global bioenergy scenarios – future forest development, land-use implications, and trade-offs

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Global bioenergy scenarios e Future forest development,land-use implications, and trade-offs

Florian Kraxner a,*, Eva-Maria Nordstrom a,b, Petr Havlık a,c, Mykola Gusti a,d,Aline Mosnier a, Stefan Frank a, Hugo Valin a, Steffen Fritz a, Sabine Fuss a,h,Georg Kindermann a, Ian McCallum a, Nikolay Khabarov a, Hannes Bottcher a, Linda See a,Kentaro Aoki a,f, Erwin Schmid e, Laszlo Mathe g, Michael Obersteiner a

a International Institute for Applied Systems Analysis (IIASA), Ecosystems Services and Management Program (ESM), Schlossplatz 1, A-2361

Laxenburg, Austriab Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Umea, Swedenc International Livestock Research Institute (ILRI), Nairobi, Kenyad Lviv Polytechnic National University, Lviv, UkraineeUniversity of Natural Resources and Life Sciences, Vienna (BOKU), Institute for Sustainable Economic Development, Vienna, AustriafRural and Renewable Energy Unit, Energy and Climate Change Branch, United Nations Industrial Development Organisation (UNIDO),

Vienna, AustriagWWF International, Gland, SwitzerlandhMercator Research Institute on Global Commons and Climate Change (MCC), Working Group on Resources and International Trade,

Torgauer Str. 12–15, 10829 Berlin, Germany

a r t i c l e i n f o

Article history:

Received 23 July 2012

Received in revised form

30 January 2013

Accepted 3 February 2013

Available online xxx

Keywords:

Global bioenergy feedstock

Integrated land-use modeling

Scenario assessment

Deforestation

Forest management

* Corresponding author. Tel.: þ43 2236807233E-mail address: [email protected] (F. Kr

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0961-9534/$ e see front matter ª 2013 Elsevhttp://dx.doi.org/10.1016/j.biombioe.2013.02.

a b s t r a c t

Preservation of biodiversity and reduction of deforestation are considered as key elements

when addressing an increased use of bioenergy in the future. This paper presents different

combinations of scenarios for global feedstock supply for the production of bioenergy

under specified social and environmental safeguard provisions. The objectives of this study

were threefold: a) to present a global perspective using an integrated modeling approach;

b) to frame the boundaries for lower scale assessments; and c) to identify potential trade-

offs to be considered in future research. The aggregate results, achieved through the

application of an integrated global modeling cluster, indicate that under a high global

demand for bioenergy by mid-century, biomass will to a large extent be sourced from the

conversion of unmanaged forest into managed forest, from new fast-growing short-rota-

tion plantations, intensification, and optimization of land use. Depending on the under-

lying scenario, zero net deforestation by 2020 could be reached and maintained with only a

minor conversion of managed forests into other land cover types. Results further indicate

that with rising populations and projected consumption levels, there will not be enough

land to simultaneously conserve natural areas completely, halt forest loss, and switch to

100% renewable energy. Especially in the tropical regions of the southern hemisphere, it

will be important to achieve a controlled conversion from unmanaged to sustainably

managed forest as well as increased protection of areas for ecosystems services such as

biodiversity. The study concludes with the recommendation to focus on targeted regional

policy design and its implementation based on integrated global assessment modeling.

ª 2013 Elsevier Ltd. All rights reserved.

; fax: þ43 2236807599.axner).

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1. Introduction

We are facing a future where a sustainable use of the planet’s

resources calls for renewable and carbon-neutral energy

sources to meet increasing demands for energy. According to

the International Energy Agency (IEA), a major opportunity to

reduce fossil CO2 emissions is the transition to alternative

sources of materials for energy production, including biomass

from forests or agricultural crops [1]. Biomass can be used for

heating, cooling, producing electricity and liquid biofuels, and

the use of biomass may help to reduce greenhouse gas (GHG)

emissions. Emissions from biomass are generally accounted

for as carbon-neutral, for example, see the EU Renewable

Energy Directive 2009/28/EC [2]. However, whether biomass is

entirely carbon-neutral is disputed and very much dependent

on system boundaries; in addition to indirect land-use change

(iLUC), there are always some emissions from processes like

cultivation, transport, or fuel production.

In 2011 more than 60 countries had some type of national

renewable energy target or support policy, according to the

IEA/IRENA Global Renewable Energy Policies and Measures

Database [3]. Climate change mitigation, energy security, and

protection of national industries and agricultural sectors are

the major rationales for supporting renewable energy. How-

ever, the extent of social and environmental regulations for

the production of bioenergy feedstock varies very much ac-

cording to country. Many developed and developing countries

have ambitious bioenergy targets but lack sound supporting

legislation. Where legislation exists, it is often confused, fails

to address socioeconomic and environmental aspects prop-

erly [4], and may create perverse incentives [5]. Country-level

requirements for GHG emissions reductions are also highly

variable, as is the assessment of compliance. Moreover, in

most countries, forestry legislation does not contain specific

regulations concerning harvesting and the use of forest

biomass for bioenergy [6].

Deficiencies in policies, in legislation, and in management

guidelines in both developed and developing countries indi-

cate that especially on global level, basic social and environ-

mental values are at serious risk from increasing bioenergy

production. Pristine forests, biodiversity, agricultural land, as

well as soil and water resources will all be under additional

pressure from a substantially increased use of biomass from

agriculture, forestry, and waste for producing energy [7]. This

development may also counteract other environmental pol-

icies and objectives, such as waste minimization or organic

farming. Lack of proper planning and management of feed-

stock production could also have severe socioeconomic ef-

fects such as conversion of farmland and forest at the expense

of small farmers and people living in the forest, concentration

of land ownership, increasing food prices, and additional

pressure on food supply in already vulnerable regions [7].

Based on these insights, we conclude that it is of utmost

importance to define and analyze scenarios of global feed-

stock supply for the production of bioenergy to identify

boundaries for future development and to guide further

research and policymaking. As this is done, economic devel-

opment, population growth, and social and environmental

safeguard provisions should be taken into account. However,

Please cite this article in press as: Kraxner F, et al., Global bioencations, and trade-offs, Biomass and Bioenergy (2013), http://dx.

few studies have so far addressed this issue on a global scale

[7], the reason being that this kind of analysis calls for inte-

grated modeling, that is, an interdisciplinary approach that

combines economic and biophysical land-use models. For

large-scale and global analysis of land-based sectors, the

economic models used are general and partial equilibrium

models. These models comprise demand for and supply of

commodities, trade flows between different regions, and land-

use competition. In a general equilibrium model all economic

activities and sectors are considered, while a partial equilib-

rium model is specialized in one or a few specific sectors like

forestry or agriculture, and elaborates them in more detail. In

integrated modeling, a biophysical land-use model is usually

linked to the equilibrium model to provide information on

constraints on supply and the actual, spatially explicit effects

of land-use change processes. A limited number of equilib-

riummodels of truly global scope have been used formodeling

land use and land-use change [8,9]. Most of these are focused

on agriculture, and only a few comprise the forest sector

[10e13]. To the best of our knowledge, there has been no

attempt to use integrated modeling for a global and spatially

explicit assessment of bioenergy feedstock combining both

the forestry and agricultural sectors.

