The livestock sector is currently implemented in a simplified way

at the regional level, i.e. it is assured that total regional feed demand for grains and green fodder (from

cropland), based on aggregate regional feed mixes from FAO food balance sheets, are taken into

account in the land use allocation process. It is assumed that the use of currently available pastureland

can be intensified in the future to meet increasing demand. As a consequence, pasture areas remain

constant. As we focus on cellulosic bioenergy crops in this study, bioenergy is supplied from

specialized grassy and woody bioenergy crops such as Miscanthus or Poplar. All bioenergy products in

the model are delivered into an aggregated demand pool. Variable inputs of production are labour,

chemicals, and other capital (all measured in US$). Moreover, the model can endogenously decide to

acquire yield-increasing technological change at additional costs (Schmitz et al. 2010). The costs for

technological change for each economic region are based on its current agricultural land-use intensity

and grow with each further investment in technological change, i.e. increasing land use intensity.

Therefore, increases in agricultural production are more costly in industrialized regions compared to

developing countries. Agricultural land-use intensity, that means here i.e. the degree of yield

amplification caused by human activities, is estimated by calculating the ratio between currently

observed yields as reported by FAO (FAOSTAT, 2009) and simulated yields under globally constant

management derived by LPJmL (for more detail see Dietrich et al. 2010). The use of technological

change is either triggered by a better cost-effectiveness of technological change compared to other

investments or as an answer for a response to production constraints, such as land scarcity. The use of

technological change is either triggered by a better cost-effectiveness of technological change

compared to other investments or as an answer for production constraints such as land scarcity. For

future projections the model works on a time step of 10 years in a recursive dynamic mode. The link

between two consecutive periods is established through the land-use pattern. The optimized land-use

3

62 3 Implications for the land system

pattern from one period is taken as the initial land constraint in the next. If necessary, additional land

from the non-agricultural area can be converted into cropland at additional costs. Carbon content for

different land use activities and crop yields are supplied by the Lund-Potsdam-Jena dynamic global

vegetation model with managed Lands (LPJmL) (Bondeau et al., 2007) for each grid cell. CO2

emissions from land use change occur if the carbon content of the previous land use activity exceeds

the carbon content of the new land use activity. Potential crop yields for MAgPIE are originally

computed with LPJmL at a 0.5° resolution as a weighted average of irrigated and non-irrigated

production, if part of the grid cell is equipped for irrigation according to the global map of irrigated

areas (Döll and Siebert, 2000). LPJmL computes potential irrigated and non-irrigated yields for each

crop within each grid cell as an input for MAgPIE. In case of pure rain-fed production, no additional

water is required, but yields are generally lower than under irrigation. In addition, LPJmL has been

applied a priori to simulate cell specific available water discharge under potential natural vegetation

and its downstream movement according to the river routing scheme implemented in LPJmL. Then, if a

certain area share is assumed to be irrigated in MAgPIE, additional water for agriculture is taken from

available discharge in the grid cell. MAgPIE endogenously decides on the basis of minimizing the costs

of agricultural production where to irrigate which crops. Each cell of the geographic grid is assigned to

1 of 10 economic world regions: Sub-Saharan Africa (AFR), Centrally Planned Asia including China

(CPA), Europe including Turkey (EUR), the Newly Independent States of the Former Soviet Union

(FSU), Latin America (LAM), Middle East/North Africa (MEA), North America (NAM), Pacific

OECD including Japan, Australia, New Zealand (PAO), Pacific (or Southeast) Asia (PAS), and South

Asia including India (SAS). The regions are initially characterized by data for the year 1995 on

population (CIESIN et al., 2000), gross domestic product (GDP) (World Bank, 2001), food energy

demand (FAOSTAT, 2008), average production costs for different production activities (McDougall et

al., 1998), and current self-sufficiency ratios for food (FAOSTAT, 2008). Self-sufficiency ratios for

food are kept constant over time, i.e. we are not simulating further trade liberalization in the scenarios.

4

3.6 Supporting Online Information 63

Land-conversion activities provide for potential expansion and shifts of agricultural land in specific

locations. For the base year 1995, total agricultural land is constrained to the area currently used within

each grid cell, according to the dataset of Ramankutty and Foley (1999) as extended by Rost et al.

(2008). However, if additional land is required for fulfilling demand, this can be taken from the pool of

non-agricultural land at additional costs. These land-conversion costs force the model to utilize

available cropland first, and land conversion will become relevant only if land becomes scarce in a

certain location or if the marginal cost reductions by producing crops on converted land outweigh the

costs of conversion. The model computes a shadow price for binding constraints in specific grid cells,

e.g. related to water availability, reflecting the amount a land manager would be willing to pay for

relaxing the constraint by one unit. Furthermore, since the model minimizes production costs for a

given demand for food and bioenergy products, these numbers show the marginal increase in

production costs for an additional unit of output.

The macro-economy-energy-climate model ReMIND

The ReMIND model is an integrative framework that embeds a detailed energy system model (ESM)

into a macro-economic growth model (MGM) and a climate system model (CSM) that computes the

effect of GHG emissions; see Bauer et al. (2010) and Leimbach et al. (2009). ReMIND is completely

hard-linked and solves the three integrated models simultaneously considering all interactions with

perfect foresight. The model is formulated as a non-linear programming problem. The present study

uses a global single region version, which is equivalent to the assumption of completely integrated

world markets for traded goods. This is equivalent to a multi-regional model with completely

integrated markets and zero transportation costs that would lead to full price equalization of all traded

goods. It solves a general equilibrium problem by maximizing inter-temporal social welfare with

perfect foresight of the household sector subject to constraints of the macroeconomic, the energy and

5

64 3 Implications for the land system

the climate system. This methodological approach is well-established in the literature on climate

change mitigation. The household sector owns all production factors – labor, resources, capital stocks

and emission permits – that are supplied to the economic sectors, which in turn pay factor prices that

make up the income of households that they allocate to consumption and saving. The macroeconomic

production sector combines an aggregate capital stock, labor and various types of final energy to

produce an aggregate economic good. The value of this aggregate good is completely exhausted to pay

for the production factors. In the ESM, the energy sector demands financial means for investments,

operation and maintenance, and primary energy in order to produce final energy carriers that are

supplied to the macroeconomic production sector. The energy sector comprises a large number of

energy conversion technologies – i.e. a heterogeneous capital stock – that convert scarce primary

energy carriers into final energy carriers that are supplied to the macro-economical framework. The use

of fossil energy carriers leads to CO2 emissions depending on the technologies that are used. The use of

biomass is assumed to be carbon neutral and if it is combined with carbon capture and sequestration the

net emission can even be negative. Some energy technologies improve endogenously from

accumulating experience known as learning by doing. The macro-economy and the energy sector

interact via energy and capital markets. The hard-link between the ESM and the MGM solves for a

social optimum that establishes a simultaneous equilibrium on these markets as has been shown in

Bauer et al. (2008). Hence, the ReMIND model considers all interactions between the various markets

and investments change accordingly. The CSM considers the accumulation of CO2 and other

greenhouse gases. The interactions of these gases are explicitly considered regarding the atmospheric

chemistry and the changes of radiative forcing that finally trigger global warming. The model computes

the global mean temperature based on an energy balance model. The inertia of the climate system is

represented by the accumulation and physical depreciation of GHG as well as the thermal inertia of the

oceans; see Tanaka and Kriegler (2007). Climate policies can be analyzed by limiting the increase of

global mean temperature (GMT) to a certain level. The model then computes the first-best cost-

6

3.6 Supporting Online Information 65

minimal solution for keeping the climate system within this limit regarding the emission pathway in

general and the investments in particular.

The most notable part of the ESM is the conversion of primary energy into secondary energy by

applying specific technologies. The alternative conversion routes and the potential of energy from

various primary energy determine the flexibility to transform the energy sector in order to cope with

constraints on emissions. Table SI1 provides an overview of all technologies that convert primary into

secondary energy carriers. A special role is played by biomass, since it is a low carbon energy carrier

that can be converted in nearly all other types of secondary energy carriers. This is a major difference

to the other renewables and nuclear that mainly are source for producing electricity.

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66 3 Implications for the land system

Table SI1: Overview of primary energy carriers, secondary energy carriers and the technologies for conversion.

Primary energy carriers

Exhaustible Renewable

Coa

l

Oil

Gas

Ura

nium

Sola

r,

Win

d,

Hyd

ro

Geo

-th

erm

al

Bio

mas

s

Electricity PC*, IGCC*, CoalCHP DOT GT, NGCC*,

GasCHP TNR, FNR SPV, WT, Hydro HDR BioCHP,

BIGCC*

H2 C2H2 SMR* B2H2*

Gases C2G GasTR B2G

Heat CoalHP, CoalCHP

GasHP, GasCHP GeoHP BioHP, BioCHP

Liquid fuels C2L* Refin. B2L*,

BioEthanolOther

Liquids Refin. Se

cond

ary

ener

gy

carr

iers

Solids CoalTR BioTR

Abbreviations: PC = conventional coal power plant, IGCC = integrated coal gasification combined cycle, CoalCHP = coal combined heat power, C2H2 = coal to H2, C2G = coal to gas, CoalHP = coal heating plant, C2L = coal to liquids, CoalTR = coal transformation, DOT = diesel oil turbine, Refin. = Refinery, GT = gas turbine, NGCC = natural gas combined cycle, GasCHP = Gas combined heat power, SMR = steam methan reforming, GasTR = gas transformation, GasHP= gas heating plant, TNR = thermal nuclear reactor, FNR = Fast nuclear reactor, SPV = solar photovoltaic, WT = wind turbine, Hydro = hydro power, HDR = hot-dry-rock, GeoHP = heating pump, BioCHP = biomass combined heat and power, BIGCC = Biomass IGCC, B2H2 = biomass to H2, B2G = biogas, BioHP = biomass heating plant, B2L = biomass to liquids, BioEthanol = biomass to ethanol, BioTR = biomass transformation * These technologies are also available with carbon capture.

The energy sector part of the REMIND model – that is of particular importance in this study – relies on

a number of assumptions of which the most important are reported next. Figure SI1 presents the fossil

fuel extraction cost curves that are used for the present study. The data was mainly based on the study

by Rogner (1997). The original costs reported in Rogner started at about 2.5$US per GJ for oil and gas

and 1.5$US per GJ for coal, which are much lower than market prices in 2005. The initial extraction

costs were corrected up-wards to meet current market prices.

8

3.6 Supporting Online Information 67

0,0

2,0

4,0

6,0

8,0

10,0

12,0

14,0

16,0

18,0

20,0

0,0 10,0 20,0 30,0 40,0 50,0 60,0

Cumulative Extraction [ZJ]

Mar

gina

l Ext

ract

ion

Cos

ts [$

US2

005

per G

J] OilNat. GasCoal

Figure SI1: Extraction cost curves for the fossil energy carriers. Extraction costs for uranium are based on NEA (2003). Uranium extraction costs increase from initially

30$/kgU to 300$/kgU at a cumulated extraction of 15.8MtU.

Figure SI2 presents the renewable energy potentials. For geo-thermal Hot Dry Rock only a small

potential of 1EJ p.a. is assumed; Turkenburg (2001) reported a maximum electricity production

potential of 43EJ p.a., but a final assessment is difficult to make because HDR is highly site dependent.

Figure SI2: Availability of renewable energy carriers in terms of energy production potentials differentiated by technologies and grades. The gray color indicates the capacity factor as the fraction per year a technology is available. Sources: ENERDATA (2006), Trieb et al. (2009), Hoogwijk (2004), Sims et al. (2007).

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68 3 Implications for the land system

Table SI2: Techno-economic characteristics of technologies based on exhaustible energy sources and biomass (cf. Iwasaki (2003), Hamelinck (2004), Bauer (2005), MIT (2007), Ragettli, (2007), Rubin et al. (2007), Schulz et al. (2007), Uddin and Barreto (2007), Takeshita and Yaaij (2008); Gül et al. (2008), Brown et al. (2009), Chen and Rubin (2009), Klimantos et al. (2009). All $US values refer to 2005 values. Original literature values are normalized to this value taking into account general inflation, the CERA (2009) power plant price index and – if necessary – exchange rates. Techno-economic Parameters Life-

time Investment costs O&M costs Conversion

efficiency Capture

rate years $US/kW $US/GJ % %

No CCS With CCS

No CCS With CCS

No CCS

With CCS

With CCS

PC 55 1400 2400 2.57 5.04 45 36 90 Oxyfuel 55 2150 4.32 37 99 IGCC 45 1650 2050 3.09 4.20 43 38 90 C2H2* 45 1264 1430 1.65 1.87 59 57 90 C2L* 45 1000 1040 1.99 2.27 40 40 70

Coal

C2G 45 900 0.95 60 NGCC 40 650 1100 0.95 1.62 56 48 90 Gas SMR 40 498 552 0.58 0.67 73 70 90 BIGCC* 40 1860 2560 3.95 5.66 42 31 90 BioCHP 40 1700 5.06 43.3 B2H2* 40 1400 1700 5.27 6.32 61 55 90 B2L* 40 2500 3000 3.48 4.51 40 41 50

Biomass

B2G 40 1000 1.56 55 Nuclear TNR 35 3000 5.04 33~ *) these technologies represent joint processes ~) thermal efficiency Abbreviations: PC – conventional coal power plant, Oxyfuel – coal power plant with oxyfuel capture, IGCC – integrated coal gasification combined cycle power plant, C2H2 – coal to hydrogen, C2L – coal to liquids, NGCC – Natural gas combined cycle power plant, SMR – steam methane reforming, B2H2 – biomass to hydrogen, B2G – biogas plant, B2L – biomass to liquid, TNR – thermal nuclear reactor, SPV – solar photovoltaic, WT – wind turbine, Hydro – hydroelectric power plant. Note: technologies marked with a * are joint production processes; for these technologies, capturing does not necessarily result in higher investment costs and lower efficiency in producing the main product.

10

3.6 Supporting Online Information 69

Techno-economic details for most exhaustible and biomass fueled conversion technologies are

provided in Table SI2. Over the last few years the more pessimistic assessment of coal fired IGCC

plants was the most important shift. The assumptions take this more careful interpretation into account:

without CCS investment costs for IGCC are lower than for conventional pulverized coal (PC) plants,

and the advantage in the case with CCS is greatly reduced. The general assessment to be found in the

literature about electricity plants fueled with gas did not change that much over the last few years. The

assumptions used here are generally in line with the literature. For biomass IGCC with and without

CCS the parameters are chosen based on a broad literature review for a plant size of 100MW electrical

output. Less optimistic assessments about the investment costs than those applied in the present study

are provided by Faaij (2006) and IEA (2008a).

Coal and biomass can also be converted into gases, liquids and hydrogen based on gasification. The

conversion of coal, gas and biomass into liquid fuels and hydrogen can be augmented by carbon

capture. The values for biomass technologies are at the pessimistic end of the range to be found in the

literature. Those for coal and gas are in the medium range.

Table SI3: Techno-economic characteristics of technologies based on renewable energy sources and biomass. For details see Neij (2003), Nitsch et al. (2004), IEA (2008a), Junginger et al. (2008), Lemming et al. (2008).

Lifetime Investment costs

Floor costs

Learning Rate

Cumulative capacity 2005

O&M costs

Years $US/kW $US/kW % GW $US/GJ Hydro 95 3000 - - - 3.46 Geo HDR

35 3000 - - - 4.2

Wind onshore

35 1200 883 12 60 2.9

Wind offshore

35 2200 1372 8 1 4.7

SPV 35 4900 600 20 5 10.33

The techno-economic parameters for renewable technologies producing electricity are given in Table

SI3. Hydro power has investment costs of 3000$US per kW. The exact number is highly site-

dependent; see IEA (2008a). For wind power stations we distinguish on- and off-shore locations

11

70 3 Implications for the land system

separately because both technologies are very different with respect to costs and other technological

features. The floor costs are derived from cost projections for the year 2050.

Table SI4: Techno-economic parameters of storage technologies; based on Chen et al. (2009) and expert interviews. Units Daily variation Weekly variation Seasonal variation Technology Redox-Flow-batteries H2 electrolysis +

combined cycle gas turbine

Efficiency % 80 40 Storage capacity Hours 12 160 Investment costs $US/kW 4000 6000 Floor costs $US/kW 1000 3000 Learning rate % 10 10 Cumulative capacity in 2005

TW 0.7 0.7

Life time Years 15 15 Cheaper technologies but not included due to limited potential

Pump-storage hydro & compressed air

storage

Pump-storage hydro & compressed air

storage

Capacity penalty to secure supply

The approach for balancing fluctuations of renewable energy technologies wind and solar PV

implemented into the ReMIND model distinguishes between variations on the daily, weekly and

seasonal time scale. Increasing market shares of fluctuating energy sources increase the need for

storage to guarantee stable electricity supply. The superposition of variations on the three time scales is

completely represented. Daily and weekly variations are compensated by installation of storage plants;

see Table 4. Seasonal variations imply a capacity penalty.

The sequestration part of CCS requires equipment and energy for transportation and injection. The

investment costs for having available the equipment for injecting one GtC per year are 175 bil.$US; see

Broek et al. (2008) and Kjärstad and Johnsson (2009). The upper limit for cumulative sequestration is

2775GtC; see Benson and Cook (2005) and IEA (2008b). The model does not consider leakage of

injected CO2.

ReMIND uses adjustment costs for thermal nuclear reactors. For this technology it is assumed that in

2005 a maximum of 5GW could be installed increasing by 1GW p.a. Each percent investment beyond

this limit increases the investment costs by 0.5%.

12

3.6 Supporting Online Information 71

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3.6 Supporting Online Information 75

SI-Figures

Figure SI3: Primary energy consumption for the scenario without forest conservation (M).

Figure SI4: Primary energy consumption of biomass by the different bioenergy conversion technologies (for the scenario without forest conservation).

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76 3 Implications for the land system

Figure SI5: Additional global CO2 emissions from land use change due to bioenergy production for the M (brown line) and the M_FC (orange line) scenario.

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3.6 Supporting Online Information 77

4 Bio-IGCC with CCS as a long-termmitigation option in a coupledenergy-system and land-use model

David KleinNico Bauer

Benjamin BodirskyJan Philipp Dietrich

Alexander Popp

Published as Klein, D.; Bauer, N.; Bodirsky, B.; Dietrich, J.P.; Popp, A. (2011): Bio-IGCC with CCSas a long-term mitigation option in a coupled energy-system and land-use model, Energy Procedia,4, 2933–2940.

79

Available online at www.sciencedirect.com

Energy Procedia 00 (2010) 000–000

EnergyProcedia

www.elsevier.com/locate/XXX

GHGT-10

Bio-IGCC with CCS as a long-term mitigation option in a coupled energy-system and land-use model

David Kleina*, Nico Bauera, Benjamin Bodirskya,Jan Philipp Dietricha, Alexander Poppa

a Potsdam Institute for Climate Impact Research, Germany

Elsevier use only: Received date here; revised date here; accepted date here

Abstract

This study analyses the impact of techno-economic performance of the BIGCC process and the effect of different biomass feedstocks on the technology’s long term deployment in climate change mitigation scenarios. As the BIGCC technology demands high amounts of biomass raw material it also affects the land-use sector and is dependent on conditions and constraints on the land-use side. To represent the interaction of biomass demand and supply side the global energy-economy-climate model ReMIND is linked to the global land-use model MAgPIE. The link integrates biomass demand and price as well as emission prices and land-use emissions. Results indicate that BIGCC with CCS could serve as an important mitigation option and that it could even be the main bioenergy conversion technology sharing 33% of overall mitigation in 2100. The contribution of BIGCC technology to long-term climate change mitigation is much higher if grass is used as fuel instead of wood, provided that the grass-based process is highly efficient. The capture rate has to significantly exceed 60 % otherwise the technology is not applied. The overall primary energy consumption of biomass reacts much more sensitive to price changes of the biomass than to techno-economic performance of the BIGCC process. As biomass is mainly used with CCS technologies high amounts of carbon are captured ranging from 130 GtC to 240 GtC (cumulated from 2005-2100) in different scenarios.

© 2010 Elsevier Ltd. All rights reserved

Keywords: Biomass, IGCC, Carbon Capture and Sequestration, Land use, Soft link

1. Introduction

Negative CO2 emission technologies can play an important role on the way towards a low emissions energy system if they are available at reasonable costs [5]. One way of achieving negative emissions is applying CCS on biomass conversion processes. One promising biomass-to-electricity conversion technology suitable for CCS with a high capture rate is the integrated biomass gasification combined cycle (BIGCC), which is expected to be highly efficient and economically feasible as it is technically similar to the efficient coal IGCC process and can profit from the experiences made with those plants [19]. Moreover, there are several other biomass CCS technologies but with less favorable techno-economic performance. However, only few non-commercial BIGCC demonstration plants

* Corresponding author. david.klein@pik-potsdam.de , Tel.: ++49+331 288 2488, P.O.Box 601203, 14412 Potsdam, Germany..

c⃝ 2011 Published by Elsevier Ltd.

Energy Procedia 4 (2011) 2933–2940www.elsevier.com/locate/procedia

doi:10.1016/j.egypro.2011.02.201

4.1 Introduction 81

2 Author name / Energy Procedia 00 (2010) 000–000

without CCS have been realized [4, 6]. Thus economic BIGCC studies analyzing different plant configurations are hypothetical. This results in high uncertainty about techno-economic performance of potential BIGCC plants.

Additional uncertainty occurs concerning different biomass raw materials feeding the plant. There are two main types of cellulosic short rotation energy crops: wood (poplar, willow, eucalyptus, pine) and grass (primarily miscanthus). Most techno-economic BIGCC studies assume wood as biomass fuel because it is easier to gasify. But grass is also considered to be a high energetic easy-to-grow short rotation crop. As grass contains more ash, which complicates the gasification and gas cleaning process, it is expected to lower the performance. There is scarce information about the effects of these different biomass fuels on the performance of the overall BIGCC process.

To assess the impact of techno-economic performance of the BIGCC process and the effect of different biomass feedstocks on the technology’s long term application in climate change mitigation scenarios we use the global energy-economy-climate model ReMIND [1]. As the BIGCC technology demands high amounts of biomass raw material it also affects the land-use sector and is dependent on conditions and constraints on the land-use side. The production of bioenergy crops causes greenhouse gas (GHG) emissions in the agricultural sector due to fertilisation of dedicated bioenergy crops and land-use change such as deforestation. The rising demand for food and bioenergy production leads to enhanced competition for limited crop land and subsequently to rising prices of biomass, food and water. To represent the dynamic interactions of the energy sector demanding and the agricultural sector supplying biomass for energy production ReMIND is linked to the global land-use model MAgPIE [15]. The link integrates biomass demand and price as well as emission prices and land-use emissions.

Using this integrated modeling framework our study analyses how the BIGCC technology contributes to long term climate change mitigation by answering the following questions:

1. What are the main factors that decide about the application of the BIGCC technology? 2. What is the mitigation potential of the technology? 3. Which biomass-technologies compete with BIGCC in the energy system?

To answer the listed research questions the following scenarios were performed:

� Varying the techno-economic performance: investment costs, electrical efficiency and capture rate, � choosing different biomass raw materials for the process: grass or wood, � comparing emissions of a business as usual scenario and a climate policy scenario,

The remainder of this paper is structured as follows: Section 2 first introduces both the energy-economy-climate model ReMIND and the land-use model MAgPIE separately and then explains the model coupling. Moreover it presents the results of a broad literature review concerning the techno-economic characteristics of the BIGCC process. Section 3 gives a detailed description of the scenarios and presents the results. The last section closes with a discussion and outlook on future work.

