Biobased economy: outlook on sustainable supplies and ......2013/05/22 · DH&CHP Bulk pellets...

Copernicus Institute Sustainable Development and Innovation Management

Biobased economy: outlook

on sustainable supplies and

future demand

UNECE/FAO workshop – Forest Products and Technologies for the

Future

22th May 2013, St Petersburg - Russia

André Faaij Scientific Director, Copernicus Institute – Utrecht University;

Head of Unit, Energy & Resources


Why biobased economy?


Biomass & bioenergy

flows according to IEA

+ other refs (2008)

[IPCC-

SRREN, 2011]


Energy system transformation…

[GEA/van Vuuren et al CoSust, 2012]


Advancing markets…pushed

by technological progress and

pulled by high oil prices • Advanced biofuels…(strong economic

perspective)

• Biorefining, biochemicals, biomaterials…

• Aviation and shipping…

• Likely to compete for the same resources…

• Should meet the same sustainability criteria…(but that is not the case today!)

• Competition or synergy?


Breakdown of CO2 reduction

options for aviation till 2050

[IIATA, 2010]


Biobased chemicals; not covered in

current global scenario’s (to date…)!

[Daioglou et al., 2013 (forthcoming)]

Energy demand

for major

Chemicals

towards

2100 with

and without

Biomass

deployment HVC’s,

including

recycling


Biofuels; they are not

going away.

Large-scale deployment of advanced biofuels vital to meet the roadmap targets

Advanced biofuels reach cost parity around 2030 in an optimistic case

Fin

al e

ner

gy (

EJ)

[IEA Biofuels Roadmap]


Current & future biomass

markets


Global biodiesel & fuel ethanol production

2000-2009

Biodiesel

EU

Ethanol

USA Brazil Argentina Others

(Source: Lamers et al., RSER, 15 (2011) 2655– 2676)


Global (fuel) ethanol trade streams of

minimum 1 PJ in 2009.



Global (fuel) ethanol trade streams of

minimum 1 PJ in 2011.

(Source: Lamers et al., in Faaij & Junginger (eds), forthcoming in 2013)



Global biodiesel trade streams of minimum 1

PJ in 2009.


(Source: Lamers et al., in Faaij & Junginger (eds), forthcoming in 2013)

Global biodiesel trade streams of minimum 1

PJ in 2011.


(Source: Lamers et al. RSER, 16(2012) 3176-3199

Global wood pellet production 2000 - 2010


(Source: Lamers et al. 2012)

Global wood pellet trade 2010

Source: Lamers et al., RSER, 16(2012) 3176-3199


European pellet markets

2

1

4

Bulk large

scale power

Bulk medium

DH&CHP

Bulk pellets

households

Pellets in bags

households

Major

exporters

2

4

1

3 3

NE Europe (coasters)

Central Europe (trucks)

[Sikkema et al, BioFPR, 2011]


Simulated Biomass trade flows 2020

RU

FI

SE

FR

UA

ES

NO

TR

PL

DE

IT

UKBY

RO

IE

LT

BG

AT

LV

HU

CZ

PT

RS

GR

EE

SK

BA

HR

NL

CH

DK

BE

MD

AL

SI

MK

ME KS

CY

LU

MT

MC

RU

FI

SE

FR

UA

ES

NO

TR

PL

DE

IT

UKBY

RO

IE

LT

BG

AT

LV

HU

CZ

PT

RS

GR

EE

SK

BA

HR

NL

CH

DK

BE

MD

AL

SI

MK

ME KS

CY

LU

MT

MC

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Import non-EU

Low Import scenario High Import scenario

Year: 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

2009 2015 2020

(pellets) Low Import High Import Low Import High Import

Total trade (Mtoe) 1.6 5.4 6.2 12.6 17.4

Total trade (Mt wood pellet

eq.)* 3.8 12 14 29 40

Of which Intra-EU 55% 38% 32% 52% 32%

Of which Inter-EU 45% 62% 68% 48% 68%

*) Mt eq. = million metric tonne pellet equivalent (18 MJ/kg)

[Hoefnagels et al, UU/Task 40, 2011]


A future vision on global

bioenergy markets (2050…)

[GIRACT FFF Scenario project; Faaij, 2008]

250 Mha = 100 EJ

= 5% ag land + pasture

= 1/3 Brazilie


Forestry biomass supply chain

Source:

Röser &

Sikanen


Biomass densification options BALING

• pressing and mechanical dewatering

• mostly straw, but also forest residues

BRIQUETTING

• popular with straw

• easy moisture uptake

PELLETIZING

• mostly woody biomass e.g. saw dust

• straw difficult, but employed (DK,PL)

• mechanical damaging, moisture uptake

TORREFACTION

• thermochemical process (200ºC, 1h)

• anaerobic conditions,

• solid product (char)

• any type of biomass

• fibrous structure loose

• hydrophobic character

• low energy density – volume reduced

only slightly in the process


+++

0** ++ ++* ++* ++ PELLETIZING

__ +++ +++ +++ PYROLYSIS

+ +++ +++ +++ TORREFACTION

+ PELLETIZING

+ +++ +++ +++ 0 TORREFACTION

0** + ++* ++* ++ BRIQUETTING

0 0

__

+ + BALING

0 0 __ __ + SIZING

CORROSION FEEDING STORAGE

HEALTH &

SAFETY

STORAGE

ENERGY

LOSSES

TRANSPORT

Comparison of densification options

+ Positive

- Negative

0 Neutral

*if proper storage conditions provided **unless pelletized with additives lowering the ash melting point (then +)

+


Experience curve for primary forest fuels

in Sweden and Finland (1975 and 2003).

Sourc

e:

Jungin

ger

Faaij

et al., 2005


Estimated future costs of

sugarcane and ethanol production

assuming 8% annual growth

Cumulative sugarcane production [106 TC]

1000 2000 4000 8000 16000 32000Pro

duction c

osts

sugarc

ane [U

S$/tonne] and e

thanol [U

S$/m

3]

10

20

40

200

400

800

10 20 40 80 160 320 640 1280

Sugarcane

Ethanol prod. cost (excl. feedstock)

Expected range of cane prod. costs in 2020

Expected range of ethanol prod. costs in 2020

PR = 0.68 + 0.03

PR = 0.81 + 0.02

2020

2020

Cumulative ethanol production [106 m

3]

Explaining the experience

curve:

Cost reductions of

Brazilian ethanol from

sugarcane

J.D. van den Wall Bake, M.

Junginger, A. Faaij, T.Poot,

A. da Silva Walter

Biomass & Bioenergy, 2008


Biomass resources;

potentials <-> preconditions


Biobased economy;

friend or foe?

• Food vs. Fuel

• Biofuels a crime against humanity

• Threats for biodiversity, water,

farmers…

• LUC & iLUC, Carbon Payback…

result in poor GHG balances

• Large number of external damages.


2050 Bioenergy Potentials &

Deployment Levels

2008 Global

Energy Total

Chapter 2

Possible

Deployment

Levels

2011 IPCC Review*

Land Use

3 and 5

million km 2

Chapter 10 Modelled

Deployment Levels for CO2 Concentration

Targets

Past Literature

Range of

Technical

Potentials

0-1500 EJ

Glo

bal

Pri

mar

y En

erg

y Su

pp

ly, E

J/y

2008 Global

Biomass Energy

2050

Global

Energy

AR4,

2007

2050 Global

Biomass

AR4,

2007

<440 ppm

440-600 ppm

Technical Potential

2050 Projections

Minimum

median 75th

Maximum

100

300

150 190

80

265 300

Technical Potential Based on 2008

Model and Literature Assessment

118

20 25

25th

Percentile

2000 Total Biomass Harvest for Food/Fodder/Fiber as Energy Content

[IPCC-SRREN, 2011]


[IPCC-SRREN, 2011]