The objective of this paper is to provide an outlook on the

potential feedstock for bioenergy as a contribution to the

decarbonization of the energy sector

- with a global perspective using an integrated modeling

approach,

- to frame the boundaries for lower scale assessments and to

justify research on bioenergy on various scales,

- to identify potential trade-offs to be considered in future

research.

Scenarios similar to those presented in this paper were

developed jointly with WWF and used for analysis of future

forest development with focus on deforestation. In this paper

we provide additional results and more in-depth analysis and

interpretation for the bioenergy feedstock relevant selection

of scenarios.

2. Method and main model descriptions

2.1. Modeling biomass supply at global scale e anintegrated modeling approach

The models GLOBIOM, EPIC, and G4M have been used for a

long time in an integrated modeling framework at IIASA (and

readers interested in details going beyond the scope of this

paper are invited to directly contact the authors and to visit

www.globiom.org). The economic model GLOBIOM [13,14]

encompasses all countries of the world, aggregated to 28

world regions [16], and bases its crop and forest sector details

on biophysical parameters supplied by the more specialized

models G4M for forestry and EPIC for agriculture. The global

agricultural and forest market equilibrium is computed by

choosing land use and processing activities to maximize the

sum of producer and consumer surplus subject to resource,

ergy scenarios e Future forest development, land-use impli-doi.org/10.1016/j.biombioe.2013.02.003

http://www.globiom.org



b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 3 ) 1e1 1 3

technological, and policy constraints, as described by McCarl

and Spreen [17]. The general concept and structure of GLO-

BIOM is similar to the US Agricultural Sector and Mitigation of

Greenhouse Gas (ASMGHG) model [18].

EPIC [19,20] supplies to GLOBIOM detailed information on

management-related yields according to fertilizer and irriga-

tion rates. EPIC is set up globally for 20 crops (barley, dry

beans, cassava, chickpea, corn, cotton, cowpea, ground nuts,

millet, oats, potatoes, rapeseed, rice, rye, soybeans, sorghum,

sugarcane, sunflowers, sweet potatoes, and wheat). Four

management systems are simulated by EPIC and imple-

mented in GLOBIOM (irrigated, high input e rainfed, low

input e rainfed and subsistence management systems). For

each management system, EPIC provides GLOBIOM with in-

formation about yield, fertilizer, and water requirements, as

well as various environmental parameters including carbon

and nutrient balance, and the connected greenhouse gas

emissions, nitrogen leaching, soil erosion, and other

biophysical indicators (Table 1).

On the forestry sector side, G4M [21,22] supplies GLOBIOM

with information on mean annual increment, maximum

share of biomass usable as saw logs in the mean annual

increment, and harvesting costs. G4M also supplies GLOBIOM

with consistent accounts of carbon stocks in forests which are

then used to assess GHG emissions related to deforestation. In

an iterative procedure, G4M in turn uses GLOBIOM projections

on wood and agricultural land prices, and wood demand

quantities [21] to consistently estimate future forest dynamics

at high spatial resolution (currently at a 0.5� � 0.5� resolution)[23e25].

Additionally, GLOBIOM is linked to the JRC global energy

model POLES (Prospective Outlook for the Long-term Energy

System) [26] for regions outside Europe and the PRIMESmodel

[27,28] for EU27 countries through information on macroeco-

nomic indicators and bioenergy demand. Alternatively, other

energymodels, for example, MESSAGE [29,30], can be linked to

GLOBIOM [31]. Bioenergy demand is split into first-generation

biofuels, second-generation biofuels, bioenergy plants, and

direct biomass use for energy. The same population and GDP

projections that are used for input to the POLES and PRIMES

models are also used as exogenous drivers for the G4M

Table 1 e Parameters exchanged between the different model

Model linkage

EPIC / GLOBIOM [19,20] Fo

(h

-

-

-

G4M / GLOBIOM [21,22] -

-

-

-

POLES (or other energy model) / GLOBIOM [26] -

GLOBIOM / G4M [13,16] -

-

-

-

Please cite this article in press as: Kraxner F, et al., Global bioencations, and trade-offs, Biomass and Bioenergy (2013), http://dx.d

baseline. Food (calorie) consumption per capita mimics pro-

jections from FAO [32].

For amore detailed description of themodeling framework

see, for example [13,33].

2.2. Scenario settings, assumptions and definitions

The vision of “Zero Net Deforestation and Forest Degradation

(ZNDD) by 2020” in accordance with WWF’s Living Forests

Report [15] is a substantial component of our scenarios. ZNDD

means that there is no net forest loss through deforestation

and no net decline in forest quality through degradation at the

global level. Thus, for our scenarios we compare a future

development of feedstock under i) the assumption that there

are no restrictions with respect to deforestation, with the

exception of protected areas, to ii) the assumption that there

is a strong restriction on deforestation (RED ¼ Reducing

Emissions from Deforestation). However, ZNDD does not

mean that there can be no forest clearing at all; it recognizes

people’s right to clear some forests for agriculture and the

value in occasionally “trading off” degraded forests, provided

that biodiversity values and the net quantity and quality of

forests are maintained. Note that short-rotation tree planta-

tions are not equatedwith natural forests, asmany ecosystem

services are diminished when a plantation replaces a natural

forest.

The scenarios are summarized in Table 2 and described in

more detail in the following subsections.

2.2.1. “BAU” scenarioThe “business as usual” (BAU ) scenario assumes that current

and future behavior can be extrapolated from historical

trends. Under the BAU scenario, land-use change is anticipated

due to (a) demands for land to supply a growing global human

population with food, fiber, and fuel; and (b) continuation of

historical patterns of poorly planned and governed exploita-

tion of forest resources. Key assumptions in this scenario

concerning growing world population and increasing GDP,

which drive demands for commodities and energy, are drawn

from the European Commission [34]. Assumptions for human

diet changes follow FAO projections toward 2050 [32,35] and

s of the modeling framework.

Parameters exchanged

r 20 crops (>75% of harvested area) and 4 management systems

igh input, low input, irrigated, subsistence)

Crop yields

Water balance (including irrigation water)

Carbon, nitrogen and phosphorus balance

Mean annual increment

Share of biomass suitable for sawnwood

Harvesting cost

Carbon stock in forests

Bioenergy demand (fuelwood, biomass for energy industry, biofuels)

Wood price projections

Land price projections

Agricultural commodity price projections

Demand for forest biomass by type

ergy scenarios e Future forest development, land-use impli-oi.org/10.1016/j.biombioe.2013.02.003



Table 2 e Overview of the five scenarios.

