The Role of Land Use in
Determining Greenhouse Gases
Mitigation Costs
Thomas Hertel (Purdue Univ.), Huey-Lin Lee (NIES),
Steven Rose (U.S. EPA), andBrent Sohngen (Ohio State Univ.)
GTAP Working Paper No. 36(Output from the research project sponsored by the US-EPA)

Motivation• Land is a significant source of GHG emissions– Deforestation: 1/3 of total emissions since 1850
– Land management: 75% of N2O, 50% of CH4• Previous studies suggest land-based mitigation is cost-effective– e.g., Sohngen and Mendelsohn (2003), Rao and Riahi (inpress), van Vuuren et al. (in press)• Analytical challenges for land modeling– Competition for land between land-based sectors– Land-based mitigation competition and net emissions effects– Land heterogeneity and dynamics– Lack of key consistent global data—land, emissions,mitigation costs• New global datasets—land, emissions, mitigation costs
Î Provide opportunities for improving our understanding of
the role of land in determining GHG mitigation costs.
2

Objective:
To analyze the impact of GHG mitigationon land use change in general equilibrium
frameworkOutline of this presentation:• Land, GHG emissions/sequestration data
• Land supply and demand and land-basedemissions modeling in GTAP
• Analysis set-up• Results• Conclusions
3

GTAP AEZ Land Use Data
• Our work builds on path-breaking workby Darwin et al. at ERS/USDA, by adding:– More refined definition of AEZs
– Climate dimension—tropical, temperate,boreal– Implementation at the 226-country level– Documented in Lee et al. (2005) andavailable on the GTAP website
4

Definition of AEZs in GTAP
• 18 AEZs = 6 LGPs x 3 climatic zones– 6 LGPs = 0–59, 60–119, 120–179,…., 300–365 days
– 3 climatic zones = boreal, temperate, tropical• Follows pioneering work by FAO and IIASA in
definition of an AEZ as– land with given “length of growing period” (LGP), as
determined by: temperature, precipitation, soil
condition and topography, combined withinformation from a water balance model andknowledge of physical requirements for growing
certain crop.

• Lands classified in same AEZ have homogeneousunits within the country—i.e., with similar
climate and soil conditions for crop growing.
5

Global Distribution of AEZs

6

Global Land Cover: distribution
1800E+61600E+61400E+61000E+6

Boreal
0800E+6

TropicalTemperate

0600E+60400E+6

LGP1

LGP3
LGP4
LGP5
LGP6

Pasture

LGP1
LGP2
LGP3
LGP4
LGP5
LGP6

Cropland

LGP1
LGP2

LGP4
LGP5
LGP6

LGP1
LGP2
LGP3

LGP5
LGP6

LGP1
LGP2
LGP3
LGP4

Savan./grassland Shrubland

LGP2
LGP3
LGP4
LGP5
LGP6

ForestLGP6000000E+0

LGP1
LGP2
LGP3
LGP4
LGP5

0200E+6

LGP1
LGP2
LGP3
LGP4
LGP5
LGP6

Area (ha)1200E+6

Built-up land

Other land7

Cropland Hectares by AEZ and crop
180,000
160,000
Paddy rice

140,000

Wheat

Paddy rice
Wheat
Cereals grain nec
Crops nec
Vegetables, fruit, nuts
Plant-based fibres
Oil seeds
Sugar cane, sugar beet

Cereal grain

100,000
80,00060,00040,00020,000TropicalTemperate

AEZ18

AEZ17

AEZ16

AEZ15

AEZ14

AEZ13

AEZ12

AEZ11

AEZ10

AEZ9

AEZ8

AEZ7

AEZ6

AEZ5

AEZ4

AEZ3

AEZ2

AEZ1

1000 hectares

120,000

Boreal8

Distribution of Crop Land Rents,
within AEZs
100%

80%
8 o cr7 p fb60%

6 c_b

Vegies/fruits: quick growing, higher value.

5 os d4 v_f

40%

3 gro
2 w ht1 pdr

20%

18 AEZ18

17 AEZ17

16 AEZ16

15 AEZ15

14 AEZ14

13 AEZ13

12 AEZ12

11 AEZ11

10 AEZ10

9 AEZ98 AEZ8

7 AEZ7

6 AEZ6

5 AEZ5

4 AEZ4

3 AEZ3

2 AEZ2

1 AEZ10%9

Non-CO2 emissions & forest sequest’n data
• New 2001 non-CO2 emissions data
– Corresponds to GTAP v6 data 2001 base year andcomplements GTAP 2001 CO2 emissions data
– Highly disaggregated – explicitly for more precisemapping to economic activity (output and input)• 226 countries• 21 non-CO2 GHG emissions categories (N2O,CH4, F-gases)– ~145 types of emissions with subcategory
disaggregation• Regional 2000 forest carbon stock data by AEZ,
management type, and tree age cohort• Soil carbon stock data and Other CO2 (non-fossil fuelcombustion) emissions data also available (but yetimplemented in the model)
10Sectoral distribution of non-CO2emissions, by region
GTAP-AEZ sectoral Non-CO2 emissions distribution by region

