Well-to-Wheel GHG Emissions and Land 

Use Change Impacts of Biodiesel from 

Malaysian Palm Oil 

Stefan Unnasch 

Susan Tarka Sanchez 

Brent Riffel 

July 2011 

Under contract with 

The Malaysian Palm Oil Council 

LCA.6015.50P.2011 

July 2011 

Life Cycle Associates, LLC
DISCLAIMER 

This report was prepared by Life Cycle Associates, LLC for The Malaysian Palm Oil 

Council. Life Cycle Associates is not liable to any third parties who might make use of 

this work. No warranty or representation, express or implied, is made with respect to the 

accuracy, completeness, and/or usefulness of information contained in this report. 

Finally, no liability is assumed with respect to the use of, or for damages resulting from 

the use of, any information, method or process disclosed in this report. In accepting this 

report, the reader agrees to these terms. 

 ACKNOWLEDGEMENT 

Life Cycle Associates, LLC performed this study with funding from The Malaysian Palm 

Oil Council. MPOC’s Project Manager was Dr. Kalyana Sundram. The study’s authors 

are indicated in the citation below. 

Prepared for 

Malaysian Palm Oil Council 

2nd Floor, Wisma Sawit, Lot 6, SS6 

Jalan Perbandaran, 47301 

Kelana Jaya, Selangor, Malaysia 

www.mpoc.org.my 

Contact Information: 

Stefan Unnasch 

Life Cycle Associates, LLC 

1.650.461.9048 

unnasch@LifeCycleAssociates.com

www.LifeCycleAssociates.com

Recommended Citation: Unnasch, S. S. T. Sanchez, and B. Riffel (2011) Well-to-Wheel 

GHG Emissions and Land Use Change Impacts of Biodiesel from Malaysian Palm Oil. 

Prepared for Malaysian Palm Oil Council. Life Cycle Associates Report 

LCA.6015.50P.2011.
i | Life Cycle Associates, LLC 

CONTENTS 

SUMMARY...................................................................................................................... 1

1 INTRODUCTION...................................................................................................... 4

1.1 OBJECTIVE...................................................................................................... 4

1.2 LAND USE CONVERSION............................................................................... 4

1.3 BACKGROUND ................................................................................................ 6

1.4 CATEGORIZING DIRECT AND INDIRECT LUC.............................................. 7

2 WELL TO WHEEL GHG EMISSIONS...................................................................... 9

2.1 MALAYSIA BIODIESEL PATHWAY ................................................................. 9

2.2 CO-PRODUCT ALLOCATION METHOD AND CARBON BALANCE ............. 10

2.3 OIL PALM CULTIVATION............................................................................... 11

2.4 PALM OIL PRODUCTION .............................................................................. 16

2.5 SCOPE AND DEFINITION.............................................................................. 17

2.5.1 Project Objective...................................................................................... 17

2.5.2 Functional Unit......................................................................................... 18

2.5.3 System Boundary .................................................................................... 18

2.6 ANALYSIS METHODS ................................................................................... 18

2.7 OVERVIEW OF MODEL APPROACH. ERROR! BOOKMARK NOT DEFINED.

2.8 LIFE CYCLE PROCESSES AND INPUTS ..................................................... 19

2.8.1 Oil Palm Cultivation Energy ..................................................................... 19

2.8.2 Fertilizers and Pesticides......................................................................... 19

2.8.3 FFB and EFB Transport........................................................................... 21

2.8.4 Oil Extraction and Processing.................................................................. 21

2.8.5 Palm Oil Transport................................................................................... 26

2.8.6 Biodiesel production ................................................................................ 27

2.8.7 Fuel Transport and Distribution................................................................ 29

2.8.8 Fuel Combustion...................................................................................... 30

3 DIRECT LAND USEEFFECTS............................................................................... 33

3.1 CARBON ACCOUNTING IN MALAYSIA ........................................................ 33

3.1.1 Land Classification in Malaysia................................................................ 33

3.1.2 Malaysian soil types................................................................................. 34

3.1.3 Palm Carbon Cycle.................................................................................. 35

3.1.1 Carbon Accounting for Palm.................................................................... 40

4 LUC MODEL APPROACH ..................................................................................... 45

5 LCA ISSUES......................................................................................................... 50

6 ILUC RESULTS ..................................................................................................... 56

7 DISCUSSION......................................................................................................... 58

8 REFERENCES....................................................................................................... 61

APPENDIX A. CARBON STOCKS................................................................................ 67

8.1 NATURAL FORESTS IN MALAYSIA C BUDGET .ERROR! BOOKMARK NOT 

DEFINED.

8.1.1 Forest Clearing Impacts............................. Error! Bookmark not defined.

8.1.2 New European Commission Recommendations for Carbon Stock 

Measurements ......................................................... Error! Bookmark not defined.

8.2 ABOVE GROUND CARBON ONLY IDENTIFIES COVER NOT CHANGE

ERROR! BOOKMARK NOT DEFINED.
ii | Life Cycle Associates, LLC 

TABLES 

........................................................................................................................................ 6

Table 1. Base Case and Sub-pathway Palm Oil Mill POME Scenarios......................... 10

Table 2. Fertilizer Input Parameters. ............................................................................. 20

Table 3 Herbicide and Pesticide Consumption on Palm Oil Plantations........................ 20

Table 4. Herbicide and Pesticide Consumption on Palm Oil Plantations....................... 21

Table 5. Palm Oil Mill Energy and Material Inputs......................................................... 22

Table 6. Palm Oil Mill Outputs....................................................................................... 23

Table 7. Feedstock components and Allocation shares. ............................................... 26

Table 8. CPO Transport Parameters............................................................................. 26

Table 9. Transesterification Energy and Chemical Inputs ............................................. 28

Table 10. Palm Oil Fatty Acid Profile............................................................................. 28

Table 11. Biodiesel Transport Parameters.................................................................... 30

Table 12. Biodiesel Transport Parameters.................................................................... 31

Table 13. Vehicle CH4 and N2O Emissions. .................................................................. 31

Table 13 Approaches to Carbon Accounting................................................................. 40

Table 14 Three Approaches to measuring stock change/ GHGs from HWP................. 44

Table 15 Relative Stock Change Factors (FLU, FMG, AND FI) (Over 20 years) for 

Different Management Activities on Cropland ............................................................... 45

Table 16. Carbon release factors and 100 year emissions with 2% discount factor...... 49

Table 17 Estimates for Emission Factors vary considerably for the different land use 

change types................................................................................................................. 59

FIGURES 

Figure 1. Summary of LUC and direct emission results for Malaysian Palm Biodiesel.... 3

Figure 2. General Approach for counting direct and indirect LUC ................................... 5

Figure 3. System Boundary Diagram for Palm Biodiesel Production .............................. 6

Figure 4. Stages of Oil Palm Cultivation........................................................................ 12

Figure 5. Palm Oil Plantation Aerial Photos .................................................................. 13

Figure 6. Plantation Photos........................................................................................... 14

Figure 7.. Palm Oil Mill Photos. ..................................................................................... 16

Figure 8. Palm Oil Mill Photos. ...................................................................................... 16

Figure 9. Oil Palm Mill Process Diagram....................................................................... 24

Figure 10. Biomass Boiler and Untreated POME Effluent............................................. 25

Figure 11. Transesterification Reaction of Triglycerides to Methyl Esters..................... 27

Figure 12. AEZ for Peninsular Malaysia........................................................................ 34

Figure 13 Estimated annual carbon stocks in tropical Asian and Malaysian forests ..... 35

Figure 14 Malaysian study for SOC measurements within the harvesting and frond file 

paths representing three sampling depths (a, b and c) ................................................. 38

Figure 12 Conventional pastoral agriculture vs. agroforestry system such as for palm 

represent very different carbon cycles........................................................................... 39
iii | Life Cycle Associates, LLC 

Figure 13 Panamanian forest study with agroforest and pasture land compared.......... 42

Figure 14. Calculating LUC impacts from palm oil ........................................................ 46

Figure 15. Carbon stock emission factors used in the LCFS assume the same stored 

carbon from all crops. However, in Malaysia, the biofuel crops would contain much 

higher carbon stocks. .................................................................................................... 47

Figure 16. Carbon release and uptake from Malaysian palm plantations...................... 48

Figure 17 VISIT Ecosystem Model................................................................................ 51

Figure 18. Carbon cycle of palm plantation (source: derived from EPA RFS2) Photo 

credits: Stefan Unnasch ................................................................................................ 52

Figure 19. Palm tree root mass (Photo credit: Stefan Unnasch) ................................... 52

Figure 20. Global Oil Production ................................................................................... 53

Figure 21 Oil palm products .......................................................................................... 54

Figure 22. Direct fuel LCA emissions and LUC emissions compared to soy biodiesel 

and petroleum diesel (100 year time horizon) ............................................................... 57
1 | Life Cycle Associates, LLC 

Summary 

The well-to-wheel (WTW) GHG emissions and land-use change (LUC) impacts 

associated with Malaysian palm oil derived biodiesel are examined in this study. WTW 

emissions include direct emissions associated with palm oil production, processing, 

transport, and vehicle use. 

The approaches to evaluating indirect direct LUC impacts are reviewed with an overall 

LUC estimated. Direct LUC impacts are discussed and also analyzed separately in the 

full fuel cycle report. Combined, LUC impacts include the effects associated with the 

usage of crude palm oil (CPO) feedstock, the co-production of palm kernel meal and 

other co-products, and the net carbon flux from the different growth stages within the 

palm plantation. 

Key parameters affected the WTW emissions include the following: 

• Methane capture from palm mill. The decomposition of palm oil mill effluent 

(POME) results in the production of methane emissions if the material is pumped 

into lagoons, which is currently the predominant practice. Malaysia will be 

requiring the conversion of palm mills to methane capture. This technology is 

examined separately so that regulators can require certification of the 

technology. 

• Co-products. Palm production results in several co-products. Assessing the 

impacts through substitution is problematic because such an analysis requires a 

system expansion to other tropical oils. An allocation approach may be best for 

the palm kernel. Crude glycerin produced from Malaysian palm oil is sold for 

reprocessing as green glycerin. The practice of selling crude glycerin is 

sufficiently common and Malaysian palm oil biodiesel should be treated with a 

substitution credit for this co-product. 

• Yields. Both fresh fruit bunch (FFB) and oil extraction yields are improving due to 

various technology and agronomy efforts. FFB yields should grow from 20 to 30 

tonne/ha and oil extraction ratios from 21% to over 26%. This combination of 

yield improvement results in a palm oil productivity increase from 4 to 7.8 

tonne/ha or almost doubling of oil yield. The doubling of oil yield would reduce 

the WTW emissions by almost 50% for farming and transport to the mill. 