Driving forces, dimensions, scales…


Key factors

biomass potentials Issue/effect Importance Impact on biomass

potentials

Supply potential of biomass

supply as estimated in recent studies

Improvement agricultural management *** Choice of crops

***

Food demands and human diet

*** Use of degraded land

***

Competition for water

*** Use of agricultural/forestry by-products ** Protected area expansion

**

Water use efficiency

** Climate change ** Alternative protein chains

**

Demand for biomaterials

*

Demand potential of biomass

demand as estimated in recent studies

Bio-energy demand versus supply

** Cost of biomass supply **

Learning in energy conversion ** Market mechanism food-feed-fuel **

Dornburg et al., Energy &

Environmental Science 2010


Contributors to land use

change…


World

North America

75

168204

39

Oceania

America

5593

114

40

Japan

Ameri

2 2 2 2

19 25 30

W.Europe

13 E.Europe

13 24295

CIS &

Baltic States

111

223

269

83

Caribean &

Latin America

160

232

279

87

sub-Saharan

Africa

117

282

347

49

Middle East &

North Africa

2 31 39

2

South Asia

26 31 3723

East Asia

28

158194

22

1270

1545

607

364 forest growth

agricultural and forestry

wastes and residues

dedicated woody

bioenergy crops

surplus forest growth

agricultural and forestry

wastes and residues

dedicated woody bioenergy crops on surplus agricultural land

Total bioenergy production potential in 2050 based on system 1 to 4 (EJy-1; the left bar is system 1, the

right bar is system 4

Bioenergy production potential in

2050 for different levels of change in

agricultural management

Sourc

e:

Sm

eets

, F

aaij

2007

Pro

gre

ss in E

nerg

y &

Com

bustion S

cie

nce


[See e.g: van Dam et al., RSER, 2010]


Copernicus Institute Sustainable Development and Innovation

Operationalisation of sustainability criteria

costs

land

availability

Criteria

deforestation

competition with

food production

biodiversity

soil erosion

fresh water

nutrient leaching

pollution from

chemicals

employment

child labour

wages

Impact

crop

management

system

yield quantity

cost supply

curve

[Smeets et al., 2010]


Cramer Cie.: minimum safeguard->

stabilisation-> improvement…

1. GHG balance -> Chain performance (30-80%+..)

2. Land-use/competition with food: reporting; to be developed.

3. Biodiversity -> reporting/FSC/RSPO; to be developed.

4. Wellfare -> Reporting EPI; to be developed further.

5. Well being -> ILO, Social accountability standards, etc.

6. Environment – Waste; law, GPG’s

– Agrochemicals; law, GPG’s (further development).

– Soil quality; reporting/monitoring (further development).

– Water quality & quantity; law, reporting/monitoring (further development).

Cramer et al., 2007


Snapshot on certification

• RED crucial; proposal to CAP 1st gen biofuels. iLUC factors ‘put aside’. Evaluated in 2014 (likely to expand). Runs till 2020.

• ISCC gained ground. RSB lags behind. Variety of other systems RED approved.

• ISO process ongoing…

• Announcement that criteria for solid biomass will be introduced (meets resistance as well).

• Response from IWPB (large utilities).

• Attention for iLUC prevention and carbon payback (additional criteria?)


GHG mitigation peformance


GHG/MJ of major modern bioenergy chains vs.

conventional fossil fuel options

Excluding

(i)LUC

effects;

these can

have

strong

impacts

[IPCC-SRREN, 2011]



Uncertain!!!

‘depreciation

Carbon losses

over 20 years;

after that iLUC

= zero.

Carbon intensity

fossil ref

excludes upstream

Emissions.

These will increase

(>200 g/MJ

possible)


GHG emissions per km driven

[Van Vliet et al., 2009]

No CCS CCS


iLUC; scientific status, gaps

next steps…


Confrontation

bottom-up vs. top down

iLUC modelling Key steps iLUC

modelling efforts:

• CGE; historic data basis

• Model shock, short term, BAU, current technology.

• Quantify LUC

• Quantify GHG implications (carbon stocks)

Bottom-up insights:

• Coverage of BBE options, advancements in agriculture, verification of changes (land, production)

• Gradual, sustainability driven, longer term, technological change (BBE, Agriculture

• LUC depends on zoning, productivity, socio-economic drivers

• Governing of forest, agriculture, identification of ‘’best’’ lands.

[IEA & other workshops, 2011-2013; pubs under preparation]


Example: Corn ethanol

Results from PE & CGE models

[Wicke et al., Biofuels, 2012]

-100 -50 0 50 100

Searchinger et al. [3]

CARB [13]

EPA [18]

Hertel et al. [14]

Tyner et al. [15] – Group 1



Al-Riffai et al. [16]

Laborde [17]

Lywood et al. [25]

Tipper et al. [2] – marginal

Tipper et al. [2] – average

LUC-related GHG emissions (g CO2e/MJ)

Corn

B: Ethanol


• Controlling (i)LUC

– Increasing efficiency in agriculture, livestock and

bioenergy production

– Integrating food, feed and fuel production

– Increasing chain efficiencies

– Minimizing degradation and abandonment of

agricultural land

• Controlling type of LUC

– Sustainable land use planning (incl. monitoring)

– Excluding high carbon stock and biodiversity areas

– Using set-aside, idle or abandoned agricultural land

– Using degraded and marginal land

ilUC mitigation options…


Contrast:

• Modeling for iLUC factors is only half the science we need; reactive instead of pro-active concept.

