European Carbon Sinks
Modeling Status, Data, Analytical Gaps,
EUFASOM
Uwe A. Schneider
Research Unit Sustainability and Global Change
Hamburg University
Sink Modeling Status
• EU Commission 2002: Potential of
European sinks from both agriculture and
forestry unclear
• Fast analysis needed for
– International negotiation of Kyoto Protocol
(define own position and understand others)
– EU emission trading system
EU Emission Trading - Sinks
• No initial allowance to use credits from
carbon sinks projects such as forestry to
meet emission targets
• Review of the emissions trading directive in
2006: if reporting and accounting
uncertainties surrounding sinks can be
lifted, it leaves open the possibility of using
the credits from 2008.
Integrated Sink Enhancement
Assessment (INSEA) Project
• Funded by European Commission to
address analytical gap of carbon sinks in
European Agricultural and Forestry
• January 2004 – July 2006
INSEA Model Structure
Common Data
• Soil
• Forests
• Climate
• Technologies
• Markets
• Model Results
Geographical
Analysis
Biophysical Models
• EPIC
• PICUS
Economic Models
• Hohenheim
• AROPAJ
• EFI
• EU-FASOM
• AGRIPOL
Available Data
•
•
•
•
Soils (MOSES, JRC)
Climate (MARS)
Forest Inventories (EFI)
Conventional Management (FADN,
EUROCARE, EUROSTAT, IIASA)
Problems: Confidentiality restrictions, Data
quality, Property rights
Soil Data
Source:
Luca Montanarella,
Joint Research Center,
Ispra, Italy
Analytical and Data Gaps
• Farm level impacts of alternative
agricultural and forest management
–
–
–
–
Costs
Inputs
Outputs
Environmental Impacts
Addressing the Gaps
• Engineering Analysis
• Link to other (European) projects
– GREENGRASS - Sources and Sinks of Greenhouse Gases from
managed European Grasslands and Mitigation Strategies
– CARBOINVENT - Multi-Source Inventory Methods For
Quantifying Carbon Stocks And Stock Changes In European
Forests
– MIDAIR - Greenhouse Gas Mitigation for Organic and
Conventional Dairy Production
– CARBO-AGE - Age-related dynamics of carbon exchange in
European forests
European Non-Food Agriculture
(ENFA) Project
• Starting in 2005
• Includes detailed biofuel analysis
• Environmental impact analysis consistent
with food options
• Integration in EUFASOM
• Analysis of fuel directives
Land use option
Non-food product options
Miscanthus, Switchgrass
Bioethanol, Pellets, Electricity,
Heat, Biomaterial
Red Canary Grass
Pellets and briquettes, Hot water
energy
Willow, Poplar, Eucalyptus,
Arundo
Energy
Hemp, Flax, Kenaf
Fibre products
Maize, Sugar beet, Potatoes
Bioethanol
Rape, Sunflower
Biodiesel
Forest Activities
Pulp, Paper, Timber, Fuel
Benefits for North American Sink
Analysis
• Refinement of European Data in global
models
• Parallel links, i.e. USFASOM and
EUFASOM
• Extrapolation of European Strategies
currently not modeled in US
European Forest and Agricultural
Sector Model (EU-FASOM)
• Model built from scratch
• Uses conceptual approach of (US)-FASOM
• Mathematical programming based
optimization model
• Partial equilibrium
Industry
Demands
Resource
Endowments
Production
factors
Climate
Data
Soil
Data
Management
Data
Simulation of
Environmental Field
Impacts with EPIC
Microeconomic, Community,
and Environmental Analysis
Traditional
Agricultural
Technologies
Non-Food
Technologies /
Engineering Models
Forest Inventory and
Management
Alternatives
Existing and
Potential
Agricultural or
Other Policies
Fully Integrated European NonFood Agriculture and Forest Model
EU-FASOM - Deviations from
USFASOM
•
•
•
•
Texture based land quality classifications
Rotations vs. individual crops
Dynamic soil carbon rates
Validation
Dynamic Soil Carbon Coefficients
• Soil-climate-regime and soil
management history determines soil
carbon coefficients
• Various strategies can be a source or
sink depending on the carbon level of
the associated land unit
Why changing coefficients?
1
Conventional Tillage
Zero Tillage
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
5
10
Time [Years]
15
20
Problem of Dimensionality
• Consider a forward looking decision model
with 20 alternative soil management
practices and 30 time periods
• The number of possible management
sequences equals 2030 ~ 1E+39
• Many models yield more combinations
(regions, crops, …)
Technical Implementation
•
•
•
•
•
•
•
•
•
•
•
Details available in paper available from author
X = land use variable
S = Soil carbon variable
t = time index
r = region index
i = soil type index
u = land use index
o = soil carbon class index
s = sequestration coefficient
c = carbon content coefficient
 = soil carbon class transistion probability
Soil Carbon Class Distribution
X
u
t,r,i,u,o
   r,i,u,o,o  Xt 1,r,i,u,o 
u,o
Calculation of probabilities is not shown but available in the paper
Soil Carbon Levels
St,r,i  St 1,r,i  St,r,i
Soil Carbon Change S t ,r ,i
 s
a)
r,i,u,o
 X t,r,i,u,o 
u,o
b)
c
u,o
r,i,u,o
 Xt,r,i,u,o     cr,i,u,o  Xt 1,r,i,u,o 
u,o
Average Deviation between Carbon Measures
8
Low Initial Carbon Status
Middle Initial Carbon Status
High Initial Carbon Status
7
6
5
4
3
2
1
0
0
100
200
300
400
500
600
700
Number of Soil Carbon Status Classes
800
900
1000
1100