Key parameters affecting the LUC impact of palm oil biodiesel include the following: 

• Carbon storage. Palm trees uptake a substantial amount of CO2 during their 

growth cycle. The carbon is stored in the soil, roots, above ground biomass, and 

litter. The net carbon storage needs to be accounted for in the LUC analysis1. 

The carbon stored in harvested wood products (HWP) from conversion also 

requires assessment. Finally, the inclusion of credits from biomass uses and co-

 

1 Some iLUC studies consider the shift from forest to crops or grass land to crops in Malaysia. The 

carbon stock values for crops considered in the iLUC analysis (EPA RFS2 and ARB GTAP) represent 

very low levels of carbon storage. 
2 | Life Cycle Associates, LLC 

products are highly dependent on system boundary choice and allocation 

method. 

• Time horizon. Palm plantations require a 25 to 30 year growth cycle and are 

costly to develop. A minimum of three cycles are necessary to achieve economic 

viability; so a 100 year time horizon is appropriate for assessing time varying 

GHG releases. 

• iLUC of fungible oils. Indirect LUC (iLUC) effects from the use of CPO would be 

comparable to those from soy oil as these vegetable oils are largely fungible in 

the food oil markets. Palm oil is the low cost producer and considered the 

marginal source on the market. Differences in marketability depend on a wide 

range of customer preferences, marketing, the fat profile, and other parameters. 

Because palm, soy, and rapeseed are highly substitutable, this creates 

fractionation in the palm market. Thus, difficult to analyze indirect market effects. 

• Projected yield increases. As in the case of other biofuels, palm oil yields can 

be projected to improve as technology and practices improve and demand 

increases. The “price induced yield” effect outside of Malaysia can be substantial 

as palm productivity in Africa and South America are much lower than those in 

Malaysia. 

These principles are taken into account in the analysis presented in this study. Figure 1 

shows the LUC results for palm oil combined with the direct emission effects, which are 

documented in a separate report. The GHG emission estimates are consistent with 

EPA’s analysis of soy biodiesel under the RFS2 (EPA 2010). The analysis is presented 

over a 100 year time horizon to be consistent with the long commercial life of a palm 

plantation. The emissions are summed over the time horizon using a 2% discount 

factor. The fuel production, transport, and tailpipe emissions correspond to the direct 

and upstream fuel cycle emissions for fuel production equipment and vehicle operation. 

These emissions are constant over time. The next category is farm inputs, fertilizer, 

and associated N2O emissions, which are based on the inputs for palm oil production2

. 

The first set of scenarios shows LUC emissions from palm oil with the iLUC estimated to 

be the same as those for soy oil because the feedstock oils are traded and are largely 

fungible. The iLUC result here is based on EPA’s analysis for soy biodiesel and 

includes all of the LUC effects except for the farm inputs and N2O. Finally, the carbon 

storage associated with the palm plantation is taken into account. Note that any 

changes in carbon stocks associated with the production of the CPO feedstock are 

taken into account with the iLUC results. 

 

2

 EPA calculates U.S. and international fertilizer emissions using FASOM and international fertilizer data. 

Since FASOM is not available to assess the emissions from Malaysian agriculture, the analysis 

represents the direct and fuel cycle emissions associated with palm oil. The same approach is taken by 

ARB under the LCFS; where fertilizer impacts are not included in the LUC analysis.
3 | Life Cycle Associates, LLC 

-2,000,000

-1,000,000

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

2005 Diesel Baseline

Soy Biodiesel base yield

Palm

Biodiesel, CH4 Capture

Palm Biodiesel, CH4 Capture, Biomass Sales

Palm Biodiesel, No CH4 Capture

Palm Biodiesel, Palm Bole Products

Palm Biodiesel Malaysia only LUC

GHG Emissions (g CO2 e / mmBtu)

Fuel Production

International Farm Inputs and

Fert N2O

U.S. Farm Inputs and Fert

N2O

Fuel & Feed Transport

Tailpipe

International Land Use

Change

Palm Net dLUC

International Rice Methane

International Livestock

U.S. Livestock

U.S. Soil Carbon

U.S. Rice Methane

50% Reduction Threshold

Net Emissions

100 year time horizon

2% Discount Factor

Figure 1. Summary of LUC and direct emission results for Malaysian Palm Biodiesel 

Source Life Cycle Associates, EPA 2010

The direct emissions component from the palm plantation is not included in EPA’s 

carbon stock emission factors and is therefore included here as an additional effect. 

This scenario for LUC represents the high range for palm oil because the higher 

productivity of palm and carbon uptake from the palm plantation. The far right bar 

represents the LUC impacts if all marginal palm were grown on Malaysian land that 

is currently reserved for agriculture with a forest cover3. Here the carbon storage 

from the palm plantation largely offsets any loss of carbon from forest vegetation. 

Even this scenario represents a high emission case as palm could also expand onto 

non forested lands. 

 

3

 This scenario is comparable to corn ethanol expanding onto CRP land in the U.S. indirect effects are not 

relevant if the expansion is onto land that may not be otherwise available for agriculture. 
4 | Life Cycle Associates, LLC 

1 Introduction 

Palm oil is derived from the fruit of the oil palm, a tropical species (Elaeis guineensis) 

that originated in West Africa, but now grows as a hybrid in many parts of the world, 

particularly in Southeast Asia and Central and South America

The oil is used for a variety of purposes. Palm stearin (solid at room temperature) is 

used almost exclusively for industrial uses such as in cosmetics, soaps, detergents etc. 

Palm olein (liquid at room temperature) is used exclusively for food use. In some 

Western countries, greater use of palm oil in food products is driven by a switch away 

from unhealthy trans-fats. The world demand for palm oil has soared in the last two 

decades, first for its use in food or consumer products and more recently as the raw 

material for biofuel. 

1.1 Objective

This document provides detailed calculations, assumptions, inputs and other necessary 

information to calculate the energy requirements and GHG emissions for the palm oil to 

biodiesel (BD) pathway. This document provides a summary of the direct energy inputs 

and direct GHG emissions from palm BD production. In this case, “direct” refers to 

direct and upstream energy and emissions, in contrast to indirect economic effects. The 

functional unit is a megajoule (MJ) of fuel produced. 

1.2 Land Use Conversion 

LUC includes the direct emissions (dLUC) associated with land conversion to 

agricultural production and indirect land use change (iLUC) effects associated with land 

change (locally or elsewhere), including economic impacts induced by perturbation of 

global commodity markets. Estimating this change includes estimates of conversion of 

land between ecosystem types resulting in a new balance of carbon storage levels over 

time, with associated storage or release of carbon. The exchange in carbon over time 

can be induced through land conversion resulting from the expanded production of 

biofuels, yet the temporal and spatial pattern of this change is difficult to measure with 

certainty (Searchinger, Heimlich et al., 2008, Fargione et al., 2008, Liska et al., 2009, 

EPA 2010). 

Two papers facilitated the current iLUC global debate: Fargione (et al., 2008) and 

Searchinger (et al., 2008). Fargione stresses the importance of nature for carbon 

storage, and uses the carbon payback time to calculate different biofuel scenarios in 

terms of carbon savings per biofuel (or debt) from avoided fossil fuel combustion. 

Searchinger examines the indirect effects of land use impacts from corn ethanol based 

on agro-economic modeling of the food crop. Previously, Wang and Delucchi (2005) 

included iLUC in the LEM model and a preliminary iLUC factor has been included in 

GREET. Since these publications, the topic of LUC in biofuels LCA has been 

incorporated in the policies and regulations affecting GHG emissions from biofuels (EPA 

2010, ARB 2010). 

. 
5 | Life Cycle Associates, LLC 

Inputs from the well to wheel (WTW) or tank to wheel (TTW) reflect a direct causal 

relationship (Figure 2) between energy inputs and GHG emissions, which is often 

referred to as an attributional approach. Indirect impacts are determined by changes in 

spatial distribution of cropping systems throughout the world based on agro-economic 

modeling. The changes in land are combined with datasets for carbon stocks to 

estimate overall carbon release. The assessment of indirect effects (including iLUC) is 

often termed a ‘consequential analysis’. Some analyses aggregate dLUC emissions 

with the iLUC emissions or treat all land cover change as iLUC (Tyner et al., 2010). 

This approach needs to be examined carefully with palm plantations as palm plantations 

store more carbon than conventional crops. 

Direct Effects

Raw material 

extraction GHGs

including agricultural 

operations 

(machinery)

Incorporation of cogeneration or use of 

co-products into 

production systems 

(LC inputs)

Includes Biorefinery

Operations, Coproduct allocation 

and improvements 

(lower GHGs)

Blending and 

Distribution of Biofuel

Indirect Effects

Land Use Change 

(area ha) for 

Extraction

GHG effects from 

Carbon (release now, 

interim, later?) 

Temporal , Spatial

Land Use Activity 

Patterns (Temporal, 

Spatial)

Replacement 

Production and Yield 

Improvements (lower 

GHGs)

Effects on Global 

Commodity Markets 

(Elasticities$) and 

Food (IMPACT)

Approach: LCA 

Models: GREET, 

SimaPro, Ecoinvent

(EMPA)

Approach: PE/GE 

Models + Carbon 

Stock +Historical 

Patterns +Policy 

Mandates+ Land Use 

Employed +...

Figure 2. General Approach for counting direct and indirect LUC 

Indirect impacts include economic impacts associated with this change, and subsequent 

flux of carbon from production, and are difficult to quantify with certainty. Using the 

consequential method requires projections of biofuels demand over time and debate 

continues about how to include demand projections from current and future legislation 

that mandate biofuel quantities. Market effects are determined by many factors, 

including regional supply and demand curves and elasticity factors (price, yield, etc.). 

Partial and general equilibrium models are employed to calculate these changes. 
6 | Life Cycle Associates, LLC 

Lastly, historical trends of land use patterns fail to represent LUC as a geospatial and 

non-linear function of output because this is difficult to judge the attribution of land cover 

change to agriculture. Additional applications such as the use of geographic information 

system (GIS) mapping coupled with LCA attempt to address this issue. Studies are 

beginning to focus on the complexity of relaying use data to ecosystem cover ((i.e. 

Farming vs. another activity in grassland vs. forest) Geyer et al 2010)). Figure 3 shows 

the combination of the direct WTW effects plus the dLUC and iLUC effects from palm 

biodiesel. The direct energy inputs are described in a separate report and the iLUC and 

dLUC components are covered in this report. 