Scenario name Description

BAU ”Business as usual”: projection of future

development in line with historical trends

BE2010 As for BAU, but the production of

bioenergy fixed at the level in 2010

BEPlus Projection of bioenergy demand by 2050

as in the 100% renewable energy vision

by the Ecofys Energy Model [36]

BEPlusRED As for BEPlus, but with target “no net

deforestation” (RED ¼ Reducing

Emissions from Deforestation)

BiodivRED Stricter biodiversity protection combined

with target “no net deforestation”

Table 3e Production of bioenergy in PJ under the differentscenarios as given by the POLES and PRIMES models.

Carrier of bioenergy Scenario 2010 2030 2050

Heat and

power generation

BAU 3391 18,288 36,372

BiodivRED 3391 18,288 32,686

BEPlus 3517 22,108 44,451

BEPlusRED 3517 22,108 43,185

BE2010 3391 3391 3391

Direct biomass use BAU 14,135 14,887 15,713

BiodivRED 14,135 14,887 15,713

BEPlus 14,133 14,763 14,235

BEPlusRED 14,133 14,763 14,235

BE2010 14,135 14,135 14,135

Liquid fuels,

first-generation

BAU 1103 2335 1732

BiodivRED 1103 2335 1732

BEPlus 1069 2175 1864

BEPlusRED 1069 2175 1864

BE2010 1103 1103 1103

Liquid fuels,

second-generation

BAU 161 2835 10,853

BiodivRED 161 2835 10,853

BEPlus 156 2250 15,065

BEPlusRED 156 2250 15,065

BE2010 161 161 161

Total bioenergy BAU 18,790 38,344 64,670

BiodivRED 18,790 38,344 60,985

BEPlus 18,875 41,296 75,614

BEPlusRED 18,875 41,296 74,348

BE2010 18,790 18,790 18,790


agricultural productivity gains follow a low bound assumption

of 0.5% increase per year for all regions.

2.2.2. “BE2010” scenarioIn the “Bioenergy 2010” (BE2010) scenario it is assumed that the

bioenergy feedstock demand is “frozen” at its 2010 level and

does not change beyond 2010. The BE2010 scenario contains no

restriction with respect to deforestation, as explained in Sec-

tion 3, and is used as a comparison scenario in the Results

section of this study.

2.2.3. “BEPlus” and “BEPlusRED” scenariosIn the “Bioenergy Plus” (BEPlus) scenario, the bioenergy feed-

stock demand is based on the “global 2 �C scenario” derived

from the POLES model [26,34 ] and is an approximation of the

projected bioenergy demand by 2050 in the 100% renewable

energy vision generated by the Ecofys Energy Model [36]. This

scenario projects demand for bioenergy from land-based

feedstock (excluding those not competing for land, such as

municipal solid waste, industrial waste, and algae) of 75.6 EJ

final energy supply in 2050, of which 16.9 EJ are liquid biofuels.

The BEPlus scenario helps explore implications for global

land availability and productivity of producing sufficient bio-

energy feedstock to meet future demand.

Some important assumptions of the BEPlus scenarios

include:

� A higher carbon price and more ambitious GHG emission

reduction targets in the energy sector are used as input from

the POLESandPRIMESmodels, compared to theBAUscenario.

This makes bioenergy more competitive relative to fossil

fuels, provided bioenergy use delivers genuine, full life-cycle

carbonsavings.This competitiveness ishampered,however,

by higher bioenergy feedstock prices as more bioenergy is

used.

� The land-based bioenergy feedstock is produced in natural

forests managed jointly for biomass and timber production,

in timber plantations, and in croplands. Harvesting in nat-

ural forests ismodeled on a sustained yield basis. Themodel

assumes that tree tops, branches, and stumps (harvesting

residues) are not removed from forests to ensure soil pro-

tection and long-term fertility.

� Current harvesting practices that cause deforestation

or forest degradation are phased out. This shift is assumed

to take place, despite population growth, by fuelwood for


domestic use being increasingly sourced from sustainably

managed forests and dedicated plantations and reducing

per capita fuelwood demand through introduction of more

efficient stoves and heating systems that are less detri-

mental to human health.

2.2.4. “BiodivRED” scenarioThe “Biodiversity” (Biodiv) RED scenario was developed to

explore the impact of stricter biodiversity protection. Here it is

assumed that remaining natural ecosystems are protected i.e.,

no further conversion of these ecosystems to cropland, graz-

ing land, or plantations in areas identified as important for

biodiversity by any one of the conservation mapping pro-

cesses mentioned in the following. This scenario assumes

that current land uses (e.g., cropland or forestry) in these areas

remain constant, or decrease, in the biodiverse zones and

continue to produce food or timber. The UNEP-WCMC World

Database on Protected Areas [37] lists most existing protected

areas; the Model uses 2009 data, with no land conversion

allowed within these areas even under the BAU scenario. Other

data sources on areas important to biodiversity include WWF

Global 200 Ecoregions [38], WWF/IUCN Centres of Plant Di-

versity [39], Amphibian Diversity Areas [40], Conservation In-

ternational’s Hotspots [41], BirdLife International Endemic

Bird Areas [42], Alliance for Zero Extinction sites [43], and a

review of the effectiveness of sustainability criteria accom-

panying the EU’s 2020 biofuel targets [4].

2.3. Bioenergy potential under different scenarios

Table 3 shows the bioenergy production under the different

scenarios as assumed based on input from the POLES and




Fig. 1 e Cumulative deforestation 2010e2050 caused by

land-use change according to the different scenarios.


PRIMES models. In this study, the four main carriers of bio-

energy are heat and power, direct biomass use, and first- and

second-generation liquid fuels. Heat and power generation

takes place in plants where primary energy from mostly

woody biomass is turned into electricity or heat (or both, i.e.,

combined heat and power e CHP). Direct biomass use means

that primary energy from wood is turned into heat for do-

mestic cooking and heating. First-generation liquid fuels are

mainly bioethanol and FAME (Fatty Acid Methyl Esters) pro-

duced from agricultural crops; the main crops are sugarcane,

corn, wheat, rapeseed, soya, and palm oil. Second-generation

liquid fuels will be produced mainly from wood and turned

into transport fuel, gas, electricity, and heat.

The bioenergy production will increase more under the

BEPlus and BEPlusRED scenarios than under the BAU and Bio-

divRED scenarios. The main reasons for this are the assump-

tions about higher carbon price and more ambitious emission

reduction targets under the BEPlus and BEPlusRED scenarios.

The same total bioenergy production is assumed under the

BEPlus scenarios, irrespective of whether or not there is a

deforestation restriction. The main difference is that under

non-RED most of the bioenergy will be sourced in unmanaged

or pristine forest while under RED most of the bioenergy

production will be sourced by intensification (short-rotation

plantations) and partially through conversion from unman-

aged to managed forest.