2. Modeling energy-system and land-use interactions

2.1. The global energy system model ReMIND

The BIGCC technology is integrated into the global energy-economy-climate model ReMIND (Refined model of Investments and Technological Development) that hard-links a macroeconomic growth model with a bottom-up energy system model and a simplified climate module [1]. The macroeconomic growth model of the Ramsey-type solves a general equilibrium problem by maximizing inter-temporal social welfare with perfect foresight subject to constraints of the macroeconomic, the energy and the climate system. This results in optimal distribution of investments among energy conversion technologies in the energy system and in the case of a climate target constraint like it is considered in this study it leads to minimized mitigation costs.

The bottom-up energy system module represents the energy sector on a detailed level of various conversion technologies along the path from primary to final energy production and estimates resulting CO2 emissions. Technologies are characterized by a set of techno-economic parameters such as specific investment costs, conversion efficiency, operation and maintenance costs or technical life-time. Non-energy GHG emissions (CO2,

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82 4 Coupled energy-system and land-use model

Author name / Energy Procedia 00 (2010) 000–000 3

CH4 and N2O) from land-use, land-use change and other sources are estimated by marginal abatement cost curves (MAC) [16]. The climate system model calculates the global mean temperature from the accumulation of CO2 and other GHG [23].

There are multiple bioenergy conversion technologies available in the model: Transportation fuels can be obtained either from Fischer-Tropsch processes (once through or with producer gas recycling) with and without CCS or from a biomass-to-ethanol technology without CCS. Besides the BIGCC plant electricity can be produced by a combined heat and power biomass plant without CCS. A simple biomass heating plant without CCS is also available. Moreover hydrogen can be supplied by gasification of biomass with and without CCS.

As lignocellulosic bioenergy crops are expected to cause much less GHG emissions and less competition with food production in the land-use sector than 1st generation energy crops, the bioenergy technologies considered in this study focus on 2nd generation purpose-grown bioenergy conversion routes. The biomass supply side is represented by the global land-use model MAgPIE. It computes the corresponding biomass price and resulting land-use emissions for a given biomass demand.

2.2. The global land-use model MAgPIE

MAgPIE (Model of Agricultural Production and its Impact on the Environment) is a global land use allocation model [14, 17, 18]. It takes regional economic conditions as well as spatially explicit data on potential crop yields, land and water constraints into account and derives specific land-use patterns, crop yields, emissions and total costs of agricultural production for each grid cell. Spatially explicit data is provided to MAgPIE on a regular geographic grid, with a resolution of 0.5 by 0.5 degrees. The objective function of the land-use model is to minimize total cost of production for a given amount of agricultural and biomass demand in a recursive dynamic mode. To fulfill the given demands the model can endogenously decide to acquire yield-increasing technological change at additional costs or to convert land from the non-agricultural area into cropland at additional costs.

Regional food demand is defined for an exogenously given population and income growth based on regional diets [7]. Food and feed energy for ten demand categories can be produced by 20 cropping activities and three livestock activities. Biomass demand is also given exogenously and can be satisfied by the production of two cellulose-based bioenergy crops: a grassy (herbaceous) one like Miscanthus and a woody one (ligneous) like Poplar [2]. Residues from forestry and agriculture are not integated. Cropland, pasture land and irrigation water are fixed inputs in limited supply in each grid cell. Variable inputs of production are labour, chemicals, and other capital, which are assumed to be in unlimited supply to the agricultural sector at a given price.

MAgPIE incorporates a representation of the dominant greenhouse gas emissions from land use change and different agricultural activities. CO2 emissions are caused by land use change and occur if the carbon content of the previous land use activity exceeds the carbon content of the new land use activity. Emissions from agricultural activities focus on N2O-emissions from the soil and manure storage as well as CH4-emissions from rice cultivation, enteric fermentation and manure storage that add up to 87 % of total agricultural (land use) emissions in the year 2000 [26]. As agricultural emissions arise from multiple causes, they depend on the type of agricultural activity. Their extent is heavily influenced by crop or animal type, fertilizer input, climate, soil quality or farm management.

2.3. Coupling ReMIND and MAgPIE

To represent the interaction of biomass demand and supply side ReMIND is coupled to MAgPIE via a soft-link meaning that both models stay separated and are solved sequentially in an iteration loop. The link consists of exchanging time paths of certain variables in which the output of one model serves as input for the other model. The link integrates biomass demand and price as well as emissions prices and land-use emissions for CO2 and N2O:ReMIND estimates the demand of lignocellulosic biomass and GHG emissions prices for CO2 and N2O. Based on this exogenous input, MAgPIE computes corresponding biomass prices and resulting GHG emissions for the given demand. Using this updated information of biomass supply ReMIND is solved again. This is repeated until the exchanged time paths do not show changes from one iteration to the next.

The overall agricultural N2O emissions estimated by the land-use model are taken as baselines of marginal abatement cost curves within ReMIND from which further abatement is possible. Concerning CO2 only the additional land-use change emissions due to biomass production are considered. Other land use, land-use change

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and forestry (LULUCF) CO2 emissions are covered endogenously by a MAC in ReMIND [8]. Methane is not included into the liking as there is only little direct or indirect effect on CH4 emissions caused by biomass production. Global methane emissions are also estimated endogenously in ReMIND from a MAC.

2.4. Techno-economic characteristic of the BIGCC process

Several suggestions for possible BIGCC process configurations exist. The most important characteristics are the type of biomass raw material input and the design of gasifier, gas cleaning unit and carbon capture process. All plant configurations depicted here use fluidized bed gasifiers as they are appropriate for medium to large-scale application and can cope with varying quality of input material while reducing ash-related problems [29, 28]. The gasifier can be blown with air, steam or oxygen and can be operated at atmospheric or elevated pressure (15-30 bar). Hot gas cleaning is favorable for pressurized gasification whereas for atmospheric gasification cold gas cleaning is preferable. Only few studies consider carbon capture for BIGCC power plants [11, 21, 25]. The CO2 separation is realized by pre-combustion capture with physical absorption after a water-shift reaction. The gas turbine has to be prepared for low calorific gases like those produced from biomass gasification [22]. However, as there are no commercially running plants, none of these configurations could give proof of being superior in terms of economic feasibility and technological reliability.

From a literature review we obtain techno-economic data of BIGCC-C processes shown in Figure 1 [3, 4, 10, 12, 11, 20, 21, 25, 9]. Both specific investment costs and electrical efficiency show economies of scale. Adding carbon capture to the process increases investment costs and decreases efficiency. For our calculations we use the values displayed in Table 1. As a compromise between large-scale plants which profit from economies of scale and on the other hand costs for transportation of biomass we choose a plant size of 100 MWe [27, 13], which is also in range of the size assumed by several studies (see Figure 1).

Figure 1: Techno-economic data of BIGCC processes with and without CCS obtained from the literature. Left: Specific investment costs($2005/kWe) vs. installed capacity (MWe). Right: Electrical efficiency vs. installed capacity (MWe).

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BIGCC BIGCC+CCS default

BIGCC+CCS10%

BIGCC+CCS20%

Plant size Specific investment costs Electrical efficiency Operating capacity factor O&M costs Capture rate

[MWe] [$2005/kWe] [%] [%] [$2005/GJ] [%]

100 1860

4280

3.95 0

100 2560

3180

5.66 90

2816 28

3072 25

Table 1: Techno-economic parameterization of BIGCC process with and without CCS used in the energy system model.

3. Results

3.1. Mitigation potential

Figure 2 compares emissions of the business as usual scenario (upper black line) which does not impose any restrictions on the energy system emissions to a policy scenario with a limit of a global mean temperature rise of 2°C above pre-industrial level (lower black line). The colored areas in between display the contributions of different mitigation options to the overall emission reductions. Biomass production is limited to grass only. Starting at the middle of the century the BIGCC+CCS technology reaches a share of 33% in 2100 on the overall mitigation. The heavy use of biomass with CCS even facilitates slightly negative total energy system emissions at the end of the century which in turn allows for rising emissions at the beginning of the century.

Figure 2: Comparing emissions of business as usual and policy scenario emissions: mitigation shares of technologies, fuel switchand efficiency gains (biomass type: grass).

3.2. Techno-economic performance and biomass type

We perform a sensitivity analysis by varying the techno-economic parameters of the BIGCC+CCS process obtained form the literature. Three cases are considered, as shown in Table 1: (i) default, (ii) electrical efficiency 10% lower and specific investment costs 10% higher than in default case (eff10), (iii) electrical efficiency 20% lower and specific investment costs 20% higher than in default case (eff20). Each case was combined with two

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4.3 Results 85

6 Author name / Energy Procedia 00 (2010) 000–000

different lignocellulosic bioenergy feedstocks, wood (wo) and grass (gr), delivered by the land-use model. For all cases a climate change mitigation target of 2°C is applied.

Figure 3 shows the carbon captured by the BIGCC+CCS process with different techno-economic performances cumulated from 2005-2100. Even in the most pessimistic case of a grass-fed process (gr-eff20) almost the same amount of carbon is captured as in the most optimistic wood-fired scenario (wo). The reason is the lower price for grass, starting at 4 $US/GJ in 2005 and rising to 10 $US/GJ in 2100 compared to wood ranging from 7 $US/GJ to 25 $US/GJ in the same period. However, the grass-fed process is much more sensitive to the degradation of techno-economic performance than the wood fired processes. The results show that the contribution of BIGCC-C technology to long-term climate change mitigation could be much higher if grass is used as fuel instead of wood, provided that the grass process is highly efficient. Even though BIGCC without CCS provides electricity with low carbon emissions it is hardly applied by the model. As can be seen on the right in Figure 3 the most important parameter, which decides about the application of the technology, is the capture rate. It has to significantly exceed 60 % otherwise the technology is not applied.

Almost all biomass is used with CCS technologies resulting in high amounts of captured carbon from 2005-2100. In the case of grass raw material the cumulated captured carbon for all cases of efficiencies and capture rates exceeds 170 GtC and reaches 240 GtC for the most efficient grass-case. In all wood-based scenarios the cumulated amount is lower and accounts for about 130 GtC.

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00Biomass�FT�(OT)+CCSH2�Prod.�(Biomass)+CCSBiomass�IGCC+CCS

Figure 3: Carbon captured from Bio-CCS technologies cumulated form 2005-2100 in GtC. Left: Variation of techno-economic performance(see Table 1) and biomass fuel. Right: Variation of capture rate: 90%, 80%, 70%, 60% (fuel: grass)

3.3. Competing Biomass Technologies

Figure 3 (right) already points out that a decreasing capture rate leads to less deployment of the BIGCC+CCS technology. As there is no change on the land-use side, the same amount of biomass is still available at same prices. In Figure 4 (right) the share of primary energy consumption of different bioenergy technologies is depicted. It shows the competing technologies which step in when the BIGCC+CCS technology becomes unattractive due to low capture rates. It is the Biomass-to-liquids Fischer-Tropsch technology which takes over and also provides negative emissions as the CCS type of this technology is used. Its capture rate of 48% is lower than in all depicted cases of BIGCC+CCS, but in contrast to BIGCC (that produces electricity) it produces Diesel for the transportation sector and thus helps substituting fossil oil. An opposing trend can be observed if parameters on the energy system side stay constant but biomass prices rise (represented here by a fuel switch from grass to wood). As Figure 4 (left)

2938 D. Klein et al. / Energy Procedia 4 (2011) 2933–2940

86 4 Coupled energy-system and land-use model

Author name / Energy Procedia 00 (2010) 000–000 7

shows, the BIGCC+CCS technology displaces the Fischer-Tropsch technology as it can provide more negative emissions from the more expensive biomass. The cumulated amount of biomass used from 2005-2100 in all efficiency cases of the more expensive raw material wood is about 5500 EJ, which is less than half of the amount used in all efficiency cases with grass (about 12500 EJ). This shows that if biomass is scarce it is predominantly used by the BioIGCC+CCS technology to produce as much negative emissions as possible. Moreover the overall primary energy consumption of biomass reacts much less sensitive to techno-economic performance of the BIGCC (efficiency and capture rate) process than to price changes of the biomass (grass and wood).

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Biomass�CHP

Synthetic�Nat.�Gas

Figure 4: Competing biomass technologies: shares of biomass primary energy consumption of technologies cumulated from 2005-2100. Left: Fuel switch from grass to wood. Right: Variation of BIGCC capture rate.

4. Conclusion and Outlook

Results indicate that BioIGCC with CCS could serve as an important mitigation option and that it could even be the main bioenergy conversion technology as it efficiently provides negative emissions and produces electricity. Future techno-economic studies and demonstration plants of the BIGCC+CCS process should focus on the more challenging biomass raw material grass instead of wood as it can be produced at lower costs and enables high amounts of negative emissions in case of good techno-economic performance of the process. The capability of producing negative emissions decides on the application of BIGCC+CCS as a mitigation technology. Given a high capture rate, BIGCC+CCS can significantly contribute to mitigation of the energy system emissions. All considered scenarios show high cumulated amounts of captured carbon that range from 130 GtC to 240 GtC. In case of expensive lignocellulosic biomass raw material BIGCC+CCS with a high capture rate turns out to be the preferred bioenergy conversion technology. The overall primary energy consumption of biomass reacts much more sensitive to price changes of the biomass than to techno-economic performance of the BIGCC.

As biomass production and demand do not coincide in identical world regions we will couple regionalized models with regions linked by biomass trade. Moreover the new HTC technology (HTC = hydrothermal carbonisation) will be assessed [24]. The product of the HTC process is similar to charcoal and thus is expected to be appropriate for various alternative downstream applications like fuel or electricity production (with CCS). It could also be used for soil improvement and thus could serve as a simple carbon sink provided it is kept in the soil for a long time. Besides that it can be used as source material for chemical processes. As HTC is a densification process of biomass raw material it may facilitate long-distance transport of biomass.

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88 4 Coupled energy-system and land-use model

5 The global economic long-termpotential of modern biomass in aclimate-constrained world

David KleinFlorian Humpenoder

Nico BauerJan Philipp Dietrich

Alexander PoppBenjamin Bodirsky

Markus BonschHermann Lotze-Campen

Published as Klein, D.; Humpenoder, F.; Bauer, N.; Dietrich, J.P.; Popp, A.; Bodirsky, B.; Bonsch,M.; Lotze-Campen, H.; (2014): The global economic long-term potential of modern biomass in aclimate-constrained world, Environmental Research Letters, 9, 074017.

89

The global economic long-term potential ofmodern biomass in a climate-constrainedworld

David Klein, Florian Humpenöder, Nico Bauer, Jan Philipp Dietrich,Alexander Popp, Benjamin Leon Bodirsky, Markus Bonsch andHermann Lotze-Campen

Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany

E-mail: david.klein@pik-potsdam.de

Received 9 February 2014, revised 3 July 2014Accepted for publication 4 July 2014Published 31 July 2014

AbstractLow-stabilization scenarios consistent with the 2 °C target project large-scale deployment ofpurpose-grown lignocellulosic biomass. In case a GHG price regime integrates emissions fromenergy conversion and from land-use/land-use change, the strong demand for bioenergy and thepricing of terrestrial emissions are likely to coincide. We explore the global potential of purpose-grown lignocellulosic biomass and ask the question how the supply prices of biomass depend onprices for greenhouse gas (GHG) emissions from the land-use sector. Using the spatially explicitglobal land-use optimization model MAgPIE, we construct bioenergy supply curves for tenworld regions and a global aggregate in two scenarios, with and without a GHG tax. We find thatthe implementation of GHG taxes is crucial for the slope of the supply function and the GHGemissions from the land-use sector. Global supply prices start at $5 GJ−1 and increase almostlinearly, doubling at 150 EJ (in 2055 and 2095). The GHG tax increases bioenergy prices by$5 GJ−1 in 2055 and by $10 GJ−1 in 2095, since it effectively stops deforestation and thusexcludes large amounts of high-productivity land. Prices additionally increase due to costs forN2O emissions from fertilizer use. The GHG tax decreases global land-use change emissions byone-third. However, the carbon emissions due to bioenergy production increase by more than50% from conversion of land that is not under emission control. Average yields required toproduce 240 EJ in 2095 are roughly 600 GJ ha−1 yr−1 with and without tax.

S Online supplementary data available from stacks.iop.org/ERL/9/074017/mmedia

Keywords: biomass, climate change mitigation, land use, resource potential, biomass supplycurve, carbon tax, energy

1. Introduction

Energy from biomass as a substitute for fossil energy is notonly supposed to improve energy security. Several studiesinvestigating the transition of the energy system under climatechange stabilization targets consider bioenergy a large-scale

and cost-effective mitigation option (Riahi et al 2007, Calvinet al 2009, Luckow et al 2010, Van Vuuren et al 2010a, Roseet al 2013). In particular, bioenergy with CCS (BECCS) maysignificantly reduce stabilization costs, since its negativeemissions compensate emissions from other sources andacross time (Van Vuuren et al, 2010b, 2013, Kriegleret al 2013, Azar et al 2010, 2013, Klein et al 2013). Theamount of realizable negative emission directly depends onthe amount of biomass available. Thus, the biomass potentialand its cost become crucial factors that affect overall miti-gation costs (Rose et al 2013, Klein et al 2013). While the

Environmental Research Letters

Environ. Res. Lett. 9 (2014) 074017 (11pp) doi:10.1088/1748-9326/9/7/074017

Content from this work may be used under the terms of theCreative Commons Attribution 3.0 licence. Any further

distribution of this work must maintain attribution to the author(s) and thetitle of the work, journal citation and DOI.

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5.1 Introduction 91

scientific consensus on the importance of bioenergy for cli-mate change mitigation is strong (Rose et al 2013), highuncertainties remain regarding the biomass potential, resultingin wide ranges of estimates (28–655 EJ yr−1, see alsosection 2). This is mainly due to uncertainties about futuredevelopments of agricultural yields1, demand for food andfeed, and availability of land and water for agricultural pro-duction. In particular, there are only a few global studiesattributing costs or prices to the estimated bioenergy potential(see also section 2). The purpose of this study is to providesupply price curves for lignocellulosic biomass that can serveas a basis for the economic assessment of bioenergy in cli-mate change mitigation scenarios.

Low-stabilization scenarios consistent with the 2 °C tar-get project large-scale deployment of biomass necessitatingdedicated production of modern lignocellulosic biomass atlevels that exceed the potential of residues and first-generationbiofuels (Popp et al 2013). Therefore, this study focuses onpurpose-grown lignocellulosic and herbaceous biomass. Amajor concern about the sustainability of large-scale bioe-nergy production is its potential to induce deforestation. First,deforestation causes carbon emissions and counteracts theobjective of emission mitigation if no effective forest pro-tection regime is in place (Wise et al 2009, Poppet al 2011a, 2012, Calvin et al 2013). Second, deforestationentails substantial biodiversity loss, as forests are the mostbiologically diverse terrestrial ecosystems (Turner 1996,Hassan et al 2005). Both adverse effects could be con-siderably mitigated if GHG emissions from the land-usesector (including non-CO2 emissions such as N2O from fer-tilizer use) were equally priced with energy emissions. In thecase of a GHG price regime comprising energy and land-use/land-use change emissions, the strong demand for bioenergyand pricing of terrestrial emissions are likely to coincide, andthe GHG pricing is likely to affect the availability and pro-ductivity of land for bioenergy, and thus bioenergy prices fora given level of demand. However, to our knowledge theavailable literature on bioenergy potentials does not considerGHG pricing in the land-use sector (Hoogwijk 2004, Hoog-wijk et al 2005, 2009, Smeets et al 2007, Erb et al 2009, VanVuuren et al 2009, Dornburg et al 2010, Haberl et al 2010,Beringer et al 2011a). Therefore, this study investigates theimpact of GHG prices on the potential and the supply pricesof bioenergy.

Furthermore, these studies assume bioenergy productiononly on land not required for food production, and theyassume yield improvements to be independent of bioenergydemand. However, these assumptions may not hold if thedemand for bioenergy strongly increases, as projected by low-stabilization scenarios. In contrast, the approach applied forthis study incorporates land competition between bioenergyand other crops. Moreover, it allows derivation of yieldimprovement rates required to satisfy given levels of bioe-nergy and food demand, since technological development is

endogenous in this approach. Using the land-use optimizationmodel MAgPIE (Model of Agricultural Production and itsImpacts on the Environment) (Lotze-Campen et al 2008,Popp et al 2010), we construct bioenergy supply curves forten world regions by measuring the bioenergy price responseto different scenarios of bioenergy demand and GHG prices.The model endogenously treats the trade-off between landexpansion (causing costs for land conversion and for resultingcarbon emissions) and intensification (requiring investmentsfor research and development) by minimizing the total agri-cultural production costs. GHG emissions from the land-usesystem are priced, and resulting costs for emissions accruingfrom bioenergy production are reflected in bioenergy prices.

The purpose of this study is to quantitatively assess theglobal economic potential of lignocellulosic purpose-grownbiomass under different climate policy proposals. It presentsbioenergy supply price curves on a regional level. Two keyquestions are addressed: what is the global potential of pur-pose-grown second-generation biomass and how are potentialand corresponding supply prices dependent on GHG taxes?

2. Present bioenergy potential studies

It is important to note that this study focuses on second-generation biomass. The literature about first-generation bio-mass is larger, but not a concern here. Several studies haveinvestigated the global potential of second-generation pur-pose-grown bioenergy under different constraints. There aremainly two types of global bioenergy potential study so far.The first group identifies the potential of bioenergy bydefining the area of land available for bioenergy productionand by making assumptions about the productivity of thisland (Hoogwijk et al 2005, Smeets et al 2007, Erbet al 2009, 2012, Van Vuuren et al 2009, Dornburget al 2010, Haberl et al 2010, 2013, Smith et al 2012, Ber-inger et al 2011a). These studies assume some kind of food-first policy, as they exclude land that is needed for food andfeed production and allow bioenergy production only on landthat is not used for food production or might in future becomeavailable due to intensification or decreasing demand foragricultural commodities. The development of technologicalchange is included in these studies by exogenous assumptionson food and feed crop yield growth that largely determine theland available for bioenergy production. Other importantfactors are food demand, trade, and livestock production.Some studies consider additional sustainability constraints byexcluding land for forest and nature conservation or due towater scarcity or degradation (Van Vuuren et al 2009, Ber-inger et al 2011a). Based on these studies, the estimates of thepurpose-grown biomass potential for 2050 range from28–265 EJ yr−1 at the lower end to 128–655 EJ yr−1 at theupper end2.

The wide range can be explained by different assump-tions on food demand, availability of land, and development

1 In particular, there is lack of experience with the production oflignocellulosic feedstock for energy purposes, since it has not been producedon a commercial scale yet.

2 This range excludes results from Smeets (2007), who reports a potential of215–1272 EJ yr−1 in 2050 assuming large land area with high productivity.

