Suk-Won Choi (NCAR)Brent Sohngen (The Ohio State University)Steven Rose (EPRI)

April 8, 2009Forestry and Agriculture Greenhouse Gas Modeling Forum

Shepherdstown, WV

Global Forestry and Agriculture Land Use Model

The authors would like to acknowledge Alla Golub and Tom Hertel for data and helpful comments.

1.Motivation• Most land use models do not account for dynamic forest

stock adjustments, e.g., – IMAGE (Alcamo et al,1998)– GTAP (Hertel et al,1997), FARM (Darwin et al, 1996) , Ianchovichina, et al.

(2001)

• Managing forest composition is important—vintages, species, management intensity—for timber and carbon production, as well as other environmental amenities

• In addition, need for– Explicit consideration of alternative land-uses – Examining intensive and extensive margins (i.e., changes in land

management as well as land-use)– Modeling access to unmanaged lands – Global market feedbacks and production and land-use re-allocations

2.Objectives• Develop dynamic optimization model of global

land use– Dynamics in forestry and competition with

agricultural uses– Technological change (Total Factor Productivity)– Agricultural expansion into “virgin” forests

• Develop baseline• Explore baseline sensitivity

– Alternative assumptions on technological change

3. Model & Data• Maximize welfare in crop, livestock, and forestry

sectors:

16

1

18

1

16

1

18

1

16

1

18

1

6

1

*

**

,,,,,

)()),,((

)()),,(()()),;((

r jLv

r jCr

r j kF

QLv

LvLvLvLvLvLv

QCr

CrCrCrCrCrCrF

QF

kjrakjrFF

t

CCCtdQLKXQD

tdQLKXQDtdQmvHQD

Max

DF,DCr,DLv : Global Demand functionQF,QCr,QLv : Production functionCF,CCr,CLv : Cost functionX, K, L : Land, Capital, Labor inputH, V, m : Timber Harvest, Yield, ManagementIndices: region (r), AEZ (j), timber type (k)

• Assumptions– Single global demand for each product.

• Assumes perfect substitution among regional agricultural outputs.• Quality and market adjusted substitution of timber (regions, species)

– Heterogeneous land types – agro-ecological zones

– Crop & Livestock production modeled with nested Constant Elasticity of Substitution (CES) production functions.

• Demand for land in AEZs derived from CES functions.

– Land Supply modeled via Constant Elasticity of Transformation (CET) functions across AEZ in each region.

– Total Factor Productivity (TFP) for crop and livestock sectors assumed to change over time, following Ludena et al (2006).

• Forestry sector : - Tracking forest vintages by species within AEZs.- Up to 6 timber types in each AEZ (total 401 managed timber types globally—species/management combos)

1 2 3 4 5 6 78

China

CanadaUS

0

1

2

3

4

5

6

7

8

9

Age (decade)

Mill ha

Timber Age distribution (Base year)

US:AEZ16, timber type 1

China:AEZ15, timber type 5

Canada:AEZ15, timber type 4

• Forestry sector (continued)- Tracking forest vintages by species within AEZs

0

0.5

1

1.5

2

Mill ha

1 2 3 4 5 6 7

2005

2015

2025

2035

Timber Age (Decade)

Year

Result example for China (AEZ 9, timber type 5)

2005201520252035

• Forestry sector (Continued) - Tracking timber management intensity over time

Timber yield by different management(US softwood example)

0

20

40

60

80

100

120

140

160

10 20 30 40 50 60 70

Age

Mill cu ft

High Management

Low Managemet

Livestock output

Intermediate inputs

Value added nest( = 0.2391)

Capital Land Labor

FeedLand

Land (AEZ 1) Land (AEZ j) Land (AEZ 18)

Feed and land input nest (ω = 0.5)

Land input nest (β= 20)

• Agriculture structure -Livestock example

4. Data: – Crop and Livestock Sector

Global economic data: GTAP (Dimaranan, 2006 ) Global output demand: AIDADS (Yu et al, 2004) Technology changes: Ludena et al (2006) Land Use: Ramankutty et al (2004)

– Forestry Sector Economic data and timber inventory: Sedjo & Lyon

(1990), Sohngen et al (1999), and Sohngen & Mendelsohn (2007)

•Global output demand: Yu et al (2004)