24 RWTrade
23 NTrdServices

2000

22 OthManufact
21 OthExtractn
20 Transport1800

19 Services
18 EnrgIntnsMnf

1600

1400

MtCeq

1200

China Paddy Rice
85% CH4
15% N2O

17 WoodProcessn(in Ceq)

12 GasDistribut

1000

16 OthFoodPrcsn
15 ProcessdRice
14 OtherMeatPrd
13 Rumint_Prods

11 Electricity
10 RefinedFuels

800

9 Gas
8 Oil

600

7 Coal6 Forest
400

5 NonRuminLivs
4 Ruminants

200

3 OtherCrops
2 OtherGrain

0
1 USA

2 CHN

3 ROW

1 PaddyRice

11The GTAP-AEZ model• Static global CGE• 3 Regions (for now): USA, China, ROW• 24 Sectors – 5 land-based sectors (3 crops, ruminantlivestock, forestry)• Key features:– Land in 6 AEZs: aggregated from the 18 AEZs
– 3-tier CET structure of AEZ-specific land supply• GHG emissions and sequestration modelling– Incorporate new detailed non-CO2 GHG emissions
data (N2O, CH4, F-gases) and forest carbon
sequestration data
– 3 classifications of emissions – output, intermediate
inputs, primary factor related emissions– Introduce emissions pricing– Calibrate mitigation responses
12Land supply in GTAP
• Standard, yet counterfactual– 1 type of ag. land, imperfect mobility acrossuses• Now: Agro-ecologically zoned land
– Heterogeneous in terms of rainfall,temperature, topography, soil type andmoisture, etc.
– length of growing period (LGP) varies– Suitability for growing of certain crops– Restricting land mobility across uses13

3-tier CET structure for
AEZ-specific land supply
Crop 1

Crop 2.....

Crop NETRAEL3 = -yi

CropsGrazing
ETRAEL2 = -0.5yi

ForestryAgriculture
ETREAL1 = -0.25yi

Land of AEZ iIndex i allows for
AEZ heterogeneity.
14Sector-specific CES structure for
AEZ land demand
Total land demanded by sector jESUBAEZ = 20••.....

AEZ 1AEZ 2AEZ N

Big enough ESUBAEZ ensures returns to AEZ lands to move closely together
A good approximate of an alternative specification where:– one prod. function for each AEZ in each activity– AEZ-specific comm. are perfect substitutes– Similar production function for AEZ-specific activity– Each AEZ-specific sector faces same input/factor prices.
15

The GTAP-AEZ model• Static global CGE• 3 Regions (for now): USA, China, ROW• 24 Sectors – 5 land-based sectors (3 crops, ruminant
livestock, forestry)• Production with intra- and inter-regional landheterogeneity– Land in 6 AEZs: aggregated from the 18 AEZs
– CET – 6 different AEZ land endowments• GHG emissions and sequestration modelling– Incorporate new detailed non-CO2 GHG emissions
data (N2O, CH4, F-gases) and forest carbon
sequestration data
– 3 classifications of emissions – output, intermediate
inputs, primary factor related emissions– Introduce emissions pricing– Calibrate mitigation responses
16Modeling emissions- 3 categories •Output – emissions treated as an
input to production, represents
alternative technologies,introduce new CES elasticity

1 PaddyRice– Follows Hyman et al. (2003)– e.g., coal, oil, energy intensive
manufacturing•Input – emissions proportional
to input use– Endowment – e.g, ruminant :capital stock (animal herd)– Intermediate input – e.g., grain
crop : fertilizer use

Total non-CO2 GHG emissions (MtCeq)
USA
ChinaROW
2.971
70.160137.364

Paddy Rice
100%
90%
80%
70%
60%

Intermediate input
Endowment
Output

50%
40%
30%
20%
10%
0%
USAChinaROW

17

Prod. structure: output related emissions incl.
Output-Emissions
composite

OutputOutput-related emissions

Intermediate Inputs

Value Added

SkilledLabor

Unskilled
Labor

NaturalResource

Land

Capital18

Emissions pricing• The economic impact of an emissions tax associatedwith input usage depends on the size of the tax AND
the emissions intensity (tC/$) of the input.
• The larger the emissions intensity, the greater theimpact of a given carbon tax on the sector’s input useand production.
Emission intensities (tC/$ of input)
InputFertilizer in crops production
Ruminant livestock capitalLand in paddy riceUSA
0.00610.00990.0040China0.0043
0.95620.012519

