# Revisions to Land Conversion Emission

**Factors since the RFS2 Final Rule Nancy L. Harris**  

# report **December 2011 2** 

**ACKNOWLEDGEMENTS This**  **work was**  **carried out**  **in collaboration**  **with EPA’s**  **Office of**  **Transportation and**  **Air Quality**  **under the**  **direction of**  **Aaron Levy,**  **who provided**  **useful comments**  **and guidance during all stages of the work.**

**Cite as: Harris, N.L. 2011. Revisions to Land Conversion**  **Emission Factors since the RFS2 Final Rule. Report submitted to EPA.**  **ACKNOWLEDGEMENTS work**  **was carried**  **out in**  **collaboration with**  **EPA’s Office**  **of Transportation**  **and Air**  **Quality under**  **the direction**  **of Aaron**  **Levy, who**  **provided useful**  **comments and guidance during all stages of the work.** 

**Harris, N.L. 2011. Revisions to Land Conversion Emission Factors since the RFS2 Final Rule. Report**  **submitted to EPA.**

# TABLE OF CONTENTS **1** 1 **INTRODUCTION ................................**  1.1 EPA’s Renewable Fuel Standard 1.2 Summary of Changes since RF 2 **HARVESTED WOOD PRODUCTS ................................**  2.1 Summary
................................ 2.2 Initial Assessment of carbon storage in HWP 2.3 Updated Assessment of carbon 3 **OIL PALM CARBON STOCKS ................................**  3.1 Summary
................................ 3.2 Initial Assessment of carbon storage in oil palm plantations 3.3 Updated Assessment of carbon storage in oil palm plantations 4 **PEAT ASSUMPTIONS ................................**  4.1 Summary
................................ 4.2 Initial Assessment of Peat Emissions 4.3 Updated Assessment of Peat Emissions 4.3.1 Estimation of peat depth burned 4.3.2 Estimation of peat bulk density and carbon content 4.3.3 Methane emissions from fire 5 **FOREST CARBON STOCKS ................................**  5.1 Summary
................................ 5.2 Initial Assessment of Forest Carbon Stocks 5.3 Updated Assessment of Forest Carbon Stocks 6 **REFERENCES ................................**  **................................**

**................................ ................................**  **................................ ................................**  EPA’s Renewable Fuel Standard
................................ ................................ ................................ Summary of Changes since RF S2
................................ ................................ ................................

**................................ ................................**  **................................**

................................ ................................ ................................ ................................ Initial Assessment of carbon storage in HWP
................................ ................................ Updated Assessment of carbon storage in HWP
................................ ................................

**................................ ................................**  **................................**

................................ ................................ ................................ ................................ 3.2 Initial Assessment of carbon storage in oil palm plantations
................................ Updated Assessment of carbon storage in oil palm plantations
................................

**................................ ................................**  **................................ .........................** 

................................ ................................ ................................ ................................ Initial Assessment of Peat Emissions
................................ ................................ .............................. Updated Assessment of Peat Emissions
................................ ................................ 4.3.1 Estimation of peat depth burned
................................ ................... **Error! Bookmark not defined.** 4.3.2 Estimation of peat bulk density and carbon content
..................... **Error! Bookmark not defined.** 4.3.3 Methane emissions from fire
................................ ......................... **Error! Bookmark not defined.**

**................................ ................................**  **................................**

................................ ................................ ................................ ................................ Initial Assessment of Forest Carbon Stocks
................................ ................................ Updated Assessment of Forest Carbon Stocks
................................ ................................

**................................ ................................**  **................................ 3** 

**................................ ..**  **4**

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................................ ......................... 14 **Error! Bookmark not defined.** 14 **Error! Bookmark not defined.** 15 **Error! Bookmark not defined.** 15

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## 1.1 **EPA’s Renewable Fuel Standard** The Renewable Fuel Standard (RFS) program was created under the Energy Policy Act (EPAct) of 2005 and established the first renewable changes to the RFS program as required by the Energy Indepe and lays the foundation for achieving significant reductions of greenhouse gas emissions from the use of renewable fuels, for reducing imported petroleum, and encouraging the development and expansion of the U.S. ren ewable fuels sector. Biofuel policies use the term “land use change effect” to refer to the sum of direct and indirect land use impacts of biofuel production. Greenhouse gas (GHG) emissions that occur when converting land are often considered the main impact, but other impacts include those on biodiversity, water availability, water and soil quality, and social impacts. The US Environmental Protection Agency (US EPA) commissioned Winrock International to estimate greenhouse gas ( GHG ) emission and rever indirect land use change analysis for various fuel options. EPA is currently in the process of updating the assumption s and data used in its analysis. T the final RFS2 rule was released
. 

## 1.2 **Summary of Changes since RFS2** After receiving several comments related to various aspects of our emission factor analysis stakeholders and reviewers, we explored assumptions: 1. **Harvested Wood**  **Products** harvested wood products after 30 years for conclude d that the impact on emission factors continues to be 

# 1 **INTRODUCTION**

**EPA’s Renewable Fuel Standard** Renewable Fuel Standard (RFS) program was created under the Energy Policy Act (EPAct) of 2005 and established the first renewable fuel volume mandate in the United States. The RFS2 changes to the RFS program as required by the Energy Indepe ndence and Security Act of 2007 (EISA), lays the foundation for achieving significant reductions of greenhouse gas emissions from the use of renewable fuels, for reducing imported petroleum, and encouraging the development and expansion of
Biofuel policies use the term “land use change effect” to refer to the sum of direct and indirect land use impacts of biofuel production. Greenhouse gas (GHG) emissions that occur when converting land are impact, but other impacts include those on biodiversity, water availability, water and soil quality, and social impacts. The US Environmental Protection Agency (US EPA) commissioned Winrock International to estimate

emission and rever sion factors for various land use transitions as part of its indirect land use change analysis for various fuel options. EPA is currently in the process of updating the

s and data used in its analysis. T his report summarizes the main changes to t 

## Summary of Changes since RFS2

comments related to various aspects of our emission factor analysis explored the following topics as part of a re
-assessment of our
**Harvested Wood**  **Products**
. We tested an alternate method for estimating harvested wood products after 30 years for the countries of Indonesia and Malaysia, and

that the impact on emission factors continues to be very small
. 