There are different patterns for the different categories of

bioenergy. For all scenarios except the BE2010 scenario pro-

duction of bioenergy for heat and power will be increasingly

important in the future and will form a large part of the total

bioenergy. Around 2030e2040 a shift from first- to second-

generation biofuels is assumed to take place. The production

of second-generation biofuels was very low in 2010 but is

assumed to increase considerably in all scenarios except for

BE2010. This shift to second-generation biofuels also means a

shift in feedstock, from mainly agricultural crops to forest or

plantation-based feedstock. Consequently, there will be

higher pressure on unmanaged forest to be converted into

managed forest to provide bioenergy feedstock. Both natural

and cultivated land will also be converted into plantations for

the same purpose. Moreover, an overall intensification of

agriculture and forestry is to be expected.

2.4. Bioenergy definition for this study

Under the different scenarios e especially with respect to

ZNDD e the (bio-)energy markets and policies will be differ-

ently affected regarding land availability for bioenergy crops,

fast-growing tree plantations, and the supply of wood from

existing natural or semi-natural forests. Bioenergy is seen as

an inevitable component of future energy portfolios and as a

result carries significant environmental and social risk [44].

In this study, we distinguish between wood-based bio-

energy and crop-based bioenergy. All biomass used for elec-

tricity, heating, and second-generation biofuels is wood-

based, while first-generation biofuels are produced from

crop-based biomass. In the first case, bioenergy can either be

produced from forests or from fast-growing short-rotation

plantations. Depending on the source and on the current

characteristics of standing biomass (i.e., growth rate,


disturbances, soil carbon, and management activities, etc.),

the climate balance of wood-based bioenergy can vary. For

example, intensive management practices like whole tree

harvesting and use of fast-growing exotic species or of fertil-

izers generally have worse carbon balance and ecological

impacts than bioenergy which is supplied from fast-growing

plantations on degraded lands, using sustainable forest

management practices and is hence able to provide climate-

friendly fuel and increase carbon storage.

In the case of crop-based bioenergy, there is a clearly

competitive situation for the world’s productive arable land.

With respect to GHG-saving potentials, biofuels from agri-

cultural origin should ideally be sourced from land that causes

neither direct nor indirect forest conversion. Some of the

currently produced bioenergy products (i.e., some first-

generation biofuels) generate substantial environmental and

social costs through their irrigation needs or by displacing

essential food crops causing deforestation as the ultimate

consequence [13]. Thus, a careful balancing between all these

factors and their clearly positive aspects in terms of GHG

emission reductions was considered for our analysis. By tak-

ing a global perspective, it is possible to track any sort of

leakage or indirect land-use change.

3. Results

The scenarios produce a multitude of information; here, our

aim is not to present a full overview of the results but rather to

focus on some specific aspects and how they are interlinked.

First, we give an outlook on forest development in general.

Then we present results concerning land-use change caused

by feedstock production and effects on ecosystem services

from bioenergy production (i.e., regional effects, effects on

GHG emissions and water consumption).

3.1. Outlook on forest development

When analyzing the general development of global biomass

feedstock from a deforestation point of view, a comparison

between the different scenarios shows that under the BAU

scenario, 286 million hectares of forest are lost through land-

use change between 2010 and 2050 (accumulated area).

Slightly more forest will be lost under the BEPlus scenario:

303 million hectares (Fig. 1). This comparatively small




Fig. 3 e Cumulative land-use change under the BAU

scenario.


difference between the baseline and the increased bioenergy

scenario can be explained by the fact that under BAU almost

the same demand for bioenergy exists in 2050 as under the

BEPlus. In other words, by 2050 most options for the energy

portfolio include huge shares of bioenergy to meet the

increasing global energy demand (see also comparison of the

energy portfolios by Azar [44]). Fig. 1 shows that even when

keeping bioenergy demand at a constant level between 2010

and 2050 (BE2010 scenario) the accumulated deforested area is

260million hectares in 2050, almost as high as for the BAU and

the BEPlus scenarios. This phenomenon can be explained by the

fact that there is no restriction on deforestation under the

BE2010 scenario, and the bioenergy demand may indirectly

affect (non-protected) pristine forest. The difference in area

between BEPlus and the BE2010 scenarios, 43 million hectares,

represents the area deforested due to additional bioenergy

production under the BEPlus scenario, which roughly corre-

sponds to an area the size of Sweden. In contrast, under those

scenarios combined with RED less than 20 million hectares of

forest will be lost.

Since conversion of unmanaged forest into sustainably

managed forest is allowed under all scenarios, the area of

managed forest will increase (Fig. 2). The largest increase, of

more than 300 million hectares, takes place under the BEPlus

and BEPlusRED scenarios, while the increase under the BE2010

scenario is less than half of thisdonly 124 million hectares.

Again, in Fig. 2 differences can be explained by additional

bioenergy production under the BEPlus and BEPlusRED sce-

narios and the fact that under those scenarios combined with

RED, no net deforestation takes place but conversion from

unmanaged into managed forest is possible.

3.2. Land-use change under additional bioenergydemand

Under the BAU scenario, the pattern of land-use change shows

increasing areas of cropland, short-rotation coppice, managed

forest, and grassland, while the areas of unmanaged forest

and other natural vegetation are decreasing (Fig. 3). This

conversion of pristine forests and other natural habitats is

caused by an increased demand for food and energy by a

growingworld populationwith no strictmeasures being taken

to stop deforestation. Under the RED scenarios, the pattern is

Fig. 2 e Cumulative increase in area of managed forest

2000e2050 according to the different scenarios.


somewhat different. The areas of cropland, short-rotation

coppice, and managed forest are increasing, while the

decrease in unmanaged forest area is smaller at the expense

of grassland area, which is decreasing, and other natural

vegetation, which is decreasing more than under the BAU

scenario.

Now we turn to look at what the effects of a demand for

bioenergy higher than the one in 2010 could be. In this case,

land-use change under the BE2010 scenario is used as the

baseline and by calculating the difference between this

baseline and another scenario, we can find out what land-use

changes would take place. Relative to the BAU scenario, this

comparison reveals that the area of short-rotation coppice

will increase by 192 million hectares and the area of managed

forest by 119 million hectares until 2050 due to additional

bioenergy feedstock production (Fig. 4). By using the land-use

change under the BE2010 scenario as a baseline and calculating

additional land-use change in the other scenarios, the effect of

increased bioenergy feedstock production compared to the

2010 level can be assessed. Under the BAU scenario, this com-

parison reveals that the area of short-rotation coppice will

Fig. 4 e Cumulative land-use change and net forest cover

change (managed D unmanaged forest area) caused by

additional bioenergy production under the BAU scenario

(compared to the 2010 level of bioenergy production).





increase by 192 million hectares and the area of managed

forest by 119 million hectares until 2050 due to additional

bioenergy feedstock production (Fig. 4). The area of other

natural vegetation will decrease by 133 million hectares and

areas of unmanaged forest will decrease even more, by

145 million hectares. Cropland and grassland areas will also

decrease slightly. Thus, much of the total increase in area of

short-rotation coppice (see Fig. 3) can be explained as caused

by increased future bioenergy demand. Similarly, about half of

the total increase in managed forest area is also caused by

bioenergy demand. Fig. 4 also shows that there is a loss of total

forest area (net forest change) of 26 million hectares which is

caused by additional bioenergy production when there is no

restriction on deforestation. Although bioenergy (i.e. biofuels)

has to be seen as a direct driving factor behind the loss of

pristine forest and other natural habitat, it is “not the major

driving force”. Rather, the exploitation of these habitats is

caused by other factors; for instance, expansion of agricultural

land for food production and inefficient land use.