Immature 

Trees

Nursery

Palm 

Trunk

Transport

Land 

Establishment

Indirect 

LUC

Other 

Products

Digester

Mature Tree

Tree 

Removal

Mill

POME

Lagoon 

Digester

Refinery

Fresh Fruit Bunches

Open 

Lagoon

Fertilizer

Biodiesel 

Vehicle

Biodiesel 

Plant

Transport 

& Delivery

Cooking 

Oil 

Palm 

Kernel Chemical 

Fertilizer

Methanol

Chemicals

Direct 

LUC

Vegetable 

OIls

Figure 3. System Boundary Diagram for Palm Biodiesel Production 

1.3 Background 

To understand the complex effects and impacts of biofuel production, iLUC has been 

introduced, with much speculation due to variable results thus far across the biofuel 

production chain profile. To analyze these impacts indirectly, the approach must include 

the effect of an increase in demand for palm biodiesel. 

With the exception of marginal or abandoned croplands, direct land use change (dLUC) 

results in an initial net loss of sequestered carbon in the form of both aboveground 

(plants, litter) or belowground (roots, soil) biomass when crops are planted in place of 

existing land cover (Fargione et al, 2008). The direct effects are established in one 

place in time and include the additional land required. The questions specific to palm 

biodiesel include: 

• What happens to the increase of additional co-products to the market? There 

are a minimum of three for palm biodiesel: palm kernel oil, palm kernel expeller 

and glycerin. How to calculate these as avoided emissions requires choices of 
7 | Life Cycle Associates, LLC 

allocation and this is covered in direct emissions report. The indirect effects are 

less certain and include analysis of the fungibility of the various palm products. 

• How is the additional demand met—by either yield or by land increase—and 

how to calculate the projected increases or decreases in each per year? 

• What are current calculations of carbon stocks for palm plantationsrepresenting the differences in life cycle, HWP potential and what is missing or 

uncertain (eg. roots, litter, depth for soil carbon). 

• Estimate of peat use and forest conversion for new plantation development. 

An increasingly intense debate in the biofuels industry now centers on LUC affiliated 

with biofuels crop growth. The case of palm differs from an annual rotation and is the 

only biofuel (currently) representing a perennial tree species that is a long term 

production system (>25 years). Therefore, the carbon sequestration potential of this 

system has not yet been adequately addressed in recent studies on LUC, particularly 

for roots and below ground carbon. Palm grown on peat soil does represent a difference 

in emissions and is included in the direct emissions report because the land cover area 

under development in peat soils is known. 

Different approaches have produced variable results for overall iLUC estimates. For 

example, E4Tech in the UK is working on a casual descriptive model approach; Joint 

Research Council (JRC) in Europe is also analyzing the palm biodiesel iLUC results; 

Environmental Protection Agency (EPA) and California Air Resources Board (ARB) 

have analyzed the LUC effects associated with various biofuels including corn and 

sugarcane ethanol, soy biodiesel, and are also looking into analyses of palm LUC. The 

Roundtable on Sustainable Palm Oil (RSPO) has not yet made a decision on inclusion 

of LUC or GHG model at this time. 

Policies around the globe have addressed iLUC differently so far. However, measuring 

iLUC in practice is fraught with difficulty and there is no agreed methodology on how to 

analyze iLUC and dLUC at present. The distinction between the impacts is often 

blurred. 

In the U.S., EISA defines lifecycle GHG emissions as follows: “The term ‘lifecycle 

greenhouse gas emissions’ means the aggregate quantity of greenhouse gas emissions 

(including direct emissions and significant indirect emissions such as significant 

emissions from land use changes), as determined by the Administrator, related to the 

full fuel lifecycle, including all stages of fuel and feedstock production and distribution, 

from feedstock generation or extraction through the distribution and delivery and use of 

the finished fuel to the ultimate consumer, where the mass values for all greenhouse 

gases are adjusted to account for their relative global warming potential.” 

1.4 Categorizing Direct and Indirect LUC 

The categorization of dLUC and iLUC emissions provides opportunities to estimate 

emissions and yet no clear method has evolved for counting these impacts. In the 

simplest analysis, a grain crop is converted to biofuel. The grain is a global commodity 
8 | Life Cycle Associates, LLC 

and the new production of the crop could occur anywhere in the world. Furthermore 

other crops might provide a substitute and other shifts in the agricultural system could 

occur. The GHG emissions associated with changes in cropping are referred to as 

iLUC. However, expanding crop emissions may also have some effects associated with 

the crop itself. For example, corn cropping can build up root mass and soil carbon due 

to no till practice. Similarly switch grass and palm trees also build up a root mass. 

Biomass from the crop could also be harvested. The GHG flux associated with the 

biofuel crop could be categorized as dLUC or examined in a modeling system that 

accounts for all crop changes globally, thus placing the net carbon flux from the crop 

into the iLUC category. 

Detailed modeling of dLUC effects are complicated by factors such as soil conditions, 

fate of the biomass and many other factors. For the purposes of this study, dLUC 

impacts are treated as an adder4 to iLUC emissions. Alternatively, a bounding case

examines the expansion of palm onto only Malaysian land with no impact on other 

agriculture. 

 

4 The GREET model treats dLUC emissions as an adder for switch grass and farmed trees. EPA models 

net GHG flux with the FASOM model, which captures dLUC impacts in the U.S. International emissions 

are estimated by assuming a switch from one land classification to another thus simplifying the dLUC 

issue.
9 | Life Cycle Associates, LLC 

2 Well to Wheel GHG Emissions 

2.1 Malaysia Biodiesel Pathway 

The choice of aggregation for palm oil production and processing options is important in 

terms of establishing pathways under the LCFS. The choice of pathway and 

aggregation affects the requirements for fuel producers to provide data to establish 

project specific CI values. The following factors affect pathway aggregation: 

Farming Inputs

Palm oil is grown in plantations throughout peninsular Malaysia, Sabah, and Sarawak. 

The plantations occur over a range of soil conditions, age of plantation, transport 

logistics, and other factors. FFBs are processed in 400 palm mills in Malaysia. The 

source of FFBs to the palm mills is not dedicated and varies with market conditions. 

The key LCA inputs for farming are fertilizer inputs and FFB yield. These data are 

collected by the Malaysian Palm Oil Board (MPOB). Individual palm oil mills would not 

always have access to plantation level data. For the purpose of the pathway, the 

average Malaysian farming inputs and yields are recommended for pathway 

development. This situation is comparable to the data aggregation for corn ethanol and 

soy biodiesel. Average national statistics are used to characterize the fuel pathway. 

Aggregating crop production by region or farm level is problematic because data 

collection becomes too difficult for most fuel producers. Furthermore, allowing both 

country average and regional data creates a situation where producers with lower inputs 

will use local data while producers with higher inputs will choose average or default 

pathways. Therefore, using Malaysian aggregate statistics for farm inputs and yields is 

the best approach. 

Palm Oil MiIl

The palm oil mill processes FFBs to crude palm oil (CPO). Palm kernels and biomass 

products are produced as co-products. The biomass products can be exported or used 

on the plantation for energy inputs and fertilizer. This processing step is functionally 

similar to soy oil extraction where soybean oil and soybean meal are produced prior to 

shipping soy oil for processing to biodiesel. Estimates of typical U.S. facilities are used 

as inputs to the GREET model for soy oil extraction. 

However, CPO extraction is accomplished with steam, leaving the FFB residue in a wet 

solution. GHG emissions vary substantially with palm oil mill effluent (POME) 

processing options. Methane emissions from POME lagoons are a dominant source of 

fuel pathway emissions when the methane is not captured (in addition to agricultural 

chemical use). A base case and five sub-pathways were developed to address 

methane capture and use. The oil mill base case and sub-pathway scenarios are 

presented below in Table 1. 
10 | Life Cycle Associates, LLC 

Table 1. Base Case and Sub-pathway Palm Oil Mill POME Scenarios 

Oil Mill Factors Oil Mill 

Case 1 

Oil Mill 

Case 2 

Oil Mill 

Case 3 

Oil Mill 

Case 4 

POME Methane 

Capture 

No 

Capture

Lagoon 

Capture 

Lagoon 

Capture 

POME 

Digester 

tank 

POME Methane Use Released 

methane

Palm Mill 

Fuel 

Electricity 

Production 

and Export

Electricity 

Production 

and Export

POME Fate Spread 

on Field

Spread on 

Field 

Spread on 

Field 

Spread on 

Field 

Biodiesel Production

The LCA inputs for biodiesel production include processing fuel, methanol, and 

biodiesel, and glycerin yield. Transportation logistics also affect the fuel pathway 

depending on whether CPO or biodiesel is shipped to California. 

The energy inputs and yields for biodiesel production depend on the plant technology. 

More sophisticated plants will achieve higher levels of methanol recover, higher yields, 

and lower fuel use. The CA LCFS defaults for biodiesel are analyzed here. 

Transportation logistics are examined for biodiesel transport to California. 

2.2 Co-product Allocation Method and Carbon Balance

The results for all of the sub-pathways considered depend on the treatment of coproducts produced at the oil mill and biodiesel facility. The palm oil to biodiesel fuel 

pathway produces palm kernels, glycerin and various palm tree biomass products that 

can be used for energy production, lumber and other products. The main co-product 

methods used for biofuel analysis and considered here are allocation and displacement. 

The allocation method allocates production energy and emissions among the products 

produced based on some property, while the displacement method attributes all energy 

and emissions to the dominant product and grants a credit for co-products produced. 

The baseline co-product method recommended here is the hybrid allocation approach 

utilized by ARB for soy biodiesel. In this method, the energy inputs and emissions from 

agriculture, feedstock transport and oil extraction are distributed between the oil, feed 

products based on the mass share of the product streams. The merits of this allocation 

method include: 

• Analysis is consistent with soy oil pathway 

• Data is available in GREET calculations 

• Credit for palm kernel is based on mass rather than energy since kernels are 

used as food 

• Avoids substitution data for palm kernel such as coconut oil and coconut meal 
11 | Life Cycle Associates, LLC 

In the case of palm oil, the emissions are allocated to CPO and palm kernels. The 

share of energy and emissions from agriculture, feedstock transport and oil extraction 

allocated to CPO is then further allocated between the downstream biodiesel and 

glycerin products based on the energy share of each product stream. This allocation 

procedure assigns 67.4% (product of 70.9% CPO mass share and 95.0% biodiesel 

energy share) of energy and emissions to biodiesel production. The share allocated to 

palm kernels is effectively excluded from the results and represents the co-product 

credit. 

Other fuel LCA studies calculate a substitution credit based on the production of other 

tropical oils. However, this approach is complicated and prone to increased uncertainty 

since the land use impacts of products like coconut oil would also need to be taken into 

account. 

Energy and emission results are also allocated for CPO transport and transesterification 

based on the energy content of glycerin relative to the energy content of the biodiesel 

produced (95.0%). Energy and emissions from biodiesel transport and distribution are 

not allocated. This approach results in a relatively low valuation of glycerin. If the 

glycerin were valued as a chemical substitute displacing petro-glycerin, the co-product 

credit would be -21 g/MJ. The soy biodiesel developers have also made this 

observation. We believe that the approach conducted in this analysis is consistent with 

soy biodiesel and ARB should also apply it to palm oil-derived fuels. 