# INTRODUCTION

Renewable Fuel Standard (RFS) program was created under the Energy Policy Act (EPAct) of 2005 RFS2 final rule made ndence and Security Act of 2007 (EISA), lays the foundation for achieving significant reductions of greenhouse gas emissions from the use of renewable fuels, for reducing imported petroleum, and encouraging the development and expansion of
Biofuel policies use the term “land use change effect” to refer to the sum of direct and indirect land use impacts of biofuel production. Greenhouse gas (GHG) emissions that occur when converting land are impact, but other impacts include those on biodiversity, water availability,
The US Environmental Protection Agency (US EPA) commissioned Winrock International to estimate sion factors for various land use transitions as part of its indirect land use change analysis for various fuel options. EPA is currently in the process of updating the his report summarizes the main changes to t he analysis since

comments related to various aspects of our emission factor analysis from assessment of our
method for estimating carbon stored in the countries of Indonesia and Malaysia, and**2. Revised**  **carbon stock**  **estimates** approach for estimating carbon stocks on oil palm plantations was changed from assuming one year of growt h to using a time 3. **Revised estimates**  **of GHG**  **emissions on**  **peat soils**  from peat drainage based on new literature. 4. **Revised c arbon stock estimates for forests thr**  for Latin America and Africa were estimated in the final RFS2 rule using draft values from Saatchi et al. (2011). This work has now been published and covers continental regions: Latin America, Africa and South and Southeast Asi carbon stock values were included in our revisions. were also updated based on developed by Gallaun et al. (2010 **Revised carbon**  **stock estimates**  **on oil**  **palm plantations**  **in Indonesia**  **and Malaysia**  approach for estimating carbon stocks on oil palm plantations was changed from assuming one

h to using a time
-averaged approach. **GHG emissions**  **on peat**  **soils**
. We updated our estimates of emissions from peat drainage based on new literature.

**arbon stock estimates for forests thr oughout the tropics**  **.** Forest carbon stocks for Latin America and Africa were estimated in the final RFS2 rule using draft values from Saatchi et al. (2011). This work has now been published and covers

Latin America, Africa and South and Southeast Asi a. Final published carbon stock values were included in our revisions. Forest carbon stocks for the EU were also updated based on EU
-wide maps of above
- ground biomass in forests

Gallaun et al. (2010 ). **5**

**and Malaysia**  **.** The approach for estimating carbon stocks on oil palm plantations was changed from assuming one We updated our estimates of emissions

Forest carbon stocks for Latin America and Africa were estimated in the final RFS2 rule using draft values from Saatchi et al. (2011). This work has now been published and covers three a. Final published Forest carbon stocks for the EU ground biomass in forests**6**



## 2.1 **Summary** After re
-assessing our analysis of carbon stored in harvested wood products after 30 years, we conclude that the impact of harvested wood products on 

## 2.2 **Initial Assessment of carbon** For the final RFS2 rule, we addressed the potential significance of the harvested wood product pool and concluded that for most regions of the world, the amount of carbon stored in wood products long was insignificant, especially if considering a ti EPA’s lifecycle analysis). Therefore, carbon storage in harvested wood products was not incorporated into emission factors. We originally reached this conclusion based on data presente FAO forest products database (FAO 1995) source -sink balance. This database, spanning more than 30 yr (1961 recognized source of forest products data. Based on the Winjum et al. methodology, t (>5 yr) wood products was calculated by dividing the reported carbon in net production of industrial roundwood by the reported to tal carbon in commodity uses sawnwood, other industrial roundwood, woodbase panels and paper and paperboard. Of the eight countries highlighted by Winjum et al., this proportion ranged from 13% for Finland to 67% fo Across the developing world, the proportion in long Due to initial time constraints associated with the finalization of RFS2, we analyzed only the case study of Indonesia to assess the magnitude of carbon stored in H had the highest reported timber extraction rate (50 m would have the highest amount of carbon potentially stored in wood products that would influence the emissio n factor. Based on data reported in Winjum et al. (1998), 25% of extracted carbon ends up in long
- lived ( ≥5 yrs) wood products. Using an assumed extraction rate of 50 m



# 2

assessing our analysis of carbon stored in harvested wood products after 30 years, we conclude

harvested wood products on emission factors continues to be very small 

## Initial Assessment of carbon **storage in HWP** For the final RFS2 rule, we addressed the potential significance of the harvested wood product pool and concluded that for most regions of the world, the amount of carbon stored in wood products long considering a ti meframe of 30 years (cor responding to the timeframe of EPA’s lifecycle analysis). Therefore, carbon storage in harvested wood products was not incorporated

reached this conclusion based on data presente d in Winjum et al. (1998), who used the FAO forest products database (FAO 1995) to estimate the influence of wood harvesting on a nation’s C sink balance. This database, spanning more than 30 yr (1961
- 1993), is an internationally

f forest products data. Based on the Winjum et al. methodology, t he proportion of extracted timber that ends up in long (>5 yr) wood products was calculated by dividing the reported carbon in net production of industrial tal carbon in commodity uses ≥5 yr for each of four product classes: sawnwood, other industrial roundwood, woodbase panels and paper and paperboard. Of the eight countries highlighted by Winjum et al., this proportion ranged from 13% for Finland to 67% fo Across the developing world, the proportion in long
- lived wood products was 53%. Due to initial time constraints associated with the finalization of RFS2, we analyzed only the case study of Indonesia to assess the magnitude of carbon stored in H WP. We chose Indonesia because this country had the highest reported timber extraction rate (50 m 3 ha - 1 ) of the countries we reviewed and therefore would have the highest amount of carbon potentially stored in wood products that would influence the n factor. Based on data reported in Winjum et al. (1998), 25% of extracted carbon ends up in ≥5 yrs) wood products. Using an assumed extraction rate of 50 m 3 ha - 1 for Indonesia (13.75 t 

# 2 **HARVESTED WOOD PRODU**

assessing our analysis of carbon stored in harvested wood products after 30 years, we conclude very small
.
For the final RFS2 rule, we addressed the potential significance of the harvested wood product pool and concluded that for most regions of the world, the amount of carbon stored in wood products long
-term responding to the timeframe of EPA’s lifecycle analysis). Therefore, carbon storage in harvested wood products was not incorporated d in Winjum et al. (1998), who used the to estimate the influence of wood harvesting on a nation’s C 1993), is an internationally he proportion of extracted timber that ends up in long
- lived (>5 yr) wood products was calculated by dividing the reported carbon in net production of industrial ≥5 yr for each of four product classes: sawnwood, other industrial roundwood, woodbase panels and paper and paperboard. Of the eight countries highlighted by Winjum et al., this proportion ranged from 13% for Finland to 67% fo r India. Due to initial time constraints associated with the finalization of RFS2, we analyzed only the case study of WP. We chose Indonesia because this country ) of the countries we reviewed and therefore would have the highest amount of carbon potentially stored in wood products that would influence the n factor. Based on data reported in Winjum et al. (1998), 25% of extracted carbon ends up in for Indonesia (13.75 t 

# HARVESTED WOOD PRODU **CTS**C ha - 1 ), and assuming 25% of this is stored in HWP, the e

reduced by 11 t CO 2 ha - 1, or approximately 1 Furthermore, based on Winjum et al.’s analysis, we estimated the additional emissions that emanate from the oxidation (i.e. burnin g and decay) of year 30 (i.e., annual retirement rate of wood products) additional oxidized fraction , the Indonesia value of carbon stored in wood products decreased further to 7 t CO 2 ha - 1

. Our initial approach used only the compiled summary tables for individual countries reported in Winjum et al. (1998); we did not attempt to re how the values in the summary tables were calculated. 