• Biofuel policies also half the policy we need; mandates without proper preconditions, resulting in CONFLICTS

Versus

• Interlinked agricultural& biobased economy

policies (agri, clima, energy…).

• Investigate (and implement) Integral land use strategies (agriculture, BBE, nature, rural development) to achieve SYNERGIES


Ins and outs carbon debt debate


Basic principle of GHG emission

reductions through bioenergy

Source: adapted from

IEA Bioenergy Task 38

The fact that bioenergy is ultimately renewable is

not debated, but the time until the repayment of

any potential carbon debt is repaid is under

debate

Rapid removal

Slow uptake


Two very important

methodological choices:

1. Does the analysis consider the stand-level

and/or the landscape level

2. Does the study analyse the time until the

initial carbon-debt is repaid, or does it

compare the carbon flows of a bioenergy

scenario with a reference scenario (e.g. a

no-use scenario)


Stand-level

Source: Eliasson et al. 2011


Landscape-level

Source: Eliasson et al. 2011


CT

“parity point”

“carbon debt

repayment”

t C0

Bioenergy scenario (landscape)

Bioenergy scenario (plot)

No harvest scenario (plot)

No harvest scenario (landscape)

Change in carbon stored in forest from t = 0

CT = -DCstorage + Cff saving

DCstorage

Cff saving Carbon saved from displacing fossil fuel

energy generation

Notes:

• Both bioenergy scenarios account for loss of carbon in one

plot

• Landscape scenario accounts for growth over all plots

therefore has faster growth

• No harvest landscape also, therefore, accounts for growth

that would have occurred had harvest not taken place

• Concept based on Mitchell (2012) with extension to

stand/landscape level by Robin Grenfell / MWH

51

Carbon debt & parity points –

stand & landscape level “Foregone

sequestration”


Overview of parity times

per biome •

Source: Lamers and Junginger, 2013, BioFPR, in press


C-debt mitigation options

1. New plantations on degraded/C-poor land -> imminent

carbon credit!

2. For managed/commercial forests: Use of fertilizer and

weed control (within SFM limits) – increases productivity

strongly

3. Increased early stand density & use of pre-commercial

thinnings

Options 2 & 3 cause no additional land use and reduce any

C-payback times strongly (+ additional output for pulp &

timber), but all need incentives


No use of plantation for fossil fuel

substitution

-3000

-2000

-1000

0

1000

2000

3000

4000

5000

6000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Sum

of c

arb

on

flo

ws

[Mg

Car

bo

n /

25

ha]

Tree carbon

Forest floor and litter

Balance no use of forest

Balance low productive

plantation

Balance medium productive plantation

Balance high productive plantation

[Jonker et al., GCB-Bioenergy, 2013]


[Magnus Fridh,Swedish Forest Agency]


Avoided emissions 1970-2010

Substitution with bioenergy cut emissions

of 550 Mton CO2 in 40 yrs

0

500

1000

1500

2000

2500

3000

3500

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Döda trädDead orwindthrown trees

LövträdBroad-leaved

GranNorway spruce

TallScots pine

Carbon stock: 1970-2010 = + 840 Mton Million m3

[Magnus Fridh Swedish Forest Agency]


General conclusions

Payback times reported in scientific literature vary

widely, but many of them are based on rather

hypothetical scenarios.

Vast majority of currently utilized solid biomass in the EU

is still residue based – but primary forest biomass share

will increase in the future.

Determining the most adequate method / reference

scenario etc. is strongly case–dependent – there is no

‘one-size fits all solution’


Challenges for science,

business and policy • Land & natural resources (local – global)

– Integral land use strategies (agriculture, BBE, nature, rural development)

– Full impact analyses and optimization;

– Include ‘macro’’-themes; iLUC, food security, rural development, water/biodiversity.