2.3 Oil Palm Cultivation 

The first step in the oil palm to biodiesel pathway is cultivation. The oil palm (Elaeis 

guineensis) is a perennial crop and it grows to a height of approximately 10 m before 

replanting after 25 years. The oil palm fruits are attached to bunches (FFB – fresh fruit 

bunches) weighing around 25 kg and each carrying 1,500-2,000 single fruits (for oil 

palms 10-15 years old). The harvested FFB contain around 20% oil, 25% nuts (5% 

kernels, 13% fiber and 7% shell) and 23% empty fruit bunches. The kernels contain 

approximately 55% oil and 8% protein.5

The agricultural stage for oil palm includes activities related to cultivation of oil palm. 

The inventory is normalized to 1 hectare oil palm plantation in Malaysia. The planted 

area includes mature and immature palms. Most data are based on collected data on 

cultivation practices in Malaysia. The yield of fresh fruit bunches (FFB) is applied as the 

average yield in Malaysia. 

Since oil palm is a perennial, three different stages are considered for the plantation 

site: immature plantation, mature plantation and a transition period for repositioning of 

next generation palms. Furthermore, every plantation has a nursery for sowing the 

seed. The palms are mature from two years and the economically viable life span of an 

oil palm is typically 22 to 25 years depending upon oil price, economically harvestable 

 

5

 Corley R H V and Tinker P B (2003), The Oil Palm. Fourth Edition. Blackwell Publishing 
12 | Life Cycle Associates, LLC 

height, and yield. Oil palms can exceed 70 to 100 feet in height; however they are 

typically removed from production when they reach 25 feet, which coincides with an 

average age of 25 years. The 25 feet height is an industry limit which is based loosely 

on the height of the average harvester plus the length of the long sickle harvesting pole. 

1.Land preparation

2.Planting of seedlings

3.Establishment of cover 

crop

4.Maintenance

(fertilizers, pesticides, 

etc.)

1. Immature palms

(0-2 years)

1.Harvesting

2.Maintenance

(fertilizers, pesticides, 

etc.)

1.Clearing of palms

2.Land preparation for 

next generation

2. Mature palms

(3-25 years)

3.Transition period

(1-3 month)

Nursery

1.Land preparation

2.Planting of seedlings

3.Establishment of cover 

crop

4.Maintenance

(fertilizers, pesticides, 

etc.)

1. Immature palms

(0-2 years)

1.Harvesting

2.Maintenance

(fertilizers, pesticides, 

etc.)

1.Clearing of palms

2.Land preparation for 

next generation

2. Mature palms

(3-25 years)

3.Transition period

(1-3 month)

1.Land preparation

2.Planting of seedlings

3.Establishment of cover 

crop

4.Maintenance

(fertilizers, pesticides, 

etc.)

1. Immature palms

(0-2 years)

1.Harvesting

2.Maintenance

(fertilizers, pesticides, 

etc.)

1.Clearing of palms

2.Land preparation for 

next generation

2. Mature palms

(3-25 years)

3.Transition period

(1-3 month)

Nursery

Figure 4. Stages of Oil Palm Cultivation. 

Before the oil palms are planted in the plantation they are grown in poly bags in the 

nursery. The nursery consists of two main stages. In the pre-nursery that last from the 

sowing of the seed in a small poly bags until the seedling is approximately three to four 

months. The seedlings in the small poly bag are kept under a cover protecting them for 

direct sunlight. After that, the seedling is planted in a larger poly bag in the mainnursery where it is cultivated without a protecting cover until it is 12 to 13 months old 

and ready for planting in the plantation.6

 

The palms are planted with a density of 136-148 palms per ha on mineral soils 

(assumed average of 142 palms per ha) and approximately 160 palms per ha on peat 

soils. Before the palms are planted, the soil is sprayed with pesticides, plowed, 

compacted and legume cover is sown, typically pueraria. The cover crop prevents 

erosion and fixes nitrogen from the atmosphere, especially when the palms are young. 

The oil palms are harvested the first time when they are three years old, two years after 

planting in the plantation. 

The age distribution of the oil palms in Malaysian plantations affects the yield as 

replanting rates vary with market conditions. Current yields are over 4.5 tons of CPO/ha 

with projections towards 8 tons/ha based on improvements in cultivation and more 

productive cultivars. Thus, one average hectare consists of 8% immature palms and 

92% mature palms. When the palms become unproductive (oil yield plateaus and 

 

6 Møller J, R Thøgersen, A M Kjeldsen, M R Weisberg, K Soegaard, T Hvelplund and C F Børsting 

(2000), Fodermiddeltabel - Sammen-sætning og foderværdi af fodermidler til kvæg (English: Feeding 

component table – Composition and feeding value of feeding components for cattle), Rapport nr 91 

Landsudvalget for Kvæg. Foulum, Denmark
13 | Life Cycle Associates, LLC 

declines), the palms are poisoned and felled and the land is totally cleared with 

bulldozers. The common practice today is shredding of the trunks from the clearing of 

the field and using them as mulch. The third stage is clearing and replanting. 

The average FFB yield in Malaysian estates in 2004 to 2006 was 19.03 t/ha yr for 

mature plantations (Schmidt 2008).7 The MPOB has calculated an average Malaysian 

yield of 20 tonne FFB/ha yr, based on the overall effective yield, including all stages. 

The MPOB yield value is assumed in this analysis. The yield is not directly input in the 

analysis, but is used to determine fertilizer input parameters in kg/tonne FFB from 

fertilizer data in kg/ha units. In general, higher yields are associated with larger fertilizer 

inputs. The FFB yield and fertilizer rate have a quadratic relationship; higher fertilizer 

input results in higher crop yield over a limited range and plateaus thereafter. Higher 

yields are also attributed to improved tree varieties, improved cultural practices. 

Malaysian oil yields are higher than Indonesian yields because of the large numbers of 

young palms due to great expansions of the oil palm planted area in recent years. 

Nearly 100 percent of oil palms grown today in Indonesia and Malaysia are the DxP or 

Tenera hybrid. The Tenera is a hybrid cross of the Dura and Pisifera oil palm varieties. 

The Dura line is the mother line, while pollen is used from the Pisifera. Until recently 

planting material was only available in the form of germinated hybrid seeds. In the last 

few years some plantation research and development laboratories have successfully 

cloned parent lines to produce hybrid seed of high yielding Tenera from young leaf 

tissue cultures. Figures 5 and 6 show oil palm plantations, a mature tree and a fresh 

fruit bunch (FFB). 

© Life Cycle Associates © Life Cycle Associates 

Palm oil nursery Palm oil plantation (1-year) 

Figure 5. Palm Oil Plantation Aerial Photos 

An important factor to consider is the type of cultivated soil (mineral soil versus peat 

soil). Farmers prefer mineral soil and choose peat soil for oil palm cultivation as a last 

resort because of higher labor, equipment and chemical costs and lower yields. Lands 

with peat soils require much more soil compaction and preparation than mineral soils to 

 

7

 Schmidt J.H., 2008c. Life cycle assessment of palm oil at United Plantations Berhad, (In press). Teluk 

Intan, Malaysia: United Plantations Berhad. 
14 | Life Cycle Associates, LLC 

prevent root oxidation and prevent trees from toppling over. Special equipment is 

required to compact the soil and then dig a hole-in-a-hole to plant each young palm—

the best-practice technique for planting in peat soil. Careful planning on peat soil fields 

is required to ensure suitable water conditions during the dry season and monsoon 

season. The water table should be maintained near the surface in peat soils to prevent 

roots from drying out. Oil palms grown in peat soils require a different fertilizer mix than 

palms grown in mineral soils (discussed below). Yields for oil palms grown on peat soils 

vary significantly based on the nutrient inputs, water management practices and peat 

depth. Oil palm yields from shallow peat soils are generally less than yields from 

mineral soils but similar or greater yields can be achieved on deep peat with significant 

nutrient inputs. 

© Life Cycle Associates © Life Cycle Associates 

Mature Palm oil plantation Fresh Fruit bunch (FFB) 

Figure 6. Plantation Photos. 

Approximately 11% of land used for oil palm cultivation in Malaysia contains peat soils 

and the Malaysian Palm Oil Board estimates a 60:30:10 ratio for shallow, medium and 

deep peat soils in Malaysia.8

 

The palm’s nutrient requirements are met with recycled nutrients from mulching of 

pruned fronds, EFB from the palm oil mill and land application of anaerobically treated 

POME (Palm oil mill effluent) and solid sludge, as well as synthetic fertilizers. The 

fertilizer input is made based on the expected yield and nutrient balance. FFB 

compositional data is used to estimate the quantity of each nutrient in the feedstock. 

The nutrient balance establishes the limiting nutrient(s) and accounts for nutrients 

supplied by soil, legumes and nutrient recycling. Palms planted in mineral soil require 

nitrogen input (primarily ammonium sulfate), phosphorus (as phosphate), potassium 

(potash) and magnesium (magnesium oxide). Peat soils, by contrast, contain large 

amounts of nitrogen and require phosphate rock and burnt lime (calcium oxide) to break 

down the peat and make nitrogen available. Peat soils have a low pH and require 

addition of lime or other amendments to raise the pH. The concentration of nutrients 

 

8

 Global Oils & Fats Business Magazine, 2007, Vol. 4, Issue 3. 
15 | Life Cycle Associates, LLC 

other than nitrogen is very low and these must be supplied to the palms. In particular, 

retention of potassium in peat soils is very poor and the nutrient must be supplemented. 

The Base Case life cycle pathway is based on fertilizer data reviewed and converted to 

input parameters by MPOB. The data is based on surveys of 102 oil palm plantations, 

comprising 1.1 million ha of land (approximately 25% of oil palm land in Malaysia), over 

three months in 2007. The data includes immature and mature palms grown on mineral 

and peat soil and represents a wide range of plantations at all stages of development. 

The MPOB survey data is one of the best sources available to represent average 

fertilizer inputs in Malaysia. The data is well documented, converted to input 

parameters transparently and represents a large number of producers at one time (3 

months). However, longitudinal fertilizer data (inputs over the entire life cycle of oil 

palms) is difficult or impossible to obtain. Ideally, the fertilizer inputs for the Malaysian 

average case should be based on longitudinal data of a large group of representative 

plantations. Moreover, new techniques are emerging that may improve nutrient uptake 

and reduce application requirements. These complexities make it difficult to develop 

representative fertilizer input parameters. The input values are presented in Section 

5.2. MPOB has also developed recommended nitrogen inputs and standard (average) 

Malaysian plantation nitrogen inputs by palm age based on FFB yield and palm age 

data acquired from the MPOB Observation Block Network for low, medium and high 

yielding palms. 