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Environ. Res. Lett. 9 (2014) 074017 D Klein et al

92 5 Bioenergy supply under full land-use competition

of yields, which are identified by these studies as the maindeterminants of the bioenergy potential. Low estimates aremainly driven by assuming high population growth (resultingin high food demand) and excluding water scarce areas andnature conservation areas, resulting in low availability of landfor bioenergy production. Projected future yields are anothercrucial (yet highly uncertain) parameter determining thebioenergy potential. Haberl et al 2010 report a wide range of7–60 GJ ha−1 yr−1 used in the literature. The estimates at theupper end of bioenergy potentials are mainly based on high-yield growth rates for food and bioenergy crops over the nextdecades that are at present level or higher (Hoogwijket al 2005, Van Vuuren et al 2009, Smeets et al 2007,Dornburg et al 2010).

These studies use simulation models to project the futuredevelopment of the land-use system and feature differentlevels of spatial explicit biophysical conditions. The projec-tions applying average yields over large areas consideredsuitable for bioenergy production tend to project high bioe-nergy potentials, 120–660 EJ yr−1 (Hoogwijk et al 2005, VanVuuren et al 2009, Smeets et al 2007, Dornburg et al 2010),while process-based studies that aim to include spatiallyexplicit data on local biophysical conditions estimate lowerranges, 37–141 EJ yr−1 (Beringer et al 2011b, Erbet al 2009, 2012). Another approach derives the actual netprimary productivity (NPP) from satellite data and argues thatthe NPP poses an upper bound to bioenergy production. Theestimates reported by these studies (excluding residues) are121 (Smith et al 2012) and 190 (Haberl et al 2013).

However, to be able to assess the economic potential andhence the competitiveness of bioenergy in the energy system,one needs information about the supply costs of biomass.Therefore the second group of studies additionally assignscosts for bioenergy production to the different types of landcell and constructs supply cost curves by sorting the cells bytheir biomass production costs. Only a few studies provideinformation about the production costs, particularly con-cerning second-generation bioenergy on a global scale(Hoogwijk 2004, Hoogwijk et al 2009, Van Vuurenet al 2009). Hoogwijk (2009) introduces costs for land,capital, and labor to the technical potential identified inHoogwijk (2005) and finds that 130–270 EJ yr−1 in 2050 maybe produced at costs below $2.2 GJ−1 and 180–440 EJ yr−1

below $4.5 GJ−1 3. The underlying scenarios assume sig-nificant land productivity improvements and cost reductionsdue to learning and capital–labor substitution. Using a similarapproach (but assuming less available land due to a loweraccessibility factor), Van Vuuren et al (2009) excludes furtherareas from biomass production due to biodiversity con-servation, water scarcity, and land degradation. This reducesthe global biomass potential in 2050 from 150 EJ yr−1 withoutthese land constraints to 65 EJ yr−1. Following the same costapproach as Hoogwijk (2005), Van Vuuren et al (2009) findsthat in 2050 about 50 EJ could be produced at costs below

$2.2 GJ−1 and 125 EJ below $4.8 GJ−1. Realizing the fullpotential of 150 EJ by taking biomass from degraded landinto account would cost up to $8 GJ−1. Both studies allowbioenergy production on abandoned and rest land only andconsider only woody bioenergy crops.

3. Methods

3.1. The land-use model MAgPIE

MAgPIE is a spatially explicit, global land-use optimizationmodel (Lotze-Campen et al 2008, Popp et al 2010). Theobjective function of MAgPIE is the fulfillment of food,livestock, material, and bioenergy demand at least costs undersocio-economic, political, and biophysical constraints.Demand is income elastic, but price-induced changes indemand are not reflected. Major cost types in MAgPIE arefactor requirement costs (capital, labor, and fertilizer), landconversion costs, transportation costs to the closest market,investment cost for technological change (TC), and costs forGHG emission rights. The cost minimization problem issolved in 10-year time steps until 2095 in recursive dynamicmode by varying the spatial production patterns, by expand-ing crop land, and by investing in yield-increasing TC (Lotze-Campen et al 2010, Dietrich et al 2012). TC increases thepotential yields of all crops within a region by the samefactor. The costs for enhancing the yields in a specific regionincrease with the level of agricultural development of theparticular region; i.e., the higher the actual yields in a regionthe higher the costs for one additional unit of yield increase(Dietrich et al 2014). The model distinguishes ten economicworld regions with global coverage (cf supplementary mate-rial section S1.2): Sub-Saharan Africa (AFR), CentrallyPlanned Asia including China (CPA), Europe includingTurkey (EUR), states of the former Soviet Union (FSU), LatinAmerica (LAM), Middle East/North Africa (MEA), NorthAmerica (NAM), Pacific OECD including Japan, Australia,New Zealand (PAO), Pacific (or Southeast) Asia (PAS), andSouth Asia including India (SAS). MAgPIE considers spa-tially explicit biophysical constraints such as crop yields andavailability of water (Bondeau et al 2007, Müller andRobertson 2013) and land (Krause et al 2013) as well associo-economic constraints such as trade liberalization, forestprotection, and GHG prices.

MAgPIE differentiates between the land types cropland,pasture, forest, and other land (e.g. non-forest natural vege-tation, present and future abandoned land, desert). Unlike thecropland sector, which is subject to optimization, the areas inthe pasture sector, the forestry sector, and parts of forestland(mainly undisturbed natural forest within protected forestareas, FAO 2010) are fixed at their initial value in this study.Considering this, about 7900Mha (∼61%) of the world’s landsurface is freely available in the optimization of the initialtime step, of which about 3000Mha are suitable for cropping.Since all crops including bioenergy have equal access to theavailable land (no underlying food-first policy), the resultingcompetition for land is reflected in shadow-prices for

3 Dollars are given as US $2005 in this study. Dollars from other years areconverted using the consumer price index http://oregonstate.edu/cla/polisci/sites/default/files/faculty-research/sahr/inflation-conversion/excel/cv2008.xls

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5.3 Methods 93

bioenergy derived from the demand-balance equation. As weconsider bioenergy crops to be a globally tradable good,emerging bioenergy prices are equal across regions. However,interregional bioenergy transport as such is not considered.

Yields of dedicated grassy and woody bioenergy crops(rainfed only) obtained from the vegetation model LPJmL(Beringer et al 2011a, Bondeau et al 2007) represent yieldsachieved under the best available management options. SinceMAgPIE aims to represent actual yields in its initial time step,these yields are downscaled using information about observedland-use intensity (Dietrich et al 2012) and FAO yields(FAO 2013). For instance, in AFR yields are reduced byabout 70% (supplementary material figure S12). However, byinvesting in yield increasing technologies this yield gap canbe closed, and technological progress over a long time caneven increase yields beyond LPJmL yields since it pushes thetechnology frontier.

MAgPIE calculates emissions of the Kyoto GHGs carbondioxide (CO2), nitrous oxide (N2O), and methane (CH4)(Bodirsky et al 2012, Popp et al 2010, 2012). Carbon emis-sions from land conversion occur if the carbon content(aboveground and belowground vegetation carbon) of thenew land type is lower than the carbon content of the previousland type (e.g. if forest is converted to cropland). The amountof carbon stored differs across land types and the values arederived from LPJmL. Soil carbon and decay time of onsitebiomass are not considered, whereas the regrowth of naturalvegetation on abandoned land and the resulting increase of itscarbon stock are considered. Costs accruing due to the taxa-tion of emissions are added to the production costs. Thus, theGHG tax incentivizes the reduction of emissions resultingfrom land-use change (CO2) and agricultural production(N2O, CH4). It is important to note that in our analysis carbonemissions from all types of land conversion are accounted forand reported in the results, but only carbon emissions fromdeforestation (conversion of forest land into any other landtype) are taxed. We consider this type of carbon tax regime tobe closest to a REDD scheme (reducing emissions fromdeforestation and forest degradation, Ebeling andYasué 2008), which is currently discussed by the internationalcommunity and expected to contribute to a post-Kyotoemission reduction treaty (Phelps et al 2010). AgriculturalN2O and CH4 emissions can be reduced according to mar-ginal abatement cost curves based on the work of Lucas et al(2007) and Popp et al (2010).

3.2. Scenarios

The socio-economic assumptions regarding trade liberal-ization, forest protection, and demand for food, feed, andmaterial are geared to the ‘middle of the road’ narrative ofshared socio-economic development pathways (SSPs), withintermediate challenges for adaptation and mitigation (O’Neilet al 2012, see supplementary material for more information).The SSPs do not incorporate climate policy by definition. Wesimulate the outcome of climate policy by applying GHGtaxes and bioenergy demand scenarios as exogenous para-meters. While bioenergy demand is varied in order to derive

the bioenergy supply price curves (see supplementary mate-rial), the GHG tax is varied for the sensitivity analyses of thesupply curves.

The global uniform GHG tax on CO2, N2O, and CH4 inthe tax30 scenario starts in 2015 increasing by 5% per year(2020, $30 tCO2eq

−1, giving the scenario its name; 2055,$165 tCO2eq

−1; 2095, $1165 tCO2eq−1). It is close to CO2

prices required to reach low stabilization targets at 450 ppmCO2eq (Rogelj et al 2013, IEA 2012a, Luderer et al 2013).The N2O and CH4 taxes are calculated from the CO2 tax usingthe GWP100. In the tax0 scenario there is no GHG tax.

The bioenergy supply price curves are derived by mea-suring the price response of the MAgPIE model to 73 dif-ferent global bioenergy demand scenarios. Each bioenergydemand scenario yields a time path of regional allocation ofbioenergy production and global bioenergy prices. For eachregion and time step the supply curve was fitted to theresulting 73 combinations of bioenergy production andbioenergy prices (see supplementary material for detailsand data).

4. Results

4.1. Bioenergy prices

Figure 1 shows the globally aggregated supply curves for2055 and 2095. Without a GHG tax in the land-use sector,bioenergy in 2055 can be supplied starting at $5 GJ−1.Introducing a global uniform GHG tax substantially increasessupply prices for biomass by about $2 GJ−1 at low bioenergydemands (below 30 EJ yr−1) and $5 GJ−1 at medium to highdemands (above 120 EJ yr−1) in 2055. In 2095 the taxincreases bioenergy prices by $10 GJ−1.

Conditions of bioenergy production differ across regions,as do resulting bioenergy supply curves. Figure 2 shows theregional breakdown of the global supply curve for majorproducers. Without a GHG tax these are the tropical regionsAFR and LAM (figure 1, bottom), which offer access to largeareas of forest that can be converted to high-productivity landfor crop and bioenergy production. This results in relativelyflat supply curves in the tax0 scenario (figure 2, left). CPAand NAM contribute most of the remainder. There are onlyminor contributions from EUR, FSU, and PAS and almostnone from PAO and MEA.

Introducing a GHG tax changes the relative position ofthe regional supply curves, since the consequences of pricingemissions are different across regions (figure 2, right). Theprice-elevating effect can be separated into two components: asteepening of the supply curve due to land exclusion and atranslation effect due to non-CO2 co-emissions from bioe-nergy (supplementary material figure S5). The steepening ofthe supply curve is caused by the component of the GHG taxthat affects the carbon emissions from land conversion (CO2

price), since it effectively stops deforestation (in 2055 and2095) and thus reduces the amount of land available for theexpansion of bioenergy production. The translation effect iscaused by pricing nitrogen emissions that accrue from

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94 5 Bioenergy supply under full land-use competition

fertilizer use for bioenergy crop cultivation. It does not scalewith the bioenergy demand, since the amount of organic andinorganic fertilizer used per unit of bioenergy is constant.

The translation effect applies to the supply curves of allregions (supplementary material figure S6) and is stronger in2095 than in 2055 since the GHG tax is substantially higher($1165 versus $165 tCOeq−1). Regions where no forest landis used in the tax0 scenario, such as CPA, PAS, and SAS, areonly affected by this N2O-price effect. The supply curves ofregions that deforest in the tax0 scenario additionally show asteepening due to CO2 pricing of forest land (strongest inAFR and LAM). The combined effects significantly increaseregional supply prices and change the relative position of thesupply curves (figure 2). This is reflected in the reallocation ofthe bioenergy production depicted at the bottom of figure 1:production shifts from AFR and LAM mainly to PAS, CPA,and SAS under the GHG tax. The tax makes PAS competi-tive, which features a relatively high but flat supply curve.The land restriction in PAS is not as strong as in otherregions, since it can expand into productive land that is notunder emission control (see below)4.

4.2. Land and yields

To illustrate the effects of bioenergy demand and GHG tax onprocesses that drive the allocation and prices of bioenergy,such as changes in land cover, yields, and emissions, theremainder of the analysis focuses on the 2095 results of amedium bioenergy demand scenario selected as a sample outof the full portfolio (2055 results are included in the supple-mentary material). This is done to keep the analysis com-prehensible. The characteristics of the effects observed in

other demand scenarios are similar and qualitatively the same.To identify the effect of bioenergy production we compareresults of this sample scenario to a zero-demand scenario (seesupplementary material figure S4 for the respective scenariosand the full portfolio). Figure 3 shows the global land cover in2095 and the initial value in 2005 for the four land types thatare subject to optimization (top) and their changes from 2005to 2095 (bottom). Figure 4 depicts the regional breakdown ofthe changes. Bioenergy production requires substantialamounts of land, almost 500Mha for 240 EJ in 2095. Withand without tax this is predominantly realized by crop landreduction (intensification) and usage of other land.

Without a GHG tax bioenergy causes only little addi-tional deforestation (-55Mha, in LAM mainly), since largeamounts of accessible forest are already cleared for food andfeed production (−250Mha), (tax0 Bio versus tax0 NoBio).Bioenergy reduces cropland globally by 300Mha (−17%) in2095, mainly in AFR, LAM, CPA, and NAM. The increasedusage of other land (−130Mha) due to bioenergy productionin the tax0 Bio scenario has two sources: increased conver-sion of existing other land (AFR) and usage of land that isabandoned in the tax0 NoBio scenario (LAM, EUR, FSU)(see also figure 4). Under the GHG tax, bioenergy and foodproduction cannot access high-productivity forest land inAFR and LAM since it is effectively protected by the tax.Therefore, bioenergy plantations are partly pushed out ofregions that formerly had access to forest (300Mha in AFRand LAM). In parts this is compensated by further expansioninto other land (−100Mha), since resulting emissions are notpenalized by the GHG tax. Substantial amounts of other landare converted in PAS that would have not been touched bythe single effect of bioenergy or tax. The remaining part iscompensated by the replacement of cropland with bioenergycropland (−200Mha), predominantly in SAS. Again, only the

Figure 1.Globally aggregated bioenergy supply curves for 2055 (top left) and 2095 (top right) without (black line) and with a global uniformGHG tax (red line). The gray lines in the 2095 figure (right) indicate the positions of the 2055 supply curves. The colored bars at the bottomshow the underlying regional bioenergy production pattern for the sample scenario (145 EJ in 2055 and 240 EJ in 2095). The shaded areas inthe upper part indicate the standard deviation of the aggregated fit. Since the fit in 2095 is based on higher demand values than the 2055 fit,the absolute value of the spread is larger in 2095, as is the standard deviation.

4 Under the GHG tax the global supply curves begin to flatten from the pointwhere PAS becomes competitive.

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5.4 Results 95

combination of bioenergy and tax leads to bioenergy pro-duction in SAS.

Bioenergy yields required for the global production of240 EJ in 2095 vary substantially across regions and rangefrom 80 GJ ha−1 yr−1 (FSU) to 690 GJ ha−1 yr−1 (LAM) in thetax0 scenario and 715 GJ ha−1 yr−1 (PAS) in the tax30 sce-nario (supplementary material figure S12). While the globalaverage yield remains unchanged at 500 GJ ha−1 yr−1

(27 t ha−1 yr−1), the GHG tax requires substantial yieldincreases for energy crops in all major producer regions,mostly in PAS (from 300 to 715 GJ ha−1 yr−1), which com-pensates for the exclusion of productive land in AFR andLAM. If only major producers that cover more than 93%(224 EJ) of the global production (AFR, CPA, LAM, andNAM in the tax0 scenario and additionally PAS and SAS inthe tax30 scenario) are taken into account, average yieldincreases from 596 to 611 GJ ha−1 yr−1 driven by the GHGtax5. The average yield of regions producing the remaining

17 EJ decreases from 157 to 136 GJ ha−1 yr−1. Further infor-mation on yields can be found in the supplementary material.

4.3. Emissions

Figure 5 shows the carbon emissions from the land-use sectorcumulated from 2005 to 2095 separated into emissions fromfood and energy crop production. Without the GHG tax, foodproduction accounts for roughly 234 GtCO2 (80%) of totalemissions, mainly caused by deforestation in AFR (120GtCO2) and LAM (70 GtCO2). Since the GHG tax almoststops deforestation, it substantially reduces carbon emissionsfrom food crop production by 56% (to 102 GtCO2).Remaining carbon emissions are caused by conversion ofother land. The production of bioenergy causes additionalemissions. If forest is not protected by the GHG tax, bioe-nergy emissions account for 63 GtCO2, mainly due todeforestation in LAM (40 GtCO2). Under the GHG tax thereis no deforestation for bioenergy, but substantial expansion

Figure 2. Impact of the GHG tax on the regional supply curves: regional breakdown of the global supply curve for major producers for thetax0 (left) and tax30 scenarios (right), and for 2055 (top) and 2095 (bottom) with the sample scenario marked. The shaded areas indicate thestandard deviation of the fit. Since the fit in 2095 includes higher demands than the 2055 fit, the standard deviation is greater.

5 The difference from the global average is mainly due to FSU’s lowproduction on large areas of land.

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Figure 3. Global cover of land suitable and available (except protected forest) for agricultural production in 2095 with the initial value of2005 (top). Global changes in land cover between 2005 and 2095 (bottom).

Figure 4. Breakdown of the global changes of land cover between 2005 and 2095 into regional changes.

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5.4 Results 97

into other land6, predominantly in PAS (73 GtCO2). Thisleakage effect increases bioenergy emissions by 54% to 97GtCO2 cumulated from 2005 to 2095.

5. Discussion and conclusion

We constructed bioenergy supply curves for ten world regionsand a global aggregate under full land-use competition using ahigh-resolution land-use model with endogenous technologi-cal change. We find that the implementation of GHG taxes forland-use and land-use change emissions is crucial for theslope of the supply function and the GHG emissions from theland-use sector.

Climate policy not only increases the demand for bioe-nergy, as several studies show (Rose et al 2013, Calvinet al 2009, Van Vuuren et al 2010a); it could also substantiallyincrease supply prices of biomass raw material, as the presentstudy shows (+$5 GJ−1 in 2055, +$10 GJ−1 in 2095). This ismainly due to the fact that large amounts of high-productivityforest land are de facto excluded by the GHG tax, sinceexpanding into forests would entail substantial carbon emis-sions and related emission costs. Imposing the GHG tax thusprevents deforestation, lowers carbon emissions, reduces landavailable for bioenergy production, and increases the oppor-tunity costs of land that is in competition with food production.

N2O emissions from fertilizer use further increase bioenergyprices. The GHG tax also reduces the emissions in the case ofno bioenergy demand, because the agricultural demand aloneis a strong driver for cropland expansion. The bioenergy pricespresented in this study emerge under full land-use competitionwith other crops and are therefore higher than pure productioncosts on abandoned land found by Hoogwijk et al (2009) andVan Vuuren et al 2009. The potential supply price of biomassraw material is a crucial parameter for the deployment ofbioenergy. However, how much and along which conversionroutes (e.g. fuels, electricity, heat) bioenergy might bedeployed emerges from the balancing of supply and demand,the latter of which is crucially determined by emission reduc-tion targets, carbon prices, biofuel mandates, and technologyavailability (Mullins et al 2014, Klein et al 2013, Roseet al 2013). Although bioenergy prices presented here mayseem high compared to other energy carriers (4.6 $2005 GJ−1

for coal in 2011, IEA 2012b), bioenergy supply at these pricescould become relevant, since under climate policy the energysystem shows high willingness to pay for bioenergy (Kleinet al 2013). The incentive to pay high prices for bioenergy andto create negative emissions from it increases with the carbonprice.

Results show that large-scale bioenergy production andhigh GHG prices, which are likely to coincide under a climatepolicy that embraces the energy and the land-use sector, canput substantial pressure on the land-use system. In the sce-narios of this study bioenergy production requires largeamounts of land, predominantly realized by intensificationand increased usage of other land. Thus, two measures aimingat climate change mitigation (carbon taxes and bioenergy)could pose a threat to food security since they could drama-tically reduce the land available for food production.Although not in the focus of this study, it is important to notethat the resulting needs for intensification are likely toincrease food prices. However, there is some indication thatfood-price effects of large-scale bioenergy production couldbe lower than price effects caused by climate change (Lotze-Campen et al 2013). Adding to a study by Wise et al (2009),who found substantial emissions from deforestation if ter-restrial emissions are not priced at all (corresponding to thetax0 scenario used here), this study illustrates the potentialconsequences of a sectoral fragmented climate policy in theland-use sector: while effectively preventing deforestation thetax cannot prevent considerable carbon emissions resultingfrom conversion of land that is not covered by the tax or aforest conservation scheme. In our scenarios this is pre-dominantly the case for other land in PAS. Its special roleemerges from its high productivity and its high carbon con-tent. Based on data from Erb et al 2007 and FAO 2013 it wasaccounted ‘other land’ (cf supplementary material 3.1). Itshigh carbon content, however, would also justify including itin the forest pool, which would protect it from conversion andthus reduce land-use emissions and alter the supply ofbioenergy.

Increased N2O emissions are another adverse effect ofbioenergy production reducing the GHG mitigation potentialof bioenergy. They are of the same order of magnitude as the

Figure 5. CO2 emissions from land-use change due to bioenergyproduction and other agricultural activities cumulated from 2005to 2095.

6 In cases where bioenergy production inhibits or reverses regrowth ofnatural vegetation (other land), inhibited negative emissions are counted aspositive emissions due to bioenergy production.

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CO2 emissions due to bioenergy production. The tax clearlyreduces total and bioenergy N2O emissions. The bioenergyemissions comprise emissions from bioenergy productionitself (direct) and increased emissions from agricultureresulting from intensification due to bioenergy production(indirect). With the finding that N2O emissions might becomea major part of the GHG balance of lignocellulosic biofuels,this study is in line with findings by Melillo et al (2009) andPopp et al (2011b). However, the latter study argues that thesubstantial negative emissions that could potentially be gen-erated from biomass can overcompensate bioenergy N2Oemissions.

The energy yields of bioenergy presented in this study(200 GJ ha−1 yr−1 in 2005, around 600 EJ ha−1 yr−1 in 2055,and up to 700 GJ ha−1 yr−1 in 2095) are at the upper end of therange (130–600 GJ ha−1 yr−1) reported by Haberl et al (2010)for 2055. This partly results from the fact that the model inour study predominantly chooses to produce biomass fromherbaceous bioenergy crops (such as Miscanthus), which tendto feature higher yields than woody bioenergy crops (Fischeret al 2005) used in studies presented by Haberl et al (2010).Second, and in contrast to existing studies, bioenergy cropproduction in the present study competes for cropland withfood crop production and thus bio-energy can be grown onhighly productive land. Furthermore, the observed prices andunderlying production patterns of bioenergy and food cropsare based on decisive preconditions in the land-use model, i.e.(i) optimal global allocation of bioenergy and food productionand (ii) optimal and timely investments into research anddevelopment (R&D) and full impact of R&D on all cropswithin a region. These optimal conditions are difficult toachieve in the real-world production system, exhibitingunderinvestment into research and development (Alstonet al 2009), consisting of numerous individual farmers whodo not have equal access to technology, and facing landdegradation, pests, and changes in weather and climate.