## 2.3 **Updated Assessment of carbon storage in HWP** Our updated analysis uses basically analysis to be more comprehensive by including HW the EPA analysis for which we had ready access to study of Indonesia). Also, the Winjum et al. (1998) methodology and equations are explained in detail i the approved Verified Carbon Standard (VCS) methodology module VMD0005: Estimation of carbon stocks in the long
-term wood products pool (CP in our updated assessment to provide better documentation on The carbon in each wood product class paper and paperboard) can end up in one of four categories: (1) lost immediately to wood waste during production (2) emitted to the atmosphere wit (3) emitted to the atmosphere between 5 and 30 years after production; or (4) long
-term wood products pool (stock remaining i Carbon in long
-term wood products after 30 years (timeframe [C in HWP] = [total C extracted]
– [wood waste] **Timber extraction rates** (volume extracted; m so urces, ranging from in
-country consultant data, neighboring countries when FAO data failed to give realistic estimates. volumes of each wood product class were obtained from FAO available at the time of analysis) and converted to carbon using biomass conversion factors from Winjum et al. (1998) (Table 1) and a carbon conversion factor of 0.5. The paper and paperboard class is repor by FAO in units of biomass, therefore no biomass conversion factor was needed. After all product classes

1 http://www.v
-c -s.org/methodologies/find ), and assuming 25% of this is stored in HWP, the e mission factor for forest conversion would be

, or approximately 1
- 2% of the emission factor value. Furthermore, based on Winjum et al.’s analysis, we estimated the additional emissions that emanate g and decay) of in
- use wood commodities each year between year 5 and year 30 (i.e., annual retirement rate of wood products)
. After accounting for emissions , the Indonesia value of carbon stored in wood products after Our initial approach used only the compiled summary tables for individual countries reported in Winjum et al. (1998); we did not attempt to re
-create their methodology or re
-do any of the steps involved in the values in the summary tables were calculated. 

## Updated Assessment of carbon storage in HWP

basically the same approach as Winjum et al. , but we were able to revise our analysis to be more comprehensive by including HW P estimates for 95 of the 135 countries included in

which we had ready access to timber extraction rates (rather than just the case study of Indonesia). Also, the Winjum et al. (1998) methodology and equations are explained in detail i

Carbon Standard (VCS) methodology module VMD0005: Estimation of carbon term wood products pool (CP
-W), Sectoral Scope 14 1 . We followed this methodology in our updated assessment to provide better documentation on how our estimates were derived. product class (sawnwood, woodbase panels, other industrial roundwood, can end up in one of four categories: ood waste during production ; atmosphere wit hin five years of production ; emitted to the atmosphere between 5 and 30 years after production; or term wood products pool (stock remaining i n wood products after 30 years ) term wood products after 30 years (timeframe of EPA analysis) is therefore estimated

[wood waste]
- [short
- lived products]
– [additional oxidized fraction] extracted; m 3 ha - 1 ) were estimated based on a number of different data country consultant data, FAO data tables, or assumed to be the same as other neighboring countries when FAO data failed to give realistic estimates. In most cases, p

product class were obtained from FAO databases for the year 2008 and converted to carbon using biomass conversion factors from Winjum and a carbon conversion factor of 0.5. The paper and paperboard class is repor O in units of biomass, therefore no biomass conversion factor was needed. After all product classes

s.org/methodologies/find **7**

mission factor for forest conversion would be Furthermore, based on Winjum et al.’s analysis, we estimated the additional emissions that emanate use wood commodities each year between year 5 and from this after 30 years Our initial approach used only the compiled summary tables for individual countries reported in Winjum do any of the steps involved in

, but we were able to revise our P estimates for 95 of the 135 countries included in timber extraction rates (rather than just the case study of Indonesia). Also, the Winjum et al. (1998) methodology and equations are explained in detail i n

Carbon Standard (VCS) methodology module VMD0005: Estimation of carbon . We followed this methodology how our estimates were derived. (sawnwood, woodbase panels, other industrial roundwood,

of EPA analysis) is therefore estimated as

[additional oxidized fraction]
. ) were estimated based on a number of different data

, or assumed to be the same as other In most cases, p roduction databases for the year 2008 (latest year and converted to carbon using biomass conversion factors from Winjum and a carbon conversion factor of 0.5. The paper and paperboard class is repor ted O in units of biomass, therefore no biomass conversion factor was needed. After all product classes**8**

were represented in a consistent unit (carbon), proportions of the total class could be derived for eac h country **Table 1**  **. Biomass conversion factors (Mg m roundwood and commodity biomass from volumes reported in m** 

The fraction of extracted biomass tha constant 19% for developed countries and 24% for developing countries (Winjum et al. 1998). The fraction of wood products with estimated by product class (Winjum et al. 1998):  Sawnwood  Woodbase panels  Other industrial roundwood  Paper and Paperboard The additional oxidized fraction of wood products that will be emitted to the atmosphere between **30 years after production**  is estimated by product class harvested (Winjum et al. 1998):

 Sawnwood  Woodbase panels  Other industrial roundwood  Paper and Paperboard After accounting for wood waste, carbon in short fraction, the remainder is assumed to be the carbon stored in harvested wood products after 30 years. Using this m ethodology for Indonesia, with an assumed extraction rate of 50 m carbon in HWP was estimated as 1.93 t C ha were represented in a consistent unit (carbon), proportions of the total extracted carbon in each product h country
. **. Biomass conversion factors (Mg m -**  **3 green volume) by region and species group used to compute**  **roundwood and commodity biomass from volumes reported in m 3**  **by region. From Winjum et al. (1998).**

The fraction of extracted biomass tha t ends up as **wood waste** during production is estimated as a constant 19% for developed countries and 24% for developing countries (Winjum et al. 1998). The fraction of wood products with **short -** **term (<5 yr) uses** after which they are retired and oxidized estimated by product class (Winjum et al. 1998): 0.2 0.1 0.3 0.4 fraction of wood products that will be emitted to the atmosphere between is estimated by product class according to the biome where the wood was

Boreal Temperate Tropical 0.12 0.22 0.4 0.22 0.40 0.64 0.40 0.64 0.88 0.12 0.22 0.93 After accounting for wood waste, carbon in short
- lived products and carbon in the additional oxidized fraction, the remainder is assumed to be the carbon stored in harvested wood products after 30 years.