In addition to plantation surveys and nutrient balance analysis, fertilizer purchase data 

can be used to estimate fertilizer inputs. MPOB reviewed import data and converted the 

values to input parameters for comparison with the survey data.9

 Most of the fertilizers 

are imported and the import data provides the mass of each fertilizer imported (tonnes 

or kg). The mass of the fertilizer absorbed by oil palm is estimated, and the absorbed 

nutrient values are converted to kg/ha units based on the oil palm planted area (ha). 

The resulting fertilizer data is converted to kg/tonne FFB units based on the FFB yield 

(tonne/ha). The data indicates an increase in average fertilizer use from 2002 to 2008. 

Overall, the input parameters derived from import data are consistent with the inputs 

derived from the plantation survey data. The nitrogen input parameter derived from the 

import data is slightly lower than the similar value from the survey data, the phosphate 

inputs are the same and the potash input parameter derived from import data is 

approximately 30% lower than the survey value. 

Weeds are treated with application of herbicides and in addition insecticides and 

fungicides are applied in order to control insects and funguses. The use of insecticides 

and rodenticide is often reduced by an integrated pest management program. Usually 

that includes planting of beneficial flowering plants which attract parasites and predators 

of the common pests of the oil palm. In an integrated pest management program, the 

number of rats is reduced by barn owls. Barn owls are attracted by setting up nesting 

boxes. Another part of integrated pest management is setting up of rhinoceros beetle 

 

9 Mohamed Ali Sabre, Fertilizer Industry Association of Malaysia, (FIAM), 2009, Evolution of fertilizer use 

by crops in Malaysia: recent trends and prospects. 
16 | Life Cycle Associates, LLC 

traps.10 More complete recovery of all fruits from the ground also reduces food supplies 

for rats. 

2.4 Palm Oil Production 

The FFBs are collected and transported to a palm oil extraction facility where the oil is 

extracted and co-products separated. Figure 7 shows several photos of palm oil mill 

components. 

© Life Cycle Associates © Life Cycle Associates 

Palm oil mill FFB Loading ramp 

Figure 7. Palm Oil Mill and Loading Ramp Photographs

© Life Cycle Associates © Life Cycle Associates 

FFB sterilization facility Empty fruit bunches (EFB) 

Figure 8. FFB Sterilization and EFB Photographs 

Production and processing of crude palm oil involves several unit processes, including 

the following: 

 

10 Jannick H Schmidt, 2007, Life cycle assessment of rapeseed oil and Ph.D. thesis, Part 3: Life cycle 

inventory of rapeseed oil and palm oil, Department of Development and Planning, Aalborg University, 

June 15. 
17 | Life Cycle Associates, LLC 

• Loading ramp: after passing over the weighbridge, the fruit has to be held for a 

time until it can enter the first stage of processing. 

• Sterilization of the FFB is done batchwise in an autoclave for 1 hour 40 minutes 

for the FFB to be completely cooked. The temperature inside is approximately 

120-130°C. The steam condensate is the wastewater generated at this step. 

• Stripping (threshing): to separate the sterilized fruits from bunch stalks. This 

processing step generates the empty fruit bunches (EFB). 

• Digestion: the separated fresh fruits are put into the digester where they are 

mashed under steam-heated conditions. No residue occurs in this step. 

• Crude palm oil extraction and purification: the homogenous oil mash from the 

digester is pushed through a screw press, and later passes through a vibrating 

screen, a hydrocyclone and decanters to remove fine solids and water. Decanter 

wastewater and decanter cake are the major wastes at this step. Centrifugal and 

vacuum driers are used to further purify the oil before sending it to a storage 

tank. The purpose of purification is to boil the mixture of oil and water to remove 

water-soluble gums and resins in the oil. The temperature of oil (60°C) in the 

storage is maintained with steam coil heating before the CPO is sold. 

• Nut/fiber separation: the fiber and nuts from the screw press are separated in a 

cyclone. The fiber that passes out of the bottom of the cyclone is used as boiler 

fuel from which ash is produced after combustion. 

• Nut cracking: the nuts are cracked in a centrifugal cracker. After the cracking 

process, the kernels and shells are separated by clay suspension (Kaolin). The 

separated shells from the kernels are used as fuel or sold to other mills. The 

kernels are sent to the kernel drying process in a silo dryer. 

2.5 Scope and Definition 

2.5.1 Project Objective 

The objective of this analysis is to calculate the total fuel cycle emissions for Malaysian 

palm oil converted to biodiesel in Malaysia and used as transport fuel in California 

based on the CA-GREET model methodology in an external analysis framework. The 

analysis uses life cycle inventory (LCI) data from the CA-GREET in a separate 

workbook to calculate disaggregated fuel cycle emissions in g CO2e/MJ biodiesel. 

A major objective of the analysis is to examine the sensitivity of the results to key inputs 

and analytical methods. Two of the most important factors examined are the co-product 

credit method used and the POME scenario. The study examines the impact on coproduct credits for palm kernels and glycerin using the two different methods: allocation 

and substitution. This approach illustrates the differences in biodiesel carbon intensity 

when emissions are allocated to co-products and when co-products are instead credited 

as substituting for other products. The analysis examines the range of POME 

emissions associated with different POME management scenarios and the resulting 

impact on the fuel pathway carbon intensity. This report briefly discusses the 
18 | Life Cycle Associates, LLC 

differences in chemical inputs and feedstock yield associated with cultivation on the two 

main soil types, but the results presented here correspond to oil palm cultivation on 

mineral soil. 

2.5.2 Functional Unit 

The functional units of the analysis are the total energy and greenhouse gas emissions 

per unit fuel energy (mmBtu or MJ). Energy consumption is expressed as a unitless 

ratio of total energy input (including fuel energy) per unit of fuel output (Btu/mmBtu or 

k/MJ). GHG emissions are expressed as grams of CO2 equivalent per unit of fuel (g 

CO2e/MJ), and are referred to as the carbon intensity (CI). The GHGs considered in the 

analysis are CO2, N2O, CH4, CO and volatile organic compounds (VOC). GHG 

emissions from TTW portion of the analysis arise from the carbon content of the fuel (g 

C/MJ fuel) converted to CO2 (44 g CO2/12 g C), plus vehicle emissions of CH4

(evaporative and exhaust) and N2O. Global warming potentials (GWP) (g 

CO2e/g constituent) for CH4 and N2O are taken from the Intergovernmental Panel on 

Climate Change (IPCC) global warming potential (GWP) values (IPCC 2007) for a 100 

year time horizon. CO and VOC are assumed converted to CO2 in the atmosphere, and 

thus have GWPs of 1 when expressed as CO2 (fully oxidized form). The analysis 

excludes the climate impact of secondary and higher order atmospheric species that 

arise from direct emissions, including ozone, oxides of nitrogen (NOx) and aerosols. 

2.5.3 System Boundary 

The Malaysian palm oil to biodiesel fuel pathway presented in this report includes oil 

palm farming, fresh fruit bunch (FFB) transport, palm oil production at the oil mill, oil 

transport, biodiesel production, transport and distribution (T&D) and use of biodiesel in 

an internal combustion engine. Figure 1.1 shows the discrete components that form the 

biodiesel pathway including farming, biodiesel production, distribution to refueling 

stations and final use in a transportation vehicle. Note that the system boundary 

excludes processing of the palm kernels to produce palm kernel oil and press cake. 

These processes are outside the scope of the analysis and a co-product credit is 

assessed for whole palm kernels. Palm oil production and prior production steps occur 

in Malaysia, followed by transportation of palm oil by ocean tanker to California. 

Biodiesel is produced from palm oil and transported to fueling stations for use in internal 

combustion vehicles. 

2.6 Analysis Methods 

This analysis used LCI data extracted from the California GREET for the individual 

process fuels, chemical inputs and transport segments contained in the fuel pathway. 

These data vectors were assembled in an off-model matrix format (discussed in Section 

4.3) that uses the input energy for each process fuel or chemical (specific energy) with 

the LCI data to calculate results for each step of the fuel pathway. 

The CA-GREET model was set to target year 2010 and configured to “Southeast Asia” 

for Malaysian palm oil farming, transport, oil production and transesterification, to “U.S. 

average” for biodiesel transport by ocean tanker and to “CA Marginal” for fuel transport 

and distribution in California. Fuel combustion is a function of the fuel carbon content 
19 | Life Cycle Associates, LLC 

and heating value and the gram per mile vehicle emission factors for methane (CH4) 

and nitrous oxide (N2O). The following two subsections describe LCI data and the 

matrix format in greater detail. 

2.7 Life Cycle Processes and Inputs 

2.7.1 Oil Palm Cultivation Energy 

Energy use was derived mainly from diesel consumption in agricultural machinery used 

in nursery, maintenance, harvesting and collection procedures. It has not been possible 

to determine the specific diesel consumption per single field work process per hectare. 

Data has only been available as the total diesel consumption per year on the estate 

level. Therefore, diesel consumption in machinery in the plantation is determined as a 

total value including all field work processes per ha per year. A recently published study 

about the United Kingdom Renewable Transport Fuel Obligation estimates diesel 

consumption for the oil palm farming of approximately 18.5 gallons (70 liters)11 per 

hectare per year. This input per area is converted to an input per mass feedstock (MJ 

diesel/MJ biodiesel produced as follows: 

70.0 l ha 35.8 MJ 125.4 MJ/tonne FFB

ha 20 tonne l

       =         

This parameter referred to as a specific energy in the analysis, although a true specific 

energy value is dimensionless, reflecting input energy divided by output energy. The 

process specific energy is converted to a net specific energy (MJ diesel/MJ biodiesel) 

by multiplying it by the feedstock use factor. This is accomplished with a matrix 

calculation and is done for most life cycle components in the analysis and will not be 

explicitly shown for each pathway step. 

125.4 MJ 4.01 tonne FFB tonne 1.04 tonne CPO kg BD 

tonne FFB tonne CPO tonne BD 1,000 kg 37.9 MJ

0.017 MJ diesel/MJ BD

           =           

2.7.2 Fertilizers and Pesticides 

The next component of the farming energy use is the energy and emissions associated 

with production and transport of fertilizers, pesticides and herbicides. The LCI chemical 

data includes transportation of each product from plant to field. The farming data is 

converted from g/ha per year to g/tonne for use in the LCA calculation based on FFB 

yield per hectare (tonne FFB/ha/yr). 

During lifetime of plantation several nutrients are needed. Values given in the literature 

reflect total nutrient needs. Table 2 summarizes the Base Case and sub-pathway 

inputs used in this analysis. Other inputs include magnesium and trace minerals copper 

 

11 RTFO, 2008. United Kingdom Renewable Transport Fuel Obligation (RTFO). Carbon and Sustainability 

Reporting Within the Renewable Transport Fuel Obligation Requirements and Guidance Draft 

Government Recommendation to RTFO Administrator. London. Referenced [30.1.2008]. 

