global forestry and agriculture land use model
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Global Forestry and Agriculture Land Use Model. Suk-Won Choi (NCAR) Brent Sohngen (The Ohio State University) Steven Rose (EPRI) April 8, 2009 Forestry and Agriculture Greenhouse Gas Modeling Forum Shepherdstown, WV. - PowerPoint PPT PresentationTRANSCRIPT
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