ethodology for Indonesia, with an assumed extraction rate of 50 m 3 ha - 1 as before, the carbon in HWP was estimated as 1.93 t C ha - 1, or 7 t CO 2 ha - 1 . This is the same value as was estimated carbon in each product
**green volume) by region and species group used to compute by region. From Winjum et al. (1998).** 

during production is estimated as a constant 19% for developed countries and 24% for developing countries (Winjum et al. 1998). after which they are retired and oxidized is

fraction of wood products that will be emitted to the atmosphere between **5 and** according to the biome where the wood was

lived products and carbon in the additional oxidized fraction, the remainder is assumed to be the carbon stored in harvested wood products after 30 years. as before, the . This is the same value as was estimatedpreviously because the approach is the same (but now explained in

calculated carbon stored in HWP for other countries using this approach. The maximum amount of carbon stored in HWP was 11 t CO 2 CO 2 ha - 1 for more than 70% of countries analyzed ( for most tropical developing countries are so low. **Table 2**  **. Reductions in country -** **level emission factors after accounting for carbon stored in harvested wo products longer than 30 years.**  **Reduction in Emission Factor (t CO**  **2 ha**  **- 1** **)** >10 5 – 10 4 – 5 3 – 4 2 – 3 1 – 2
< 1 **Total Countries Analyzed** previously because the approach is the same (but now explained in more detail). However, we also calculated carbon stored in HWP for other countries using this approach. The maximum amount of ha - 1 for Malaysia, and the emission factor changed by less than 1 t f countries analyzed ( Table 2 ), mainly because per
- he ctare extraction rates tropical developing countries are so low.

**level emission factors after accounting for carbon stored in harvested wo # Countries**  **Countries** 1 Malaysia 4 India, China, Indonesia, South Africa 1 Guatemala 3 Brazil, Belize, Nepal 8 Ghana, Cote d’Ivoire, Ecuador, Russia,

Bangladesh, Honduras, Costa Rica, Gabon 10 Angola, Haiti, Guyana, Madagascar, Myanmar,

Egypt, Philippines, Cameroon, Republic of Congo, Pakistan 68 **95 9** 

more detail). However, we also calculated carbon stored in HWP for other countries using this approach. The maximum amount of for Malaysia, and the emission factor changed by less than 1 t ctare extraction rates **level emission factors after accounting for carbon stored in harvested wo od** 

India, China, Indonesia, South Africa

Ecuador, Russia, Bangladesh, Honduras, Costa Rica, Gabon Angola, Haiti, Guyana, Madagascar, Myanmar, Egypt, Philippines, Cameroon, Republic of**1**

**0**



## 3.1 **Summary** We increased our estimate of carbon stocks on oil palm plantations in Indonesia and Malaysia from 15 t CO 2 ha - 1 to 128 t CO 2 ha - 1

. This resulted from plantations from only one year of carbon accumulation 

## 3.2 Initial Assessment of carbon storage in In the final RFS2 rule, conversion to perennial cropland was assumed to be sugarcane, except in the case s of I ndonesia and Malaysia in which case the in oil palm plantations were estimated as the stocks present after one year of growth at based on Table 5.3 of the 2006 IPCC Guidelines for AFOLU, wh plantations as 136 t biomass per hectare, or carbon stock value was divided by an assumed 15 year growth period to derive a linear growth rate of 4 t C ha - 1 yr- 1 (15 t CO 2 ha - 1 yr- 1). 

## 3.3 **Updated Assessment of carbon storage in oil palm plantations** Several comments were received on our estimates provide addi tional documentation to justi palm, a request to cha nge to a time plantations, and a suggestion to include the soil organic carbon pool in our estimates of palm oil conversion based on the arg ument that carbon than the baseline land use ( Imperata 

### 3.3.1 **Further Support for IPCC Carbon Stock Estimate for Oil Palm**  We compiled additional information to subs plantations given in the 2006 IPCC Guidelines were compiled by Niklas and Enquist (2004).



# 3

our estimate of carbon stocks on oil palm plantations in Indonesia and Malaysia from 15 t

This resulted from changing our estimate of carbon stocks on oil palm only one year of carbon accumulation to using a time
-averaged approach 

## 3.2 Initial Assessment of carbon storage in **oil palm plantations** inal RFS2 rule, conversion to perennial cropland was assumed to be sugarcane, except in the ndonesia and Malaysia in which case the assumption was conversion to oil palm.

were estimated as the stocks present after one year of growth at based on Table 5.3 of the 2006 IPCC Guidelines for AFOLU, wh ich gives biomass stocks on oil palm

biomass per hectare, or 68 t C ha - 1 assuming 1 t biomass = 0.5 t carbon carbon stock value was divided by an assumed 15 year growth period to derive a linear growth rate of 4 t 

## Updated Assessment of carbon storage in oil palm plantations Several comments were received on our estimates of oil palm carbon stocks, including a request to to justi fy the use of the IPCC de fault carbon stock estimate for oil nge to a time
-averaged approach to estimating carbon stocks on oil palm and a suggestion to include the soil organic carbon pool in our estimates of palm oil

ument that palm oil plantations sequester higher amounts of soil organic Imperata grassland or primary/secondary forest). 

### IPCC Carbon Stock Estimate for Oil Palm information to substantiate the default estimate of carbon stocks in oil palm plantations given in the 2006 IPCC Guidelines . Biomass values for seeded plants throughout the world were compiled by Niklas and Enquist (2004). For oil palm ( Elaeis guineensis ), data were cited from



# 3 **OIL PALM CARBON STOCKS** 

our estimate of carbon stocks on oil palm plantations in Indonesia and Malaysia from 15 t tocks on oil palm averaged approach
. 

## oil palm plantations inal RFS2 rule, conversion to perennial cropland was assumed to be sugarcane, except in the oil palm. Carbon stocks were estimated as the stocks present after one year of growth at 15 t CO 2 ha - 1 ich gives biomass stocks on oil palm assuming 1 t biomass = 0.5 t carbon . The total carbon stock value was divided by an assumed 15 year growth period to derive a linear growth rate of 4 t 

## Updated Assessment of carbon storage in oil palm plantations of oil palm carbon stocks, including a request to

fault carbon stock estimate for oil averaged approach to estimating carbon stocks on oil palm and a suggestion to include the soil organic carbon pool in our estimates of palm oil palm oil plantations sequester higher amounts of soil organic

tantiate the default estimate of carbon stocks in oil palm . Biomass values for seeded plants throughout the world data were cited from 

# STOCKSCannell (1982) but reported originally in Ng et al. (1968). Aboveground biomass values were measured in

high
-yielding, fertilized stands of palm oil plantations of various ages in Malaysia. Plantations were growing on marine clay with fine sandy loams. One or two palms were sampled from stands of various ages and stand values were obtained by multiplying mean palm values by the numbers of palms per hectare (148 stems/ha). Data from this study are summarized in Figure 1. The data show t stocks of oil palm plantations increase through year 15, which was the oldest plantation in the dataset. We fitted a Chapman
- Richards logistic growth curve to these data, and carbon stocks at age 15 are approximately 66 t C/ha, a value which is Other studies report slightly higher or lower estimates estimates aboveground biomass for a 30 year palm oil planting as C/ha belowgrou nd, for a total carbon stock of 84.6 t C/ha. aboveground carb on stocks as approximately 40 t C/ha, for a 25 year oil palm planting Kalimantan. Assuming a belowground biomass ratio of 33%, t estimate of approximately 53 t C/ha.

