Addressing Non-CO2 Gases & Sinks in GHG
Scenarios: Experience from Energy Modeling
Forum 21Francisco C. de la ChesnayeJohn P. Weyant

US Environmental Protection Agency

Stanford University

NIES - EMF Workshop on GHG Stabilization Scenarios,Tsukuba, 22-23 January 2004

Outlineºººº

Introduction to the EMF 21 Study
Data Development on Non-CO2 GHG and Sinks
Results part A: Non-CO2 GHGs
Areas for further workº Results part B: Recent EMF 21 Scenario Runs

EMF 21 Working Group Objectives
1) Conduct a new comprehensive, multi-gas policy
assessment to improve the understanding of the affects ofincluding non-CO2 GHGs (NCGGs) and sinks (terrestrialsequestration) into short- and long-term mitigation
policies. Answer the question: How important are NCGGs& Sinks in climate policies?.
2) Advance the state-of-the-art in integrated assessment /economic modeling3) Strengthen collaboration between NCGG and Sinks
experts and modeling teams
4) Publish results in a special issue of the Energy Journal

Economy, Technology, & Integrated Assessment Models (18)Asia / Australia
ABARE (Guy Jakeman & Brian Fisher) with GTEM
Energy Research Institute China (Jiang Kejun) with IPAC
IAE Japan (Atsushi Kurosawa) with GRAPE
Indian Institute of Management (P. Shukla) with SGM-India
National Institute for Environmental Studies, Japan (Junichi Fujino) with AIM
Europe
CEA - IDEI (Marc Vielle) with GEMINI-E3
CICERO - University of Oslo (H.A. Aaheim) with COMBAT
Cntr for European Econ Research-(C. Boehringer & A. Loschel) with EU PACE
Copenhagen Economics (Jesper Jensen) with the EDGE Model
Hamburg Univ. (Richard Tol) with FUNDIIASA (Shilpa Rao) with MESSAGE
Oldenburg University, Germany (Claudia Kemfert) with WIAGEM
RIVM (Detlef van Vuuren, Tom Kram, & Bas Eickhout) with IMAGE
UPMF (Patrick Criqui) & CIRAD (Daniel Deybe) with POLES/AGRIPOL
USArgonne Nat Lab (Don Hanson) & EPA (Skip Laitner) with AMIGAEPRI (Rich Richels) & Stanford Univ (Alan Manne) with MERGE
MIT (John Reilly) with EPPAPNNL-JGCRI (Jae Edmonds, Hugh Pitcher, & Steve Smith) with SGM & MiniCAM

Non-CO2 GHG Experts
Dina Kruger and Francisco de la Chesnaye, USEPAPaul Freund and John Gale, IEA Greenhouse Gas R&D Programme
Methane & N2O
Ann Gardiner, Judith Bates, AEA TechnologyCasey Delhotal, Dina Kruger, Elizabeth Scheehle, USEPA
Chris Hendriks, Niklas Hoehne, Ecofys
Fluorinated (HGWP) Gases
Jochen Harnish, Ecofys, Germany
Deborah Ottinger and Dave Godwin, USEPA
Sinks (Terrestrial Sequestration)
Bruce McCarl, Texas A&M
Ken Andrasko, USEPA & Jayant Sathaye, LBNL
Roger Sedjo, RFF & Brent Sohngen, Ohio State Univ
Ron Sands, PNNL-JGCRI

2000 Global Net GHG Emissions
CO2 LUCF
19%

CO2 Fuel/cement55%

CH416%

F-gases
1%

N2O
9%

Total 11,100
MMTCE

Non-CO2 GHG & sequestration datarequirements• Global, consistent non-CO2 GHG emission baselines for2000 and projections 2020 by region. And key emissions
drivers.
• Comparable marginal abatement curves– by region, by gas, and by sector– sensitivities to energy, material prices– in MMTCE w/ 100-yr GWP & gas specific units– Various discount and tax rates• Assessment of how marginal abatement curves vary overtime, from 2010 to 2100 by decade.

Global Non-CO2 GHG Emissions for 2000 in MMTCE
Sectors
Sub-sectors
Methane N2O
F-gases
Coal123
ENERGYNat Gas244459
Petroleum Syst17165917%
Stationary/MobileSAdipic & Nitric Acid Prd
60
INDUSTRY
182
7%

HFCs
PFCs
SF6
Substitution of ODS
Biomass
AGRICULTURE Soils
1610
Enteric Fermentation
61%
Manure Management
Rice
WASTE
Landfills
388
Wastewater
15%
2,639
TOTAL NCGG

26
2915
52
134
476
61
177
213
154
1,615
61%

51656
55

21
902
34%

1225%

Regional Methane Marginal Abatement Curves forEnergy& Waste
Sectors:
2010
MethaneMarginal
Abatement
Curves,20210$200
MexicoEU-15

RussiaUSA

China$/TCE (2000 USD)

$150

$100

$50

$00102030

4050-$50

MMTCE

60

708090100

Global Non-CO2 Marginal Abatement Curves forEnergy,& Waste Sectors: 2010
Global
Non-COIndustry
2 GHG Marginal Abatement Curves, 20210$200