– Governance…

• Drive down the learning curves – Technologies (fuels, biomaterials, power, carbon

management (CCS)

– Cropping systems

– Logistics, markets, CoC

– Business models & investment.


Thanks for your attention

For more information, see:

- Sciencedirect/Scopus (scientific)

- Google scholar citations (personal)

- http://srren.ipcc-wg3.de/report (IPCC)

- www.bioenergytrade.org (IEA)


Perspective on Europe


Factors complicating the debate

• Carbon debt vs. carbon parity

• Stand level vs. landscape level

• Large variety in different sourcing areas (EU, US SE, Ca BC, Russia

etc.) and different feedstocks , such as precommercial & commercial

thinnings, forest residues left after clear-cut for timber, insect-damaged

(e.g. MPB) wood

• Choice of fossil fuel reference system (coal, EU average, NG)

• Use (and substitution/displacement factors) of (by-) products

• What is the reference (aka counterfactual) scenario: protection, use for

timber or paper, or land use change (e.g. conversion to agricultural

crops, or urban development)?

• Carbon debt vs. carbon credit (in case of man-made plantations)

=> There is no single correct method. These choices depend on the

specific situation and political preference.


0

1

2

3

4

5

6

7

8

9

10

1960 1970 1980 1990 2000 2010 2020 2030

Yie

ld [to

n/h

a]

Source FAOSTAT

Observed historic yields

Yield projections Europe

Observed yield CEEC and WEC

Linear

extrapolation of

historic trends Widening yield gap

Applied scenarios Low, baseline and high 0

1

2

3

4

5

6

7

8

9

10

1960 1970 1980 1990 2000 2010 2020 2030

Yie

ld [to

n/h

a]

Source FAOSTAT

Observed historic yields Projections

0

1

2

3

4

5

6

7

8

9

10

1960 1970 1980 1990 2000 2010 2020 2030

Yie

ld [to

n/h

a]

Source FAOSTAT

Observed historic yields Projections

[Wit & Faaij, Biomass & bioenergy, 2010]


Average annual yield growth rate

projections for Europe for the

period 2000-30 for four studies

0.35%

2.3%

1.5%

0.82%

1.12%

0.62%

0.02%

0.31%

1.2%

0.4%

0.9%

0.6%

0.9%

1.1%

0% 1% 2% 3%

Sugar

Oils

Grains FAO

REFUEL

EEA

Ew ert et al.

5.2%

3.2%

Aggr. crops

Sugar

Oils

Grains

Cereals

Rapeseed

Wheat (durum)

(durum)Cereals

Rapeseed

Wheat (durum)

Aggr. crops

Aggr. crops

Aggr. crops

WEC CEEC Ukraine

De Wit, et al., RSER 2011


Absolute productivity increases and

relative growth rates for the period

1961-2007 and per decade. Absolute Relative

1961-2007 1961-2007 ‘61-‘69 ‘70-‘79 ‘80-‘89 ‘90-‘99 ‘00-‘07 kg ha

-1 y

-2

kg animal-1 y

-1 % y

-1

France Wheat 104 3.6 5.2 2.5 2.5 1.6 -0.9 Rapeseed 40 2.5 1.4 0.3 -0.3 2.1 1.2 Sugarbeet 1024 3.1 3.6 0.2 2.4 1.0 2.8 Cattle 2.8 1.6 0.5 1.2 0.9 -0.1 0.9

Netherlands Wheat 110 2.7 0.7 3.8 1.4 0.5 -0.6 Rapeseed 25 1.0 -0.6 -1.8 -0.1 0.6 0.2 Sugarbeet 489 1.2 2.6 0.1 1.4 -1.9 2.5 Cattle 1.1 0.6 0.7 0.9 2.1 -0.9 -1.0

Poland Wheat 39 1.8 3.6 2.3 4.1 -0.6 1.6 Rapeseed 21 1.4 1.7 0.4 -0.4 -0.6 4.0 Sugarbeet 319 1.2 3.5 -0.5 2.6 1.0 3.7 Cattle 2.5 2.7 3.6 6.1 4.9 0.6 10.1

Ukraine (USSR) a Wheat n.a. n.a. 5.1 1.0 3.6 -4.5 -0.2

Rapeseed n.a. n.a. 3.5 -2.7 -0.4 -7.4 9.4 Sugarbeet n.a. n.a. 9.0 0.3 5.0 -3.2 11.3 Cattle n.a. n.a. 6.3 2.1 2.1 -4.9 1.2