Land-use change caused by bioenergy production displays

a similar pattern under the BEPlus scenario, although the

changes are of a larger magnitude.

The additional land-use change due to bioenergy produc-

tion under the biodiversity-protecting BiodivRED scenario

shows that the RED target of avoiding deforestation creates a

typical pattern of land-use change with respect to the

different land-use types. In 2050 areas of other natural vege-

tation have decreased by 210 million hectares, grasslands by

167 million hectares, and croplands by 56 million hectares

(Fig. 5). Areas of short-rotation coppice have increased by

191 million hectares and managed forest by

120 million hectares. Unmanaged forest area has also

increased by 124 million hectares; this expansion of unman-

aged forest takes place with a certain time lag in formerly

managed forests and croplands where management has

ceased. Overall, the RED restriction on deforestation results in

an increase of forest area under additional bioenergy demand

compared to the baseline 2010 level (see net forest change in

Fig. 5). The protection of biodiversity within pristine and other

Fig. 5 e Cumulative land-use change and net forest cover

change (managed D unmanaged forest area) caused by

additional bioenergy production under the BiodivRED

scenario (compared to the 2010 level of bioenergy

production).


types of forest would clearly be at the costs of, for example,

grassland and savannah (which are mostly located in the

southern hemisphere).

3.3. Regional effects on ecosystem services fromadditional bioenergy demand

Increased bioenergy feedstock production directly affects land

use and land-use change and thereby various ecosystem ser-

vices like biodiversity, carbon fixation and water resources.

The effects of feedstock production on biodiversity cannot

be directly assessed with the model cluster used. However,

here we use the area of unmanaged forest as a proxy for

biodiversity of forest ecosystems. In 2000 the area of un-

managed forest was 3146 million hectares, compared to the

area of managed forest, which was 719 million hectares and

the area of short-rotation plantations which was

47 million hectares. Unmanaged forest will be lost under all

scenarios but under the RED scenarios the loss is only half that

under the BAU scenario.

Under the BAU scenario, most of the loss of unmanaged

forest takes place in the tropical areas of South America,

Africa, and Asia (Fig. 6). Compared to that, under the

BEPlusRED scenario, the loss of unmanaged forest is not only

considerably smaller but also more evenly distributed from a

global perspective (Fig. 7). This is also the general pattern

under the BiodivRED scenario. One reason for this is that since

much of the world’s unmanaged forests are located in the

tropical areas, the greatest effects of the RED target will

naturally also be seen in these areas. Nevertheless, under the

BEPlusRED scenario, loss of unmanaged forest takes place

relatively late in some regions (i.e., Planned Asia China, Latin

America Carib and Other Pacific Asia), where unmanaged

forest is converted into managed forest and, to some extent,

short-rotation coppice. This development is forced by an

increasing energy demand from a growing world population.

3.4. GHG emission effects from agriculture and land-usechange

A comparison of the GHG emissions from agriculture and

land-use change under the different scenarios shows that

although bioenergy is a better alternative than fossil fuels,

bioenergy production indirectly affects GHG emissions

Fig. 6 e Cumulative loss of area of unmanaged forest

2000e2050 in different regions under the BAU scenario.




Fig. 7 e Cumulative loss of area of unmanaged forest

2000e2050 in different regions under the BEPlusRED

scenario.


considerably through deforestation. Under the BE2010 sce-

nario, the bioenergy use is small compared to the other sce-

narios, and the GHG emissions measured as CO2 equivalent

are the highest, 8.1 Gt y�1 in 2050. The GHG emissions are

lower under the BAU and BEPlus scenarios, where bioenergy use

is more extensive. However, under the BEPlusRED and Bio-

divRED scenarios the GHG emissions measured as CO2 equiva-

lent are lowest, around 5 Gt y�1 in 2050, due to the restrictions

on deforestation (Fig. 8).

Background calculations to Fig. 8 show that the GHG emis-

sions from deforestation are highest under the BEPlus scenario,

increasing notably, especially between 2020 and 2030. One

reason is that unmanaged forest is converted to cropland for

bioenergy feedstock production and food production. Another

critical factor in terms of GHG emissions is the agricultural

sector, that is, soil emissions (N2O), livestock (CH4, N2O) and

rice cultivation (CH4). Overall, additional bioenergy production

does not change the general pattern of emissions from agri-

culture compared to the other scenarios. In other words,

deforestation can be seen as the largest emission factor even

under pressure for intensification in the agricultural sector.

3.5. Effects on water from additional bioenergy demand

Water resources will be affected by increased bioenergy

feedstock production directly but also indirectly through

Fig. 8 e GHG emissions measured as CO2 equivalent from

total land use 2000e2050 under the different scenarios.


higher pressure on agricultural land due to intensification and

optimization of use. Consequently, the irrigated water

demand for agriculture is lowest under the BE2010 scenario

(only 970 km3 in 2050) because of the low level of bioenergy

production (Fig. 9). Under the BAU and the BEPlus scenarios,

with higher bioenergy production, the water demand for

agriculture is 1,000 km3 per year in 2050. However, under the

BEPlusRED and BiodivRED scenarios the water demand is even

higher. One explanation for this is that the RED target implies

that less land is available for food production which leads to

more intensive use of the agricultural land, including the need

for increased irrigation.

4. Discussion, conclusions, and outlook

According to the scenarios presented here, bioenergy pro-

duction is not a major driver of forest loss (see also Ref. [45])

but it does have significant influence on other negative or

potentially negative land-use change dynamics. However,

determining the direct drivers of deforestation and forest

degradation is complex. Forests are cleared for a range of

reasons and by individual families as well as by some of the

world’s largest corporations.

The scenarios also showus that it is possible to avoid large-

scale deforestation, even under expanded bioenergy produc-

tion. Today 1200 million hectares, or 30% of forests, have

production designated as their primary function [46]. The

projected expansion of forest management based on the

model results is driven primarily by demand for bioenergy.

The aim of minimizing deforestation does not have much

impact on the rate of expansion of forest management, as the

scenarios in question assume that the expansion is via sus-

tainable forest management that does not cause forest loss or

degradation. Moreover, especially in degraded forests man-

agement may substantially improve the forest condition and

also the value of ecosystem services.