**Figure 1**  **. Biomass carbon accumulation in palm oil plantations in Malaysia. Data originally from Ng et al. (1968).** 

### 3.3.2 Switching to a Time**- Averaged**  Cannell (1982) but reported originally in Ng et al. (1968). Aboveground biomass values were measured in yielding, fertilized stands of palm oil plantations of various ages in Malaysia. Plantations were

fine sandy loams. One or two palms were sampled from stands of various ages and stand values were obtained by multiplying mean palm values by the numbers of palms per

Data from this study are summarized in Figure 1. The data show t stocks of oil palm plantations increase through year 15, which was the oldest plantation in the dataset.

Richards logistic growth curve to these data, and carbon stocks at age 15 are approximately 66 t C/ha, a value which is almost identical to the IPCC Tier 1 estimated value of 68 t C/ha. Other studies report slightly higher or lower estimates , such as that by Syahranudin (2005) estimates aboveground biomass for a 30 year palm oil planting as **62.8 t C/ha** , with an additio

nd, for a total carbon stock of 84.6 t C/ha. Jiwan and Saharjo (2009) estimate

approximately 40 t C/ha, for a 25 year oil palm planting Assuming a belowground biomass ratio of 33%, t his translates into a total carbon stock estimate of approximately 53 t C/ha.

**. Biomass carbon accumulation in palm oil plantations in Malaysia. Data originally from Ng et al. (1968).**



### Averaged **Approach 1**  **1**

Cannell (1982) but reported originally in Ng et al. (1968). Aboveground biomass values were measured in yielding, fertilized stands of palm oil plantations of various ages in Malaysia. Plantations were

fine sandy loams. One or two palms were sampled from stands of various ages and stand values were obtained by multiplying mean palm values by the numbers of palms per

Data from this study are summarized in Figure 1. The data show t hat carbon stocks of oil palm plantations increase through year 15, which was the oldest plantation in the dataset.

Richards logistic growth curve to these data, and carbon stocks at age 15 are 1 estimated value of 68 t C/ha. Syahranudin (2005) who , with an additio nal 21.8 t estimate in East his translates into a total carbon stock

**. Biomass carbon accumulation in palm oil plantations in Malaysia. Data originally from Ng et al. (1968). 1** 

**2**

Our original analysis accounted for only one year of growth when estimating carbon storage on palm oil plantations. We revised this approach to use a time whereby average carbon stocks over the life o Based on a review of the literature, averaged value of 35 t C ha - 1, or 128 t CO t C ha - 1 is a chieved by approximately Year 8 **Germer and Sauerborn (2007) : 35 t C/ha**  In Figure 2 , taken from Germer and Sauerborn (2007), biomass data from 51 oil palm fields were plotted. Integration of the fitted (nonlinear) aboveground biomass of 60 +/

- 20 tonnes biomass deviation is an estimation taking the large variation of the plotted data into acc Sauerb orn calculated a time
-averaged oil palm root biomass of 20 +/ total ground cover biomass of 2.5 +/ maximum of 10 tonnes/ha and a linear biomass loss through closure at 5 years after planting). This yields an estimate of total above 82.5 +/

- 26 t/ha. Considering an assumed carbon content of 40.4% for oil palm biomass (Syahrinudin 2005) and of 50% for remaining vegetation, both together fix a time averaged quantity of **C/ha** within the economic lifespan of oil palm.

**Figure 2**  **. Aboveground oil palm biomass**  **horizontal line indicates the time -** **averaged AGB as obtained by integration of the potential curve; the dashed horizontal line represents the time** **- averaged biomass of the linear curve. From Germer and Sauerborn (2007).**  Our original analysis accounted for only one year of growth when estimating carbon storage on palm oil plantations. We revised this approach to use a time
-averaged approach to carbon stock estimation, whereby average carbon stocks over the life o f the plantation were used as the carbon stock value.

of the literature, we modified the carbon stocks of palm oil plantations to a time , or 128 t CO 2 ha - 1 . This estimate is also in line with Figure 1 above, chieved by approximately Year 8
.

**: 35 t C/ha** , taken from Germer and Sauerborn (2007), biomass data from 51 oil palm fields were plotted.

(nonlinear) equation shown in Figure 2 returns a time averaged oil palm

tonnes biomass/ha for 25 years after planting, where the standard deviation is an estimation taking the large variation of the plotted data into acc ount. Germer and averaged oil palm root biomass of 20 +/

- 5 tonnes/ha and a time total ground cover biomass of 2.5 +/

- 1.0 tonnes/ha (assuming a fast ground cover establishment with a maximum of 10 tonnes/ha and a linear biomass loss through increased shading to 1 tonne/ha at canopy This yields an estimate of total above
- and belowground biomass of 26 t/ha. Considering an assumed carbon content of 40.4% for oil palm biomass (Syahrinudin 50% for remaining vegetation, both together fix a time averaged quantity of within the economic lifespan of oil palm.

**Aboveground oil palm biomass –**  **potential vs. linear estimation of time -** **average d biomass. The thin solid**  **averaged AGB as obtained by integration of the potential curve; the dashed averaged biomass of the linear curve. From Germer and Sauerborn (2007).**  Our original analysis accounted for only one year of growth when estimating carbon storage on palm oil averaged approach to carbon stock estimation, f the plantation were used as the carbon stock value. we modified the carbon stocks of palm oil plantations to a time
-

. This estimate is also in line with Figure 1 above, where 35

, taken from Germer and Sauerborn (2007), biomass data from 51 oil palm fields were plotted. returns a time averaged oil palm /ha for 25 years after planting, where the standard ount. Germer and 5 tonnes/ha and a time

-averaged 1.0 tonnes/ha (assuming a fast ground cover establishment with a

increased shading to 1 tonne/ha at canopy

and belowground biomass of 26 t/ha. Considering an assumed carbon content of 40.4% for oil palm biomass (Syahrinudin 50% for remaining vegetation, both together fix a time averaged quantity of **35.3 +/ -**  **11.0 t**

**d biomass. The thin solid averaged AGB as obtained by integration of the potential curve; the dashed**  **averaged biomass of the linear curve. From Germer and Sauerborn (2007). Henson (200** **7) : 35.4 t C/ha** 