Available:www.dft.gov.uk/roads/RTFO 
20 | Life Cycle Associates, LLC 

and zinc. Although other trace inputs are often used, these are excluded for lack of 

data and these inputs have a negligible impact on fuel cycle emissions. 

Table 2. Fertilizer Input Parameters.

Malaysia Average 

(MPOB Data) 

Data kg/ha/yr kg/tonne FFB 

 N (N) 69.8 3.5 

 P (P2O5) 56.0 2.8 

 K (K2O) 230.0 11.5 

Mg (Mg) 18.0 0.9 

FFB Yield 

(tonne/ha) 

19 - 2010 

25 - 2020 

This need can be fulfilled either by using artificial synthetic fertilizers, biomass residuals 

(pruned fronds, EFB, and POME), decomposition from atmosphere or decrease from 

soil nitrogen pool. In this study it was assumed that nutrient requirements are fulfilled 

by using synthetic fertilizers. Ammonium sulfate is the primary nitrogen fertilizer used 

for Malaysian palm oil production. Although GREET does not include ammonium 

sulfate, it models ammonia and sulfuric acid, which are used to manufacture ammonium 

sulfate. Therefore, an LCI vector representing ammonium sulfate was constructed for 

the analysis based on these precursor chemical results from GREET. MPOB survey 

data representative of oil palm cultivation in Malaysia was used to characterize the base 

case fuel pathway. 

The applied consumption of pesticides and herbicide are the average of 1 year of 

nursery, 1 year immature and 23 years mature oil palm and results in annual application 

of 2.59 kg herbicide/ha, 0.58 kg pesticide/ha (Schmidt, 2007). These values are similar 

to the value given (3 kg/ha year) in the RTFO methodology documentation (RTFO, 

2008), where no differentiation between herbicide and pesticide has been done. 

The typical herbicide used for Malaysian oil palm cultivation is glyphosate, but this 

herbicide is not included in the GREET model. The closest herbicide in applicability that 

is included in GREET and also used on oil palm plantations is atrazine, which is 

assumed as herbicide in this analysis. 

Table 3 Herbicide and Pesticide Consumption on Palm Oil Plantations. 

Input Mature Immature Nursery Applied Value 

Input 

Value 

Active Components kg/ha yr kg/ha yr kg/ha yr kg/ha yr g/tonne 

FFB 

Atrazine Herbicide 2.30 3.60 8.20 2.59 121 

Insecticide 0.30 0.61 7.08 0.58 27 

2.7.3 Field and Downstream N2O Emissions 

The analysis includes the direct field and downstream N2O emissions resulting from 

conversion of nitrogen in the fertilizer input and above and below ground biomass. 

Agricultural N2O emissions result from conversion of fixed (natural and anthropogenic) 

nitrogen in the soil. Fixed nitrogen applied to field crops is either extracted by the crop 
21 | Life Cycle Associates, LLC 

as a nutrient, absorbed (chemically bound) into organic soil components or entrapped in 

soil aggregates (chemically unbound). The majority of the chemically bound nitrogen 

remains stabilized in the organic form in the soil system, while the unbound nitrogen is 

converted to N2O, volatilized as nitrate or ammonia, or leached out as nitrate. Field and 

downstream inputs are significant components of agricultural emissions associated with 

Oil palm cultivation. GREET assumes 1.3% of fertilizer-N is ultimately converted to 

N2O, including 1.1% conversion on the field and 0.2% conversion downstream. The 

calculation of downstream N2O emissions is presented below: 

( ) 2 2

2

3,490 g N 44 g N 72.65 g N 1.3%

tonne FFB 28 g N tonne FFB

    O O

    =

   

The calculation uses the mass ratio of N2O to N2 (44/(2x14)). 

2.7.4 FFB and EFB Transport 

Fresh fruit bunches are transported 17 miles (27 km) by medium duty diesel truck to a 

fuel production plant in Malaysia. Crude palm oil (CPO), palm kernels, fiber and other 

palm tree products are extracted from the FFB, yielding empty fruit bunches (EFB). 

This analysis assumes half (50%) of the EFB remain at the plant and half is transported 

back to the field by medium duty truck. The key transport parameters are presented in 

Table 4. Approximately 5.07 kg of FFB are required per kg of CPO produced and 1.04 

kg of CPO is required per kg of biodiesel produced. The conversion factors are used to 

convert the calculated results per ton of transported product to energy and emissions 

per MJ of biodiesel produced. 

Table 4. Herbicide and Pesticide Consumption on Palm Oil Plantations. 

Transport Mode Medium Duty Truck Medium Duty Truck 

Cargo Fresh Fruit Bunch Empty Fruit Bunch 

Fuel Diesel Diesel 

Fuel Economy (mi/gal) 7.3 7.3 

Capacity (tons) 12 12 

Share of FFB, EFB 100% 50% 

Distance (mi) 17 17 

Energy Intensity (Btu/ton-mi) 1,466 1,466 

Fuel Conversion factor (kg/kg RD) 5.27 0.69 

2.7.5 Oil Extraction and Processing 

Data from twelve Malaysian palm oil mills were used for this study (see Table 5 and 6). 

MPOB converted the survey results in life cycle input parameters. The data were 

published by Subramaniam in 2008 and provides a good representation of palm oil mills 

in Malaysia (Vijaya Subramaniam, 2008). These palm oil mills were selected based on 

the type of mill which were either plantation based mills or private mills. They have 

different processing capacities of oil palm FFB ranging from 20 tonnes per hour up to 90 

tonnes per hour. 
22 | Life Cycle Associates, LLC 

Table 5. Palm Oil Mill Energy and Material Inputs. 

Palm Oil Mill 

Output: 1 

tonne of CPO 1 2 3 4 5 6 7 8 9 10 11 12

Applied 

values 

 Processing 

Capacity (t/hr) 20.00 30.00 40.00 40.00 45.00 45.00 50.00 50.00 60.00 65.00 70.00 90.00 50.42

 Fuel Flow 

Rate (t/hr) 2.50 3.50 5.60 4.50 4.88 5.00 6.00 5.90 7.50 7.50 13.00 11.00 6.41

 Steam output 

(t/hr) 10.00 14.00 22.50 17.50 19.00 18.00 24.00 23.50 30.00 30.50 52.00 42.50 25.29

 Environmental Inputs 

 Fresh fruit 

bunch (t) 4.93 5.07 5.34 5.48 5.27 4.67 5.47 5.10 4.67 4.67 4.87 5.39 5.07

 Power from 

turbine (kWh) 99 101 107 110 105 93 109 102 93 93 97 108 101

 Power from 

Grid (kWh) 2.00 1.99 0.18 0.25 2.95 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0

 Diesel for Mill 

Use (l) 2.17 0.00 1.29 1.98 2.92 2.86 2.45 2.12 4.47 3.37 4.51 0.38 2.48

 Diesel for 

Vehicles in Mill 

(l) 0.28 0.15 2.60 2.96 0.84 3.54 1.08 1.25 3.40 3.41 0.68 2.10 2.00

 Boiler fuel: 

shell (t) 0.13 0.27 0.22 0.14 0.20 0.06 0.14 0.13 0.11 0.11 0.36 0.07 0.16

 Mesocarp 

Fiber (t) 0.51 0.63 0.52 0.57 0.47 0.58 0.55 0.52 0.43 0.43 0.55 0.60 0.53

 Boiler water 

consumption 

(t) 2.50 3.64 2.92 2.79 2.66 2.35 2.88 2.54 2.10 2.14 3.64 2.55 2.70

 Steam for 

Sterilization (t) 2.46 3.29 2.77 2.63 2.64 2.34 2.84 2.55 2.10 2.10 3.16 2.53 2.59

 Steam to 

turbine (t) 2.50 3.64 2.92 2.79 2.66 2.35 2.88 2.54 2.10 2.14 3.64 2.55 2.70

 Process 

Water (t) 3.42 2.95 3.48 3.79 3.67 3.26 3.68 3.57 3.51 3.47 3.66 3.38 3.50

These mills are located at different zones in west Malaysia. Inventory data was 

collected from the selected palm oil mills over a period of three months from each mill, 

which consisted of raw material usage as well as the emissions of solid, liquid and 

gaseous wastes. The weighted average of data values were used to define 

representative parameters for the analysis. 

The major inputs for crude palm oil processing are energy and water. In general, the oil 

mills have their own power and steam supply derived from palm residues. Power is 

provided with some of the fiber and shell from the processing. Diesel fuel is used during 

start-ups of the boiler. Diesel is also used to fuel oil mill vehicles use to move and 

transport palm material. The weighted average oil mill input parameters indicate that 

most mills use no grid electricity and little fossil fuel inputs. The Base Case oil mill 

scenario assumes that 80% of the POME methane emissions are captured and used as 

process fuel, effectively displacing the use of an equivalent quantity of biomass process 

energy. The calculation of the total methane and captured emissions are shown below: 

3

4 4

3

55.38 m 1 tonne 777 g CH 49.9 MJ CH 1 kg 1.7 MJ/kg CPO

tonne CPO kg kg 1000 g m

                  =

       

The usual extraction rate for Malaysian palm oil mills is between 0.20 and 0.24. A 

weighted average extraction rate 0.197 was used in this study. This means that 
23 | Life Cycle Associates, LLC 

processing 5.07 tonnes FFB produces 1 tonne crude palm oil. During processing, 

significant amount of palm kernels (PK) are produced. Processing of 5 tonnes FFB 

produces approximately 0.41 tonnes palm kernels. Kernels are used for extraction of 

palm kernel oil (PKO) and the production of palm kernel meal. Oil mills also produce 

fiber in excess of that used as process fuel and woody co-products. The next section 

discusses the co-products produced and co-product life cycle treatment. The total mass 

of the products and co-products is approximately 30% of the input FFB mass. The 

remaining 70% mass becomes effluent. 

Figure 9 shows a process flow diagram for oil production and processing. Demand for 

energy and water varies from process to process and estimated plant efficiency 

influences the energy and water consumption values. Figure 10 shows a biomass 

boiler and effluent pond. 

Table 6. Palm Oil Mill Outputs. 