Forestmanagement certification is one of the key tools that

could be applied to ensure sustainable management com-

bined with control of illegal logging, yet still encourage local

forest owners to produce biomass for bioenergy [47]. However,

most of the certification activities in forestry are still only

carried out in the northern hemisphere while certification

needs to be clearly focused on the tropical belt of the southern

hemisphere [48]. In addition to improving and certifying forest

Fig. 9 e Irrigated water demand for agriculture 2000e2050

under the different scenarios.





management, credible standards are also required for com-

modities driving deforestation to ensure that commodity

production does not take place at the expense of forests. This

is also supported by the regional analysis included in this

study which shows that most forest losses and conversion (to

the cost of other land-use type) are happening in the south.

Between 2040 and 2050, the food and energy demands of a

rising global population put land competition at its most

acute. The increased pressure on agricultural land will mean

that management has to become more intense in terms of

irrigation and fertilization, which may in turn lead to high

pressure on water resources, as is shown in the scenarios.

Impacts on other ecosystems are greater if forests are more

strictly protected; under the high bioenergy scenario with

minimization of deforestation, the projected loss of other

natural habitats between 2000 and 2050 will be

263 million hectares, with more than half of that due to

bioenergy.

The scenarios show that with rising populations and

projected consumption levels, there will be high pressure on

land to simultaneously conserve natural areas completely,

halt forest loss, and switch to 100% renewable energy plans

with large scale bioenergy shares. Land, water and, possibly,

food prices may rise as a consequence of this competition

for land [49,50]. To ensure a sustainable and equitable

development, we believe that either an overall reduction in

resource consumption will have to take place globally, but

especially in the northern hemisphere, or substantial in-

creases in land-use sectors productivity, especially in the

southern hemisphere are needed. Our opinion is that the

more pristine forest and biodiversity we wish to conserve,

the more important it will be to look for solutions that can

address the limitations of biophysical and technical options

by, for example, influencing demand patterns. In addition to

the obvious trade-offs identified for the energy sector in this

article, demands in other sectors, such as food consumption

of food and other products, will be important policy targets

in the future. Another possible consequence of these pro-

tection efforts could be the increasing introduction of so-

cioeconomic tools, with behavioral change being targeted on

a global level.

There will be relatively high GHG emissions even under

high bioenergy use due to deforestation, agriculture, and

natural decomposition processes. However, the scenarios

show that a deforestation minimization target will help to

reduce the GHG emissions substantially. Consequently, well

designed mechanisms for reducing emissions from defores-

tation and forest degradation (REDD) will be crucial for GHG

emission reductions and may also contribute to protection of

forest biodiversity [51]. When assessing the relative benefits of

bioenergy we also have to consider that sustainable bioenergy

is renewable, unlike fossil energy. Moreover, in the future,

development of technologies like biomass energy with carbon

capture and storage (BECCS) may make bioenergy carbon-

neutral or even carbon negative [52e55]. However, since this

study focused only on emissions from agriculture and land-

use change, we cannot draw any conclusions on the total ef-

fect of bioenergy on GHG emissions. For this, an evaluation of

a full life-cycle assessment of GHG emission impacts would be

needed.


This study is based on global integrated modeling of

biomass production and land use. By necessity, certain as-

sumptions and simplifications have to be made as compared

to stand-level and landscape-level modeling. Availability and

quality of data as input to the models is one important re-

striction to the feasibility of global modeling. Moreover, the

scenarios should be regarded as projections, rather than pre-

dictions of the future. It is notoriously difficult to predict what

will be the leading technologies, products and markets of the

future. In this study, for instance, a large part of our future

bioenergy consumption is assumed to be second-generation

biofuels, and thus research and development need to focus

on technical and practical solutions today to ensure that

future energy demands can be satisfied in this way [56].

Nevertheless, we hope that this studywill serve as an example

of the kind of studies needed for guiding future policymaking

and research.

To conclude, we wish to highlight some factors that are

crucial to the outcome of an integrated global assessment like

the present study. First, to enable intensified production of

agricultural commodities as well as intensified production of

bioenergy, mainly from forest plantations, we allocate pro-

duction to areas with the highest productivity globally. Thus,

we have to make well founded assumptions on agricultural

and forest productivity. Consequently, and only under the

condition of functioning trade, optimization of land use and

zoning can be achieved (e.g., land sharing vs. land sparing

[57]). Second, we may allocate the production of bioenergy to

managed forests, to plantations, or even to pristine forests.

Thus, to achieve both an efficient and sustainable production

of bioenergy we need to take into account i) how much

biomass can be mobilized additionally from managed forests;

ii) how much the forest area under management can be

expanded, and where this expansion is to take place; and

iii) sustainability criteria for global, national, and regional

levels. Third, we also have to consider safeguard mecha-

nisms, such as certification, and land cover uncertainty (i.e.,

what is the real land reserve on which we can, for instance,

produce additional bioenergy). Finally, we have to take

into account the access to these land resources in terms of

biophysical, legal, logistic, economic, and socioeconomic

accessibility.

The projections presented in this study regarding trade-

offs between deforestation, land-use change, and other

climate effective aspects such as GHG emissions or water

consumption make urgent action with respect to policy-

making and good governance inevitable. Renewable energy

policies and forest policies should directly address defores-

tation and forest degradation linked to bioenergy production

in order to avoid undesirable effects or even perverse in-

centives [58]. Moreover, the need for transdisciplinary

research in this field is immense [59]. To work toward a broad

and mainly renewable resources-based energy portfolio by

2050, focus on intensification, and technological and behav-

ioral shifts is needed tominimize the negative effects of trade-

offs from deforestation and forest degradation. Various policy

areas, inter alia, energy, climate, land-use and rural develop-

ment, need to be coordinated at all geographic levels and

supported by integrated assessments to ensure sustainable

use of our common resources.





Acknowledgments

The research leading to these results received funding fromthe

European Community’s Seventh Framework Programme (FP7)

under grant agreement no 212535, Climate Change e Terres-

trial Adaptation and Mitigation in Europe (CC-TAME), www.

cctame.eu, (see Article II.30. of the Grant Agreement), as well

as from the BIOMASS FUTURES project (www.biomassfutures.

eu), funded by the European Union’s Intelligent Energy Pro-

gramme under grant number IEE 08 653 SI2. 529 241, and grant

agreement no 244766 e PASHMINA (www.pashmina-project-

eu). Eva-Maria Nordstrom received funding from the Kempe

Foundations (Kempestiftelserna) and Future.

Forests, a multi-disciplinary research programme sup-

ported by the Foundation for Strategic Environmental

Research (MISTRA), the Swedish Forestry Industry, the

Swedish University of Agricultural Sciences (SLU), Umea

University, and the Forestry Research Institute of Sweden. The

authors thank the cooperation and support from the partner

research institutions, mainly the International Institute for

Applied Systems Analysis (IIASA) and the World Wide Fund

for Nature (WWF). Special gratitude goes to the International

Research NGO Global Alpine Synergies (GAS), www.global-

syn.org, in Austria for its dedicated support.

r e f e r e n c e s

[1] IEA. Energy technology perspectives. Scenarios & strategiesto 2050. Executive summary. Paris: OECD/IEA; 2010. p. 16.