Henson (2007) has also quantified carbon sequestration of sequestration of an oil palm stand with a 25 year life was found to 1 ), with the oil palms accounting for 80% of the mean carbon storage by the system (and ground cover, litter, and palm products accounting accumulated by the palms was 44 tonnes C/ha stock over 30 years was calculated to be C/ha for oil palm and understorey determined **Morel (2009) : 24.2 t C/ha**  **(aboveground only)** Morel (2009), on the basis of field measurements at oil palm plantings in Sabah, calculated a weigh average value of **24.2 t C /ha**  for aboveground biomass, assuming a 25 year planting period. assumed belowground to aboveground biomass ratio of 0. biomass ratio of 0.042 (from Germer and Sauerborn estimate approximately 33 t C/ha.

has also quantified carbon sequestration of oil palms in Malaysia. The mean sequestration of an oil palm stand with a 25 year life was found to be 2. 09 t C ha - 1 yr- 1 (

accounting for 80% of the mean carbon storage by the system (and ground cover, litter, and palm products accounting for the remaining 20%). At the end of the cycle, t otal carbon 44 tonnes C/ha and the
. time
-averaged in situ stan ding plantation carbon stock over 30 years was calculated to be **35.4 t C /ha** , which is similar to the 25 year mean value of 35.3 t /ha for oil palm and understorey determined by Germer and Sauerborn (2007). **(aboveground only)** Morel (2009), on the basis of field measurements at oil palm plantings in Sabah, calculated a weigh for aboveground biomass, assuming a 25 year planting period. assumed belowground to aboveground biomass ratio of 0. 33 and an understorey to aboveground

(from Germer and Sauerborn estimate s above), total estimate d biomass would be **1 3** 

oil palms in Malaysia. The mean carbon ( 7.66 t CO2 ha - 1 yraccounting for 80% of the mean carbon storage by the system (and ground cover, otal carbon ding plantation carbon mean value of 35.3 t

Morel (2009), on the basis of field measurements at oil palm plantings in Sabah, calculated a weigh ted for aboveground biomass, assuming a 25 year planting period. Using an and an understorey to aboveground

d biomass would be**1**

**4**



## 4.1 **Summary** In our revised assessment, n ew literature was used to estimate peat emissions from drainage considered including GHG emissions from factor analysis, but decided to continue to exclude them d Emissions from peat burning will be an area of continued research. burning vegetation continue to be included in the emission factor analysis. 

## 4.2 **Initial Assessment of Peat Emissions** In addition to changes in aboveground in GHG emissions from soil. These emissions are particularly important to include when land is cleared on peat soils due to their high organic matter c clearing land for oil palm plantations on peat to a depth of 80 cm , which resulted in emissions of 73 t CO relationship from Couwenberg et al. (2009) where every cm of peat drainage resulted in emissions of 0.9 1 t CO 2 ha - 1 yr- 1 . An assumed drainage depth of 80 cm therefore resulted in emissions of 0.9 t CO 2 ha - 1 yr- 1

. 

## 4.3 **Update d Assessment of Peat Emissions**  Estimates of emissions from peat drainage were revised based 2011) . Table 2, derived from Hooijer et al. (in press), presents estimates of a different time peri ods, taking into account the nonlinear relationship between CO since drainage. (Emissions are highest directly after conversion and then decrease through time.) Based on these new data, e missions from peat drainage over 30 years at a t are estimated as **95 t CO 2**  **ha -**  **1 yr** **- 1** , with an uncertainty maximum values of 54 to 115 t CO 2 review on the state of the science of emissions from drained peatlands, with measurements included from both closed chamber flux studies as well as peat subsidence monitoring studies. ew literature was used to estimate peat emissions from drainage GHG emissions from burning a portion of the peat layer into our updated emission factor analysis, but decided to continue to exclude them d ue to uncertainty in methods and data Emissions from peat burning will be an area of continued research. GHG emissions associated with burning vegetation continue to be included in the emission factor analysis. 

## Initial Assessment of Peat Emissions

aboveground carbon stocks, emissions from land clearing also include increases in GHG emissions from soil. These emissions are particularly important to include when land is cleared on peat soils due to their high organic matter c ontent. For the final RFS2 rule, GHG emissions from clearing land for oil palm plantations on peat originally included emissions associated with peat dra

, which resulted in emissions of 73 t CO 2 ha - 1 yr- 1 based on a CO 2 vs. drainage depth

et al. (2009) where every cm of peat drainage resulted in emissions of . An assumed drainage depth of 80 cm therefore resulted in emissions of 0.9 

## d Assessment of Peat Emissions Estimates of emissions from peat drainage were revised based on a new literature review . Table 2, derived from Hooijer et al. (in press), presents estimates of a nnualized emissions over

, taking into account the nonlinear relationship between CO 2 emissions vs. time (Emissions are highest directly after conversion and then decrease through time.) Based missions from peat drainage over 30 years at a typical drainage depth of 60

, with an uncertainty range represented by the minimum and ha - 1 yr- 1

. This estimate is the result of a comprehensive literature review on the state of the science of emissions from drained peatlands, with measurements included studies as well as peat subsidence monitoring studies. 

# 4 **PEAT ASSUMPTIONS**

ew literature was used to estimate peat emissions from drainage
. We into our updated emission ue to uncertainty in methods and data
. GHG emissions associated with

carbon stocks, emissions from land clearing also include increases in GHG emissions from soil. These emissions are particularly important to include when land is cleared ontent. For the final RFS2 rule, GHG emissions from included emissions associated with peat dra inage vs. drainage depth et al. (2009) where every cm of peat drainage resulted in emissions of . An assumed drainage depth of 80 cm therefore resulted in emissions of 0.9 1 x 80 = 73

on a new literature review (Page et al.

nnualized emissions over emissions vs. time (Emissions are highest directly after conversion and then decrease through time.) Based ypical drainage depth of 60
-85 cm represented by the minimum and This estimate is the result of a comprehensive literature review on the state of the science of emissions from drained peatlands, with measurements includedFire is oft en used when land is cleared for oil palm cultivation

vegetation are included in our assessment not included in the updated emission factors for peatlands bec to the most appropriate method s and data **Table 3.**  **Annuali z** **ed values**  **for peat**  **carbon higher**  **rates of**  **em ission** **s in**  **the years**  **immediately following**  **drainage (values**  **derived from**  **H ooijer**  **2011 ).**  **Number of years**

5 10 20 25 30 40 50 en used when land is cleared for oil palm cultivation . GHG emissions associated with burning vegetation are included in our assessment , but GHG emissions associated with burning peat soil not included in the updated emission factors for peatlands bec ause there is still significant uncer s and data for quantifying these emissions
. **ed values**  **for peat**  **carbon losses**  **from plantations**  **over various**  **time scales,**  **accounting for**  **in the**  **years immediately**  **following drainage**  **(values derived**  **from H** **ooijer**

**Carbon loss**  **(Mg CO**  **2 -** **eq ha**  **- 1**  **yr -**  **1 )** 

178 121 106 100 95 90 86 **1 5** 

. GHG emissions associated with burning associated with burning peat soil were ause there is still significant uncertainty as
**from plantations**  **over various**  **time scales,**  **accounting for**  **in the**  **years immediately**  **following drainage**  **(values derived**  **from H** **ooijer et**  **al., 1** 

**6**



## 5.1 **Summary** Forest carbon stock estimates for tropical countries were revised based on pantropical analysis by Saatchi et al. (2011). 