N2O Industrial
HGWPs

Methane

Total

500

600$/TCE (2000 USD)

$150

$100

$50

$0
-100

200

300

400

-$50

MMTCE

700

$200

$150

$100

$50

$250

0
2
4

$500

$450

$400

$350

$300

6810

M M TCE/Ye ar12
14.F ertilizer Free Zone

Sub-optimal fertilizer applications, winterwheat: reduce application by 100kg/ha

Sub-optimal fertilizer applications, winterwheat: reduce application by 50kg/haPrecision Farming

Spreader Maintenance$/tCESoil Management
Marginal Abatement Cost Curve
CHINA
2010

$1618

EMF 21 Scenarios:
1) Modeler’s Reference Case
2) Long-term, Cost-minimizing
Case A - achieved through CO2 mitigation only, andCase B - achieved through multi-gas mitigation.
• Climate Change Target: Stabilize radiative forcing at 4.5 W/m2
relative to pre-Industrial times by 2150.
• Time frame: 2000 to 2100. From 2002 to 2012, KP is NOT inreference scenario.
• Emissions: Based on meeting climate target at lowest globalcost.

EMF 21 Scenarios:3) Combined Decadal Rate of Change and Long-Term
Cost-minimizing
Achieved through multi-gas mitigation.
• Climate Change Target: Hold global mean decadal rate oftemperature change from 2010 to 2100 at 0.2ºC. (starting in2030) and meet LT at 4.5 W/m2 by 2150.
• Time frame: 2000 to 2100. From 2002 to 2012, KP is NOT inreference scenario.
• Emissions: Based on meeting climate target at lowest global
cost.

4) CO2, Multigas + Sinks with selected price path(s)Global Anthropogenic Methane Emissions
6000

5000
A2-ASFAMIGA
CICEROEDGEGEMINI-E3
B2-MESSAGEMt CEq

4000

GRAPE
IMAGE
IPAC
MERGE
MESSAGE
MiniCAM
SGM
A1-AIM
B1-IMAGE

3000

2000

1000

02000

2025

2050

2075

2100

Global Anthropogenic Nitrous Oxide Emissions
3000

2500
AMIGA
CICEROEDGEGEMINI-E3GRAPE
IMAGE

Mt Ceq

2000

IPAC
MERGE
MESSAGE
MiniCAM
SGM
A1-AIM
A2-ASF
B1-IMAGE
B2-MESSAGE

1500

1000

500

02000

2025

2050

2075

2100

Emission Comparison for 2100

40000

35000

30000

Mt Ceq

25000

N2O total
CH4 total
Fossil Fuel CO2

20000

15000

10000

5000

0
IPAC

GRAPE

MiniCAM

MERGE

B1-IMAGE

A1-AIM

B2MESSAGE

A2-ASFFurther work on Non-CO2 GHGs
• Improve coverage of Non-CO2 sources, principallyagriculture• Evaluation of Non-CO2 GHG as offsets (agriculture &waste), including transactions costs• Estimate rates of technical change in mitigation
options, especially for the long-run type, 2100
analysis• Improve estimates of emissions factors for long-termemissions projections, i.e., across space and time
• Conduct uncertainty analysis for both emissions(activity drivers, emission factors) and mitigation
estimates

EMF 21 Sinks Subgroup• Conduct comparison of land use data across models, bothclimate economic and Ag/Forestry.
• Compare key drivers and dynamics in future use and expansionof land for agriculture, forestry, & biofuels.
• Evaluate paired prices in models, i.e., timber-carbon,agriculture-carbon, biofuels-carbon.
• How does all this affect competition for land use in thereference and mitigation scenarios ?
• How do we match up the sinks mitigation scenarios with theclimate scenarios ?
• How best to incorporate the results from the sinks models into
the climate economic models and how to handle the priceinteractions?

EMF 21 Sinks Subgroup• Models including sinks in reference and/or mitigation
cases, in some form:
AIMEPPAABARE
IMAGE 2.2
IPAC
POLES/Agripol
MERGE
MiniCAM• Forest and/or agric. sector models:
GCOMAPGTMFASOM-GHG (US)Comparison of Reference Cases:
3 LT, global models --GCOMAP, GTM, IMAGE• Land Area in forest varies:• across regions, and totals• GTM has managed vs. unmanaged, inaccessible forest• GTM has age classes for existing & new forest; allows
forest mgmt. option. GCOMAP only new forest.
• LUCF Activities included vary:• Assumptions about land -use change & C cycling vary:– Makes annual time-slice hard to compare across models– Thus: best to use cumulative C gain by a dateActions That Affect Carbon• Land Use– Reduce deforestation or increase afforestation– Change inaccessible margin.
• General Management of Forest Stands– Replant rather than naturally regenerate– Enhance stocking density: fertilize, chemical weed
suppression, thinning (remove dead or slow growing stockand replace with faster growing stock).
• Rotation ages
– Generally, longer rotations enhance carbon storage.
• Harvest Quantity (storage in markets)Sequestration Scenarios
Scenario 1♦$5 in 2010, rising by 5% per year

Scenario 2■$10 in 2010, rising by 5% per year

Scenario 3▲$10 in 2010, rising by 3% per year

Scenario 4■$20 in 2010, rising by 3% per year

Scenario 5

X$100 Constant Price

Scenario 6●$75 in 2010, rising by $5 per year through 2050

900

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Scenario 6

800$$ per ton (Mg) C

700
600
500
400
300
200
10002010

2030

2050

2070

2090

ScaleResults for 2100
Price

Cum. CLandTemp.