[2] Directive 2009/28/EC of the European Parliament and of thecouncil of 23 April 2009 on the promotion of the use of energyfrom renewable sources and amending and subsequentlyrepealing directives 2001/77/EC and 2003/30/EC. Off J EurUnion 5.6.2009. L 140/16.

[3] IEA/IRENA Global Renewable Energy Policies and MeasuresDatabase. Available from: http://www.iea.org/policiesandmeasures/renewableenergy/; [accessed05.03.2013].

[4] Frank S, Bottcher H, Havlık P, Valin H, Mosnier A, ObersteinerM, et al. How effective are the sustainability criteriaaccompanying the European Union 2020 biofuel targets? GCBBioenergy [advance online publication]. http://dx.doi.org/10.1111/j.1757-1707.2012.01188.x.

[5] Jull C, Carmona Redondo P, Motosi V, Vapnek M. Recenttrends in the law and policy of bioenergy production,promotion and use. Rome: Food and AgricultureOrganization of the United Nations; 2007 Sep. p. 56. FAOLegislative Study 95.

[6] Stupak I, Asikainen A, Jonsell M, Karltun E, Lunnan A,Mizaraite D, et al. Sustainable utilisation of forest biomassfor energy e possibilities and problems: policy, legislation,certification, and recommendations and guidelines in theNordic, Baltic, and other European countries. BiomassBioenergy 2007;31(10):666e84.

[7] FAO. Forests and energy. Key issues. Rome: Food andAgriculture Organization of the United Nations; 2008. p. 56.FAO Forestry Paper 154.

[8] Heistermann M, Muller C, Ronneberger K. Land in sight?Achievements, deficits and potentials of continental toglobal scale land-use modeling. Agric Ecosyst Environ 2006;114:141e58.


[9] Lotze-Campen H. The role of modelling tools in integratedsustainability assessment (ISA). Int J Innov Sustain Dev 2008;3(1e2):70e92.

[10] Buongiorno J, Zhu S, Zhang D, Turner J, Tomberlin D. Theglobal forest products model. New York: Elsevier Science;2003.

[11] Cardellichio P, Youn Y, Adams D, Joo R, Chmelik J.A preliminary analysis of timber and timber productsproduction, consumption, trade and prices in the PacificRim until 2000. Seattle: College of Forest Resources,University of Washington; 1989. p. 214. CINTRAFORWorking Paper 22.

[12] Hertel TW, editor. Global trade analysis. Modeling andapplications. Cambridge: Cambridge University Press; 1997.

[13] Havlık P, Schneider UA, Schmid E, Bottcher H, Fritz S,Skalsky R, et al. Global land-use implications of first andsecond generation biofuel targets. Energy Policy 2011;39:5690e702.

[14] Havlık P, Valin H, Mosnier A, Obersteiner M, Baker JS,Herrero M, et al. Crop productivity and the global livestocksector: implications for land use change and greenhouse gasemissions. American Journal of Agricultural Economics 2013;95(2):442e8.

[15] Taylor R, editor. Chapter 1: forests for a living planet. Gland:World Wide Fund for Nature; 2011. p. 35. WWF Living ForestsReport.

[16] IIASA. www.globiom.org. [webpage]; Available from: www.globiom.org [accessed 07.05.2012].

[17] McCarl BA, Spreen TH. Price endogenous mathematicalprogramming as a tool for sector analysis. Am J Agric Econ1980;62:87e102.

[18] Schneider UA, McCarl BA, Schmid E. Agricultural sectoranalysis on greenhouse gas mitigation in US agriculture andforestry. Agric Syst 2007;94:128e40.

[19] Williams JR. The EPIC model. In: Singh VP, editor. Computermodels of watershed hydrology. Highlands Ranch, Colorado:Water Resources Publications; 1995. p. 909e1000.

[20] Izaurralde RC, Williams JR, McGill WB, Rosenberg NJ,Jakas MCQ. Simulating soil C dynamics with EPIC: modeldescription and testing against long-term data. Ecol Model2006;192(3e4):362e84.

[21] Kindermann GE, McCallum I, Fritz S, Obersteiner M. A globalforest growing stock, biomass and carbon map based on FAOstatistics. Silva Fenn 2008;42(3):387e96.

[22] In: Gusti M, Kindermann G, editors. An approach to modelinglanduse change and forest management on a global scale.Proceedings of 1st international conference on simulationand modeling methodologies, technologies and applications(SIMULTECH 2011). Noordwijkerhout; July 29e31, 2011.p. 180e5.

[23] Rametsteiner E, Nilsson S, Bottcher H, Havlık P, Kraxner F,Leduc S, et al. Study of the effects of globalization on theeconomic viability of EU forestry. Laxenburg: InternationalInstitute for Applied Systems Analysis; 2007. p. 185. FinalReport of the AGRI Tender Project: AGRI-G4-2006-06.

[24] Bottcher H, Verkerk PJ, Gusti M, Havlik P, Grassi G. Projectionof the future EU forest CO2 sink as affected by recentbioenergy policies using two advanced forest managementmodels. GCB Bioenergy 2012;4(6):774e83.

[25] Gusti M, Havlık P, Obersteiner M. How much additionalcarbon can be stored in forests if economic measures areused and how much could it cost?. Research Reports of theNational University of Bioresources and Nature Managementof Ukraine 135; 2009. p. 5.

[26] Russ P, Wiesenthal T, van Regemorter D, Cıscar JC. Globalclimate policy scenarios for 2030 and beyond: analysis ofgreenhouse gas emission reduction pathway scenarios withthe POLES and GEME3 models. Luxembourg: Joint Research


http://www.cctame.eu

http://www.cctame.eu

http://www.biomassfutures.eu

http://www.biomassfutures.eu

http://www.pashmina-project-eu

http://www.pashmina-project-eu

http://www.global-syn.org

http://www.global-syn.org

http://www.iea.org/policiesandmeasures/renewableenergy/

http://www.iea.org/policiesandmeasures/renewableenergy/

http://dx.doi.org/10.1111/j.1757-1707.2012.01188.x

http://dx.doi.org/10.1111/j.1757-1707.2012.01188.x







Centre, Institute for Prospective Technological Studies; 2007.p. 92.

[27] Capros P, Mantzos L, Tasios N, De Vita A, Kouvaritakis N. EUenergy trends to 2030 e update 2009. Brussels: EuropeanCommission Directorate e General for Energy incollaboration with Climate Action DG and Mobility andTransport DG; 2010. p. 180.

[28] EC. Impact assessment for the energy roadmap 2050.Brussels: European Commission; 2011. p. 218. CommissionStaff Working Paper.

