LCFS Indirect Land Use Change Expert Workgroup Carbon Emission Factors Subworkgroup

LCFS Indirect Land Use Change Expert Workgroup

Carbon Emission Factors Subworkgroup

Presentation to California Air Resources BoardSacramento, CAOctober 14, 2010

Objective

• Provide a summary of our preliminary considerations of most recent GTAP analysis (Tyner et al. 2010) and the appropriateness of the assumption/changes – Biomass and soil carbon stock– Biomass and soil carbon EF

• assumption of 25 percent of forest biomass is indefinitely sequestered in wood products

2CARB ILUC EWG Emission Factors

Our Preliminary Considerations and Suggestions

#1. Evaluate the spatially-explicit Winrock database as a basis for estimating biomass C stock by AEZ

#2. Supplement with databases to improve the accuracy of certain regions/eco-system types (such as peatlands), or to include the consideration of certain factors (e.g. forest degradation and fire)

#3. Provide clear justification for the consideration of C stored in harvested wood product (HWP)


Our Preliminary Considerations and Suggestions -Cont’d

#4. Provide clear justification for the consideration of other non-land conversion emissions• Livestock emissions• Rice production emissions• Crop switching• Differences in on-farm energy and agrichemical use

#5. Conduct sensitivity analysis on the effects of non-Kyoto climate forcing gases and particles

#6. Perform uncertainty analysis, including uncertainty propagation and uncertainty importance analysis

CARB ILUC EWG Emission Factors 4

#1. GTAP, Woods Hole and Winrock Regions


GTAP Winrock* WHRC Winrock*United States 49 United States 49Canada 13 Canada 13Sub Saharan Africa 85 Africa 85EU 27 26

Europe

43

E Europe and Rest of Former Soviet Union 10Rest of European Countries 10Russia 88 Former Soviet Union 93Brazil 29

Latin America

124Central and Caribbean Americas 39S & Other Americas 56Mid East & N Africa 45 N Africa 45E Asia 4

Pac Developed15

Oceania 10Japan 1China & Hong Kong 31 China/India/Pakistan

67India 35Rest of SE Asia 172

S & SE Asia222

Rest of S Asia 6Malaysia & Indonesia 45

* Winrock data is divided into countries and for some a further breakdown by administrative unit. The quantity given is the number of regions for each given GTAP region (Note: Some values are different because the regions are not exactly the same)

GTAP 18 Regions + AEZs

Map by Sahoko Yui, UC-Davis

Use GIS data to estimate C stocks for GTAP region / AEZsCARB ILUC EWG Emission Factors 6

• Uses a range of geographically explicit data of varying quality– Some based on field data + GIS modeling– Others based on radar– Relies on best available

Winrock Biomass Database

7

• Compiled by OSU, funded by EPA Climate Analysis Branch• Data for all GTAP Regions, by AEZ, & Accessible vs. Inaccessible

Land; • Mix of different Inventory Source Data (FAO, USDA, others)• Partially Reviewed Model and Methodology• Example:

Alternative Approach to Forest Biomass

8

United States

Ecosystem/ AEZ

Sum: (accessible ha)

Sum: (inaccessible ha)

Sum: (accessible Million tC) (above-ground

Sum: (inaccessible Million tC)

Sohngen Accessible t/C per ha

Sohngen Inaccessible t/C per ha

Woods Hole - GTAP 2010 tC/ha

1 0 0 0 0 0.0 0.02 0 0 0 0 0.0 0.03 0 0 0 0 0.0 0.04 0 0 0 0 0.0 0.05 0 0 0 0 0.0 0.06 0 0 0 0 0.0 0.07 1,262,860 1,541,948 49 113 39.1 73.38 5,051,441 6,167,791 198 452 39.1 73.39 3,367,627 4,111,861 132 301 39.1 73.3