De Wit, et al., RSER, 2012


Developments in yields and

inputs

Source: FAOSTAT and own calculations

[De Wit et al, RSER 2011]


Selected remarks on yields

• Yield growth projections in WEC at 0.5-1.5% y-1, are modest when compared to historic developments between 1961-2007, but seems high compared to developments in the last two decades. Declining growth rates in the latter period, explained by an expansion in organic farming, set-aside obligations and a decoupling of production support. REFUEL projections (0.4% y-1) for the WEC seem conservative in this respect.

• Projected growth rates for the CEEC around 1% y-1 – as projected by FAO (0.9% y-1) and EEA (1.2% y-1) – seem modest when compared to average growth figures between 1961 and 2007, even more so when compared to growth rates prior to 1990 and past 2000.

De Wit, et al., RSER 2012


Results - spatial production

potential Arable land available for dedicated

bio-energy crops divided by the

total land

Countries

Low

potential

High

potential

Moderate

potential

< 6,5%

NL, BE, LU, AT, CH, NO, SE and FI

Potential

6,5% - 17%

FR, ES, PT, GE, UK, DK, IE, IT and GR

> 17% PL, LT, LV, HU, SL, SK, CZ, EST, RO, BU and UKR

[Wit & Faaij, Biomass & Bioenergy, 2010]


Results - spatial cost

distribution Production cost (€ GJ-1) for

Grassy crops

PL, PT, CZ, LT, LV, UK, RO, BU, HU, SL, SK, EST, UKR

FR, ES, GE, IT, SE, FI, NO, IE

NL, BE, LU, UK, GR, DK, CH, AT

< 2,00 Low Cost

Moderate

Cost

2,00 – 3,20

> 3,20 High

Cost

Potential Countries



1 EJ (ExaJoule) = 24 Mtoe

Summary baseline 2030

0

3

6

9

12

15

18

21

24

0 6 12 18Supply (EJ/year)

Pro

duction C

osts

(€/G

J)

Oil


0

3

6

9

12

15

18

21

24


Pro

duction C

osts

(€/G

J) Starch

Oil


0

3

6

9

12

15

18

21

24


Pro

duction C

osts

(€/G

J) Starch

Oil

Sugar


0

3

6

9

12

15

18

21

24


Pro

duction C

osts

(€/G

J)

Wood

Starch

Oil

Sugar


0

3

6

9

12

15

18

21

24


Pro

duction C

osts

(€/G

J)

Grass

Wood

Starch

Oil

Sugar


0

3

6

9

12

15

18

21

24


Pro

duction C

osts

(€/G

J)

Grass

Wood

Starch

Oil

Sugar

Grass

Wood

1st

generation

2nd

generation

Crop specific supply curves • Feedstock potentials

Produced on 65 Mha arable and 24

Mha on pastures (grass and wood)

• Significant difference

between ‘1st and 2nd

generation crops’

• Supply potentials high

compared to demand 2010 (0,78 EJ/yr) and 2020 (1,48

EJ/yr)



Results – cost-supply curves

Production costs vs.

supply potential

for 2010, 2020 and 2030

Variation areas indicated

around the curves represent

uncertainties and scenario

variables.

Only CEEC cost level increases



Total annual biomass supply

potential, per European country.



Total energy potential under

three different crop schemes.

‘Low yielding crops’:

all arable land

available planted

with oil crops.

‘High yielding

crops’: all available

land planted with

grass crops.



Developments coupled to drivers

Example: the Netherlands

Inputs (fertilizer, machinery, labour and pesticides)

Outputs (wheat, sugarbeet, rapeseed and cattle)

90

120

150

180

210

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006

Ind

ex

, 1961=100

Supply security and

price stability

Environment

Rural development

M anure application restrictions

intensification and up-scaling Quotation and fallow regulations

intervention prices Decoupling support

Holding up-scaling by re-allo tment of agr. land Diversification of (non-)farming act.

Environmental regulations

InputOutput

The Netherlands

[De Wit et al, RSER 2011]


Example: GHG balance of combined agricultural intensification + bioenergy production in Europe + Ukraine

[Wit et al., GCB-B

Under review]


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