However, the high bioenergy yields at the end of thecentury are predominantly the result of yield increasingtechnological progress over almost 90 years. A large part ofthe yield increases in MAgPIE fills the yield gap betweenactual yields (observed land-use intensity in the starting year1995) and the potential yields (derived from the vegetationmodel LPJmL) that can be achieved under best currentlyavailable management conditions. This yield gap can besubstantial. For instance, in the regions with the highest yieldincreases, AFR, LAM, and PAS, the potential yields arereduced to obtain actual yields in 1995 by about 70, 50, and60% respectively (supplementary material figure S12).MAgPIE bioenergy yields can exceed LPJmL bioenergyyields over time as endogenous investments in R&D push thetechnology frontier. For AFR, LAM, and PAS the yields in2095 exceed the potential yields of 1995 by 80, 40, and 20%respectively. Since this study considers endogenous techno-logical change, there is a response in yield growth to thepressure of bioenergy demand and forest conservation. Thisallows identification of the need for yield growth that wouldbe required if a potential climate change mitigation policydemanded large-scale production of biomass accompanied by

GHG prices. Therefore, the development of yields should beinterpreted as projections of required yields rather than pre-dictions of expected yields. To what extent they are realistic iscurrently under discussion. The performance of R&D forsecond-generation bioenergy crops is highly uncertain. Due toa lack of experience, there are no data available which couldbe used as a point of reference. Our assumption that yieldincreases for bioenergy crops will follow yield increases offood crops could be either optimistic or pessimistic. It couldbe optimistic, since for food crops mainly the corn:shoot ratiowas improved and not the overall biomass production (as isrequired for second-generation bioenergy crops), or it couldbe pessimistic, since research on lignocellulosic bioenergycrops starts from zero, making it conceivable to assume thatthere should be a lot of ‘low hanging fruits’. Several studiesdoubt that such high yields could be achieved and argue thatthe natural productivity poses an upper bound to the pro-duction of bioenergy (Haberl et al 2013, Smith et al 2012,Field et al 2008, Erb et al 2012, Campbell et al 2008). Othersargue that transferring bioenergy yield levels that wereobserved under test conditions to huge areas might over-estimate the bioenergy potential (Johnston et al 2009). Thereare also concerns that raising energy crop yields beyond thenatural productivity over large regions and over a long time, ifpossible at all, comes at the costs of increased GHG emissionsand other adverse environmental impacts. The findings aboutbioenergy GHG emissions in the present study (see above)confirm the former at least. There is also doubt that evenwithout bioenergy demand current yield trends will be suffi-cient to meet the food demand projected for 2050 (Rayet al 2013). On the other hand, Mueller et al (2012) indicatethat substantial production increases (up to 70%) are possibleby closing the yield gap with currently available managementpractices.

The following policy relevant conclusions can be drawnfrom the results. First, a potential climate policy that pricesland-use and land-use change emissions could significantlyincrease supply prices of bioenergy, since it reduces the landavailable for bioenergy production and since it adds cost forfertilizer emissions to the production costs. Second, a carbontax can be an effective measure to protect forests (or any othercarbon stock under taxation), even if accompanied by large-scale bioenergy production. However, it can only protect landthat is defined to be under emission control. The politicalquestion of which land to put under carbon taxation defineshow much land is accessible. Thus, it is highly relevant notonly for the effectiveness of nature conservation and emissionmitigation but also for the supply of bioenergy. Third, thecombination of the carbon tax and the bioenergy demand isexpected to cause substantial pressure and strong intensifi-cation on the remaining land, particularly if biomass is pro-duced at large scale. Energy crop yields would be required torise beyond today’s potential yields. A climate policy thatbuilds on carbon taxation and bioenergy deployment thusrequires considerable accompanying R&D efforts that ensurecontinuous technological progress in the agricultural sector.

In this study, we investigated the impact of GHG priceson bioenergy supply. However, there are further crucial

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factors interacting with the long-term bioenergy supply thatcould be studied, such as food demand, different levels offorest and biodiversity protection, other land-use based miti-gation options (e.g. afforestation), and bioenergy yields. Thelatter are particularly uncertain, since there is almost nopractical experience with large-scale dedicated production oflignocellulosic biomass. Second, due to the potential com-petition with food production, the impact of bioenergydemand and GHG prices on food supply needs furtherresearch. Finally, the issue of fragmented climate policiesleading to regionally non-uniform GHG prices and potentialemission leakage needs to be considered for the environ-mental performance of bio-energy production.

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11

Environ. Res. Lett. 9 (2014) 074017 D Klein et al

5.6 References 101

Supporting Online Material for the manuscript submission

“The global economic long-term potential of modern biomass in a

climate constrained world”

1 Additional information on the MAgPIE model................................................................................................... 1

1.1 Key characteristics of the MAgPIE model .................................................................................................. 1

1.2 MAgPIE regions ..................................................................................................................................................... 2

2 Scenario design ............................................................................................................................................................... 3

2.1 Socio-economic assumptions .......................................................................................................................... 3

2.2 Tax scenarios ......................................................................................................................................................... 4

2.3 Deriving the supply price curves ................................................................................................................... 5

3 Additional results ........................................................................................................................................................... 6

3.1 Components of the effect of GHG taxes on the supply curves ............................................................ 6

3.2 Land allocation ...................................................................................................................................................... 8

3.3 Carbon emissions ............................................................................................................................................... 11

3.4 Bioenergy yields ................................................................................................................................................. 11

4 References ....................................................................................................................................................................... 14

1 Additional information on the MAgPIE model

1.1 Key characteristics of the MAgPIE model

Model feature Implementation in MAgPIE Source

Solution concept Partial equilibrium, recursive dynamic, minimize

total agricultural production costs

Time horizon 1995-2095 in 10-year time steps

Land available for food and feed

production

Crop land, forest, other natural vegetation, other

arable land

(Krause et al

2013)

Land available for bioenergy The same as for food and feed crops

Technological change (TC) Endogenous (Dietrich et al

2013)

Agricultural yields

Initial actual bioenergy yields in MAgPIE are

derived from potential bioenergy yields in LPJmL

by downscaling potential yields using information

about observed land-use intensity (Dietrich et al

2012) and yields (FAO 2013). Actual yields in

MAgPIE can increase due to endogenous TC. More

LPJmL (Bondeau

et al 2007, Müller

and Robertson

2013)

102 5 Bioenergy supply under full land-use competition

information on agricultural yields can be found in

(Humpenöder et al n.d.)

Water availability The calculation of available water per grid cell is

based on LPJmL

LPJmL (Bondeau

et al 2007, Müller

and Robertson

2013)

Water use efficiency Irrigation efficiency is static

Climate impacts Not used in this study

Food demand (Population, GDP) SSP Database and SSP storylines (O’Neil et al 2012,

IIASA 2013)

Pasture Static

Forestry Static

Biodiversity protection Forest protection, rainfed bioenergy only

(FAO and JRC

2012)

Area requirements for nature

protection

Forest protection (FAO and JRC

2012)

Trade/self-sufficiency and its

variation

Endogenous trade but exogenous self-sufficiency

Transport costs for bioenergy Based on the GTAP 7 database (Narayanan and

Walmsley 2008) and the transport distance to the

next market (Nelson 2008)

Irrigation of crops Food crops: Rainfed and irrigated

Bioenergy crops: Rainfed only

Irrigation infrastructure Starting point based on Siebert et al (2007). Can

change endogenously.

Land degradation -

GHG accounting CO2, N2O, CH4 emissions

Carbon pools that are considered Vegetation, litter and soil carbon pool

Carbon pools that are priced Vegetation, litter and soil carbon pool of forest land

Land use based mitigation Marginal abatement cost curves (N20, CH4) (Lucas et al

2007)

1.2 MAgPIE regions

Figure S1: MAgPIE regions

5.7 Supporting Online Material 103

MAgPIE Region

AFR Sub-Saharan Africa

CPA Centrally planned Asia including China

EUR Europe including Turkey

FSU States of the former Soviet Union

LAM Latin America

MEA Middle East/North Africa

NAM North America

PAO Pacific OECD including Japan, Australia, New Zealand

PAS Pacific (or Southeast) Asia

SAS South Asia including India

Table S1: Names and abbreviations of the 10 economic world regions in MAgPIE.

2 Scenario design

2.1 Socio-economic assumptions

The socio-economic scenarios employed in this study are geared to the qualitative “Middle of the

Road” narrative of Shared Socio-economic development Pathways (SSP) with intermediate socio-

economic challenges for adaptation and mitigation (O’Neil et al 2012). Based on this SSP 2 narrative

we assume a globalizing economy with medium rates of trade liberalization and medium rates of

forest protection. Food, feed and material demand (c.f. Figure S2) is calculated using the

methodology described in Bodirsky et al. (in review) based on the SSP 2 population and GDP

projections (IIASA 2013). The SSPs do not incorporate climate policy by definition. For the purpose

of this study we apply GHG taxes and bioenergy demand scenarios as exogenous parameters.

104 5 Bioenergy supply under full land-use competition

Figure S2: Global food demand scenario with regional breakdown.

2.2 Tax scenarios

Figure S3: The two tax scenarios applied in this study. The tax30 scenario increases with 5 %/yr.

5.7 Supporting Online Material 105

2.3 Deriving the supply price curves

The bioenergy supply price curves are derived by measuring the price response of the MAgPIE

model to different global bioenergy demand scenarios. The demand scenarios and resulting price

paths are available in 10-year time steps between 2005 and 2095. For each of the GHG tax scenarios

the MAgPIE model was run with 73 different bioenergy demand scenarios. Each global bioenergy

demand scenario yields a time path of regional allocation of bioenergy production and bioenergy

price. Thus, in each time step and each region 73 combinations of bioenergy production and

resulting bioenergy prices exist.

The regional supply curves were derived by fitting the function p(x,t,r) = a1(t,r) + a2(t,r) * x(t,r) ^

a3(r) using the method of least squares for p-p*, where p is the price calculated by the fit and p* the

price calculated by MAgPIE. a1, a2, and a3 are the parameters to be adjusted, t is the time step, r the

region and x the production of bioenergy (all fit coefficients are presented in the additional data-

SOM).

The supply curves were derived using the full set of 73 demand scenarios. The scenarios start with

a similar global demand of about 10 EJ in 2005 and evolve to values of 10 EJ to 270 EJ in 2055 and

50 EJ to 580 EJ in 2095. The sample scenario has a global demand of 145 EJ in 2055 and 240 in

2095. To identify the effect of bioenergy production we compare results of this sample scenario to a

zero-demand scenario (see Figure S4).

Figure S4: All bioenergy demand scenarios used for deriving the supply curves with the sample-scenario and the

zero-demand scenario marked.

106 5 Bioenergy supply under full land-use competition

3 Additional results

3.1 Components of the effect of GHG taxes on the supply curves

Figure S5: The different components of the price-elevating effect of GHG taxes: Globally aggregated bioenergy

supply curves for the scenarios without the GHG tax (tax0), with a pure carbon tax (tax30c) and the GHG tax

on all emissions (CO2,N2O, and CH4), (tax30).

5.7 Supporting Online Material 107

Figure S6: The different components of the price-elevating effect of GHG taxes: Regional bioenergy supply

curves for the major bioenergy producers for the scenarios without the GHG tax (tax0, black), with a pure

carbon tax (tax30c, green) and the GHG tax on all emissions, i.e. CO2,N2O, and CH4, (tax30, red).

On the special behavior of PAS: The supply curve of PAS changes its shape from the tax0 to the

tax30 scenario. This is due to small amounts of forest that can be cleared in the tax0 scenario at low

prices. This forest is protected by the tax in the tax30 scenario, letting the supply curve start at high

prices for low demands already. High bioenergy demands in PAS are satisfied by expansion into

other arable land both in the tax0 and the tax30 scenario, since resulting emissions are not included

in the GHG tax. Thus PAS supply curves of the tax0 and tax30 scenarios share similar features at

108 5 Bioenergy supply under full land-use competition

higher demands but differ for low demands. The low initial prices in the tax0 scenario lead to a fit of

the data that is shaped concave. In the tax30 scenario the supply curve starts at higher prices and is

convex. Other regions do not have access to high productive other land that can be used without

being penalized by the GHG tax. Thus their supply curves are convex in all scenarios.

The high productive land in PAS with, at the same time, high carbon content was accounted “other

arable land” in our scenarios. This is based on data from Erb et al 2007, who accounted huge areas

of “grazing land” in PAS, which in MAgPIE is included into the pasture land pool. Since the FAO

reports less grazing land in PAS, the difference between both sources was accounted “other arable

land” in the MAgPIE scenarios used in this study and only the intersection was defined as pasture

land.

3.2 Land allocation

Figure S7: Global land cover in 2055 and 2095 for the four land types that are subject to the optimization and

the four scenarios and the initial value in 2005.

5.7 Supporting Online Material 109

Figure S8: Global land cover in 2055 (top) and 2095 (bottom) for all land types and the four scenarios and the

initial value in 2005.

110 5 Bioenergy supply under full land-use competition

Figure S9: Regional land cover in 2055 (top) and 2095 (bottom) for the four land types that are subject to the

optimization (and additionally for protected forest) for the four scenarios and the initial value in 2005. The

figure focusses on the relevant range, thus AFR, LAM, and NAM are cut at 450 Mha.

5.7 Supporting Online Material 111

3.3 Carbon emissions

The carbon tax pushes the carbon emissions backwards in time, since early deforestation is stopped

by the tax and the land conversion in PAS (which causes CO2 emissions) starts after 2050 when the

pressure from bioenergy and food demand increases.

Figure S10: Total (agriculture + bioenergy) CO2 emissions over time for the four scenarios.

3.4 Bioenergy yields

Figure S11: Regional grassy bioenergy yields (rainfed only) from LPJmL (1995, green) and MAgPIE

(1995, 2055, 2095, orange) from the sample scenario. LPJmL (Beringer et al 2011) represents

potential yields, while MAgPIE aims to represents actual yields. Therefore, LPJmL yields are

downscaled using information about observed land-use intensity (Dietrich et al 2012) and FAO

yields (FAO 2013). It is assumed that LPJmL bioenergy yields represent yields achieved under

highest currently observed land use intensification, which is observed in EUR. Therefore, LPJmL

bioenergy yields for all other regions than EUR are downscaled proportional to the land use

intensity in the given region. In addition, yields are calibrated at regional level to meet FAO yields in

1995, resulting in a further reduction of yields in all regions. Due to yield-increasing technological

change (TC) MAgPIE yields can exceed LPJmL yields until 2095.

112 5 Bioenergy supply under full land-use competition

Figure S11: Regional average yields for herbaceous bioenergy (rain fed only) for LPJmL (1995) and MAgPIE (1995,

2055, 2095) for the scenarios without (left) and with GHG tax (right). Yields of dedicated bioenergy crops (rainfed only)

obtained from the vegetation model LPJmL (Beringer et al 2011) represent potential yields achieved under the best

available management options. Since MAgPIE aims to represent actual yields in its initial time step, these yields are

downscaled using information about observed land-use intensity (Dietrich et al 2012) and FAO yields (FAO 2013). For

instance, in AFR yields are reduced by about 70%. Due to investment in yield-increasing TC MAgPIE yields can exceed

LPJmL yields by 2095. This figure shows regional averages of yields averaged over all land in the respective region that

is potentially available for bioenergy production. Regional average yields shown in Figure S14 are potentially higher

since they only take land into account that is actually used for bioenergy production in the respective region.

Figure S12: Bioenergy land cover across regions for the scenarios with and without GHG tax in 2055 and 2095

5.7 Supporting Online Material 113

Figure S13: Bioenergy production across regions for the scenarios with and without GHG tax in 2055 and 2095

Figure S14: Realized bioenergy yields across regions for the scenarios with and without GHG tax in 2055 and 2095.

114 5 Bioenergy supply under full land-use competition

Figure S15: Bioenergy as a driver for technological change: yields versus bioenergy demand across regions for the

scenarios with and without GHG tax in 2095

4 References

Beringer T, Lucht W and Schaphoff S 2011 Bioenergy production potential of global biomass

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

Online: http://doi.wiley.com/10.1111/j.1757-1707.2010.01088.x

Bondeau A, Smith P C, Zaehle S, Schaphoff S, Lucht W, Cramer W, Gerten D, Lotze-Campen H, MüLler

C, Reichstein M and Smith B 2007 Modelling the role of agriculture for the 20th century

global terrestrial carbon balance Glob. Change Biol. 13 679–706

Dietrich J P, Popp A and Lotze-Campen H 2013 Reducing the loss of information and gaining

accuracy with clustering methods in a global land-use model Ecol. Model. 263 233–43

Dietrich J P, Schmitz C, Müller C, Fader M, Lotze-Campen H and Popp A 2012 Measuring agricultural

land-use intensity – A global analysis using a model-assisted approach Ecol. Model. 232

109–18 Online: http://linkinghub.elsevier.com/retrieve/pii/S0304380012001093

5.7 Supporting Online Material 115

Erb K-H, Gaube V, Krausmann F, Plutzar C, Bondeau A and Haberl H 2007 A comprehensive global 5

min resolution land-use data set for the year 2000 consistent with national census data J.

Land Use Sci. 2 191–224

FAO 2013 FAO statistical database (Food and Agriculture Organization of the United Nations)

Online: http://faostat.fao.org

FAO and JRC 2012 Global forest land-use change, 1990-2005 (Rome: Food and Agriculture

Organization of the United Nations)

Humpenöder F, Popp A, Dietrich J P, Klein D, Bonsch M, Lotze-Campen H, Bodirsky B L, Weindl I and

Stevanovic M 2013 Investigating afforestation and bioenergy CCS as climate change

mitigation strategies in prep.

IIASA 2013 SSP Database (version 0.93) Int. Inst. Appl. Syst. Anal. IIASA Online:

https://secure.iiasa.ac.at/web-apps/ene/SspDb

Krause M, Lotze-Campen H, Popp A, Dietrich J P and Bonsch M 2013 Conservation of undisturbed

natural forests and economic impacts on agriculture Land Use Policy 30 344–54

Lucas P L, van Vuuren D P, Olivier J G J and den Elzen M G J 2007 Long-term reduction potential of

non-CO2 greenhouse gases Eenvironmental Sci. Policy 10 85–103

Müller C and Robertson R D 2013 Projecting future crop productiviy for global economic modeling

Agricultural Economics in press

Narayanan G B and Walmsley T L 2008 Global Trade, Assistance, and Production: The GTAP 7 Data

Base (Purdue University: Center for Global Trade Analysis) Online:

http://www.gtap.agecon.purdue.edu/databases/v7/v7_doco.asp

Nelson A 2008 Estimated travel time to the nearest city of 50,000 or more people in year 2000 (Ispra

Italy: Global Environment Monitoring Unit - Joint Research Centre of the European

Commission) Online: http://bioval.jrc.ec.europa.eu/products/gam/index.htm

O’Neil B, Carter T R, Ebi K L, Edmonds J and Hallegatte S 2012 Meeting Report of the Workshop on

The Nature and Use of New Socioeconomic Pathways for Climate Change Research, Boulder,

CO, November 2-4, 2011 (Boulder, Colorado, USA: National Center for Atmospheric Research

(NCAR))

Siebert S, Döll P and Feick S 2007 Global Map of Irrigation Areas version 4.0.1. (Rome, Italy: Food

and Agriculture Organization of the United Nations)

116 5 Bioenergy supply under full land-use competition

6 The value of bioenergy in lowstabilization scenarios: anassessment using REMIND-MAgPIE

David KleinGunnar LudererElmar KrieglerJessica Strefler

Nico BauerMarian LeimbachAlexander Popp

Jan Philipp DietrichFlorian Humpenoder

Hermann Lotze-CampenOttmar Edenhofer

Published as Klein, D.; Luderer, G.; Kriegler, E.; Strefler, J.; Bauer, N.; Leimbach, M.; Popp, A.;Dietrich, J.P.; Humpenoder F.; Lotze-Campen, H.; Edenhofer, O. (2013): The value of bioenergyin low stabilization scenarios: an assessment using REMIND-MAgPIE, Climatic Change, 123(3-4),705–718.

117

The value of bioenergy in low stabilization scenarios:an assessment using REMIND-MAgPIE

David Klein & Gunnar Luderer & Elmar Kriegler & Jessica Strefler &

Nico Bauer & Marian Leimbach & Alexander Popp & Jan Philipp Dietrich &

Florian Humpenöder & Hermann Lotze-Campen & Ottmar Edenhofer

Received: 12 November 2012 /Accepted: 17 September 2013# Springer Science+Business Media Dordrecht 2013

Abstract This study investigates the use of bioenergy for achieving stringent climatestabilization targets and it analyzes the economic drivers behind the choice of bioenergytechnologies. We apply the integrated assessment framework REMIND-MAgPIE to showthat bioenergy, particularly if combined with carbon capture and storage (CCS) is a crucialmitigation option with high deployment levels and high technology value. If CCS isavailable, bioenergy is exclusively used with CCS. We find that the ability of bioenergy toprovide negative emissions gives rise to a strong nexus between biomass prices and carbonprices. Ambitious climate policy could result in bioenergy prices of 70 $/GJ (or even 430$/GJ if bioenergy potential is limited to 100 EJ/year), which indicates a strong demand forbioenergy. For low stabilization scenarios with BECCS availability, we find that the carbonvalue of biomass tends to exceed its pure energy value. Therefore, the driving factor behindinvestments into bioenergy conversion capacities for electricity and hydrogen production arethe revenues generated from negative emissions, rather than from energy production.However, in REMIND modern bioenergy is predominantly used to produce low-carbonfuels, since the transport sector has significantly fewer low-carbon alternatives to biofuelsthan the power sector. Since negative emissions increase the amount of permissible emis-sions from fossil fuels, given a climate target, bioenergy acts as a complement to fossilsrather than a substitute. This makes the short-term and long-term deployment of fossil fuelsdependent on the long-term availability of BECCS.

Climatic ChangeDOI 10.1007/s10584-013-0940-z

This article is part of the Special Issue on “The EMF27 Study on Global Technology and Climate PolicyStrategies” edited by John Weyant, Elmar Kriegler, Geoffrey Blanford, Volker Krey, Jae Edmonds, KeywanRiahi, Richard Richels, and Massimo Tavoni.

Electronic supplementary material The online version of this article (doi:10.1007/s10584-013-0940-z)contains supplementary material, which is available to authorized users.

