Birdsview of state-of-the-art scientific understanding on ... · Chapter 10 Modelled Deployment...

Copernicus Institute Sustainable Development and Innovation Management

‘Birdsview of state-of-the-art

scientific understanding on

sustainable biomass

sourcing.”

BIO-BIOTA_PFPMCG-SCOPE

Joint Workshop on Biofuels & Sustainability,

26th February 2013, Sao Paulo – Brazil

André Faaij Scientific Director, Copernicus Institute – Utrecht University;

Head of Unit, Energy & Resources


Utrecht University – NL Shanghai Ranking 2011:

Utrecht University rated as:

12th best university in Europe

best in the Netherlands 5th year in a row

48th best university worldwide

Of 56 leading universities in Europe,

Utrecht University, with ETH Zurich,

Imperial College London and Cambridge

University make up the top 4 of the CHE

Ranking.

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Copernicus Institute

- Faculty of Geosciences ‘’Connecting knowledge for sustainability’’

• Some 130 scientific staff

• Interdisciplinary; Key areas: – Energy & Resources (Energy Supply, Energy

Demand, Integrating activities (e.g. modelling)

– Innovation sciences & governance.

– Environmental sciences

• Range of bachelor & master programs

• Diverse funding of research: govt. scientific, EC, industry, international bodies, NGO’s, etc.


Energy & resources: Interlinked concepts for understanding, exploring, monitoring and evaluation.

• Technologies: Engineering – Technology Assessment – Technological Learning

• Impacts: wide portfolio of methods (environmental / ecological / socio-economic…); how to measure ‘sustainability’?

• Potential supply: options, limitations, opportunities.

• Modeling & uncertainty analysis: wide range of tools.

• Implementation: policy options, energy transition & innovation policies, roadmaps, scenario’s…


Energy demand, GHG emissions

and climate change…


Potential emissions from remaining

fossil resources could

result in GHG concentration levels far

above 600ppm.


Energy system transformation…

[GEA/van Vuuren et al CoSust, 2012]


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.


[IPCC-SRREN, 2011]

Driving forces, dimensions, scales…


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]


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


[Dornburg et al., 2010

in: IPCC-SRREN, 2011]


Global biodiesel & fuel ethanol production

2000-2009

Biodiesel

EU

Ethanol

USA Brazil Argentina Others

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


(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


Policy context Europe:

• Renewable Energy Directive (RED):

– 20% Renewable Energy in the EU in 2020 (+ specific for member states; e.g. 14% for the Netherlands).

– Subtarget of at least 10% renewable energy transport (every member state).

• European Fuel Quality Directive: Fuel suppliers at least 6% less GHG in 2020 via: biofuels, efficiency refineries, or other energy carriers (gas, electricity, hydrogen, etc.).

• 20% less GHG in 2020 (possibly 30%) -> national targets + Emission Trading. Biomass in e.g. Refining, steel, etc. can contribute.

• June 2010 National Action Plans for implementation RED.

• Intervention in RED (…): CAP on 1st generation biofuels; iLUC discussion ‘’frozen’’.

• Evaluation in 2014.

• Now: Horizon 2020 activities (JTI’s/PPP’s, outlook to 2030; heavy emphasis on biobased economy


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]


Advancing markets…pushed

by technological progress and

pulled by high oil prices • 2nd generation biofuels…

• 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?


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]


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



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 Bioenergy 38/40/43, 2011]


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


But we need the aggregated

modeling frameworks…

• World is far too complex…

• E.g. consequential LCA becomes unmanageable.

• Many interactions come from global level: trade determining factor, food & energy prices, competing (energy & mitigation) technologies, etc.

• Showing BAU IS important: markets and governments are imperfect


• Controlling extent of 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…


IMAGE /

IMAGE –TIMER

PBL

MAGNET

LEI

Bottom-up analysis of

technological learning in

bioenergy & agriculture

UU

1. Improve existing

models, knowledge

and data

2. Better integrate

existing models

Redesigning modelling frameworks & scenario’s


Bottom up IMAGE MAGNET

Bio

en

erg

y

- Current status

and future

prospects of

technologies;

- Sustainable

residue removal

(student project,

superv. by

Vassilis)

- Learning in

bioenergy chains

- Updating input data

TIMER based on

bottom-up

- Dynamic modeling

of residues –

sustainable residue

potential

- Updating crop

yields & learning

rates;

- Introduction of 2nd

generation fuels;

- Inclusion residues

as feedstock for bio-

based value chains

- Endogenize tech

learning for bioenergy

chains, shifts to new

technologies

Tech change & learning / integration


Bottom up IMAGE MAGNET

Ag

ricu

ltu

re

Agriculture &

livestock (yields,

inputs for

different mgmt

systems &

regions – effects

of shifting

systems)

Updating &

reinforcing

scenarios for

agricultural crop

yields - link to

mgmt system

Endogenize tech

change, efficiency

improvements, shift

to new mgmt

systems /

technologies;

Price effect of tech

change

Tech change & learning / integration


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.