Demand Changes

0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10

Decade

Index

Forest

Crop

Livestock

• Technology Assumptions: Annual % Change in Total Factor Productivity

Region Crop Livestock

US 1.14 0.41 CHINA 1.45 3.1 BRAZIL 0.62 2.6 CANADA 1.14 0.41 RUSSIA 1.39 1.16 EU ANNEX I 1.14 0.41 EU NON ANNEX I 1.39 1.16 SOUTH ASIA 0.96 2.1 CENTRAL AMERICA 0.62 2.6 REST OF SOUTH AMERICA 0.62 2.6 SUB SAHARAN AFRICA 0.91 0.35 SOUTH EAST ASIA -0.66 2.7 OCEANIA 1.14 0.41 JAPAN 1.14 0.41 AFRICA MIDDLE EAST 0.45 -0.3 EAST ASIA -0.66 2.7 • Source: study with 40 year global data and estimation

(Ludena et al, 2006)

Forestry sector technologyassumed globally at 3% per decade(Sohngen et al)

5. Results: Crop output increases 65% over 80 years.

Crop Output (Decade1=100)

0

50

100

150

200

250

2005 2015 2025 2035 2045 2055 2065 2075

Year

%

US CHINA

BRAZIL ROW

TOTAL EU

- Example of tech changes:

Example of TFP changes: Livestock

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

2005 2015 2025 2035 2045 2055 2065 2075

Year

TFP ratio

US

CHINA

BRAZIL

SOUTH ASIA

AF MIDDLE EAST

Results : Livestock output increases 400% over 80 years, with largest increases in China and Brazil.

Livestock Output (Decade1=100)

0

500

1000

1500

2000

2500

2005 2015 2025 2035 2045 2055 2065 2075

Year

%

US CHINA

BRAZIL EU

ROW TOTAL

Results: Deforestation Continues in Tropics (8 million ha’s/yr initially, stabilizing by 2055)

Total Inaccessible forest

0

200

400

600

800

1000

1200

2005 2015 2025 2035 2045 2055 2065 2075

Year

Mill. haBRAZIL CENT AMERICAREST SOUTH AM SUB SAHARAN AFSOUTHEAST ASIA AF MIDDLE EASTTotal

Results: Where is the deforestated land going?

Brazil (AEZ 5)

0

20

40

60

80

100

120

140

160

2005 2015 2025 2035 2045 2055

Year

Mill ha

Forest

Crop

Livestock

Rest South America (AEZ 6)

0

50

100

150

200

250

2005 2015 2025 2035 2045 2055

Year

Mill ha

Forest

Crop

Livestock

•Total carbon stock in inaccessible timber:Base case results

0

20000

40000

60000

80000

100000

120000

2005 2015 2025 2035 2045 2055 2065 2075

Year

Million tCBRAZIL CENT AMERICAREST SOUTH AM SUB SAHARAN AFSOUTHEAST ASIA AF MIDDLE ETotal820 mil tonC/year

90 mil tonC/year

6. Sensitivity Analysis

• Alternative technological change assumptions– No Tech Crop: No technological change in crop

while forest and livestock same as baseline

– No Tech Livestock: No technological change in livestock while forest and crop same as baseline

6.Sensitivity (continued)

Total land use changes (2005-2065): Difference from the BaselineNo Tech Crop No Tech Livestockcrop livestock forestry crop livestock forestry

Global 4% 0% -2% 3% -5% 2%US -4% 2% -2% -3% 6% -10%CHINA -9% 2% 0% 26% -19% 43%ROW 2% -2% -1% -1% -4% -5%Tropical 14% 0% -4% 1% -5% 5%

Total output changes (2005-2065): Difference from the BaselineNo Tech Crop No Tech Livestockcrop livestock forestry crop livestock forestry

Global -74% -89% 0% -4% -392% 20%US -69% -10% 2% 6% -2% -29%CHINA -90% -37% 15% 29% -1342% 247%ROW -84% -151% 1% -17% -160% -11%Tropical -41% -15% -9% -6% -412% 15%

6.Sensitivity (continued)

Total Annual Carbon in Tropical forest (Differences from Basecase)

-400

-300

-200

-100

0

100

200

300

400

2005 2015 2025 2035 2045 2055 2065

Year

million tC

No Tech Crop

No Tech Livestock

7. Further development

• Test different assumptions on output demand, technology, and population changes

• Analysis of forest carbon sequestration supply potential

• Carbon policy effectiveness under different technological change assumptions

• Integrated Assessment Modeling Framework