Palm Oil Mill 

Output: 1 

tonne of 

CPO 1 2 3 4 5 6 7 8 9 10 11 12

Applied 

values 

 Kernel (t) 0.39 0.41 0.43 0.44 0.42 0.37 0.44 0.41 0.37 0.37 0.39 0.43 0.41

 Mesocarp 

Fiber (t) 0.08 0.00 0.12 0.09 0.16 0.00 0.10 0.09 0.13 0.13 0.04 0.05 0.08

 Shell (t) 0.22 0.09 0.15 0.24 0.17 0.26 0.24 0.23 0.22 0.22 0.00 0.31 0.20

 Empty Fruit 

Bunch (t) 1.13 1.17 1.23 1.26 1.21 1.07 1.26 1.17 1.08 1.07 1.12 1.24 1.17

 Palm Oil Mill 

Effluent (t) 2.96 3.04 3.20 3.29 3.16 2.80 3.28 3.06 2.80 2.80 2.92 3.23 3.04

 CH4 form 

POME pond 

(m3) 53.83 55.35 58.27 59.82 57.59 51.00 59.70 55.64 51.04 51.03 53.16 58.81 55.38

 CO2 form 

POME pond 

(m3) 28.99 29.81 31.38 32.21 31.01 27.46 32.15 29.96 27.48 27.48 28.62 31.67 29.82

 Flue gas from stack

 Particulate 

Matter (kg) 0.07 0.15 0.11 0.13 0.14 0.13 0.15 0.15 0.16 0.17 0.45 0.70 0.26

 CO (kg) 0.02 0.05 0.06 0.05 0.06 0.06 0.06 0.06 0.07 0.07 0.14 0.13 0.08

 CO2 (kg) 13.74 43.47 56.87 57.39 56.06 60.72 60.70 59.41 67.99 68.81 161.20 142.18 82.71

 SOx (kg) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

 NOx (kg) 0.04 0.08 0.12 0.10 0.09 0.11 0.10 0.09 0.10 0.10 0.20 0.25 0.13

 EFB use Mulching Mulching 

Shredded 

for fuel 

use Mulching Mulching Mulching Mulching Mulching Mulching Mulching Mulching Mulching Mulching 

 POME Treated Treated Treated Treated Treated Treated Treated Treated Treated Treated Treated Treated Treated 

There are three main sources of POME in the palm oil mill: sterilizer condensate (36% 

of total POME), clarification waste water (60% of total POME) and hydro cyclone waste 

water from nut and fiber separation (4% of total POME) (Department of Environment 

1999)12.After recovering residual oil in the waste water using oil traps etc., the POME is 

treated in an anaerobic pond. The decomposition of organic matter in the POME causes 

 

12 (Department of Environment 1999), Industrial Processes & the Environment (Handbook No. 3) Crude 

Palm Oil Industry. Department of Environment, Malaysia
24 | Life Cycle Associates, LLC 

biogas. Very few of the palm oil mills in Malaysia are using the produced biogas for 

energy production or at least flaring it on order to reduce GHG emissions. 

Digested POME 3.04 MT

CH4

55.38 m3

CO229.82 m3

Sterilizer

Stripper

Digester

Screw Press

EFB hopper

Nut and fiber

separation Clarifier Drum

Decanter

Anaerobic 

POME ponds

CPO

1 MT

Nut cracking

Boiler

Turbine

Kernel

0.41 MT

Fiber

Shell

Nuts

POME

Waste water

Press cake

Steam

Electricity

Net Electricity Export 0 MWh

Grid

EFB

1.17 MT

FFB

5.07 MT

Diesel

4.48 l

System boundaries

Water

3.5 MT

Digested POME 3.04 MT

CH4

55.38 m3

CO229.82 m3

Sterilizer

Stripper

Digester

Screw Press

EFB hopper

Nut and fiber

separation Clarifier Drum

Decanter

Anaerobic 

POME ponds

CPO

1 MT

Nut cracking

Boiler

Turbine

Kernel

0.41 MT

Fiber

Shell

Nuts

POME

Waste water

Press cake

Steam

Electricity

Net Electricity Export 0 MWh

Grid

EFB

1.17 MT

FFB

5.07 MT

Diesel

4.48 l

System boundaries

Water

3.5 MT

Figure 9. Oil Palm Mill Process Diagram. 

Therefore, the main air emission from the POME ponds during the anaerobic digestion 

is the biogas which consists of methane, carbon dioxide and traces of hydrogen sulfide. 

An example of a POME biogas capture plants is a facility completed in Perak, Malaysia 

in 2006, which produce carbon credits under a CDM-EB approved methodologies, but 

this is still the exemption.13 

 

13 (Aquarius, 2008), An Integrated POME Methane Recovery - Effluent Treatment System and CDM 

Financing, Aquarius, Systems (Malaysia) Sdn Bhd, 40150 Shah Alam, Selangor, Malaysia, 2008. 
25 | Life Cycle Associates, LLC 

© Life Cycle Associates © Life Cycle Associates 

Biomass Boiler at Palm Oil Mill Untreated POME Effluent 

Figure 10. Biomass Boiler and Untreated POME Effluent. 

2.7.6 Palm Oil Mill Co-Products 

This study considers the following co-products exiting the oil mill: palm kernels, fiber 

and palm products. Co-product credits are calculating using the hybrid allocation 

method utilized by the California Air Resources Board (ARB) under the Low Carbon 

Fuel Standard for soybean-based biodiesel. This hybrid method allocates energy and 

emissions of oil extraction and upstream steps (oil palm farming, FFB transport) 

between CPO and palm kernels (PK) based on the mass of each product stream (mass 

allocation) and allocates energy and emissions for transesterification between BD and 

glycerin for transesterification and upstream steps (oil palm farming, FFB transport, oil 

extraction). The mass shares of the feed stream, and all mass-based allocation factors 

used are shown below in Table 7. The allocation factors for mass-based allocation are 

calculated based on the mass shares of the products indicated in the table. 

The hybrid method is also utilized for the case where fiber and biomass considered coproducts. Fiber and biomass could be used to generate electricity and some biomass 

could be used as lumber. The second allocation method is the same as the first, except 

that the mass-based allocation factor is calculated as the CPO mass divided by the 

collective CPO, PK, fiber and palm product mass (63.2%) rather than the CPO mass 

divided by the CPO and PK mass (70.6%). This approach effectively credits fiber and 

palm products in addition to CPO and PK based on the mass of all of the products. 

The second method is displacement, in which palm kernels are credited as displacing 

feed corn on a mass basis (based on the LCI data for feed corn). In general, the 

displacement method is the preferred approach for co-products when there is a small 

number of displaced products, the displacement factor is well characterized (kg product 

displaced/kg co-product), and the assumed displacement is economically justified. 

Although the LCI data for displaced feed is available, using the displacement method to 

credit palm kernels is difficult because of the difficulty in developing a good 

displacement metric. Ideally, feed displacement should be based on animal weight gain 

equivalence, meaning that one tonne of palm kernels would displace the mass of 

another feed required to yield the same increase in animal weight as the 1 tonne 

kernels. Unfortunately, no such metrics are currently available. Other metrics include 
26 | Life Cycle Associates, LLC 

feed mass, calories and protein content, although none of these are necessarily 

correlated to animal weight gain in the same way across feed products. The 

displacement factor for displaced glycerin is straightforward, but this approach becomes 

less justifiable when the glycerin market is saturated with bio-glycerin (as has happen in 

recent years). This analysis assumes displacement on a mass basis for PK; 

displacement credits are not considered for fiber and biomass co-products. 

Table 7. Feedstock components and Allocation shares. 

Product Mass Flow 

(kg/tonne FFB) 

Yield 

(kg/kg CPO) 

Mass Share 

(All Products) 

Mass Share 

(CPO and PK) 

CPO 197 1.0 63.4% 70.9% 

Palm Kernels 81 0.4 26.0% 29.1% 

Fiber 11 0.1 3.5% 

Palm Tree Product 22 0.1 7.1% 

Palm oil production yields palm kernels, which are transported to another facility that 

converts the kernels into kernel oil and press cake using a mechanical pressing 

process. The products are used as tensides and animal feed respectively. Production 

statistics show that 1 tonne of kernel delivers approximately 0.45 tonne of PKO and 

0.52 tonne of Palm kernel cake (PKC) (Schmidt 2007).

14 For every tonne of CPO about 

0.1845 tonne of PKO (0.41 tonne kernels/tonne CPO x 0.45 tonne PKO/tonne kernel) 

can be produced. For every tonne of CPO about 0.2132 t of PKM (0.41 tonne 

kernels/tonne CPO x 0.52 tonne PKC/tonne kernel) can be produced. This analysis 

considers only the palm kernels as a co-product; the palm kernel processing plant is 

outside of the system boundary of the analysis. 

2.7.7 Palm Oil Transport 

As discussed in the previous section, palm oil is produced at palm oil extraction plants 

in Indonesia and Malaysia and then transported via heavy duty trucks to a biodiesel 

production facility. Detailed transport parameters are provided in Table 8. 

Table 8. CPO Transport Parameters. 

Transport Mode Heavy Duty Truck 

Cargo CPO 

Fuel Diesel 

Fuel Economy (mi/gal) 5 

Capacity (tons) 25 

Share of CPO 100% 

Distance (mi) 50 

Energy Intensity (Btu/ton-mi) 1,028 

Fuel Conversion factor (kg/kg RD) 1.04 

 

14 Jannick H Schmidt (2007), Life cycle assessment of rapeseed oil and Ph.D. thesis, Part 3: Life cycle 

inventory of rapeseed oil and palm oil, Department of Development and Planning, Aalborg University, 15-

jun-2007.
27 | Life Cycle Associates, LLC 

2.7.8 Biodiesel production 

After the CPO is transported by ocean tanker to a fuel plant in California, the feedstock 

undergoes a transesterification reaction to produce biodiesel. In this process, palm oil 

(or other plant oil) is combined with methanol in the presence of a catalyst (acid or 

base) to produce fatty acid methyl esters (FAMEs) and co-product glycerin. A base 

catalyst (sodium hydroxide) is typically used to catalyze transesterification by removing 

a proton from the alcohol, making the alcohol enhances the ion more reactive. Sodium 

methoxide is also used as a strong base to help facilitate the reaction; methoxide, the 

negative ion formed when sodium methoxide dissociates, is the conjugate base on 

methanol. The mixture of FAMEs is the biodiesel product. Figure 5.3 illustrates this 

process, in which R in the figure represents the fatty acid hydrocarbon chain in the 

triglycerides comprising the oil. As the figure shows, 1 mole of oil triglycerides reacts 

with 3 moles of alcohol to yield 3 moles of FAME and 1 mole of glycerol (glycerin) in the 

transesterification reaction. 

Figure 11. Transesterification Reaction of Triglycerides to Methyl Esters. 

The inputs driving the transesterification energy and emissions calculation include the 

direct esterification energy inputs (natural gas and electricity), chemical inputs and the 

oil feedstock usage factor (kg RD/kg CPO). Table 9 below presents the direct energy 

inputs assumed for palm oil transesterification. Natural gas is used for heating and 

electricity is used for mixing and peripheral processes. The analysis includes the direct 

energy content of the chemicals used in the process, but omits upstream energy and 

emissions associated with chemical production, since the GREET model does not 

model the life cycles of these chemicals. Therefore, the GREET calculations 

underestimate the total energy and emissions associated with oil transesterification. 

