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

Biophysical Models• EPIC• PICUS

Economic Models• Hohenheim • AROPAJ• EFI• EU-FASOM• AGRIPOL

Geographical Analysis

Available Data

• Soils (MOSES, JRC)• Climate (MARS)• Forest Inventories (EFI)• Conventional Management (FADN,

EUROCARE, EUROSTAT, IIASA)

Problems: Confidentiality restrictions, Data quality, Property rights

Soil DataSource: 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

Forest Inventory and Management Alternatives

Traditional Agricultural Technologies

Soil Data

Climate Data

Management Data

Simulation of Environmental Field Impacts with EPIC

Non-Food Technologies /

Engineering Models

Microeconomic, Community, and Environmental Analysis

Existing and Potential

Agricultural or Other Policies

Indu

stry

D

eman

ds

Res

ourc

e E

ndow

men

ts

Fully Integrated European Non-Food Agriculture and Forest Model

Prod

uctio

n fa

ctor

s

• Texture based land quality classifications

• Rotations vs. individual crops

• Dynamic soil carbon rates

• Validation

EU-FASOM - Deviations from USFASOM

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?

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20

Soil o

rganic

matt

er

Time [Years]

Conventional TillageZero Tillage

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

t ,r,i,u,o r,i,u,o,o t 1,r,i,u,ou u,o

X X

t ,r,i t 1,r,i t ,r,iS S S

Soil Carbon Levels

Calculation of probabilities is not shown but available in the paper

Soil Carbon Change

r,i,u,o t ,r,i,u,ou,o

s X

r,i,u,o t ,r,i,u,o r,i,u,o t 1,r,i,u,ou,o u,o

c X c X

irtS ,,

a)

b)

0

1

2

3

4

5

6

7

8

0 100 200 300 400 500 600 700 800 900 1000 1100

Su

m o

f S

qu

ared

Dev

iati

on

s

Number of Soil Carbon Status Classes

Average Deviation between Carbon Measures

Low Initial Carbon StatusMiddle Initial Carbon Status

High Initial Carbon Status