## 5.2 **Initial Assessment of Forest Carbon Stocks** For the RFS2 final rule, forest carbon stocks were estimated from several data sources, each derived using a different methodological approach. Over the course of the past two years, several efforts have been ongoing to improve forest carbon stock estimate the increasing interest in REDD (reducing emissions from deforestation and forest degradation) mechanisms, but the accurate estimation of forest carbon stocks is equally as important for accurate estimati on of emissions from the direct or indirect expansion of biofuels onto forest land. 

## 5.3 **Updated Assessment of** Two new analyses on forest carbon stock estimation were completed since the release of the final RFS2 rule, one for th ree continental regions by Saatchi et al. (2011) and the other for the EU by Gallaun et al. (2010). Preliminary results for Latin America and Africa from Saatchi et al. (2011) were incorporated into the final RFS2 rule, but Asia results were not included d (2011) analysis is now complete, and so the final map was used to calculate updated area average forest carbon stocks for the entire area covered by the analysis (Latin America, sub Africa an d So uth and Southeast Asia; Figure 3 improvement over previous estimates because they incorporate data from more than 4,000 ground inventory plots, about 150,000 biomass values estimated from forest heig lidar, and a suite of optical and radar satellite imagery products. Estimates are spatially refined at 1 grid cell resolution and are directly comparable across countries and regions. Another advantage is the uncertainty analysis performed on the map; pixel Forest carbon stock estimates for tropical countries were revised based on the publication of a new pantropical analysis by Saatchi et al. (2011). 

## Initial Assessment of Forest Carbon Stocks For the RFS2 final rule, forest carbon stocks were estimated from several data sources, each derived using a different methodological approach. Over the course of the past two years, several efforts have been ongoing to improve forest carbon stock estimate s for large regions. This has been driven mainly by the increasing interest in REDD (reducing emissions from deforestation and forest degradation) mechanisms, but the accurate estimation of forest carbon stocks is equally as important for accurate on of emissions from the direct or indirect expansion of biofuels onto forest land. 

## Updated Assessment of **Forest Carbon Stocks** Two new analyses on forest carbon stock estimation were completed since the release of the final RFS2

ree continental regions by Saatchi et al. (2011) and the other for the EU by Gallaun et al. (2010). Preliminary results for Latin America and Africa from Saatchi et al. (2011) were incorporated into the final RFS2 rule, but Asia results were not included d ue to timing considerations. The Saatchi et al. (2011) analysis is now complete, and so the final map was used to calculate updated area average forest carbon stocks for the entire area covered by the analysis (Latin America, sub

uth and Southeast Asia; Figure 3 ). The Saatchi et al. results represent a significant improvement over previous estimates because they incorporate data from more than 4,000 ground inventory plots, about 150,000 biomass values estimated from forest heig hts measured by spaceborne lidar, and a suite of optical and radar satellite imagery products. Estimates are spatially refined at 1 grid cell resolution and are directly comparable across countries and regions. Another advantage is the

sis performed on the map; pixel
- level uncertainty ranged from 6 to 53%, but 

# 5 **FOREST CARBON STOCKS**

the publication of a new

For the RFS2 final rule, forest carbon stocks were estimated from several data sources, each derived using a different methodological approach. Over the course of the past two years, several efforts have s for large regions. This has been driven mainly by the increasing interest in REDD (reducing emissions from deforestation and forest degradation) mechanisms, but the accurate estimation of forest carbon stocks is equally as important for accurate on of emissions from the direct or indirect expansion of biofuels onto forest land.

Two new analyses on forest carbon stock estimation were completed since the release of the final RFS2

ree continental regions by Saatchi et al. (2011) and the other for the EU by Gallaun et al. (2010). Preliminary results for Latin America and Africa from Saatchi et al. (2011) were incorporated into ue to timing considerations. The Saatchi et al. (2011) analysis is now complete, and so the final map was used to calculate updated area
-weighted average forest carbon stocks for the entire area covered by the analysis (Latin America, sub
-Saharan The Saatchi et al. results represent a significant improvement over previous estimates because they incorporate data from more than 4,000 ground hts measured by spaceborne lidar, and a suite of optical and radar satellite imagery products. Estimates are spatially refined at 1
- km grid cell resolution and are directly comparable across countries and regions. Another advantage is the 6 to 53%, but 

# FOREST CARBON STOCKSuncertainty for project
- level (~10,000 ha) and national

 1%, respectively. In the final RFS2 rule, forest carbon stocks for the EU we Nabuurs et al. (2003), Myneni et al. (2001) and FAO. Issues with this ‘patchwork’ approach were that the biomass estimates were not comparable across countries due to the differences in methodological approache s, and that estimates were not spatially derived (or, the spatial data were not provided to Winrock). Since the release of the final rule, Gallaun et al. (2010) developed EU ground biomass in forests based on remote sensing and field mea the classification, and comprehensive field measurement data from national forest inventories for nearly 100,000 locations from 16 countries were also used to develop the final map. The map covers the whole European Unio n, the European Free Trade Association (EFTA) countries, the Balkans, Belarus, the Ukraine, Moldova, Armenia, Azerbaijan, Georgia and Turkey (Figure 3). Winrock obtained the GIS data for the biomass map from the corresponding author (H. Gallaun) and could weighted forest carbon stocks per country. For both data sources, we added belowground biomass to reported aboveground biomass values using an equation in Mokany et al. (2006). The data sources used per country for the estimation o f forest carbon stocks in the updated emission factor analysis are presented in Figure 4. level (~10,000 ha) and national
- level assessments was constrained at In the final RFS2 rule, forest carbon stocks for the EU we re estimated using a combination of data from Nabuurs et al. (2003), Myneni et al. (2001) and FAO. Issues with this ‘patchwork’ approach were that the biomass estimates were not comparable across countries due to the differences in methodological

s, and that estimates were not spatially derived (or, the spatial data were not provided to Winrock). Since the release of the final rule, Gallaun et al. (2010) developed EU
-wide maps of above ground biomass in forests based on remote sensing and field mea surements. MODIS data were used for the classification, and comprehensive field measurement data from national forest inventories for nearly 100,000 locations from 16 countries were also used to develop the final map. The map covers the whole

n, the European Free Trade Association (EFTA) countries, the Balkans, Belarus, the Ukraine, Moldova, Armenia, Azerbaijan, Georgia and Turkey (Figure 3). Winrock obtained the GIS data for the biomass map from the corresponding author (H. Gallaun) and could therefore calculate area weighted forest carbon stocks per country. For both data sources, we added belowground biomass to reported aboveground biomass values using an equation in Mokany et al. (2006). The data sources used f forest carbon stocks in the updated emission factor analysis are **1 7** 