[29] Messner S, Strubegger M. User’s guide for MESSAGE III.Laxenburg: International Institute for Applied SystemsAnalysis (IIASA); 1995. p. 164. Working Paper 95e69.

[30] Riahi K, Grubler A, Nakicenovic N. Scenarios of long-termsocio-economic and environmental development underclimate stabilization. Technol Forecast Soc Change 2007;74:887e935.

[31] Reisinger A, Havlik P, Riahi K, Vliet O, Obersteiner M,Herrero M. Implications of alternative metrics for globalmitigation costs and greenhouse gas emissions fromagriculture. Climatic Change 2012:1e14.

[32] FAO. World agriculture: towards 2030/2050. Prospects forfood, nutrition, agriculture and major commodity groups.Rome: Food and Agriculture Organization of the UnitedNations; 2006 June. p. 71. Interim report.

[33] Mosnier A, Havlık P, Obersteiner M, Aoki K, Schmid E, Fritz S,et al., Modeling Impact of Development Trajectories and aGlobal Agreement on Reducing Emissions fromDeforestation on Congo Basin Forests by 2030, Environmentaland Resource Economics, 1-21. (doi:10.1007/s10640-012-9618-7.)Retrieved from http://dx.doi.org/10.1007/s10640-012-9618-7.

[34] EC. A roadmap for moving to a competitive low carboneconomy in 2050. Brussels: European Commission; 2011.p. 15. Staff Working Document SEC 288.

[35] Grethe H, Dembele A, Duman N. How to feed the world’sgrowing billions e understanding FAO world foodprojections and their implications. Germany: Heinrich BollFoundation and WWF; 2011 Apr. p. 62.

[36] Singer S, editor. The energy report: 100% renewable energyby 2050. Gland: WWF, Ecofys and OMA; 2011 Jan. p. 253.

[37] UNEP-WCMC/IUCN WCPA. World database on protectedareas (WDPA): United Nations Environment Programme e

World Conservation Monitoring Centre and InternationalUnion for conservation of Nature world Commission onprotected areas; 2010.

[38] Olson DM, Dinerstein E. The global 200: priority ecoregionsfor global conservation. Ann Mo Bot Gard 2002;89:199e224.

[39] WWF/IUCN.Centres of plantdiversity: a guideand strategy fortheir conservation. Cambridge: World Wide Fund for Natureand International Union for Conservation of Nature; 1994.

[40] Duellman WE, editor. Patterns of distribution of amphibians:a global perspective. Baltimore: John Hopkins UniversityPress; 1999.

[41] Mittermeier RA, Gil PR, Hoffman M, Pilgrim J, Brooks T,Mittermeier CG, et al. Hotspots revisited: earth’s biologicallyrichest and most endangered terrestrial ecoregions.Arlington VI: Conservation International (distributed by theUniversity of Chicago Press); 2004.

[42] BirdLife International. Endemic bird areas. BirdLifeInternational; 2008.

[43] Ricketts TH, Dinerstein E, Boucher T, Brooks TM,Butchart SHM, Hoffmann M, et al. Pinpointing and


preventing imminent extinctions. Proc Natl Acad Sci U S A2005;102:18497e501.

[44] Azar C, Lindgren K, Obersteiner M, Riahi K, van Vuuren DP,den Elzen KMGJ, et al. The feasibility of lowCO2 concentrationtargets and the role of bio-energy with carbon capture andstorage (BECCS). Clim Change 2010;100(1):195e202.

[45] Taylor R, editor. Chapter 2: forests and energy. Gland: WorldWide Fund for Nature; 2011. p. 36.WWF Living Forests Report.

[46] FAO. Global forest resources assessment 2010: main report.Rome: Food and Agriculture Organization of the UnitedNations; 2010. p. 340. FAO Forestry Paper 163.

[47] Kraxner F, Yang J, Yamagata Y. Attitudes towards forest,biomass and certification e a case study approach tointegrate public opinion in Japan. Bioresour Technol 2009;100:4058e61.

[48] Kraxner F, Fernholz K, Owari T. Certified forest productsmarkets, 2010e2011. In: UNECE/FAO, editor. Forest productsannual market review 2010e2011. Geneva timber and foreststudy paper 27. Geneva: United Nations; 2011. p. 99e108.

[49] Popp A, Krause M, Dietrich JP, Lotze-Campen H, Leimbach M,Beringer T, et al. Additional CO2 emissions from land usechange e forest conservation as a precondition forsustainable production of second generation bioenergy. EcolEcon [advance Online Publication]. http://dx.doi.org/10.1016/j.ecolecon.2011.11.004.

[50] Schneider UA, Havlık P, Schmid E, Valin H, Mosnier A,Obersteiner M, et al. Impacts of population growth, economicdevelopment, and technical change on global foodproduction and consumption. Agric Syst 2011;104:204e15.

[51] Strassburg BBN, Rodrigues ASL, Gusti M, Balmford A, Fritz S,Obersteiner M, et al. Impacts of incentives to reduceemissions from deforestation on global species extinctions[advance online publication]. Nat Clim Change 2012. http://dx.doi.org/10.1038/nclimate1375.

[52] Obersteiner M, Azar C, Kauppi P, Mollersten K, Moreira J,Nilsson S, et al.Managing climate risk. Science 2001;294:786e7.

[53] Kraxner F, Nilsson S, Obersteiner M. Negative emissionsfrom bioenergy use, carbon capture and sequestration (BECS)e the case of biomass production by sustainable forestmanagement from semi-natural temperate forests. BiomassBioenergy 2003;24(4e5):285e96.

[54] Kraxner F, Aoki K, Leduc S, Kindermann G, Fuss S, Yang J,et al. BECCS in South Korea e analyzing the negativeemissions potential of bioenergy as a mitigation tool.Renew Energy 2012. http://dx.doi.org/10.1016/j.renene.2012.09.064.

[55] Fuss S, Reuter WH, Szolgayova J, Obersteiner M. Optimalmitigation strategies with negative emission technologiesand carbon sinks under uncertainty. Clim Change 2013.http://dx.doi.org/10.1007/s10584-012-0676-1.

[56] Sims REH, Mabee W, Saddler JN, Taylor M. An overview ofsecond generation biofuel technologies. Bioresour Technol2010;101:1570e80.

[57] Phalan B, Onial M, Balmford A, Green RE. Reconciling foodproduction and biodiversity conservation: land sharing andland sparing compared. Science 2011;333:1289e91.

[58] Searchinger TD, Hamburg SP, Melillo J, Chameides W,Havlik P, Kammen DM, et al. Bioenergy: counting onincentives e response. Science 2010;327:1200e1.

[59] McCarl BA. Analysis of climate change implications foragriculture and forestry: an interdisciplinary effort. ClimChange 2010;100:119e24.


http://dx.doi.org/10.1007/s10640-012-9618-7

http://dx.doi.org/10.1007/s10640-012-9618-7

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global bioenergy scenarios – future forest development, land-use implications, and trade-offs

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