Calibrating mitigation responses• Non-CO2 mitigation– Engineering mitigation cost estimates for detailed
technologies (Delhotal and Kruger, in press; USEPA,2006)– Calibrate substitution elasticities with partial
equilibrium closure• Output emissions – ESUBMAC• Endowment emissions – ESUBT• Intermediate input emissions – ESUBVA• Forest sequestration supply– Calibrated to regional forest carbon supply curves
Sohngen (2005) – afforestation (extensification) andforest management (intensification)– Calibrated to forest carbon intensities due to presenceof unmanaged land in the base year data20

Calibrated ROW forest carbon sequestration curve via
extensification (20-year annual equivalent abatement)
200

ROW forest sector sequestration MAC: extensification, 20-year annual equivalent abatement

1752001 USD $ per tonne of C

150

125

100755025
GTAP-AEZ_extensification
DGTM_Land Storage

00100

200

300

400500600700800900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200
Abatement - MMTCE
21

Calibrated USA forest carbon sequestration curve via
intensification (20-year annual equivalent abatement)
200

USA forest sector sequestration MAC: intensification, 20-year annual equivalent abatement

1752001 USD $ per tonne of C

150

125100755025
GTAP-AEZ_intensification
DGTM_age/management storage

0-551525354555657585
Abatement - MMTCE

95105115

125135145

155

22

Analysis of mitigation responses
1. GE global competition: USA-only vs.
global carbon tax
2. GE inter-sector competition: USA-onlyv.s. global carbon tax

23Mitigation affects regional landcompetitionGE % change in land rents and land use by sector due to a $50/tonne carbon tax: USA only
AEZ1
AEZ2
AEZ3
AEZ4
AEZ5
AEZ6

Forest
253.5
254.3
236.9
267.5
295.2
320.2

Percentage change in land rents
PaddyRice OtherGrain Other Crops
-15.9
2
3.3
-15.3
1.9
3.2
-15.5
1.9
3.2
-15.5
2
3.2
-16.5
2
3.3
-20.2
2.3
3.6

Ruminants
5.1
5.1
5.1
5.1
5.2
5.4

Percentage change in land use, weighted by AEZ land rent share
Forest PaddyRice OtherGrain Other Crops Ruminants-0.023-0.211-0.05
0AEZ1 0.286
0.013-0.017-0.2260.068
0.162
AEZ2-0.029-0.131
0.098
0.053
AEZ3 0.009-0.026-0.431-0.003
0.034
AEZ4 0.427
2.134-0.412-1.215-0.391-0.084AEZ5-0.626-1.366-3.242-0.153
AEZ6 5.604

1.
2.

For a given use, similar land rent responses across AEZs
Changes in land rents reflect the net effect of mitigation costs and land competition (i.e.,changes in land prices and changes in acreage) – mitigation cost/subsidy dominates in riceand forestry, land competition dominates in other ag sectors
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Mitigation affects global competition- Regional
US only tax

China100

Global tax

100

G E MAC: 3 re gions
90

ROWUSA World

80

80

ROW

2001US D pe r tonne of C.

60
50
4060
50
40
3030

2020

US A

US A
CHN

10CHN

10RO W

ROW
World

0-20

0

1.
2.

World

70

70
2 00 1US D p e r ton ne o f C .

G E MAC: 3 re gions

USAChina

90

20406080a ba te m e nt - m mtce

100120140160World00200

4006008001000a ba te me nt - mm tce

1200

1400

1600

1800

USA only carbon tax – USA less competitive, international emissions
leakage (primarily deforestation)
Vs. global carbon tax – all regions with net reductions, global emissions
reductions large, ROW mitigation least expensive (primarily forest
carbon increases), USA mitigation most expensive.
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Mitigation affects global competition– sectors
US only tax

Svc Ag100

GE MAC: U.S .A, s e ctora l, tota l90

USAForest

G E MAC: U.S .A, s e ctora l, tota l70
2001US D pe r tonne of C.

702001US D pe r tonne of C.

Ind80

USAForest

90Ind80

Global tax

Svc Ag1006050406050403030
p_RE GE MIT_AGR

20

p_REG EMIT_AG R

20

p_REG E MIT_FRS

p_RE GE MIT_FRS
p_RE GE MIT_IND

10

p_REG EMIT_IND

10

p_REG EMIT_S VC

p_RE GE MIT_S VC

020406080
100a ba te m e nt - mm tce

120140160

p_REG EMIT0

p_RE GE MIT

0180

020406080a ba te m e nt - m m tce

100120140

Global vs. US tax – US mitigation responses diminished in all sectors.

26Conclusions• Biophysical and economic landcharacteristics create comparative
abatement advantages for land endowments
Î intra- and inter-regional reallocationof production, and thus land use change.
• International market structure influences
regional mitigation responses• International leakage is an importantcomponent of total GHG emissions
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Access to LU/GHG data and WP• Land use data:– https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=1900• Greenhouse gas emissions data:– CO2:• https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=1143– CH4, N2O, F-gases:• https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=1186• GTAP Working Paper No. 36:– https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=2230
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