10 36,344,528 12,030,874 1,862 994 51.2 82.611 21,507,454 7,711,778 1,223 697 56.9 90.412 68,734,685 21,424,930 3,138 2,662 45.7 124.313 1,894,290 2,312,922 74 170 39.1 73.314 1,894,290 2,312,922 74 170 39.1 73.315 420,953 513,983 16 38 39.1 73.316 1,473,337 1,798,939 58 132 39.1 73.317 0 0 0 0 0.0 0.018 0 0 0 0 0.0 0.0

113

Accessible vs. Inaccessible Forest Area

• Methodology dependent on region

• For Europe: All Forest Area Deemed Accessible

• For USA: Accessibility is a function of timber demand / timber price

• For Tropics (China, Russia): Accessibility is based on proximity of forestland to roadways

Alternative Approach to Forest Biomass

• Need to ultimately create data set by AEZ & by Accessibility

• Can adopt OSU data since it is available or create new data set and compare to OSU

SohngenAccessible

(tC/ha)

SohngenInaccessible

(tC/ha)

WoodsHole

(tC/ha)

US 48 96 113

Brazil 102 103 102

Canada 30 30 74

India 29 70 139

Russia 17 46 65

Japan 46 108 75

Harmonized World Soil Database (HWSD)

• 0-100 cm soil C concentration can be calculated.• Last updated March 2009 though lots of missing data

especially for subsoil (30-100 cm), see following slides.

11Map by Sahoko Yui, UC-Davis

(t C/ha)

HWSD by AEZ


Map by Sahoko Yui, UC-Davis

(t C/ha)

HWSD Missing information

topsoil(0-30 cm)

subsoil(30-100 cm)


Forest EF

CARB GTAP 2010

EPA RFS2

National Inventory

Reportst CO2/ha (includes biomass and soil)

US 773 587 385 418Canada 710 458 251 295EU 109 557 299 220-366

Pasture EF

CARB GTAP 2010

EPA RFS2

National Inventory

Reportst CO2/ha (includes biomass and soil)

US 110 110 41 58Canada 199 171 59 39EU 130 199 77 73

Biomass & Soil C Recommendations

• Use best available spatially-explicit, published datasets to provide estimates by GTAP regions and AEZ combinations– Rely on largely datasets underlying Winrock biomass analysis with

improved data in a few locations

– Use GIS to estimate soil C for region / AEZ using global datasets; STATSGO for U.S.

– Use satellite-based land cover maps to pull out carbon estimates for grassland, forest, cropland etc

– Supplement with literature values for peatlands


#2. Include Peatland Emissions

• Peatland stores large amount of carbon stock and is a large sink for atmospheric carbon

• Tropical peatland is ~11% of global peatland area and 15% of global peat carbon pool– 75% is in Southeast Asia (65% in Indonesia and 10% in

Malaysia).

CARB ILUC EWG Emission Factors

Soil Carbon pool (Gt C) Soil C density (t C /ha)

Tropical forest

216 122 Global peat 598-618

Boreal/temperate peat 517-526 900-1390 Tropical peat 82-92 1400-2110 Souteast Asian peat 66-70 2200-3500

Source: based on IPCC WG1, Vitt et al (2000), and Page et al (2010)


Tropical Peatland Emission Factors

• Winrock – Peatlands cover 2-44% and 2-22% in some of the corresponding

administrative regions in Indonesia and Malaysia, respectively. – Assume cumulative emissions of 600 or 1600 t C/ha for 30 or 80

yrs, respectively (EF = 20t C/ha/yr assuming 80 cm drainage depth)

• Fargione et al. (2008) assumes 941 t C/ha (EF = 18.8 t C/ha/yr for 50 yrs)

• Both studies noted that these assumptions lack rigor, especially the duration of emissions


Peatland Drainage and Carbon Emissions

Schematic illustration of progressive subsidence of the peat surface in drained peatland, due to peat decomposition resulting in CO2 emission, as well as compaction.