D. Klein (*) : G. Luderer : E. Kriegler : J. Strefler :N. Bauer :M. Leimbach : A. Popp : J. P. Dietrich :F. Humpenöder :H. Lotze-Campen : O. EdenhoferPotsdam Institute for Climate Impact Research (PIK), Potsdam, Brandenburg, Germanye-mail: david.klein@pik-potsdam.de

119

1 Introduction

Bioenergy is expected to play an important role within the portfolio of long-term greenhousegas (GHG) mitigation options (Chum et al. 2011; Fischedick et al. 2011). Its combinationwith carbon capture and sequestration technologies (CCS) allows carbon to be removedfrom the atmosphere, making it a measure of active carbon management (Obersteiner et al.2001; Riahi et al. 2007; Tavoni et al. 2013). This feature1 of bioenergy with CCS (BECCS)alleviates the deep reductions of GHG emissions that are necessary to meet stringent climatechange mitigation targets (van Vuuren et al. 2010a, b; Azar et al. 2010; Kriegler et al. 2013a;Edenhofer et al. 2010). Another advantage of biomass is its versatility: it can be convertedinto several types of secondary energy such as electricity, heat, liquid fuels, and hydrogen(Luckow et al. 2010; van Vuuren et al. 2010a). Therefore, it can serve as a flexible measurefor mitigation across different sectors. The combination of both features makes it a valuableand robust mitigation option (Riahi et al. 2007). However, there are major uncertaintiesregarding the main factors that determine biomass deployment. First, due to uncertaintyabout future development of land use and agricultural production, there is a wide range offuture estimates of bioenergy potential, ranging from less than 50 to several hundred EJ in2050 (Chum et al. 2011). Furthermore, concerns about the negative impacts of large-scalebiomass production on food security, biodiversity and GHG emissions exist (Wise et al.2009; van Vuuren et al. 2010a; Creutzig et al. 2012; Popp et al. 2011, 2012, 2013). Second,there is major uncertainty about the availability of advanced second-generation bioenergyconversion technologies (Sims et al. 2010). Finally, large uncertainties remain regarding thefuture deployment of CCS with respect to technological challenges, constraints on storagecapacities, and limited social acceptance (Zoback and Gorelick 2012).

This study argues that two key features of bioenergy—versatility and negativeemissions—determine its use and value as a mitigation option. Versatility allows bioenergyto be deployed in the way most valuable for decarbonizing energy use (as measured in termsof revenues from its energy production), and its negative emissions capability suggests touse it in a way which maximizes the amount of CO2 withdrawn from the atmosphere. In theframework of our study, the latter is incentivized by the fact that the carbon price accrues asrevenue to BECCS operators for every ton of CO2 withdrawn (carbon revenue). Concretely,we ask the following questions:

& How do the carbon and energy value of bioenergy determine its overall value for climatechange mitigation?

& How does the structure of energy and carbon revenues differ across different bioenergytechnologies, and how do these differences affect their deployment for different levels ofclimate stabilization targets?

& How does biomass deployment interact with the deployment of fossil fuels?

Based on these questions, we aim to characterize the two key economic drivers behindbioenergy use, their interplay and the potential trade-offs between them. To our knowledge,such a detailed characterization has not yet been provided in the literature. To account for theuncertainties about crucial determinants of bioenergy deployment we additionally vary theavailability of biomass and CCS technology and study their impact on the mitigationstrategy and its costs. We shed light on the crucial factors that determine the choice ofbioenergy conversion technology if carbon and bioenergy markets are interlinked, adding to

1 There are other options to generate net negative emissions, e.g., direct air capture technologies andafforestation. In contrast to biomass, they are not usable as primary energy carriers.

Climatic Change

120 6 The value of bioenergy in low stabilization scenarios

existing studies (Luckow et al. 2010; Calvin et al. 2009; van Vuuren et al. 2010a) on thepreferred long-term and large-scale applications of bioenergy under climate policy.

2 Methodology

Bioenergy deployment depends on the evolution of both energy and land-use systems.Therefore, an in-depth analysis of future scenarios of bioenergy use requires modeling ofthe two systems. This study applies the combined model system REMIND-MAgPIE. Theintegrated assessment model REMIND represents the energy-economy-climate system andcovers a wide range of bioenergy and competing conversion technologies. Within REMIND,the land-use sector is represented by an emulation of the high-resolution land-use modelMAgPIE. This emulation focuses on bioenergy supply costs and total agricultural emissions.

2.1 The integrated assessment model REMIND

The Refined Model of Investment and technological Development (REMIND) is a globalmulti-regional model that assesses climate change mitigation policies over the course of the21st century, while integrating the interactions between the economy, the energy sector, andclimate change (Leimbach et al. 2010a, b; Luderer et al. 2012, 2013; Bauer et al. 2012a, b).

REMIND combines a macro-economic Ramsey-type growth model, a detailed bottom-upmodel of energy production and conversion, and a climate module. The macro-economyendogenously determines the demand for final energy. The final energy carriers and servicesare produced from primary energy using a broad set of technologies for conversion,transmission, and distribution (cf. supplementary online material (SOM) Table S2).Endogenous technology learning is assumed for solar photovoltaic and concentrating solarpower as well as wind turbines (see SOM Section S1 for further information on non-biomassrenewables). REMIND assumes a global storage potential for captured carbon of 3600GtCO2 and a maximal annual injection rate of 0.5 % of the regional total potential (seeSOM Section S1.6 for regional potentials). Prices for fossil and biomass resources arecalculated from supply cost curves. Regions interact via trading of goods and primaryenergy carriers, including biomass.

Bioenergy can pursue different technology routes to be converted from primary energyinto several types of secondary energy carriers. Dominant technologies are biomass-to-liquidfuels B2L, BIGCC (integrated gasification combined cycle) producing electricity, andbiomass-to-hydrogen (B2H2). BIGCC and B2H2 feature high capture rates (80 % and90 %, respectively), whereas B2L maintains a lower capture rate (48 %) since a significantshare of carbon is embedded in the resulting fuel. Detailed information about bioenergyconversion routes and competing technologies and their techno-economic characteristics canbe found in SOM Table S3. Biomass is considered a low-carbon energy source with a creditfor negative emissions from CCS in the energy system. A detailed description of theassumptions on bioenergy and the interaction of the REMIND and MAgPIE model can befound in Section 2.3 and 2.3.

The techno-economic characteristics of the technologies and the endogenously evolvingprices of energy and GHG emissions determine the size and structure of the energy sector.Climate change stabilization targets are implemented by constraining radiative forcing (cf.SOM Section S2.2). The REMIND model computes the cost-effective emission mitigationwith full where (abatement can be performed where it is cheapest), when (optimal timing ofemission reductions and investments) and what (optimal allocation of abatement among

Climatic Change

6.2 Methodology 121

emission sources and greenhouse gases) flexibility. Further key characteristics of theREMIND model can be found in SOM Table S1 and a full model description in Ludereret al. (2013).

2.2 The land-use model MAgPIE

The bioenergy supply prices and land-use emissions represented in REMIND are based ondata from the global land-use model MAgPIE (Model of Agricultural Production and itsImpact on the Environment), (Lotze-Campen et al. 2008, 2010). MAgPIE is a recursivedynamic optimization model that minimizes the total cost of production for a given amountof regional food and bioenergy demand. In order to increase total agricultural production,MAgPIE can invest either in yield-increasing technological change or in land expansion(Krause et al. 2012; Popp et al. 2011). Four categories of costs arise in the model: productioncosts for livestock and crop production, yield-increasing technological change costs(Dietrich et al. 2013), land conversion costs, and intraregional transport costs. A breakdownof total agricultural production costs into these categories can be found in SOM Figure S14.MAgPIE considers regional economic conditions such as demand for agricultural commod-ities, level of agricultural technology, and production costs as well as spatially explicit dataon potential crop yields, land, and water constraints (from the dynamic vegetation modelLPJmL, Bondeau et al. 2007) and derives specific land-use patterns, yields, and total costs ofagricultural production. The model incorporates N2O and CH4 emissions from agriculturalproduction (Bodirsky et al. 2012) as well as CO2 emissions from land-use change (Poppet al. 2012). Since the demand for food is prescribed exogenously based on the assumedpattern of regional per capita income, there is no price response of food demand.Neither is there any underlying “food-first” policy in MAgPIE. Biomass competes with theproduction of food crops for land and other agricultural resources. This competitiondetermines the biomass prices that emerge from MAgPIE. Biomass supplies special-ized 2nd generation ligno-cellulosic grassy and woody bioenergy crops, i.e. miscanthus, poplar,and eucalyptus.

2.3 Representation of the land-use sector

The supply of purpose-grown ligno-cellulosic biomass in REMIND is represented byregional supply price curves that are calculated based on the price responses of MAgPIEto different biomass demand scenarios (Klein, in prep., SOM Fig. S2). Regional biomassendowments within REMIND are not limited explicitly (apart from a global limit, seebelow). However, there is an implicit limit, since the biomass supply curves prescribe risingprices for increasing demand. Therefore, the competitiveness of bioenergy with other energycarriers is limited. The emission baselines for CH4, N2O, and CO2 from land-use and land-use changes were also obtained from MAgPIE. They are exogenously prescribed toREMIND and can be reduced according to marginal abatement cost (MAC) functions(Lucas et al. 2007). The MAC function for CO2 abatement resulting from avoided defores-tation was derived from MAgPIE by measuring the response of CO2 emissions to varyingCO2 prices.

We use different bioenergy supply cost curves for the policy and baseline scenarios inREMIND since carbon pricing not only results in lower land-use emissions, but alsoincreases bioenergy prices. Consistent with Wise et al. (2009) we find that pricing emissionsfrom land-use change induces avoided deforestation. The avoided deforestation leads to anintensification rather than extensification of land for bioenergy production and thus makes it

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more costly to produce bioenergy. Wise et al. (2009) observes that carbon prices not onlyreduce deforestation but may even lead to afforestation. However, this option is not availablein the current MAgPIE model. In the presence of carbon pricing, deforestation comes to ahalt by 2020 in all policy scenarios. The resulting carbon emissions from land use showlevels of about 4.4 GtCO2/year until 2020 and zero emissions thereafter. N2O emissionsfrom bioenergy production due to fertilization are covered by an emissions factor2 of 3.7 kgCO2 eq/GJ in REMIND. By including the emission baselines and emission factors, directand indirect emissions caused by bioenergy deployment are fully represented and are part ofthe climate change stabilization target in REMIND. Therefore, emissions from the land-usesector and the energy system are valued equally.

Bioenergy is assumed to be predominantly produced from second-generation, ligno-cellulosic, purpose grown biomass, and ligno-cellulosic agricultural and forestry residues.The traditional use of biomass phases out until 2050, based on the assumption that it isreplaced with modern, sustainable, and less harmful fuels as incomes rise, especially indeveloping countries. First-generation biofuels are expected to contribute only in the short-to mid-term and they are expected to be replaced by second-generation fuels (Sims et al.2010). Land-use impacts, co-emissions, and competition with food production from first-generation biofuels are heavily debated (Searchinger et al. 2008; Fargione et al. 2008).REMIND assumes that only small amounts of first-generation fuels exist (less than0.1 EJ/year globally). The model considers the low-cost potential of ligno-cellulosicagricultural and forest residues, which increases from 20 EJ/year in 2005 to 70EJ/year in 2100 (based on Haberl 2010 and own calculations). Given the concernsabout the sustainability implications of large-scale bioenergy production, REMINDassumes, by default, an upper global limit of 300 EJ/year for second-generationbiomass use. This constraint is consistent with the upper end of potential 2050deployment levels identified in Chum et al. (2011). Based on the current publicdebate, we consider this constraint to be a reflection of the potential institutionallimitations on the widespread use of bioenergy; however, it does not reflect alimitation of bioenergy supply in the MAgPIE model. We have run the scenariosanalyzed in this study with unconstrained bioenergy supply and found that bioenergydeployments in 2100 reach 355 EJ/year in the 550-FullTech scenario and 535 EJ/year in the 450FullTech scenario. Therefore, the bioenergy constraint becomes binding at some point in thesecond half of the 21st century (cf. Section 3 on results).

2.4 Interaction of the models

For the EMF27 scenario analysis the three models REMIND, MAgPIE and MAGICC(Meinshausen et al. 2011) were run consecutively (cf. SOM Figure S4). In a first stepREMIND calculates the optimal mitigation strategy for the energy system. Using anemulation of the MAgPIE model REMIND takes into account the bioenergy supply curves,land use emissions and land use based mitigation potentials as described above. REMINDalso includes an emulator of the MAGICC model to relate emissions to radiative forcing andtemperature. Results from the land use and climate emulation in REMIND and from thesubsequent post-runs in MAgPIE and MAGICC are very close to each other. The latter arereported to the EMF27 database.

2 This emission factor was estimated from MAgPIE results and assuming a global warming potential of 298for N2O. Van Vuuren et al. 2010c report a similar value of 2.93 kg CO2 eq/GJ.

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6.2 Methodology 123

2.5 Scenario definition

This analysis focuses on the EMF27 scenarios that combine different climate targets with theavailability of CCS and high and low bioenergy potential. The EMF 27 scenarios cover threetypes of climate targets: baseline scenarios without climate protection targets (referred to as“Base”), 3.7 W/m2 forcing stabilization targets (not to exceed, referred to as “550”), and2.8 W/m2 forcing targets with overshoot (referred to as “450”). Scenarios with CCS and anupper limit of 300 EJ/year for biomass are labeled “FullTech”. Scenarios imposing a limit of100 EJ/year on global bioenergy potential are referred to as “LimBio”. If a scenario excludesCCS, it is labeled “NoCCS”. A description of the full portfolio of EMF27 scenarios is givenin Kriegler et al. (2013b).

3 Results

The general finding across all scenarios shows that bioenergy is one of the major mitigationoptions in stringent climate policy scenarios. This paramount importance of bioenergy forachieving low-stabilization targets can be attributed to its two key characteristics: itsversatility for producing different secondary energy carriers, and the option to createnegative emissions by combining bioenergy with CCS. Section 3.1 and 3.2 focus on thedeployment of bioenergy in the energy supply mix, and how this relates to its versatility andnegative emissions capability. Section 3.3 and 3.4 investigate the value of and economicdrivers behind bioenergy deployment in climate policy scenarios.

3.1 The contribution of bioenergy to primary energy supply

The REMIND baseline scenario is characterized by a strong reliance on fossil fuels.Traditional biomass is used in the first half of the century, but modern bioenergy use remainsinsignificant. While total primary energy demand continues to increase, deployment of fossilfuels peaks in 2070 due to increasing scarcity and is subsequently replaced with renewablesources. Solar and wind energy contribute to electricity production, whereas bioenergyreplaces fossil transport fuels.

As Fig. 1 (left and middle) shows, climate policy leads to earlier deployment of bioenergyand much higher long-term deployment, reaching 300 EJ/year. While bioenergy contributessubstantially to global primary energy supply in all scenarios, primary energy mixes varyconsiderably depending on the stringency of the climate target and the availability oftechnology. All climate policy scenarios show a reduction of final energy demand comparedto the baseline (−16 % to −28 %, cf. SOM Figure S5), which results in a substantial decreasein primary energy demand.3 In all climate policy scenarios, the use of conventional coalwithout CCS is phased out quickly after 2010 and decreases to nearly zero by 2040. Inaddition, only negligible or relatively small coal use with CCS is observed across thescenarios. The 550 FullTech scenario allows for higher deployment levels of oil and gasthroughout the whole century compared to the 450 FullTech case. In both FullTech mitiga-tion scenarios, gas with CCS is a mid-term mitigation option with maximal deployment

3 The direct equivalent method was used for primary energy accounting. Since it accounts one unit of non-biomass renewable or nuclear energy for roughly three units of fossil fuels in electricity production, it tends tounderstate the contribution of renewables or nuclear in primary energy supply. Reductions in primary energyare partly due to a shift from fossil fuel combustion to non-biomasss renewables and nuclear energy.

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124 6 The value of bioenergy in low stabilization scenarios

around 2045. At the end of the century, fossil fuels account for approximately 30 % (300 EJ)in the 550 FullTech scenario and only 10 % (90 EJ) in the 450 FullTech scenario. In allpolicy scenarios, the expansion of modern bioenergy use begins around 2030, about 30 yearsearlier than in the baseline, and evolves dynamically thereafter. The maximum potential isreached between 2040 (100 EJ in 450-LimBio) and 2080 (300 EJ in 550-FullTech). BECCSconversion routes are so attractive that bioenergy without CCS is crowded out. Newconversion capacities for bioenergy are exclusively built with CCS in all policy scenariosthat allow for CCS. In contrast, fossil CCS is less favored as it entails residual emissions inthe REMIND model. In both FullTech policy scenarios, BECCS makes up approximately30 % (300 EJ) of primary energy at the end of the century.

In all policy scenarios, the non-biomass renewable energies - solar, wind, and hydro -have a dominant share in the power supply after 2060, of which solar comprises the majority.By 2100, their contribution reaches approximately 41 % (550-FullTech) to 76 % (450-LimBio) of primary energy supply.

In the 450/550-FullTech scenarios fossil fuels and industry emit 1670/2290 GtCO2 from2005 to 2100, of which 830/610 GtCO2 (50/27 %) are withdrawn by BECCS, resulting in840/1680 GtCO2 deposited in the atmosphere by 2100. Together with fossil CCS 950/770GtCO2 have been captured by 2100 (SOM Section S2.3 and S2.4).

3.2 Bioenergy: a versatile energy carrier

Due to its versatility, bioenergy assumes a unique position among non-fossil energy carriers.In contrast to nuclear or non-biomass renewables, it can be converted into different types ofsecondary energy. Figure 1 (right) depicts the demand of primary bioenergy for those typesof secondary energy cumulated for 2005–2100. Modern bioenergy is mainly deployed in thesecond half of the century. The exogenously prescribed phase out of traditional biomass isnot varied across scenarios.

In almost all scenarios including the baseline, bioenergy is predominantly used toproduce liquid fuels. In the baseline scenario, biofuel is a substitute for increasingly scarceand costly oil in the second half of the century. Under climate policy, biofuel production withCCS has two purposes. First, it lowers emissions in the transportation sector, for which otherdecarbonization methods, such as electrification, are rather costly. Second, it producesnegative emissions, which offset emissions from other sources (cf. Section 3.3). Only the450-ppm scenarios (FullTech and LimBio) show capacities for dedicated electricity produc-tion from biomass using BIGCC with CCS. In all other scenarios the small amounts of

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bioenergy demand for electricity accompanying the dominant demand for liquids is due tothe fact that electricity is a byproduct of the biomass liquefaction process represented inREMIND (cf. SOM Table S2). The BIGCC and B2H2 processes feature higher capture ratesand, therefore, enable the higher negative emissions that are required in the more stringentmitigation scenarios. In general, tightening the climate target or restricting the bioenergypotential increases the deployment of technologies with higher capture rates, in order tomaximize negative emissions per unit of primary bioenergy used. Consequently, the sharesof BIGCC (80 % capture rate) and/or hydrogen production (90 % capture rate) increase from550-FullTech to 450-FullTech, from 550-FullTech to 550-LimBio, from 450-FullTech to450-LimBio, and from 550-LimBio to 450-LimBio. Kriegler et al. (2013a) also observe ahigher share of B2H2 and BIGCC compared to B2L due to tighter bioenergy supply limitedto 200 EJ/year.

3.3 The value of bioenergy for climate change mitigation

The following sections show that bioenergy has a high value for climate mitigation. As adirect reflection of this, mitigation costs, an indicator of the economic challenges resultingfrom climate policy, rise sharply if bioenergy or CCS are limited (Fig. 2). This indicates thatwithout bioenergy and CCS, it is difficult to achieve low stabilization targets.

Aggregate mitigation costs are expressed in terms of consumption losses between aclimate policy scenario and the corresponding baseline, in net present value terms for theperiod 2005–2100 and discounted at 5 % per year, as a share of net present value consump-tion in the baseline. We find that mitigation costs depend strongly on the climate target aswell as the abundance of bioenergy and the availability of CCS technologies. Increasing thestringency of the climate target from 550 ppm to 450 ppm results in almost a doubling ofmitigation costs from 1.8 % to 3.1 %. The unavailability of CCS increases mitigation costsmore (to 2.7 % for 550-ppm and 10.5 % for 450-ppm) than lowering the bioenergy potentialfrom 300 EJ/year to 100 EJ/year (to 2.3 % and 5.7 % respectively). The costs for all 550-ppm scenarios lie below the 450-ppm policy costs. In the 450-ppm scenario, costs almostdouble (to 5.7 %) if bioenergy supply is limited to 100 EJ/year and more than triple (to10.5 %) if CCS is not available. The value of bioenergy and BECCS increases with thestringency of the mitigation target, and is particularly important for low stabilization at450 ppm CO2e.

BECCS is particularly valuable because its negative emissions provide an additionaldegree of freedom for the amount and timing of emission reductions (Kriegler et al. 2013a).Since emissions from fossil fuel combustion in earlier periods can be compensated bynegative emissions at a later stage (up to 50 % in the 450-FullTech scenario), negativeemissions allow for the postponement of some emission reductions in the short-term and thepreservation of some residual emissions in the long run. This is even more significant ifovershooting of the stabilization target (in terms of radiative forcing) is allowed before 2100.Figure 2 illustrates this emission dynamics and the challenges associated with climatestabilization with full and limited technology portfolios. The graph shows CO2 emissionsover time from fossil fuel and industry with (top left) and without accounting (top right) fornegative emissions from BECCS as well as the resulting CO2 prices (bottom left). Pathwaysof net emissions do not depend on the availability of biomass or CCS in the 550-ppm case.In the 450-ppm scenario, BECCS in the second half of the century compensates for theemissions of the preceding decades. Limiting the bioenergy potential to 100 EJ/year orexcluding CCS strongly reduces this inter-temporal flexibility. An immediate and rapidrestructuring of the energy system is required in the early decades since strong overshooting

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126 6 The value of bioenergy in low stabilization scenarios

of the forcing target (by 0.7 W/m2 as observed in the 450-FullTech scenario), is no longerpossible (0.3 W/m2 in the 450-NoCCS scenario). Consequently, carbon prices as high as 120$/tCO2 in 2020 (520 $/tCO2 with NoCCS), compared to 50 $/tCO2 in the FullTech scenario,are required to trigger this early transformation and to reach the 450-ppm target.

Figure 3 (left) shows that these emission dynamics directly translate into a relationshipbetween the pathways of fossil fuels and bioenergy deployment making the short and long-termdeployment of fossil fuels dependent on the long-term potential of biomass. Figure 3 (right)depicts the cumulated amounts (from 2005 to 2100) of fossil fuels and bioenergy for scenarioswith different bioenergy availabilities scaled to the 450-FullTech scenario. Comparing the 450-LimBio with the 450-FullTech case shows the simultaneous increase of cumulated fossil fueland bioenergy demand. Due to the creation of negative emissions, more biomass use allows forhigher deployment of fossil energy. This leads to the following conclusion: while biomass is animportant substitute for fossil fuels in climate mitigation scenarios, given a climate target it alsodisplays characteristics of a complement to fossil fuels.

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Figure 4 shows the additional 450-HighBio scenario (not part of EMF27 portfolio)which omits the 300 EJ/year constraint on global bioenergy deployment. Although thebioenergy demand strongly exceeds 300 EJ/year the corresponding amount of fossilfuels does not increase further. This is the consequence of assuming a physicallimitation of the injection rate for captured carbon in REMIND. Since this limit isreached in 2060 (450-FullTech), removing the bioenergy-constraint in the 450-HighBio scenario cannot provide additional negative emissions and there is no roomfor additional fossil fuels. Thus, the short to long-term deployment of fossil fuels andBECCS technologies additionally depend on the rate at which CO2 can be sequestered intogeological reservoirs.