level assessments was constrained at 5% and re estimated using a combination of data from Nabuurs et al. (2003), Myneni et al. (2001) and FAO. Issues with this ‘patchwork’ approach were that the biomass estimates were not comparable across countries due to the differences in methodological

s, and that estimates were not spatially derived (or, the spatial data were not provided to wide maps of above
surements. MODIS data were used for the classification, and comprehensive field measurement data from national forest inventories for nearly 100,000 locations from 16 countries were also used to develop the final map. The map covers the whole

n, the European Free Trade Association (EFTA) countries, the Balkans, Belarus, the Ukraine, Moldova, Armenia, Azerbaijan, Georgia and Turkey (Figure 3). Winrock obtained the GIS data therefore calculate area
weighted forest carbon stocks per country. For both data sources, we added belowground biomass to reported aboveground biomass values using an equation in Mokany et al. (2006). The data sources used f forest carbon stocks in the updated emission factor analysis are**1**

**8**

**Figure 3**  **. Distribution of (A) above and belowground carbon stocks and (B) uncertainty circa the year 2000. From Saatchi et al. (2011).** **Figure 4** **. Distribution of aboveground forest carbon stocks circa 2000. From Gallaun et al. (2010). Divide values in**

**the legend by 10 for carbon estimates in units of t C ha -**  **1 .**  **1 9** 

**. Distribution of aboveground forest carbon stocks circa 2000. From Gallaun et al. (2010). Divide values in 2** 

**0**

**Figure 5**  **. Updated data sources used for estimating forest carbon stocks in the emission factor analysis after the**  **release of the final RFS2 rule.**Blackard , J.A., Finco, M.V., Helmer, E.H., Holden, G.R., Hoppus

G.G., Nelson, M.D., Riemann, R., Reufenacht, B., Salajanu, D., Weyermann, D.L., Winterberger, K.C., Brandeis, T.J., Czaplewski, R.L., McRoberts, R.E., Patterson, P.L., Tymcio, R.P. 2008. Mapping U.S. forest bioma ss using nationwide forest inventory data and moderate resolution information. Remote Sensing of Environment Cannell, M.G. 1982. World Forest Biomass and Primary Production Data. Academic Press. London. 391 pp. Couwenberg, J., Dommain , R., Joosten, H. 2009. Greenhouse gas fluxes from tropical peatlands in south east Asia. Global Change Biology FAO
. 1995 . FAO Yearbook: Forest products. FAO For. Serv. No. 28, FAO, Rome. 422 p. Gallaun, H., Zanchi, G., Nabuurs, G.J., Hengeveld, G., Schardt, M., Verkerk, P.J. 2010. EU growing stock and above
-ground biomass in forests based on remote sensing and field measurements. Forest Ecology and Management Germer, J. and Sauerborn, J. 2007. Estimation of the impact of oil palm plantation establishment on greenhouse gas balance. Springer Science + Business Media BV, 2007. Henson, I.E. 2008. The carbon cost of palm oil production in Malaysia. Houghton , R.A., Butman, D., Bunn, A.G., Krankina, O., Schlesinger, P., Stone, T.A. 2007. Mapping Russian forest biomass with data from satellites and forest inventories. 2: 045032. Hooijer, A., Page, S. E., Jauhiainen, J., Lee, W. A., Idris, A., & plantations on tropical peatland: reducing uncertainty and implications for emission reduction options. Biogeosciences Discussions. (in press). 

# 6 **REFERENCES**

, J.A., Finco, M.V., Helmer, E.H., Holden, G.R., Hoppus , M.L., Jacobs, D.M., Lister, A.J., Moisen, G.G., Nelson, M.D., Riemann, R., Reufenacht, B., Salajanu, D., Weyermann, D.L., Winterberger, K.C., Brandeis, T.J., Czaplewski, R.L., McRoberts, R.E., Patterson, P.L., Tymcio, R.P. 2008. Mapping

ss using nationwide forest inventory data and moderate resolution information. Remote Sensing of Environment 112: 1658
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-2486.2009.02016.x . FAO Yearbook: Forest products. FAO For. Serv. No. 28, FAO, Rome. 422 p. G., Nabuurs, G.J., Hengeveld, G., Schardt, M., Verkerk, P.J. 2010. EU

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# REFERENCES **2 1** 

, M.L., Jacobs, D.M., Lister, A.J., Moisen, G.G., Nelson, M.D., Riemann, R., Reufenacht, B., Salajanu, D., Weyermann, D.L., Winterberger, K.C., Brandeis, T.J., Czaplewski, R.L., McRoberts, R.E., Patterson, P.L., Tymcio, R.P. 2008. Mapping

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, B.H. 2009. “The Above Ground Car bon Stock of 25 years Oil Palm Planted in the Mineral Soil ‘Case Study in Tanah Grogot, East Kalimantan, Indonesia.” July 2009. , A. 2009. Estimates of aboveground biomass forest and oil palm values for Sabah, Malaysia. University of Oxford, Environm ental Exchange Institute, September 2009. , T.R., Radojevic, M., Waugh, A., Caruana, A. 2000. Emissions from the combustion of peat: an experimental study. Atmospheric Environment 34: 3033
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-363. Winjum, J.K., Brown, S., Schlamadinger, B. 1998. Forest harvests and wood products: sources and sinks of atmospheric carbon dioxide. Forest Science 44: 272
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-
II Nutrients in Niklas, K.J. and Enquist, B.J. 2004. Biomass allocation and growth data of seeded plants. Data set. Available online [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distribut ed Review of peat International 1 global biomass carbon map for the year 2000. Available

http://cdiac.ornl.gov ), Oak Ridge Lefsky, M., Mitchard, E.T.A., Salas, W., Zutta, B.R., Buermann, W.,

, M. And Morel, A. 2011. Benchmark map of
Syahrinudin (2005). The potential of oil palm and forest plantations for carbon sequestrati on on Ward, D.E., Hardy, C.C. 1984. Advances in the characterisation and control of emissions from prescribed Annual Meeting of the Air Pollution Control Association, 24
- 2 9 June Winjum, J.K., Brown, S., Schlamadinger, B. 1998. Forest harvests and wood products: sources and sinks**D r** 

**. Nancy Harris P**  **r og** **r am**  **Officer 2**  **E c** **o s** **y st** **em Se r** **vices office**  **703.302.6519 f** **a x 703.302.6512**  **e -**  **mail nharris@win r** **ock.o r** **g 2121 C**  **r y** **s t** **al Dri v** **e, Sui t** **e 500 Arlin** **gt on,**  **V**

**A 22202, U S** **A www.winrock.org/ecosystems**  **2 3**