Work in Brazil BION/BE-BASIC

• Detailed regional analysis on land-use (potentials, dynamics).

• LCA/EIA/economics/optimisation over time of advanced biobased supply chains.

• Methodology development & demonstration regional impacts water & biodiversity.

• Socio-economic impacts on regional level.

Combined with (senior researcher capacity):

• Macro-economic analysis methods (LEI)

• Remote sensing (TUD)

• Stakeholder perspectives (TUD, univ A’dam)

In Brazil with CTBE and partners (ICONE, Embrapa, ESALQ, etc.); joint BIOEN project for 2013-2014.

Copernicus-UU is developing related efforts in: Southern Africa, Indonesia, Eastern Europe, Colombia, Argentina, SE-US and on sustainable forest management strategies.


Thanks for your attention

For more information, see:

- Sciencedirect/Scopus

- http://srren.ipcc-wg3.de/report

- www.bioenergytrade.org



Land availability for biomass production under different scenario’s in Mozambique

F. van der Hilst, J.A. Verstegen, D. Karssenberg, A.P.C. Faaij,

Spatio-temporal land use modelling for the assessment of land

availability for energy crops – illustrated for Mozambique, Global

Change Biology – Bioenergy, Volume 4, Issue 6, November 2012,

Pages 859-874

Floor van der Hilst, André Faaij, Spatio-temporal cost supply

curves for bioenergy production in Mozambique. Biofuels,

Bioproducts and Biorefining (BioFPR), Volume 6, Issue 4, July

2012, Pages 405-430.


Land use developments

Land Use

Mozambiqu

e

Economic

Environmental

Regional

‘Business as

usual’

Global

‘Progressive

and

sustainable’

Land use developments can not be predicted…

But future land use developments can be explored by means of a scenario approach.

Low technological change Low environmental concern Low agricultural productivity

High technological change High environmental concern High agricultural productivity


Yield increase

Weighted average yield 2000 = 100% weighted average based on share % in total production in hectares

100

150

200

250

300

350

2000 2005 2010 2015 2020 2025 2030

% o

f yie

ld l

eve

l 2

00

0

BAU

Prograssive


Land requirements for crop production

Business as usual scenario Progressive scenario

The area required for food production increases due to higher intake per

capita and population growth. The land requirements are smaller in the

progressive scenario, due to higher yields and higher cropping intensity.

0

2000

4000

6000

8000

10000

12000

14000

BAU 2000 BAU 2006 BAU 2015 BAU 2030 P 2000 P 2006 P 2015 P 2030

x1000 h

a

other

industrial

fruit&veg

roots&tubers

cereals

total

Additional land required due to low

Cropping Intensity

(Area harvested/Area cropland)


Nr of neighboring cells

Distance to roads

Distance to water

Distance to cities

Population density

Soil suitability

Current land use

Priority grid

Land use allocation Land is allocated to a land use function when it is most suitable for that specific faction based on several land use change determinants


Excluded areas

• For energy crops – All of the excluded land areas

• Previous slide

– Land required for crops

– Land required for pasture

– Deforested areas

– Farm areas

– DUAT

– Community areas

Excluded areas general

cropland

grazing

Deforested area

Farm areas

Community areas and DUAT

Excluded areas


2009

BAU Progressive BAU Progressive


2015



2020



2025



2030



Land availability

0

2

4

6

8

10

12

14

16

18

2005 2010 2030 2005 2010 2030

Mh

a

very suitable

suitable

moderatly suitabble

marginally suitable

not suitable

Development of land availability over time differentiated for suitability classes for the BAU scenario (left) and the Progressive scenario (right).

BAU scenario Progressive scenario


Cost breakdown of eucalyptus

related to soil suitability.


Spatial distribution of feedstock

potentials and costs (2015)

Progressive BAU


Cost Supply curves for

eucalyptus €/GJ 2005-2030.


Global (fuel) ethanol trade streams of

minimum 1 PJ in 2009.



Global (fuel) ethanol trade streams of

minimum 1 PJ in 2011.

[Lamers et al., RSER, 2012]



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.

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