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128 6 The value of bioenergy in low stabilization scenarios

3.4 The relation between bioenergy and carbon markets

Maintaining today’s fossil fuel deployment over the next decades under stringent climatepolicies induces a strong demand for BECCS in the second half of the century. This isreflected by the strongly increasing prices for biomass. While bioenergy prices in the Base-FullTech scenario range between 1 $/GJ in 2010 and 12 $/GJ in 2100, prices reach 32 $/GJin the 550-ppm scenario and 73 $/GJ in the 450-FullTech scenario in 2100. Limitingbioenergy potential or excluding CCS increases bioenergy prices to 105 $/GJ and 431$/GJ, respectively. Average production costs of bioenergy are much lower (around 6 $/GJ)indicating substantial revenues for bioenergy producers.

Figure 4 (left) reveals a strong correlation between carbon prices and bioenergy prices.This is not surprising: with increasing carbon prices, the incentive to replace fossil fuels withbioenergy increases, as do potential revenues from BECCS-generated negative emissions.The dependence of bioenergy prices on carbon prices is stronger in scenarios with CCS andsomewhat weaker in the NoCCS scenario. In the climate mitigation scenarios, the value ofbioenergy is determined by both its energy value and the value of potential negativeemissions. An analysis of the revenues gained from biomass conversion demonstrates thatunder stringent climate targets, and in the presence of BECCS, the value of negativeemissions tends to dominate over the value of the energy.

Figure 4 (right) shows the revenues from secondary energy production and capturedcarbon per unit of primary energy input for different BECCS power plants in 2050 and 2100for the US and for the 450 and 550-FullTech scenario. In both scenarios, a major share ofrevenues from BIGCC and B2H2 plants is gained from capturing carbon, whereas revenuesfrom energy production are relatively low. This effect is stronger at higher carbon prices(550 vs. 450 and 2050 vs. 2100). Compared to electricity and hydrogen, revenues fromdiesel are significantly higher since the transportation sector has fewer low-carbon alterna-tives to biomass than the electricity sector. Fossil liquids prices increase due to entailedcarbon emissions. This drives up the demand for biofuels and the resulting prices forbiofuels (cf. SOM Fig. S12 and Fig. S13 for a regional breakdown of revenues and liquidfuel prices). Thus, the B2L technology is attractive despite its lower capture rate. Only inscenarios with high carbon prices (LimBio vs. FullTech, 450 ppm vs. 550 ppm) technologieswith higher capture rates become more favorable. The driving factor for building bioenergyconversion capacities for electricity and hydrogen production are the revenues generatedfrom negative emissions, rather than from energy production.

4 Summary and conclusion

The potentially negative carbon content of biomass has far-reaching consequences formitigation strategies, mitigation costs and bioenergy deployment. Our analysis shows thatBECCS can be a crucial mitigation option with high deployment levels and a high technol-ogy value, particularly for low stabilization targets. In our model results, modern bioenergyis exclusively used with CCS if this technology is available. Not having BECCS availablestrongly increases mitigation costs.

BECCS has impact on the timing and dynamics of emission reductions over the course ofthe century. Negative emissions are valuable because they increase the amount of permis-sible carbon emissions from fossil fuels and therefore allow postponing some emissionsreductions in the short-term. Thus, for a given climate target, bioenergy acts as a comple-ment to fossils rather than a substitute. Postponing emission reductions turns this inter-

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6.4 Summary and conclusion 129

temporal flexibility into a commitment since the prolonged short-term deployment of fossilfuels rely on the long-term potential of biomass and the availability of CCS. Given theuncertainties about CCS technology and the concerns about the sustainability of large-scaleproduction of bioenergy, a strong reliance on the availability of these two options could be arisky strategy.

The potential of biomass to provide negative emissions establishes a strong link betweenbioenergy prices and carbon prices. For low stabilization scenarios with BECCS availability,we find that the carbon value of biomass tends to exceed its pure energy value. This hasconsequences for the choice of BECCS technologies: with rising carbon price the capturerate becomes the crucial deciding factor in terms of which conversion routes are taken. Highcarbon prices (as observed in the LimBio scenarios) induce investments in technologies thatwould not be built for the purpose of energy production. In our scenarios these are electricityand hydrogen. However, except in the 450-LimBio scenario exhibiting higher carbon pricesthan all other CCS-scenarios, bioenergy is predominantly used to produce low-carbon fuels,since the transport sector has significantly fewer low-carbon alternatives to biofuels than thepower sector.

The price link has another consequence: as this study shows, imposing stringent climatetargets induces a strong demand for BECCS and a high willingness-to-pay for biomass.Postponing emission reductions will further increase this demand-pull for biomass. Thus, ahigh pressure on the agricultural sector can be expected under stringent climate policy evenif production costs of purpose-grown biomass were high. In our analysis, biomass pricesexceed average production costs, giving rise to considerable land rents for producers. Theresulting large-scale bioenergy production potentially has unintended negative impacts onland-use systems, such as competition with food production, reduction of biodiversity, andadditional GHG emissions (Wise et al. 2009; Searchinger et al. 2008; Fargione et al. 2008).Imposing sustainability constraints on the production of bioenergy likely will limit the potentialof bioenergy (Haberl et al. 2010; van Vuuren et al. 2009) as a mitigation option. Therefore,further research is needed to study the potentially far-reaching implications of connectingagricultural, energy and carbon markets. In particular, unintended side effects should beincluded into the assessment of bioenergy as a mitigation measure. The investigation of thisclimate policy induced land-energy nexus requires an integrated assessment based on fullycoupled energy-economy and land use models.

Acknowledgments The research leading to these results has received support from the ERMITAGE projectfunded by the Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 265170.

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Popp A et al (2013) Land-use transition for bioenergy and climate stabilization: model comparison of drivers,impacts and interactions with other land use based mitigation options. Clim Chang (this issue)

Riahi K et al (2007) Scenarios of long-term socio-economic and environmental development under climatestabilization. Technol Forecast Soc Sci 74:887–935

Searchinger T et al (2008) Use of U.S. croplands for biofuels increases greenhouse gases through emissionsfrom land-use change. Science 319:1238–1240

Sims R et al (2010) An overview of second-generation biofuel technologies. Bioresour Technol 101:1570–1580

Tavoni M et al (2013) Modeling meets science and technology: an introduction to a special issue on negativeemissions. Clim Chang 118:1–14

van Vuuren D et al (2009) Future bio-energy potential under various natural constraints. EnergyPolicy 37:4220–4230

Climatic Change

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van Vuuren D et al (2010a) Bioenergy use and low stabilization scenarios. Energy J 31:193–221van Vuuren D et al (2010b) The relationship between short-term emissions and long-term concentration

targets. Clim Chang 104:793–801van Vuuren D et al (2010c) Exploring IMAGE model scenarios that keep greenhouse gas radiative forcing

below 3 W/m2 in 2100. Energy Econ 32:1105–1120Wise M et al (2009) Implications of limiting CO2 concentrations for land use and energy. Science

324:1183–1186Zoback MD, Gorelick StM (2012) Earthquake triggering and large-scale geologic storage of carbon dioxide.

PNAS 109(26):10164–10168

Climatic Change

132 6 The value of bioenergy in low stabilization scenarios

Supporting Online Material for the Climatic Change manuscript submission

“The value of bioenergy in low stabilization scenarios: an assessment

using ReMIND-MAgPIE”

by David Klein, Gunnar Luderer, Elmar Kriegler, Jessica Strefler, Nico Bauer, Alexander Popp,

Marian Leimbach, Jan Philipp Dietrich, Florian Humpenöder, Hermann Lotze-Campen, Ottmar

Edenhofer

1 Additional model description ........................................................................................................................................... 2

1.1 Key characteristics of the REMIND model ...................................................................................................... 2

1.2 Energy conversion routes ........................................................................................................................................ 3

1.3 Techno-economic data of bioenergy technologies...................................................................................... 4

1.4 Regional breakdown of renewable energy potentials .............................................................................. 6

1.5 Bioenergy supply price curves .............................................................................................................................. 7

1.6 Regional carbon storage potentials .................................................................................................................... 7

1.7 Nuclear technology and uranium resource potential ................................................................................ 8

1.8 Model framework and interaction between the models.......................................................................... 8

2 Detailed results ........................................................................................................................................................................ 9

2.1 Energy mixes .................................................................................................................................................................. 9

2.2 Forcing targets and resulting emission budgets in ReMIND ............................................................... 10

2.3 Contribution of BECCS to GHG emission mitigation................................................................................. 11

2.4 Captured carbon ......................................................................................................................................................... 12

2.5 Regional energy prices for liquids ..................................................................................................................... 14

2.6 Regional revenues from bioenergy ................................................................................................................... 14

2.7 Agricultural production costs .............................................................................................................................. 15

2.8 References ...................................................................................................................................................................... 15

6.6 Supporting Online Material 133

1 Additional model description

The full model documentation can be found in Luderer 2013: http://www.pik-

potsdam.de/research/sustainable-solutions/models/remind/description-of-remind-v1.5

1.1 Key characteristics of the REMIND model

Table S1: Key characteristics of the REMIND model and its implementation of climate policy

Model feature Implementation in ReMIND-R Version 1.5

Macro-economic core and

solution concept

Intertemporal optimization: Ramsey-type growth model, Negishi approach for

regional aggregation

Substitution possibilities

within the macro-

economy & sectoral

coverage

Nested CES function for production of generic consumption good from factors

capital, labor, and different end-use energy types (electricity, stationary non-

electric energy, non-electric energy used for transport).

Link between energy

system and macro-

economy

Economic activity determines demand; energy system costs (investments, fuel

costs, operation and maintenance) are included in macro-economic budget

constraint. Energy system and macro-economy are optimized jointly.

Production function in the

energy system &

substitution possibilities

Linear substitution between competing technologies for secondary energy

production. Supply curves for exhaustibles (cumulative extraction cost curves)

as well as renewables (grades with different capacity factors) introduce

convexities. Adjustment costs on the rate of capacity expansion penalize rapid

deployment.

Technological Change &

Learning

Learning by doing (LbD) for wind and solar power. A global learning curve is

assumed. LbD spillovers are internalized. Labor productivity and energy

efficiency improvements are prescribed exogenously.

International trade

Global markets for coal, oil, gas and biomass; generic consumption good and

capital; emissions permits. Interregional trade of captured CO2 is not considered.

Discounting The constant rate of pure time preference in the iso-elastic welfare function is

set to 3%. The discount rate emerges endogenously on the global capital

markets. In line with the Keynes-Ramsey-Rule, with an intertemporal elasticity

of substitution of one and an average rate of increase of global per capita

incomes of around 3%, the discount rate is between 5 and 7%.

Emissions and forcing

representation

CO2 emissions from fossil fuel use are modeled by source. A marginal abatement

cost curve (MAC) for land use CO2 emissions is used to capture the potential for

reduced deforestation. MACs for CH4 and N2O emissions integrate abatement

potentials in the energy and land use sectors. N2O emissions from bioenergy use

are modeled explicitly (3.7 kgCeq/GJ of biomass primary energy). CH4 emissions

from fossil fuel extraction depend on the amount of fossils extracted. CO2

emissions from cement production and CH4 and N2O emissions from waste are

estimated using a simple econometric model, where per capita emisisons depend

134 6 The value of bioenergy in low stabilization scenarios

on per capita capital investments or GDP, respectively. SO2 and carbonaceous

aerosol emissions from fossil fuel combustion are modeled by source. Emissions

of HFCs, PFCs, SF6, Montreal gases, ozone precursors (CO, VOC, NOx) and biomass

burning aerosols are treated exogenously. Radiative forcing from nitrate

aerosols, mineral dust, and land albedo changes is assumed exogenously.

Land use Land use is not modeled explicitly (see manuscript Section 2.3 and 2.4 and SI

Figure S4). The bioenergy supply curve is based on MAgPIE data. A marginal

abatement cost curve for REDD is included in the model. The option of

afforestation is not represented. Interactions between deforestation and

bioenergy use are not explicitly captured, but bioenergy use is capped at 300

EJ/yr or 100 EJ/yr depending on the scenario. This limit applies to modern

second-generation biomass including residues, but not including the traditional

use of biomass.

Implementation of the

CO2e stabilization target

The CO2e(quivalent) constraint is imposed on total anthropogenic forcing (a

detailed description can be found in SI Section 2.2). This leads to a uniform

shadow price for the endogenous Kyoto gas emissions in the model (CO2, CH4,

N2O). The price is established under full when (time), where (region) and what

(GHG) flexibility to meet the radiative forcing constraint.

The 450 ppm CO2e target is imposed in the year 2100, with overshoot allowed

before 2100. The REMIND model runs until 2150 to ensure that the 21st century

emissions trajectory will not violate the target beyond 2100.

Implementation of

emissions taxes

CO2, N2O and CH4 from the energy and land use sectors are subjected to the

emissions tax. The tax level for a ton of carbon dioxide (equivalent) is converted

into N2O and CH4 using their 100 year global warming potentials reported in the

4th Assessment Report of the IPCC.

1.2 Energy conversion routes

Table S2: Conversion Technologies in ReMIND

PRIMARY ENERGY CARRIERS

Exhaustible Renewable

Coal Oil Gas Uranium

Solar,

Wind,

Hydro Geothermal Biomass

SE

CO

ND

AR

Y E

NE

RG

Y

CA

RR

IER

S

Electricity PC, IGCC DOT NGCC LWR SPV, WT, Hydro, CSP

HDR BIGCC

H2 C2H2 SMR B2H2

Gases C2G GasTR B2G

Heat CoalHP, CoalCHP

GasHP, GasCHP

GeoHP BioHP, BioCHP

Liquid C2L Refin. B2L

6.6 Supporting Online Material 135

fuels Bioethanol

Other

Liquids Refin.

Solids CoalTR BioTR

Abbreviations: B2G = biogas B2H2 = biomass to H2 B2L = biomass to liquids BIGCC = Biomass IGCC BioCHP = biomass combined heat and power BioEthanol = biomass to ethanol BioHP = biomass heating plant BioTR = biomass transformation C2G = coal to gas C2H2 = coal to H2 C2L = coal to liquids CoalCHP = coal combined hat power CoalHP = coal heating plant CoalTR = coal transformation

DOT = diesel oil turbine GT = gas turbine GasCHP = Gas combined heat power GasHP= gas heating plant GasTR = gas transformation GeoHP = heating pump HDR = hot-dry-rock Hydro = hydro power IGCC = integrated coal gasification combined cycle LWR = light water reactor NGCC = natural gas combined cycle PC = conventional coal power plant Refin. = Refinery SMR = steam methane reforming SPV = solar photo voltaic WT = wind turbine

1.3 Techno-economic data of bioenergy technologies

Table S3: Techno-economic parameters of biomass conversion technologies (Iwasaki, 2003; Hamelinck, 2004;

Ragettli, 2007; Schulz et al., 2007; Uddin and Barreto, 2007; Takeshita and Yaaij, 2006; Gül et al., 2008; Brown et al.,

2009; Klimantos et al., 2009) and selected competing technologies represented in the integrated assessment model

REMIND1.5.

Investment costs [US$2005/kW]

O&M costs [$/GJ]

Conversion efficiency

Couple production*

Capture rate

Learning

NGCC 650 0.95 0.56

NGCC CCS 1100 1.62 0.48 90 %

NGT 350 1.47 0.38

GasCHP 800 2.38 0.45 0.42 Heat

GasHP 300 1.35 0.75

Gas2H2 498 0.58 0.73 -0.01 Electr.

Gas2H2 CCS 552 0.66 0.70 -0.04 Electr. 90 %

O2Ld 494 0.84 0.75 0.21 Diesel

O2Lp 222 0.55 0.75 0.21 Petrol

IGCC 1650 3.09 0.43

IGCC CCS 2050 4.20 0.38 90 %

PC 1400 2.57 0.45

PC CCS 2400 4.69 0.36 90 %

PC CCS ox 2150 4.33 0.37 99 %

CoalCHP 1350 3.74 0.40

CoalHP 400 1.90 0.70

C2L 1450 3.85 0.40

136 6 The value of bioenergy in low stabilization scenarios

C2L CCS 1520 4.54 0.40 70 %

C2H2 1260 1.64 0.59 0.08 Electr.

C2H2 CCS 1430 1.87 0.57 0.05 Electr. 90 %

BioTr 150 0.00 1.00

BioSolids 225 0.24 0.90

BioCHP 1375 4.75 0.43 0.72 Heat

BioHP 400 2.06 0.80

BIGCC 1860 3.95 0.42

BiGCC CCS 2560 5.66 0.31 80 %

B2G 1000 1.74 0.55

B2H2 1400 5.27 0.61

B2H2 CCS 1700 6.33 0.55 90 %

B2Lp 2380 8.95 0.36 0.15 Electr.

B2Ld 2500 3.48 0.40 0.16 Electr.

B2Ld CCS 3000 4.52 0.41 0.14 Electr. 48 %

Hydro 2300 1.46

Wind 1400 0.89 Floor: 900 $/kW; Learning rate: 12% of costs above floor

SPV 5300 3.36 Floor: 600 $/kW; Learning rate: 20% of costs above floor

CSP 9500 9.04 Floor: 1900 $/kW; Learning rate: 9% of costs above floor

Abbreviations: NGCC: Natural gas combined cycle NGCC CCS: NGCC with CCS NGT: Natural gas turbine GasCHP: Natural gas combined heat and power GasHP: Natural gas heating plant Gas2H2: Natural gas to hydrogen Gas2H2 CCS: Gas2H2 with CCS O2Ld: Refinery from crude oil to diesel O2Lp: Refinery from crude oil to petrol IGCC: Coal integrated gasification combined cycle IGCC CCS: IGCC with CCS PC: Pulverized coal power plant PC CCS: PC with CCS PC CCS ox: PC with CCS using oxy-fuel CoalCHP: Coal combined heat and power CoalHP: Coal heating plant C2L: Coal to diesel C2L CCS: C2L with CCS

C2H2: Coal to hydrogen C2H2 CCS: C2H2 with CCS BioTr: Traditional biomass usage BioSolids: Modern solid production from biomass BioCHP: Biomass combined heat and power BioHP: Biomass heating plant BIGCC: Biomass IGCC power plant BiGCC CCS: BIGCC with CCS B2G: Biomass to gas B2H2: Biomass to hydrogen B2H2 CCS: B2H2 with CCS B2Lp: Biomass to diesel B2Ld: Biomass to petrol B2Ld CCS: B2Ld with CCS Hydro: Hydro power plant Wind: Wind turbine (combined on- and offshore) SPV: Photovoltaic CSP: Concentrated solar power plant

* defined as units of couple product that can be additionally produced (or is consumed in case of negative vale) per unit of main product.

6.6 Supporting Online Material 137

1.4 Regional breakdown of renewable energy potentials

Figure S1: Maximal annual production across regions and grades for solar photovoltaic, concentrated solar

power, wind (on-shore and off-shore combined) and hydro power.

The resource potentials for non-biomass renewables are modeled using region-specific potentials.

For each renewable energy type, the potentials are classified into different grades, which are

specified by capacity factors. Superior grades have higher capacity factors, which correspond to

more full-load hours per year. This implies that more energy is produced for a given installed

capacity. Therefore, the grade structure leads to a gradual expansion of renewable energy

deployment over time as a result of optimization.

138 6 The value of bioenergy in low stabilization scenarios

1.5 Bioenergy supply price curves

Figure S2: Regional price curves for the supply of 2nd generation purpose grown bioenergy. For Japan it is assumed

that due to space limitation in the agricultural system there is no potential to produce purpose grown biomass.

1.6 Regional carbon storage potentials

Figure S3: Regional storage potential for carbon, for exact numbers see Table S4 below.

Table S4: Assumptions on regional storage potentials in GtCO2

AFR CHN EUR IND JPN LAM MEA OAS ROW RUS USA World

Total potential 229 367 178 183 69 550 458 183 367 917 458 3959

Realizable potential* 109 174 84 87 33 261 218 87 174 435 218 1881 * Limit is defined by the maximal annual injection rate of 0.5 % of the regional total potential and the timespan of

95 years (2005-2100).

The storage of CO2 is described by three main characteristics. First, the levelized costs for

transporting and injecting the CO2 are at 9US$ per tCO2. Second, the storage potentials are limited

for each region. The assumptions are derived from the IEA report on CCS (IEA 2008). This

assessment is relatively optimistic and therefore the most uncertain deposits have been excluded.

6.6 Supporting Online Material 139

The total global storage volume is limited to 3959 GtCO2 (1100GtC). Third, the annual injection rate

in each region is limited to 0.5% of the total storage potential. This assumption reflects that storage

sites cannot be filled with full temporal flexibility. In order not to over use storage sites and

maintain the storage integrity we apply this limit. Geo-physical factors that rationalize this

constraint are the local pressure increase that is induced at the location of the injection.

1.7 Nuclear technology and uranium resource potential

For the present study we assume 23MtU of global uranium potential with increasing extraction

costs up to 260US$ per tU. Reprocessing and fast breeding reactors are not considered here. We

only assume once-trough reactor types. We do not assume fuel recycling for reasons of operational

safety and non-proliferation. The fast breeder technology is not assumed to be available because of

reasons of technical issues, operational safety, cost, and non-proliferation. The potential of 23 MtU

comprises all conventional resources including undiscovered. We assumed 17 MtU of conventional

resources that were identified in NEA (2010) and WEC (2010), c.f. Table S5. To account for the large

uncertainties about unconventional uranium resources we only consider a share of 6 MtU to be

available in future. This adds to a global total portential of 23 MtU that is assumed in the ReMIND

model.

Table S5: Assumptions on global uranium resources

BGR (2010) NEA (2010) WEC (2010) Identified Resource < 80 $US per kg 2.5 3.7

< 130 $US per kg 1.7 5.4 < 260 $US per kg 3.8 0.9 0.9

Undiscovered

Resource

Prognosticated 2.9 2.9 6.8 Speculative 3.9 7.5 3.6

Unconventional < 22 10-22

1.8 Model framework and interaction between the models

Figure S4: Data flow and interaction between the models used for the EMF27 scenario analysis.

140 6 The value of bioenergy in low stabilization scenarios

Forcing is calculated using the climate model MAGICC 6 (Meinshausen 2011).

2 Detailed results

2.1 Energy mixes

Figure S5: Final energy mixes across scenarios and time

Figure S6: Secondary energy mixes in the power sector across scenarios and time

Similar to gas with CCS, nuclear energy is a mid-term mitigation option with maximum deployment

levels around 2050. The resource potential of 23 Mt of uranium (NEA 2010) is exhausted in all

scenarios1. Climate policy stringency and technology availability mainly affect the temporal

distribution of deployment. Studies that consider nuclear power to be unlimited, suggest higher

contributions of nuclear power to emission mitigation (Mori 2012, Luckow 2010).

6.6 Supporting Online Material 141

Figure S7: Secondary energy mixes in the non-electric sector across scenarios and time

2.2 Forcing targets and resulting emission budgets in ReMIND

The forcing targets imposed in ReMIND include the following forcing agents: CO2, CH4, N2O, F-

Gases, Montreal Gases, SO2, black and organic carbon (including BC on snow), aerosols from

biomass burning, stratospheric and tropospheric ozone, stratospheric water vapor from methane

oxidation and indirect aerosol forcing. Though negative forcing e.g. from sulfur emissions is

accounted for, it cannot be used by the model to control the total forcing level. The EMF27 forcing

targets refer to the so called AN3A forcing (Kriegler et al., this issue) that does not include forcings

from nitrate, mineral dust, and land-use albedo. The temperature calculations are based on the total

forcing and result in 1.6°C and 2.0°C in 2100, respectively, when assuming a climate sensitivity of

3 °C.