CARB ILUC EWG Emission Factors 19Source: Hooijer et al. (2010): CO2 emissions from drained peat in Southeast Asia

EF Based on Land Conversion Types

Unit CO2 emission is a linear function of groundwater depth and % area drained in converted land•Greater drainage is needed for large cropland/palm plantation•Cropland/palm plantation keeps average water tables always below 0.7 m, but they are often as deep as 1.2m on average

CARB ILUC EWG Emission Factors 20Source: Hooijer et al. (2010): CO2 emissions from drained peat in Southeast Asia

Unit CO2 emissions (t CO2/ha/yr) Large croplands, including plantations 86 (73-100)

Mixed cropland / shrubland: small-scale agriculture 48 (27-73) Shrubland; recently cleared & burnt areas 15 (6-27)

Peatland EF Recommendations

• Use best available spatially-explicit, published literature data to provide estimates– Emission factor should be weighted by peatland (conversion) area by

GTAP regions and AEZ combinations, such information should come from Land Conversion Type subgroup

– To the extend possible, use EF by land use conversion type such as the table shown in the previous slide.

Source: Hooijer et al. (2010): CO2 emissions from drained peat in Southeast Asia

21

Peatland Emissions from Fossil Fuel Land Use

Though not directly related to biofuel land use, the topic may be relevant to Other Indirect Emissions subgroup.

• Boreal peatlands store 85% of global peat, contain ~ 6 times more carbon than tropical peatlands

• LU GHG emissions (t CO2e/ha) from oil sands surface mining development can be comparable or higher than biofuels, primarily due to peatland disturbance and CH4 emissions from tailings pond (see next slide)


Source: Yeh et al. (2010)

Peatland Emissions from Fossil Fuel Land Use

• Fossil fuel LU EF (t C/ha) is significantly lower than for biofuels due to higher fossil fuel yield.


Source: Yeh, Jordaan, Brandt et al. (2010) ES&T, in press

• When wood is taken out of forest systems, C is either stored in product-in-use and landfill; or emitted, combusted, or decomposed and recycle back to the atmosphere.

• Fraction of C stored in HWP depends on – How much (and what type of wood) is removed from the forest

system; – Types of end-use products; and – Lifetime of C remains in end-use products and landfills


#3. Long-term C Storage in Harvested Wood Products (HWPs)


Fraction of C Remaining in End Use by Enduse Category, US

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

Frac

tion

of ca

rbon

rem

aini

ng in

end

us

es

Years after production

Softwood lumberHardwood lumberSoftwood plywoodOriented strandboardNonstructural panelsMiscellaneous productsPaper

Source: U.S. Department of Energy, 2006. Guidelines for Voluntary Greenhouse Gas Reporting.

Disposition Pattern of Carbon to: Products in Use and Landfilled

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

North East NorthCentral

Southeast PacificNorthwest

PacificSouthwest

UnitedStates

RegionPe

rcen

t 30 Years

50 Years

100 Years

Source: Mueller et al (2010) manuscript

#3. Long-term C Storage in HWP in the US

• In the US, on average 23 percent of the aboveground biomass removed from forest stands has been sequestered after 30 years (Mueller et al.)(see previous slide)– In the US and Canada, only roughly 50% of harvested biomass is removed

from forest system – Need adjustment to account for belowground biomass and the fraction

left on the ground (eventually decompose and is incorporated into soil or released as CO2)

– This calculation does not take into account fossil emissions displaced by wood product for energy production and by displacing energy intensive materials such as concrete and steel.

• The C storage factor may be even smaller for developing countries due to lower removal and mill efficiency, faster decomposition, etc.


HWP EF Recommendations• Based on our examination, there is sufficient data to consider

C storage HWP in the US and other developed countries. • However, data of global HWP disposition by country and long-

term carbon storage factor by wood-type and end-product (preferably by region) is difficult to obtain.

• Short-term: include sensitivity analysis of C storage in HWP (30 yrs, 50 yrs, 100 yrs)

• Long-term: global calculation of HWP and include uncertainty in HWP disposition

• Potential consideration of energy displacement, construction material displacement, and price effects (demand reduction).