Trop.

$$ per ton

Pg

Million ha%%Scenario 5$100.00

66.7

593

38%

62%

Scenario 3$143.00

56.5

726

36%

64%

Scenario 6$275.00

150.1

1,004

40%

60%

Scenario 4$286.01

98.9

1,022

41%

59%

Scenario 1$403.65

93.2

1,109

44%

56%

Scenario 2$807.30

138.9

1,403

50%

50%

• Higher long term C prices, generally increase cumulative carbon.
• Higher C prices increase the importance of temperate forests.
From Sohngen & Sedjo, EMF21

Compare Scenarios 5 & 3[$100 Constant] VS [$10 + 3% ($143)]
Scenario 5: $100

160
140

Million Tons C per Year700

Scen. 5: $100 Constant
Scen. 3: $10 +3.0%$$ per ton

120

Temperate Forests
Tropical Forests

600
500
400
300
200
1000100

2010

80

2030

2050

2070

2090

Year60

Scenario 3: $10 + 3%

40200
Year

Million Tons C per year

1400
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

1200
Temperate Forests
1000

Tropical Forests

800
600
400
2000
2010

From Sohngen & Sedjo, EMF21

2030

2050
Year2070

2090

Faster Price Increases Delay Carbon
900
Scen. 2: $10 + 5.0%
Scen 1: $5 +5%
Scen 4: $20 + 3%Scen. 3: $10 +3%800$$ per ton C

700
600
500
400
300
200
100002040

From Sohngen & Sedjo, EMF21

6080100

Tg Carbon Per Year120

140160Scenario 5: World Forest Area Increases in 3 Models
World Total Forest Area - Scenario 5 ($100/tC)
5000

4500

4000

3500

M ha3000GCOMAP

GTM

2500

IMAGE2.2
2000

1500

1000

500

02000

2010

2050

2100

But, Forest Area and C Partitioning by Region Varies.
Scenario 5 Good Agreement: NAM, LAM, EUR.. Less Good: Rest of Regions.
Scenario5 ($100/tC) - Forest Area: India
100.090.0
80.0

GCOMAP70.0M ha

60.0
50.0

GCOMAPGTM
IMAGE2.2

IMAGE

40.0
30.0
20.0

GTM

10.00.0
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Preliminary Conclusions• Lower prices and slower growth in prices favors actions intropics and subtropics.
• Faster price growth delays carbon sequestration,
particularly in tropics and subtropics.
• Profile of annual sequestration heavily dependent on pricepath– Simple functional forms seem to work for slower price growthscenarios, but are less reliable for fast growth scenarios.

• Rotations matter at the beginning and at the end…
– Early strategy for lower cost species.
– Long run strategy for setting aside timberland from production.

• Management ~ 5-10%; Rotations ~ 7-8%.
– Most Important in temperate zones.
– Just looking at land use could miss 35% of carbon in temperate or
9% in tropics

Sinks Sub-group: Continuing Issues
•

•
•
•
•
•

How to report results in roughly comparable way?
– Report by activity? (eg, forestation only, biofuels only, etc.)– Report cumulative C stock change by date, since C cycling
Avoided deforestation is significant option: 2 models include
Land availability assumptions vary & drive some mitigation options.
– Eg, what historic & projected afforestation rate to use?
How to estimate market potential, vs. technical potential?
– Decision rules (IMAGE), econ. response, barriers analysis
Boundary bet. Sinks & other sectors: eg, biofuels
How best to incorporate the results from sinks models into climate
economic models? ISSUE: Sinks price paths different from economicmodels.

**Planned Landuse and Integrated Assessment Workshop in
Spring/Summer with ABARE & RIVM.**

Carbon Storage in Forests
950Baseline$5 scenario$20 scenario

900
Billion Metric Ton

Carbon Price =$244 per ton

Carbon Price =$61 per ton

850

800
Carbon Price =$0 per ton

750

700
2000

2020

2040

2060
Year

2080

2100

How Deforestation Handled Critical for Reference& Scenarios••••

Global deforestation: c. 17 million ha/yr 2000 (FAO)
IMAGE: DEFOR in baseline & scenarios, but not as mitigation option
GTM: DEFOR baseline & as mitigation option (not reported)
GCOMAP: DEFOR in baseline & avoided deforestation as mitigation:
Scenario
GCOMAP

Avoid
DEFOR
Cum. C,
2050% of TotalMitigation
2050

Avoid
DEFOR
Cum. C,2100% of Total
Mitigation
2100

Scen. 210.9 Pg

48%

40 Pg

41%

Scen. 528.8 Pg

55%

52 Pg

64%