The non-Kyoto forcings nearly cancel each other when the targets are reached, so that the Kyoto

forcing is similar to the AN3A forcing. CH4 and N2O account for 0.7-0.8 W/m2, which leaves a CO2

forcing target of 1.9 respectively 2.7 W/m2. This defines a CO2 budget for the period 2005-2100.

From this total CO2 budget, the CO2 land use emissions have to be subtracted, which yields the

budgets for CO2 fossil fuels and industry. Budgets that have been assessed to be compatible with

450 ppm vary widely. Meinshausen et al. (2009) calculated, that with a carbon budget of 886 Gt CO2

(including carbon emissions from land use change) from 2000-2049, the chance of exceeding 2°C is

20%. The RCP 3PD scenario (v. Vuuren et al. 2007), which is also compatible with 450 ppm, arrives

at a CO2 budget of about 1400 Gt CO2 for the same time horizon. Reasons for this variation are

among others uncertainties in the climate system and the carbon cycle. When we compare the

carbon budget calculated with ReMIND with others, we find that it is at the lower end.

Table S6: Forcing targets and emission budgets (2005-2100) for the two climate policy cases.

450 ppm 550 ppm Unit AN3A forcing 2.8 3.7 W/m² Kyoto forcing 2.8 3.5 W/m² Total forcing 2.5 3.3 W/ m² CO2 forcing 1.9 2.7 W/m² CO2 budget (total) 807-920 1670-1780 Gt CO2 CO2 budget (Fossil fuel and Industry) 723-845 1600-1700 Gt CO2

142 6 The value of bioenergy in low stabilization scenarios

2.3 Contribution of BECCS to GHG emission mitigation

Figure S8: Global GHG emissions [GtCO2_eq] including negative emissions cumulated over different time periods

across all policy scenarios.

6.6 Supporting Online Material 143

Figure S9: Regional GHG emissions [GtCO2_eq] including negative emissions cumulated from 2005-2100 across the

two FullTech scenarios.

2.4 Captured carbon

144 6 The value of bioenergy in low stabilization scenarios

Figure S10: Bioenergy and CCS: Left: Primary bioenergy deployed with and without CCS cumulated (2005-2100),

Right: Captured carbon from bioenergy and fossil fuels cumulated (2005-2100)

Figure S11: Captured carbon from 2005-2100 for scenarios with CCS available (see also Figure S10)

6.6 Supporting Online Material 145

2.5 Regional energy prices for liquids

Figure S12: Prices for liquid fuels across regions and two scenarios.

2.6 Regional revenues from bioenergy

146 6 The value of bioenergy in low stabilization scenarios

Figure S13: Value of energy versus value of carbon. Revenues from production of electricity, diesel, hydrogen, and

negative emissions in 2100 across regions for the 450 FullTech scenario.

2.7 Agricultural production costs

Figure S14: Components of agricultural production cost for the Baseline scenario.

2.8 References

BGR (2010): Reserven, Ressourcen und Verfügbarkeit von Energierohstoffen (Bundesanstalt für

Geowissenschaften und Rohstoffe, Hannover, Germany).

Brown D, et al. (2009): “Thermo-economic analysis for the optimal conceptual design of biomass

gasification energy conversion systems.” Applied Thermal Engineering 29: 2137-52.

Gül T (2009): An Energy-Economic Scenarios Analysis of Alternative Fuels for Transport. ETH-

Thesis No. 17888, Zürich, Switzerland.

Hamelinck, C. (2004). Outlook for advanced biofuels. Ph.D thesis, University of Utrecht, The

Netherlands.

IEA (2008): CO2 Capture and Storage. A key carbon abatement option. Paris, France.

Iwasaki W (2003): A consideration of the economic efficiency of hydrogen production from

biomass.” International Journal of Hydrogen Energy 28: 939-44.

Klimantos P, et al. (2009): “Air-blown biomass gasification combined cycles: System analysis and

economic assessment.” Energy 34: 708-14.

Kriegler, E, et al. (this issue) Overview over the EMF27 study.

6.6 Supporting Online Material 147

Luckow P, et al. (2010) Large-scale utilization of biomass energy and carbon dioxide capture and

storage in the transport and electricity sectors under stringent CO2 concentration limit scenarios.

International Journal of Greenhouse Gas Control 4.

Luderer, G., et al. (2013) Description of the REMIND-R model. Technical Report, Potsdam Institute

for Climate Impact Research. http://www.pik-potsdam.de/research/sustainable-

solutions/models/remind/description-of-remind-v1.5

Meinshausen M., et al. (2009) Greenhouse-gas emission targets for limiting global warming to 2°C.

Nature 458, 1158-1162, doi:10.1038/nature08017

Meinshausen M, et al. (2011) Emulating coupled atmosphere-ocean and carbon cycle models with a

simpler model, MAGICC6 – Part 1: Model description and calibration. Atmos. Chem. Phys., 11.

Mori S (2012) An Assessment of the potential of nuclear power and CCS in the long-term global

warming options based on Asian Modeling Exercise scenarios. Energy Economics 34.

NEA (2010) Uranium 2009. Resources, Production, and Demand (Nuclear Energy Agency and

Organization of Economic Co-operation and Development, Paris).

Ragettli M (2007): Cost Outlook for the Production of Biofuels. Master Thesis, ETH Zürich, February,

2007.

Schulz, T. (2007): Intermediate Steps towards the 2000-Watt Society in Switzerland: An Energy-

Economic Scenario Analysis. ETH Thesis No. 17314, Zürich, Switzerland.

Takeshita T, Yamaji K, Fujii Y (2006): “Prospects for interregional energy transportation in a CO2-

constrained world.” Environmental Economics and Policy Studies 7:285-313.

Uddin S, Barreto L (2007): “Biomass-fired cogeneration systems with CO2 capture and storage.”

Renewable Energy 32: 1006-19.

van Vuuren D, et al. (2007) Stabilizing greenhouse gas concentrations at low levels: an assessment

of reduction strategies and costs. Climatic Change, doi:10.1007/s/10584-006-9172-9.

WEC (2010) 2010 Survey of Energy Resources (World Energy Council, London).

148 6 The value of bioenergy in low stabilization scenarios

7 Synthesis and Outlook

The objective of this thesis has been to examine the role bioenergy can play in achievingambitious climate change mitigation targets, to identify the economic drivers behind thechoice of bioenergy conversion technologies, and to investigate how bioenergy supply,mitigation strategies and costs depend on constraints on the supply side and the demandside of bioenergy.The results suggest that bioenergy with CCS is one of the major long-term mitigation

options, particularly for achieving low-stabilization targets. High CO2 prices offer higheconomic benefits from generating negative emissions with BECCS. Accordingly negativeemissions are found to be the main driver behind BECCS deployment with significantimpact on the cost-effective emission trajectory and overall mitigation costs. Besidesthese findings that are discussed in detail in the next sections this thesis makes fivecontributions to the assessment of bioenergy that are briefly recapitulated below:

1. By reviewing the SRREN and scrutinizing how it performs in relation to proposedassessment requirements the thesis identifies knowledge gaps that may be addressedin future bioenergy assessments and research.

2. Integrating models of energy, economy, land use, and climate the thesis providesa quantitative framework that allows to explore consistent long-term scenarios forclimate change mitigation while considering the trade-off between benefits frombioenergy deployment in the energy sector and potential additional emissions inthe land use sector.

3. It presents regional bioenergy supply curves with global coverage that can be ap-plied in IAMs to represent the bioenergy resource potential.

4. Based on a broad literature review it supplies techno-economic parameters for thebiomass-to-electricity conversion route with and without CCS.

5. It contributes long-term scenarios of bioenergy deployment under different con-straints on climate, technology, and biomass-supply.

The potential role of bioenergy in climate change mitigation has been analyzed in Chapter3 to 6 addressing specific research questions as defined in Section 1.6. The major findingsof the chapters are presented along these research questions (next section) followed by asynthesis and discussion of the results (Section 7.2) and suggestions for further research(Section 7.3).

149

150 7 Synthesis and Outlook

7.1 Synthesis of results

What is the current state of bioenergy assessment?

The complexity of the bioenergy supply system induces a high level of uncertainty aboutfuture outcomes. Policy makers therefore have a need for comprehensive and balancedbioenergy assessment. This chapter asks the question whether the Special Report onRenewable Energy Sources and Climate Change delivers such an assessment with respectto bioenergy. For this purpose three criteria for assessment making are defined: Anassessment should (1) Provide a review of literature that is comprehensive in topicsand communities. (2) Make diverging assumptions transparent and possibly reconciledisparate views by identifying trade-offs. (3) Present a consistent set of policy-relevantstorylines.

Regarding comprehensiveness the SRREN succeeds in bringing various insights fromdifferent communities together and shows a good coverage of key-issues of bioenergydeployment, such as costs, emissions, resource potentials, impacts, and governance.

Tradeoffs between bioenergy deployment and other essential land use related dimen-sions of the bio-socio-sphere such as water, food, emissions, soil, and biodiversity areidentified and discussed. A major gap, however, was identified as the missing reconcil-iation between the life-cycle-assessment (LCA) and the IAM community, representingdisparate perspectives on bioenergy-associated GHG emissions. Most scenarios exploredwith IAMs assume first-best worlds where so-called market failures, such as land useemissions from bioenergy deployment, are addressed by appropriate policy instruments.LCA researchers observe life-cycle emissions of biofuels that can be comparable to gaso-line, and are highly uncertain. High deployment levels could lead to co-emissions fromland use change and agricultural intensification.

Storylines representing various worldviews are identified but – with the exception ofdeployment costs – not systematically explored in models. SRREN Chapter 2 maps ex-pert judgment on future bioenergy deployment on four scenarios defined in the SpecialReport on Emission Scenarios (SRES), (IPCC 2000), but the SRES dimensions are onlyinsufficiently considered in IAMs. Most models do not cover the land use sector explic-itly but rely on an exogenous supply cost function. There is a lack of transparency ofunderlying assumptions. In contrast to SRREN Chapter 2 supply cost curves in SRRENChapter 10 assume “good governance” for all SRES storylines by assuming that bioen-ergy is produced on abandoned and rest land only, which implies underlying food-firstor nature protection policies. Bioenergy is generally assumed to be carbon-neutral inmany IAMs. Harmonization of assumptions with SRREN Chapter 2 is not attempted.Neither is the SRES scenario space of SRREN Chapter 2 systematically covered. Hence,the space of possible bioenergy storylines explored with IAMs is very narrow.

Chapter 2 of this thesis concludes that numerous specifications need to be introducedinto IAMs such that a more complete scenario space, representing the trade-offs identifiedin SRREN Chapter 2, can be systematically explored in an integrated setting. Aiming ata better representation of sustainability constraints in the land use sector the followingchapters use an integrated framework of energy-economy-climate and land use to explorethe potential contribution of bioenergy to climate change mitigation. The next chapterstarts with an analysis that focuses on the impacts of large-scale bioenergy deploymenton the land use system.

7.1 Synthesis of results 151

How this thesis aims to improve bioenergy assessment

Chapter 2 criticizes that so far IAMs do not represent the land use sector in detail but usesupply cost curves for bioenergy. It further criticizes that these supply functions until nowassume first-best worlds with good governance, since they allow bioenergy production onabandoned land only and thus consider zero land use change emissions from bioenergyproduction. Due to constraints to the numerical feasibility of a fully integrated energy-economy-climate-land use model this thesis maintains the concept of bioenergy supplycost curves, yet derived from a high-resolution land use model. However, in severalrespects the approach applied in this thesis aims to improve bioenergy assessment bycovering a broader perspective than previous studies.

Most importantly bioenergy production is not restricted to abandoned land as it wasin previous bioenergy potential studies but it has full access to agricultural and non-protected forest land and can induce land use change. Thus, in contrast to previousstudies criticized in Chapter 2 there is no per se assumption of good governance like anunderlying food-first policy. GHG emissions induced by bioenergy production, such ascarbon emissions from deforestation and other land use changes, and nitrogen emissionsfrom fertilizer use are fully accounted for (except in Chapter 3). Therefore, by assumingbioenergy not to be carbon neutral this thesis takes a step toward the reconciliation ofLCA and IAM community.

In this thesis the dimension of good and poor governance on the one hand is addressedby the different GHG pricing regimes applied in the land use sector (pricing vs. nopricing) resulting in different supply curves and GHG emissions (most prominently inChapter 5). On the other hand, good governance is represented by the assumption thatcarbon emissions from deforestation can be measured and priced at all. An examplefor potential consequences of poor governance is the carbon leakage effect observed inChapter 5 resulting from imperfect land classification that, in this case, excludes landwith high carbon density from emission control. This illustrates that in addition to im-plementing GHG pricing in the land use sector also the issue of land classification posesa challenge to effective land based mitigation strategies. Furthermore, the approachapplied in this thesis allows investigating the impact of bioenergy production on sustain-ability indicators like food and water prices as done in Chapter 3, which gets particularlyimportant if bioenergy production - as in this study - is not restricted to abandoned landbut has the same access to land as food production. The solution space regarding bioen-ergy deployment, criticized as too narrow in Chapter 2, is further explored in Chapter 6,where different second-best worlds with limitations of technology availability (CCS) andbioenergy supply are evaluated.

What is the cost-effective contribution of bioenergy to a low-carbon transition,paying special attention to implications for the land system?

This chapter explores the cost-effective contribution of bioenergy to low-carbon transitionincluding its costs and trade-offs with food and water security and taking the impact offorest conservation on bioenergy potentials into account. Results show that bioenergyfrom dedicated lignocellulosic energy crops is likely to be a cost-efficient contributionto the future energy mix with deployment levels of 100 EJ in 2055 and up to 300 EJin 2095. Due to rising bioenergy prices, restrictions on land availability from forestprotection reduce bioenergy deployment by about 40 EJ/yr. Cultivation of bioenergy

152 7 Synthesis and Outlook

crops has several effects: it increases crop land expansion; it takes over a huge share intotal cropland; it increases the rate of technological change required to accommodatefood and bioenergy demand (0.8% per year until 2095 with bioenergy demand comparedto 0.6% without bioenergy); it is mainly located in areas that today are occupied byintact ecosystems; and it increases CO2 emissions from deforestation. The restriction ofagricultural land expansion due to forest protection would have to be compensated byfurther increases in rates of technological change (0.9% per year until 2095 with forestprotection compared to 0.8% per year without it).Food prices and the implicit values of irrigation water are only slightly affected without

forest conservation, but rise significantly (especially in the tropics) if forests are excludedfrom available land for future cropland expansion. Higher food prices (80% increase inAfrica and 70% in Latin America) follow directly from competition with energy crops,due to limitations in land availability and associated needs for technological developmentat additional costs. Shadow prices of irrigation water rise with increasing forest conser-vation (390% in Africa and 460% in Latin America), because less land is available forrainfed agriculture, and biomass accumulation for dedicated bioenergy cultivation leadsto higher evapotranspiration rates, which can reduce water availability in regions wherewater is already scarce. This is a strong indication that the competition for water be-tween agriculture, private households, and industry is likely to increase heavily in manyregions. Avoiding deforestation for biodiversity conservation by excluding intact forests(mainly in the tropics) from suitable land for cropland expansion could add additionalpressure on other natural ecosystems with extraordinary biodiversity, such as savannas.Focusing on the land use implications of bioenergy deployment this chapter showed

that forest conservation combined with large-scale cultivation of dedicated biomass forclimate change mitigation will likely generate conflicts with respect to food supply andwater resource management. Forest protection was found to increase bioenergy prices andthus reduce bioenergy deployment. However, due to its versatility and potentially nega-tive carbon content bioenergy remains an attractive mitigation option even if bioenergyprices increase due to constraints on the land use side. The next chapter will exemplifythis focusing on the deployment of a particular conversion technology, whereas Chap-ter 6 demonstrates it with a broader perspective on technology mix, bioenergy demanddrivers, and mitigation strategies.

What is the mitigation potential of the BioIGCC technology and how does itdepend on the techno-economic performance and the biomass feedstock?

This chapter explores the mitigation potential of the high-efficient biomass-to-electricityBioIGCC technology that can cope with heterogeneous feedstocks and that is suitablefor CCS with a high capture rate.Results indicate that BioIGCC with CCS could serve as an important mitigation op-

tion under stringent climate change mitigation targets contributing one third of totalemissions reduction at the end of the century by generating large amounts of negativeemissions after 2050 (440-660 GtCO2 cumulated from 2005-2100). The most importantparameter that decides about the application of the BioIGCC technology is the capturerate. The technology is only used with CCS. A decreasing capture rate leads to lessdeployment. It has to exceed 60% otherwise the technology is not applied even thoughit could supply electricity. The BioIGCC technology competes for providing energy andnegative emissions with the fuel producing Fischer-Tropsch process which steps in when

7.1 Synthesis of results 153

the BIGCC+CCS technology becomes unattractive due to low capture rates. However,when biomass raw material becomes scarce and consequently more expensive it is ex-clusively used with the BioIGCC technology to produce as much negative emissions aspossible, provided the capture rate is high. Biomass feedstock is predominantly producedfrom lignocellulosic perennial grassy feedstock. However, this requires robust gasificationtechnologies to be available that can cope with the heterogeneous grassy feedstock. Sinceresults indicate that modern bioenergy is predominantly deployed after 2030 there is stillsome time to bring these technologies to maturity. Bioenergy prices rise if the supplyof primary biomass is constrained to woody feedstock, which can only be produced atlower production levels than grass.The previous two chapters have already indicated that global emissions constraints

could lead to high deployment levels of bioenergy. The analyses focused on land useimplications and technological aspects. The next chapter asks the question how climatepolicy affects the supply of biomass feedstock and resulting land use emissions.

How much biomass can by supplied under full land use competition at whatprice? How does the biomass potential depend on GHG prices?

This study explores the global potential of purpose-grown lignocellulosic biomass underdifferent carbon tax regimes using the land use optimization model MAgPIE. In the anal-ysis bioenergy supply curves for 10 world regions are derived by measuring the bioenergyprice response of the MAgPIE model to different scenarios of bioenergy demand andGHG prices.The results show that the implementation of GHG taxes is crucial for the slope of the

supply function and the GHG emissions from the land use sector. Global supply pricesstart at 5 $/GJ and increase almost linearly, doubling at 150 EJ (in 2055 and 2095). TheGHG tax has a clear elevating impact on bioenergy prices (roughly +5 $/GJ in 2055 and+10 $/GJ in 2095 at 150 EJ). The elevation can be separated into an effect that steepensthe supply curve (scaling with the bioenergy demand) due to reduced land availabilityand a roughly constant upwards translation effect (independent of bioenergy demand)due to non-CO2 co-emissions from bioenergy. The steepening is caused by the CO2 pricewhich effectively stops deforestation and thus excludes large amounts of high-productiveland (mainly in tropical regions). The resulting increases in land scarcity are reflected inincreasing bioenergy prices. The translation effect is caused by pricing nitrogen emissionsthat accrue from fertilizer use for bioenergy crop cultivation. It does not scale with thebioenergy demand since the amount of organic and inorganic fertilizer used per unit ofbioenergy is constant. The land exclusion is partly compensated by expansion into otherarable land, since resulting emissions are not penalized by the GHG tax (in this settingonly emissions from deforestation are covered by the GHG tax). The remaining part iscompensated by the replacement of cropland with bioenergy crop land.Conditions of bioenergy production differ across regions as do resulting bioenergy

supply curves. Without a GHG tax major producers are the tropical regions Africa(AFR) and Latin America (LAM), which offer access to large areas of forest that canbe converted to high productive land for crop and bioenergy production. This results inrelatively flat supply curves if carbon is not taxed. Introducing a GHG tax changes therelative position of the regional supply curves since the consequences of pricing emissionsare different across regions. Regions where no forest land is used, such as CentrallyPlanned Asia (CPA), Pacific Asia (PAS), and South Asia (SAS), are only affected by

154 7 Synthesis and Outlook

the N2O-price effect. The supply curves of regions that deforest without the GHG taxare additionally affected by the steepening effect of CO2 prices (strongest in AFR andLAM). The combined effects significantly increase regional supply prices and change therelative position of the supply curves. This is reflected in the reallocation of bioenergyproduction that shifts from AFR and LAM mainly to PAS, CPA, and SAS under theGHG tax. The tax lets PAS get competitive, which features a relatively high but flatsupply curve. The land restriction in PAS is not as strong as in other regions because itcan expand into other land that is not under emission control. However, this increasesland use change emissions in PAS under the GHG tax so that global land use emissionsdue to bioenergy production are higher under the tax than without tax. Nonetheless, thetax reduces total global land use change emissions by one third, since emission savingsfrom avoided deforestation overcompensate increased emissions from conversion of otherland. Average yields required for bioenergy production in 2095 are roughly between 500and 600 GJ/ha for the major producer regions with and without tax.This chapter explored the global potential of biomass feedstock and showed that GHG

prices could significantly increase biomass prices. Using the bioenergy supply curves thatwere presented in this study Chapter 6 analyzes the economic demand drivers behind thelarge-scale deployment of bioenergy in long-term climate change mitigation scenarios.

Which factors determine the willingness-to-pay for bioenergy under climatechange mitigation targets? How do these factors affect the choice of bioenergyconversion routes?

Chapter 6 investigates the use of bioenergy in long-term climate change mitigation sce-narios, how its energy and carbon content determine the value of bioenergy for climatechange mitigation and how they affect the choice of bioenergy conversion technologies ifcarbon and bioenergy markets are interlinked. The role of bioenergy is analyzed undertwo different climate targets: a weaker forcing target of 3.7 W/m2 (not to exceed) anda stringent low-stabilization target of 2.8 W/m2 (with overshoot before 2100), the latterof which holds the increase of global mean temperature below 2 �.The carbon content of biomass, which allows generating negative emissions, has far-

reaching consequences for mitigation strategies, mitigation costs and bioenergy deploy-ment. In the scenarios of this analysis modern bioenergy is exclusively used with CCSif this technology is available. BECCS is particularly important for low-stabilization,which is hard to achieve without negative emissions. The mitigation costs (calculatedas the cumulated consumption losses compared to a reference scenario without climatepolicy) for low-stabilization almost double (to 5.7%) if bioenergy supply is limited andmore than triple (to 10.5%) if CCS is not available. In the weak policy case they arebetween 1.8% and 2.7%. Biomass is predominantly used to produce liquid fuels for thetransportation sector, for which other decarbonization methods, such as electrification,are rather costly. Tightening the climate target or restricting the bioenergy potentialincreases the deployment of technologies with higher capture rates, in order to maximizenegative emissions per unit of primary bioenergy used.The potential of biomass to provide negative emissions establishes a strong link be-

tween carbon prices and bioenergy prices, the latter of which are interpreted as thewillingness-to-pay for bioenergy. The carbon price increases with the stringency of theclimate target and with the reduction of the bioenergy potential, as does the willingness-to-pay for bioenergy. While bioenergy prices are 12 $/GJ in 2100 without climate policy,

7.1 Synthesis of results 155

under low-stabilization targets prices increase to 73 $/GJ if the bioenergy potential isconstrained to 300 EJ/yr and 430 $/GJ if it is constrained to 100 EJ/yr. It is hard toimagine that these bioenergy prices and underlying carbon prices would be politicallydesirable and enforceable or would be paid in the real world. Rather they are an indicatorfor the strong need for negative emissions that occurs in the second half of the centuryfrom intertemporal emissions balancing. Since the CCS technology is the only way totransform the biomass carbon into negative emissions the price-link is weaker if CCSis not available (66 $/GJ in 2100). Consequently, the willingness-to-pay for bioenergydepends on the carbon price and the availability of CCS. For low stabilization scenarioswith BECCS availability, we find that the carbon value of biomass tends to exceed itspure energy value. This has consequences for the choice of BECCS technologies: withrising carbon price the capture rate becomes the deciding factor in terms of which con-version routes are taken. Rising carbon prices induce investments in technologies thatwould not be built for the purpose of energy production.BECCS has impact on the timing and dynamics of emission reductions over the course

of the century. Negative emissions are valuable because they increase the amount of per-missible carbon emissions from fossil fuels and therefore allow postponing some emissionsreductions in the short-term and the preservation of some residual emissions in the longrun. Thus, for a given climate target, bioenergy acts as a complement to fossils ratherthan a substitute. Postponing emission reductions turns this inter-temporal flexibilityinto a commitment since the prolonged short-term deployment of fossil fuels relies onthe long-term potential of biomass and the availability of CCS. Given the uncertaintiesabout CCS technology and the concerns about the sustainability of large-scale produc-tion of bioenergy, a strong reliance on the availability of these two options could be arisky strategy.