#4. Summary of Emission Factors of Other Indirect Effects

• There are other indirect impacts beyond changes in carbon stocks (biomass + Soil C).• Livestock emissions• Rice production emissions• Crop switching and changes in energy and chem. use

• The changes can be positive or negative.• For some pathways the other emissions are

significant (~25% of ILUC for soybean biodiesel).• See August subgroup presentation for details


Bushel Improvement per Pound N P K Applied

0.002.004.006.008.00

10.0012.0014.00

1980 1990 2000 2008

Year

Bush

el In

crea

se

CG N

SB N

CG P

SB PCG K

SB K

• Yields (bushels/acre) of two primary crops (corn and soybeans) grown in the U.S. have increased dramatically over the past approximately 30 years for the same amount of fertilizer applied

‒ Further studies are needed to determine whether the observed decrease in fertilizer use should be applied to reference case, or ILUC scenarios (e.g. increase yield with no changes in fertilizer use?)

Fertilizer Use and N2O Emissions

29

Source: Nelson et al.

Other Indirect EF Recommendations

• This whole issue of fertilizer vs. yield needs to be thought through as to how much is covered in the direct emission calculation and what the incremental emissions/unit of production might be for the indirect emissions.

• N2O emissions and N2 application rate have significant implications for GHG emissions. Better regional data would be useful in considering fertilizer use and N2O emissions in the reference case and ILUC scenarios.


#5. Non-Kyoto Climate Forcing Gases and Particles

Emission g/kg DM a GWP100 g CO2e/kg EF contrib.CO2 1640 1 b 1640 70%CO 65 3 c 195 8%CH4 2.4 25 b 60 3%

NMHC 3.1 8 c 25 1%NOX 3.1 -1 c -3 0%N2O 0.15 298 b 45 2%BC 0.8 680 d 544 23%OC 3.2 -50 e -160 -7%

Total EF 2345 100%a Delmas et al. 1995; b IPCC AR4; c Brakkee et al 2008; d Bond and Sun 2005; e Sanhueza 2009

Source: Courtesy of Richard Plevin 31

32

Savanna Burning Emission Factor

32

Non-Kyoto Climate Forcing Gases and Particles Recommendations

• Non-Kyoto climate forcing gases and particles can contribute to large uncertainties and significantly increase the estimated impacts of biofuel LUC emissions.

• The use of these Non-Kyoto gases would also require that the gasoline and diesel reference fuels would need to be re-done.

• Sensitivity analysis (short-term) and uncertainty analysis (long-term) should be performed to explicitly consider the effects of non-Kyoto climate forcing gases and particles


Recommendations on Treatment of Uncertainty

Short-term:• Incorporate range of uncertainties reported in the datasets/literature for

biomass and soil C. • Use sensitivity analysis or scenario analysis to illustrate the importance of

the consideration of certain parameters , such as HWP, other non-land conversion emissions, non-Kyoto climate forcing gases and particles.

• Use parametric analysis to estimate effect of specific time profile to examine emissions over time or scenario analysis to consider different approaches to handling time, (e.g., simple amortization, cumulative radiative forcing, discounting).

Long-term:• Include probability distributions for all estimates.• Propagate uncertainty using Monte Carlo simulation.• Use global SA to estimate uncertainty importance to identify which

parameters drive overall variance.


Remaining Important Issues • This subgroup has not had a chance to provide a

comprehensive review of the following issues:– EF related to changes in biomass C stock

• Fire emissions• Forest categories (disturbed vs undisturbed, maturity, degradation,

drought)– EF of soil C emissions after LU conversion– Dynamic modeling of LU emission changes (stock + flow) vs.

categorical modeling (percent changes in stock)– Albedo effect

• Some of these issues may be included in the final report. However, CARB should conduct independent studies to determine the effect of these issues (long-term).


Documents

LCFS Indirect Land Use Change Expert Workgroup Carbon Emission Factors Subworkgroup