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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.

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Global forestry and agriculture land use model

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

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


1 motivation
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
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
3. Model & Data

  • Maximize welfare in crop, livestock, and forestry sectors:

DF,DCr,DLv: Global Demand function

QF,QCr,QLv : Production function

CF,CCr,CLv : Cost function

X, K, L : Land, Capital, Labor input

H, V, m : Timber Harvest, Yield, Management

Indices: 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)

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


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


Livestock output

Value added nest

( = 0.2391)

Intermediate inputs

Capital

Land

Labor

Feed and land input nest

(ω = 0.5)

Land

Feed

Land input nest

(β= 20)

Land (AEZ 1)

Land (AEZ j)

Land (AEZ 18)

  • 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)



Forestry sector technology

assumed globally at

3% per decade

(Sohngen et al)

  • Source: study with 40 year global data and estimation (Ludena et al, 2006)




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


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


Results where is the deforestated land going
Results: Where is the deforestated land going? ha’s/yr initially, stabilizing by 2055)


Total carbon stock in inaccessible timber base case results
Total carbon stock in inaccessible timber: ha’s/yr initially, stabilizing by 2055)Base case results

820 mil tonC/year

90 mil tonC/year


6 sensitivity analysis
6. Sensitivity Analysis ha’s/yr initially, stabilizing by 2055)

  • 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
6.Sensitivity (continued) ha’s/yr initially, stabilizing by 2055)


6 sensitivity continued1
6.Sensitivity (continued) ha’s/yr initially, stabilizing by 2055)


7 further development
7. Further development ha’s/yr initially, stabilizing by 2055)

  • 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


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