156 7 Synthesis and Outlook

Key findings

� Bioenergy with CCS is a mitigation option with paramount importance particularlyfor achieving stringent climate change mitigation targets. If CCS is available, inmitigation scenarios bioenergy is exclusively used with CCS and it is predominantlyused to produce liquid fuels for the transportation sector. Bioenergy deploymentincreases rapidly after 2030 to around 300 EJ/yr. Mitigation costs rise sharply ifbioenergy or CCS is constrained. This indicates that without bioenergy and CCS,it is difficult to achieve low stabilization targets.

� In the climate mitigation scenarios, the value of bioenergy is determined by bothits energy value and the value of potential negative emissions. The availability ofBECCS creates a strong link between carbon prices and bioenergy prices. For lowstabilization scenarios with BECCS availability, the carbon value of biomass tendsto exceed its pure energy value. Rising carbon prices thus induce investments intechnologies that would not be built for the purpose of energy production.

� Bioenergy is so valuable because its negative emissions increase the amount of per-missible carbon emissions from fossil fuels and therefore allow postponing emissionsreductions in the short-term and the preservation of some residual emissions in thelong run. For a given climate target, bioenergy thus acts as a complement to fossilsrather than a substitute.

� However, this makes the prolonged short-term deployment of fossil fuels dependenton the long-term potential of biomass and the availability of CCS. Thus, uncertain-ties about the long-term developments of (i) conditions in the land use sector (landavailability, yields etc.) and (ii) CCS technology are highly relevant for short-termdecisions about emission reduction.

� Since bioenergy prices and carbon prices are closely linked, stringent climate protec-tion targets induce a high willingness-to-pay for bioenergy that exceeds bioenergysupply prices by far.

� Biomass feedstock can be supplied at prices above 5 $/GJ. GHG taxes increasebioenergy supply prices due to reduced availability of high-productive forest landand due to nitrogen-emissions from bioenergy production. Grassy biomass feed-stock can be produced at lower cost than woody feedstock.

� Rising bioenergy demands are generally realized by yield increasing technologicaldevelopment, i.e. intensification. Average yields required for bioenergy productionin 2095 are roughly between 500 and 600 GJ/ha for the major producer regions.Without a GHG tax in the land use sector bioenergy reduces food cropland, otherland and forest. Introducing a tax on land use change emissions from deforestationfurther reduces cropland and other land but saves forest completely. Two “valves”are used to accommodate food and bioenergy demand if land availability is reducedby GHG taxes: intensification and expansion into other land. Since GHG taxeseffectively prevent deforestation, also in the presence of bioenergy demand, theysignificantly reduce global land use change emissions. However, high bioenergydemands plus GHG taxes (as observed under low-stabilization targets) at the sametime put substantial pressure on the land use sector and could create a carbonleakage effect from conversion of land that is not under emission control.

7.2 Discussion 157

� There is a strong indication that the competition for water between agriculture,private households, and industry is likely to increase heavily in many regions, par-ticularly if forests are protected and bioenergy is used for climate change mitigation.

7.2 Discussion

Climate policy not only increases the demand for bioenergy as Chapter 6 and severalfurther studies show (Calvin et al. 2009; Rose et al. 2013; van Vuuren et al. 2010a), itcould also substantially increase supply prices of biomass raw material as Chapter 5 ofthe present study demonstrates. These prices are relatively high compared to previousstudies (about 4 $/GJ in Hoogwijk et al. (2009) and van Vuuren et al. (2009)) andother energy carriers, particularly if GHG emissions in the land use sector are priced,8-12 $2005/GJ for 100 EJ of biomass in 2055 compared to e.g. 4.6 $2005/GJ for coalin 2011 IEA (2012b). However, Chapter 6 reveals that the BECCS technology offeringnegative emissions, and the need for negative emissions under climate policy establish aclose link between carbon prices and bioenergy prices. The resulting willingness-to-payfor bioenergy by far exceeds bioenergy supply prices identified in Chapter 5. Postponingemission reductions are likely to further increase this demand-pull for biomass, if a givenstabilization target shall be achieved. According to these findings ambitious climatechange mitigation targets thus can be expected to put substantial pressure on the landuse sector in the second half of the century by demanding large-scale production ofbiomass feedstock.To the author’s knowledge there are only few studies that explicitly present supply

price curves for long-term global bioenergy supply (c.f. Section 5.2). Using a differentapproach compared to these studies the analysis in Chapter 5 finds significantly highersupply prices. Previous studies assume so called food-first policies calculating pure bioen-ergy production costs on abandoned land only that would not be used for other purposes.However, based on the rationale that farmers autonomously decide which crops to culti-vate this study rather regards bioenergy and food crops as equal competitors for fertileland, and that land allocation is solely based on cost-effectiveness. Bioenergy supplyprices in this study thus emerge under full land use competition with food and feedproduction and are therefore higher than pure production costs in the previous studies.The high willingness-to-pay for bioenergy offers substantial revenues for agricultural

producers and creates an incentive to convert high-productive forest land often located intropical ecosystems with high biodiversity (Doornbosch and Steenblik 2008) into land forbioenergy production, which could entail substantial upfront carbon emissions. There-fore, global forest protection measures are needed to reduce emissions from bioenergyproduction. Deforestation could either be prevented by explicit forest protection policiesas applied in Chapter 3. According to Chum et al. (2011) “Persson and Azar (2010)acknowledge that pricing LUC carbon emissions could potentially make many of thecurrent proximate causes of deforestation unprofitable (e.g., extensive cattle ranching,small-scale slash-and-burn agriculture and fuelwood use) but they question whether itwill suffice to make deforestation for bioenergy production unprofitable because thesebioenergy systems are highly productive”. Chapter 5 confirms findings of Wise et al.(2009) that pricing LUC carbon emissions can indeed be another effective measure toprevent deforestation and to reduce land use change emissions substantially even underlarge-scale bioenergy production. In the scenarios analyzed in Chapter 5 deforestation

158 7 Synthesis and Outlook

is completely stopped by the GHG tax. However, Chapter 5 also showed that the highpressure from bioenergy demand and LULUC emission pricing might increase emissionsfrom conversion of land that is not under emission control. Each piece of land that isprotected by emission pricing increases the incentive to expand bioenergy and food pro-duction into land that is not protected otherwise. This demonstrates the limitations ofcarbon pricing: carbon taxes can only protect land that is under emission control andonly based on its carbon content. There are other reasons, though, that make land valu-able to be excluded from agricultural use, most of all biodiversity, necessitating effectivenature protection policies that accompany GHG taxation. However, the more land isexcluded from agricultural production, the stronger gets the competition for land, waterand other agronomic inputs between bioenergy and food production (Chapter 3). At thesame time the willingness-to-pay for bioenergy can be very high, as Chapter 6 revealed,creating an incentive for producers to switch from food production to bioenergy produc-tion. This shows that land-based mitigation options, such as BECCS, could lead to arevaluation of land by creating substantial carbon-price-induced land rents. Consideringthat climate policy on the other hand devaluates fossil fuels the combined effects couldresult in a substantial redistribution of rents from fossil fuel owners to land owners.

Carbon prices induced by climate policy propagate through two channels into theland use sector, directly in the form of GHG prices on LULUC emissions and indirectlyin the form of bioenergy demand. Thus, they would reduce land for food productionin two ways, by effectively preventing deforestation and by requiring additional land forbioenergy cultivation. If so, ambitious climate change mitigation targets require substan-tial yield increases throughout the whole century to accommodate food and bioenergydemand.

As presented in Chapter 1 there is a broad portfolio of potential mitigation optionsof which BECCS is the only one considered in this thesis that can generate negativeemissions. Chapter 6 showed that the availability of BECCS is crucial for mitigationstrategies and corresponding costs. Large amounts of carbon, up to 50% (830 GtCO2)of total cumulated CO2 emissions, have been captured at the end of the century in thelow-stabilization scenarios analyzed in Chapter 6. However, the (intertemporal) balanc-ing of emissions via negative emissions from biomass relies on the availability of threecrucial chain links: the biomass potential, bioenergy conversion technologies, and thecarbon capture and storage technology. All of which are still uncertain since they haveonly been demonstrated on laboratory scale and are by no means commercial yet. Under-ground CO2 storage has to be tested at scale, long-term leakage rates remain unknown,and the availability of storage capacities in reasonable range of emission sources are un-certain. The long-distance transport of captured CO2, if needed, would require a pipelinenetwork similar to the existing gas transport infrastructure (ICF 2009). Furthermore, thelack of social acceptance constitutes a major barrier for the implementation of demon-stration and commercial deployment projects. There are alternative means for carbondioxide removal, such as direct air capture technologies (DAC) and afforestation (Tavoniand Socolow 2013) or soil carbon build-up (Smith et al. 2008), that could lower thedependency on BECCS. However, in contrast to bioenergy they do not provide energybut potentially even demand energy in the case of DAC. Furthermore DAC technologiesalso rely on large-scale and long-term underground storage capacities. This is not thecase for afforestation, which builds up above ground carbon stocks that, however, mightbe at risk of being released at a later point in time. Thus, permanence of storage is an

7.3 Outlook 159

issue for all carbon removal technologies (BECCS, DAC, and afforestation). Moreover,afforestation is similar to bioenergy in that it requires land. Many IAM models includingREMIND lack representation of these options yet (Kriegler et al. 2013c).The other determinant of the amount of realizable negative emissions, the bioenergy

potential, strongly depends on developments of yields, technological change, and landavailability. Second generation lignocellulosic feedstock, mainly short-rotation perennialgrasses, such as Miscanthus, have not been produced for energy purposes on large andcommercial scale yet. The lack of experience results in uncertainty about actual andfuture yields. As pointed out in Section 5.5 the bioenergy potential presented in thisstudy is based on optimal land allocation, solely based on cost-effectiveness, accompa-nied by optimal investments into research and development (R&D), and full impact ofR&D on all crops. However, sub-optimal real world conditions are likely to lower bioen-ergy potentials (Section 5.5). The rates of yield increases required for large-scale biomassdeployment identified in Chapter 3 (0.9%/yr) are slightly lower, though, than historicalyield growth of about 1.3% annually from 1970 to 1995 averaged across all crops. TheMAgPIE version applied for the bioenergy potential estimated in this thesis assumes theglobal pasture land to be fixed at 1995 level and thus not accessible for other agriculturalactivities. However, under real world conditions the evolvement of pasture land is, likeall other agricultural areas, subject to economic conditions and decisions. High bioen-ergy prices could create an incentive to convert pasture land to bioenergy crop land.Technological progress in livestock production could reduce required pasture land andincrease the land availability for bioenergy and food production.The major bioenergy conversion routes analyzed in this study rely on gasification of

lignocellulosic feedstock. Albeit long experience from coal gasification makes this partof the route to negative emissions the one closest to maturity it still is far from thecommercial state. First demonstrations projects have been abandoned due to technicalissues and lack of financial support (Stahl 2004; Waldheim and Carpentieri 1998).Another important and highly uncertain parameter that affects the supply side and

the demand side of bioenergy likewise is the evolvement of the world population, sinceit drives the demand for food and energy. Land requirements for food production andresulting land use competition between bioenergy and food also depend on human diet,mainly due to large land requirements for meat production. A diet shift to reduced meatconsumption could decrease global agricultural non-CO2 emissions (Popp et al. 2010)and hence the need for negative emissions.

7.3 Outlook

This thesis demonstrated the paramount importance large-scale BECCS deploymentcould gain for achieving low-stabilization targets that keep global warming below 2 �. Itshowed that mainly in the second half of the century large amounts of biomass have tobe produced and converted, and large amounts of carbon have to be captured and storedunderground. However, none of these bioenergy chain links have been demonstratedat large and commercial scale yet: neither the production of purpose-grown lignocellu-losic biomass for energy purposes nor the conversion (mainly relying on gasification) norcapturing and storing carbon. The results of this analysis indicate that investments inresearch and development (R&D) for all of these practices could be justified. The tech-nologies are not required immediately but research and development should make them

160 7 Synthesis and Outlook

available soon after 2030. In particular further research is needed how purpose-grownlignocellulosic biomass can be produced sustainably on a large-scale. The success of thelarge-scale application of BECCS not only depends on technological improvements butit crucially depends on the social acceptance of large-scale bioenergy production andunderground carbon storage, which will only be realizable if adverse effects, particularlydeforestation and competition with food production, can be mitigated. This requiresnot only R&D investments but also political institutions that enforce nature protectionand potentially necessary policies preventing adverse effects on food production. Thisbecomes even more important if yield improvements, as estimated in this study, can notbe realized under real world conditions that are characterized by market imperfectionssuch as under-investments, subsidies, trade-barriers and other regulations.

Moreover, it would be worthwhile to study how real world land owners may reactin a non-optimal way on high carbon prices that revaluate their land due to the needfor land-based mitigation options. Are institutions and policies needed to manage andhandle the potentially large redistribution of land rents that may occur under climatepolicy? Furthermore, long-term climate policy requires time consistent policies and in-stitutions. This gets particularly important if negative emissions from BECCS are usedfor intertemporal emission balancing, since the compliance with a certain climate tar-get relies on the application of negative emission technologies after 2050 to compensateemissions that will have been released at that time. In addition, results in this thesis,especially in Chapter 5, are based on an important precondition for successful emis-sion mitigation in the land use sector: the accounting of land use and land use changeemissions. In the real world this would require ubiquitous institutions that guarantee acomprehensive monitoring of agricultural activities, land use change, and resulting emis-sions. In summary, the deployment of bioenergy with CCS for climate change mitigationrequires short-term as well as long-term planning, high institutional efforts, and timeconsistent policy intervention on various levels (global, regional, and local) and vari-ous topics (rents and investments, emission accounting, climate target) throughout thewhole century to ensure timely R&D of BECCS technologies and feedstocks, sustainablelarge-scale production of biomass, and intertemporal emission balancing.

There are further challenges for future research. The interactions of bioenergy andfood production are of particular interest. The deployment levels of bioenergy presentedin this study require large amounts of land and thus reduce land available for foodproduction. First of all, the impact of the resulting competition on food supply shouldbe studied in more detail. Food prices could rise due to higher costs for land, labor,fertilizer, water, and other agronomic inputs. Including market imperfections (such astrade barriers) and sub-optimal R&D investments into the analysis and investigatingtheir impact on food and bioenergy supply could be another step. Furthermore, impactsof climate change on the biomass potential, not considered in this study, should be takeninto account. Since agricultural yields are strongly dependent on climatic conditionsglobal warming could substantially alter future yields. Finally, there are alternatives tobioenergy with CCS, such as direct air capture technologies and afforestation that havenot been covered by this study. Afforestation is of special interest because it does notrequire the development of new technologies or the availability of underground carbonstorage. In contrast to short-rotation bioenergy feedstocks afforestation builds up long-lived forests and could thus even increase biodiversity. However, since it requires land itinterferes with bioenergy and food production. In particular it competes with BECCS for

7.3 Outlook 161

land and the cost-effective reduction of carbon emissions. Therefore, the potential trade-off between BECCS deployment and afforestation should be studied in an integratedframework, as developed in this thesis, covering dynamic interactions between the energyand the land use sector.

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Nomenclature

AR4 Forth Assessment Report of the IPCC

AR5 Fifth Assessment Report of the IPCC

BECCS Bioenergy with CCS

BioIGCC Biomass integrated gasification combined cycle

CCS Carbon capture and sequestration

DAC Direct air capture

EMF Stanford Energy Modeling Forum

GEA Global Energy Assessment Report

GHG Greenhouse gas

GMT Globally averaged combined land and ocean surface temperature

IAM Integrated assessment model

IPCC Intergovernmental Panel on Climate Change

LCA Life cycle assessment

LULUC land use and land use change

MAgPIE Model of Agricultural Production and its Impact on the Environment

R&D Research and development

REMIND Regional Model of Investment and technological Development

SRES Special Report on Emission Scenarios

SRREN Special Report on Renewable Energy Sources and Climate Change Mitiga-tion

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Statement of Contributions

The five core chapters of this thesis (Chapters 2 to 6) are the result of collaborationsbetween the author of this thesis and his advisors and colleagues. The author has madesignificant contributions to all chapters ranging from conceptual design to technical de-velopment, data analysis, reviewing, and writing. This section details the authors’scontribution to the core chapters and acknowledges major contributions of others.

Chapter 2 The author contributed to the literature review and developed a formerversion of the actual paper together with Christoph von Stechow and Ottmar Edenhofer.The author contributed various text parts and revisions. In particular the author con-tributed Figure 4, performed the underlying data analysis and wrote the correspondingtext parts.

Chapter 3 This chapter uses the coupled model framework of REMIND-G-MAgPIE.The REMIND-G model and the MAgPIE model were provided by the Potsdam Insti-tute for Climate Impact Research. The code for coupling the existing models has beendesigned and implemented jointly by the author and Jan Philipp Dietrich. The develop-ment of the model framework was supervised by Nico Bauer and Alexander Popp. Theauthor performed all model experiments the analysis is based on. Alexander Popp wrotethe article with revisions by the author and further co-authors of the paper. The authorcontributed most of the data analysis regarding the energy system part of the results.

Chapter 4 The author is responsible for the conceptual design, handling and writingof the article. The analysis uses the coupled model framework of REMIND-G-MAgPIE.The REMIND-G model and the MAgPIE model were provided by the Potsdam Insti-tute for Climate Impact Research. The code for coupling the existing models has beendesigned and implemented jointly by the author and Jan Philipp Dietrich. The develop-ment of the model framework was supervised by Nico Bauer and Alexander Popp. Theauthor is solely responsible for the parametrization and implementation of the BioIGCCtechnology in the REMIND-G model, designing and performing the model experimentsand writing the article with revisions by the co-authors. Nico Bauer contributed to theframing of the research question.

Chapter 5 The author is responsible for the conceptual design, handling and writingof the article. The analysis uses results of the MAgPIE model that was provided bythe Potsdam Institute for Climate Impact Research. The author developed the data-processing and fitting framework for constructing the bioenergy supply curves. Theauthor designed and performed the underlying MAgPIE runs and wrote the paper withinput from Florian Humpenoder (description of the MAgPIE model and some of thefigures in the SOM) and with revisions by the co-authors, particularly Nico Bauer, Flo-rian Humpenoder, and Alex Popp. Florian Humpenoder and Jan Philipp Dietrich gave

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180 Bibliography

substantial support by discussing and handling MAgPIE-internal issues that occurred inthe preparation phase and they also contributed to parts of the data analysis.

Chapter 6 The author is responsible for the conceptual design, handling and writingof the article. The analysis in this chapter uses the REMIND-R model and the MAgPIEmodel provided by the Potsdam Institute for Climate Impact Research. The author issolely responsible for the parametrization and implementation of the bioenergy supplycurves in the REMIND-R model and performing the underlying MAgPIE model runs.The REMIND-R model runs were performed jointly with Jessica Strefler. The scenariodesign is based on the EMF 27 scenario protocol. The author is solely responsible forthe data analysis and writing the article with input from Nico Bauer (REMIND modeldescription), Alex Popp (MAgPIE model description) and Jessica Strefler (descriptionof forcing targets and resulting emission budgets) and with revisions by the co-authors,particularly Gunnar Luderer and Elmar Kriegler, who also contributed to the framingof the research questions.

Tools and Resources

This dissertation relies on numerical modeling. A number of software tools were usedto create and run the models, and to process, analyze and visualize the results. Thissection lists these tools.

Modeling The REMIND model, the MAgPIE model, and the supply curve fitting toolwere implemented in GAMS1. The CONOPT32 solver was used to solve the non-linearformulations.

Data Processing For the data pre- and postprocessing and figure generation the Math-Works’ MATLAB3, version 7.5 (R2007b) and the free software programming languageR4, version 3.01, were used.

Source code management All code projects were managed using the Subversion ver-sion control system5.

Typesetting This document was prepared with LATEX2ε6 using KOMA-Script7 version

2013/12/19 v3.12 KOMA-Script and the packages pdfpages, scrpage2, booktabs, hyperref,nomencl, inputenc, textcomp, caption, sidecap, and biblatex. Chapters 2 to 6 were writtenwith Microsoft Word 2010.

Literature management Zotero8 was used for literature management and providingthe bibliography to LATEX2ε and biblatex.

1http://www.gams.com2http://www.gams.com/docs/conopt3.pdf3http://www.mathworks.de/products/matlab/4http://www.r-project.org/5http://subversion.apache.org/6http://www.latex-project.org/intro.html7http://www.komascript.de8http://www.zotero.org/

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Acknowledgements

This thesis grew over years with the support of many people and in the inspiring andcollegial atmosphere at PIK that emerged from the collaboration of so many wonderfulpeople and which I am very grateful for.

I thank Ottmar Edenhofer for giving me the opportunity to work at PIK, for supportingand supervising this thesis and especially for motivating me by lively discussions in thebig round that reminded me of the broader perspective. I am also thankful to KeywanRiahi who kindly offered to review the thesis. Thank you Nico, for your constant sup-port and patience, your questions and suggestions, and the wisdom of your screensaver.Thank you Alex, for your encouraging and constructive support and your admirable goodspirits at all times. Thank you Gunnar and Elmar, for the inspiring discussions and yoursupport, particularly in the EMF and SSP process.

Thanks to the wonderful REMIND and MAgPIE crew. It was a pleasure to work withyou. If sometimes there seemed to be no other reason to enter the daily treadmill, youwere! Special thanks go to Jessi and Robert for the great times in our offices and to Jessifor accompanying me on the long winding road to the absolutely ultimate last final-finalEMF submission. Special thanks also go to Florian and Jan, who saved me several timeswhen the thieving magpie crossed my path.

Thank you Charlotte, for your enduring support, your interest and persistence in discus-sions and for sharing the view on what is important in the end.

Thank you Cora, for your trust, your tireless patience waiting for the famous “just halfan hour” and for making me forget all the infeasibilities right after.

I thank my family and friends for their enduring understanding and moral support, andI am grateful that they still know me after I disappeared for a long time.

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