The transesterification electricity input is 0.03 kWh/kg BD. 
28 | Life Cycle Associates, LLC 

Table 9. Transesterification Energy and Chemical Inputs 

Input Energy (MJ/kg) Energy Share (%) 

Natural gas 2.1 42.9% 

Methanol 2.0 41.8% 

Sodium hydroxide 0.1 2.0% 

Sodium methoxide 0.5 10.1% 

Hydrochloric acid 0.1 3.1% 

Total 4.8 100.0% 

2.7.8.1 Glycerin Co-Product 

Glycerin is produced as a byproduct of transesterification. The glycerin yield is 

calculated in this analysis based on ideal stoichiometric yield and the CPO triglyceride 

profile. The typical fatty acid profile for palm oil is shown below in Table 10, based on 

palm oil composition presented in the Renewable Energy Group report.15 As the table 

shows, the weighted average molecular weight (MW) for FAME produced from CPO is 

270.8 g/mol. 

Table 10. Palm Oil Fatty Acid Profile 

Fatty Acid Mass Share 

Fatty Acid 

MW (g/mol) 

Methyl Ester 

MW (g/mol) 

Arachidic acid 0.3% 312.5 326.6 

Linolenic acid 0.3% 278.4 292.5 

Lauric acid 0.2% 200.3 214.4 

Linoleic acid 8.6% 280.4 294.5 

Oleic acid 41.9% 282.5 296.5 

Stearic acid 4.6% 284.5 298.5 

Palmitic acid 43.4% 256.4 270.5 

Palmitoleic acid 0.1% 254.4 268.4 

Myristic acid 0.5% 228.4 242.4 

Behenic acid 0.1% 340.6 354.6 

Weighted Average 100.0% 270.8 284.8 

The theoretical stoichiometric mass ratio is then calculated based on the FAME 

weighted average molecular weight, the molar mass of glycerin and glycerin molar yield. 

The MW for glycerin is 92.1 g/mol, indicating a glycerin-to-FAME mass ratio of 0.108, as 

shown below: 

=

92.1 g/mol glycerin 0.108 g glycerin/g FAME

(3 mol FAME/mol glycerin)*(270.8 g/mol FAME)

Glycerin can be used for many purposes, including animal feed, soap production, 

energy, chemical synthesis and many applications in the food and pharmaceutical 

industries. Glycerin can be used as a feedstock for many chemical syntheses yielding 

high-value products because it functions as a versatile prochiral building block. Life 

 

15 Sanford, S., et al., 2009, “Feedstock and Biodiesel Characteristics Report”, Renewable Energy Group, 

available at www.regfuel.com
29 | Life Cycle Associates, LLC 

cycle analyses have just begun to consider chemical co-products as feedstocks for 

high-value chemical production and very little LCI data is currently available to calculate 

a displacement credit. 

Like the palm oil mill co-products, both allocation and displacement methods are used 

to calculate co-product credits for glycerin. Energy allocation is used (rather than massbased allocation) and the displacement method assumes petro-glycerin displacement 

by bio-glycerin. The energy allocation method represents glycerin used as fuel, which is 

a conservative approach that credits based on the lowest value use of glycerin. The 

calculation for the glycerin energy allocation factor is based on the glycerin yield and 

energy content; the energy allocation factor calculation is shown below: 

( )

(37.9 MJ/kg BD) = 95.0%

37.9 MJ/kg BD + (0.108 kg glycerin/kg BD)(18.6 MJ/kg glycerin)

 

The heating value for palm oil biodiesel in the above equation is derived from the 

literature.16 This allocation factor is applied in conjunction to the PK allocation factor to 

energy and emission results for palm cultivation, FFB transport and oil extraction; the 

factor is applied alone to the transesterification results and no allocation factors are 

used for the biodiesel transport and distribution results. 

The displacement method represents a more liberal credit because it assumes that the 

bio-glycerin produced displaces glycerin produced from petroleum, which has a 

relatively high carbon intensity. This assumption is valid only when petro-glycerin is the 

dominant glycerin source, the bio-glycerin displaces existing glycerin supply to meet 

demand (rather than stimulating new demand) and bio-glycerin represents an 

insignificant share of the market (the bio-glycerin does not impact market forces). 

The displacement factor for displaced glycerin is straightforward, but this approach 

becomes less justifiable when the glycerin market is saturated with bio-glycerin (as has 

happen in recent years). This analysis assumes displacement on a mass basis for PK 

and glycerin. 

2.7.9 Fuel Transport and Distribution 

As discussed in the previous section, biodiesel is produced at a biodiesel plant in 

Malaysia and then transported via heavy duty trucks to the closest port by heavy duty 

truck (150 miles, 241 km) and later on shipped with ocean tanker (9,000 miles, 14,484 

km) to California for biodiesel production. After transport to a California port, the 

biodiesel is transported 50 miles (80 km) by heavy duty truck to a bulk fuel facility and 

then distributed 90 miles (145 km) to refueling stations. Table 11 provides the transport 

input assumptions for biodiesel transport. 

The heavy duty truck transport calculations include energy and emissions associated 

with the back haul, since the truck is assumed to return empty after transporting 

 

16 Mehta, P., et al., 2009, “Estimation of a Lower Heating Value of Vegetable Oil and Biodiesel Fuel,” 

Energy and Fuels, 23, 3893 – 3898.
30 | Life Cycle Associates, LLC 

biodiesel; ocean tankers, by contrast, are assumed to carry fuel or chemicals during the 

backhaul, and therefore energy and emissions associated with the ocean tanker back 

haul are not counted in the analysis. The energy values are converted from Btu/tonmile to total energy as follows: Btu/ton-mile inputs are converted to Btu/ton, which is 

converted to Btu/mmBtu fuel for both legs of the trip. No allocation factor adjustment is 

needed for biodiesel transport. 

Table 11. Biodiesel Transport Parameters. 

Transport Mode Heavy Duty 

Truck 

Ocean 

Tanker 

Heavy Duty 

Truck 

Heavy Duty 

Truck 

Cargo Biodiesel Biodiesel Biodiesel Biodiesel 

Transport Segment BD Plant to 

Port 

Malaysia Port 

to CA Port 

CA Port to 

Bulk Dist. Distribution 

Fuel Diesel Residual Oil Diesel Diesel 

Fuel Economy (mi/gal) 5 5 5 

Capacity (tons) 25 250,000 25 25 

Share of biodiesel 100% 100% 100% 100% 

Distance (mi) 150 9,000 50 90 

Energy Intensity (Btu/ton-mi) 1,028 1,028 1,028 1,028 

The energy and greenhouse gas emissions for biodiesel transport are calculated the 

same way as the transport energy. The general formula for the direct and upstream 

transport emissions are shown below: 

The BD transport and distribution calculations are based on LCI data from GREET 

calculated as described above on an energy basis (MJ biodiesel), in contrast to the 

feedstock (FFB and CPO) transport calculations, which are based on mass-based (ton 

cargo) LCI data. 

2.7.10 Fuel Combustion 

The tank-to-wheel emissions refer to vehicle tailpipe fuel combustion emissions. Fuel 

combustion emissions per MJ of fuel are independent of the life cycle analysis 

conducted for the WTT component and contain two main parts: tailpipe CO2 calculated 

based on the fuel carbon content and fuel heating value and combustion CH4 and N2O, 

which depend on the emission factors of the specific equipment and vehicle energy use. 

Emission factors (g/km) from the EMFAC model are used to represent biodiesel 

combustion CH4 and N2O emissions and the vehicle energy use (MJ/km). 

Vehicle CO2 (Carbon in Fuel) 

This life cycle analysis considers only the fossil carbon in fuel (expressed as fully 

oxidized, g CO2/mmBtu fuel), since biologically derived fuel carbon originates from the 

atmosphere and the net greenhouse gas impact is neutral. This approach is consistent 

with the IPCC guidelines. The only fossil carbon in biodiesel originates from the 

methanol (produced from natural gas) used in palm oil transesterification. Table 12 

shows the parameters need to calculate fossil CO2 emissions per MJ of fuel and the 

result. Note that EPA and the GREET model do not count the fossil carbon in 

biodiesel, instead providing a defacto credit for the production of bio glycerin. 
31 | Life Cycle Associates, LLC 

Providing a substitution credit for the glycerin more accurately reflects the 

environmental impact of this co-product. Thus fossil carbon in the biodiesel could be 

counted with a credit for the production energy for glycerin and the displacement of 

fossil glycerin. This substitution analysis would be most consistent with the EPA’s 

RFS2 analysis. 

Table 12. Biodiesel Transport Parameters. 

Description Biodiesel 

BD Lower Heating Value (MJ/kg) 37.5 

MeOH Input to Transesterification (MJ/kg BD) 2.0 

MeOH Lower Heating Value (Btu/gal) 57,250 

MeOH Density (g/gal) 3,006 

MeOH Carbon Ratio (wt%) 37.5% 

CO2/C Mass Ratio (wt%) 44.0095/ 

12.011 

Fossil Carbon in Fuel (gCO2e/MJ) 3.67 

Vehicle CH4 and N2O emissions 

The CH4 and N2O emissions are assumed to be the same as ULSD. ULSD emission 

factors for heavy duty trucks are shown in Table based on EMFAC factors used by the 

Air Resources Board. The vehicle energy use, N2O and CH4 emission rates and final 

emissions are shown in Table 13. 

Table 13. Vehicle CH4 and N2O Emissions. 

Parameter 

2010 

Emissions 

factor (g/km) 

GWP GHG 

(gCO2e/MJ) 

N2O 0.030 298 0.692 

CH4 0.022 25 0.042 

Energy Use (MJ/km) 12.8 0.73 

The total tailpipe fossil carbon CO2 plus combustion CH4 and N2O is 4.40 g CO2e/MJ 

BD. For comparison, the combustion fossil carbon in conventional diesel (ULSD) is 

74.95 CO2e/MJ, for total TTW emissions of 75.68 g CO2e/MJ including combustion CH4

and N2O. 

2.8 Well-to-Wheel Results 

The direct CI results for palm oil biodiesel are presented below in Table 14. The table 

shows greenhouse gas emissions disaggregated by pathway stage for two different sets 

of co-product methods and the reference petroleum Ultra-Low Sulfur Diesel (ULSD) 

results. In the first case, which corresponds to the co-product treatment used by ARB 

for soy biodiesel, mass allocation was used to account for production of palm kernels 

and wood products (fiber and palm) and the energy allocation method was used to 

credit the bio-glycerin produced. The CI results for this scenario are 23.3 g CO2e/MJ, 

representing a 75% emission reduction compared to the petroleum baseline. The CI is 

very similar to the CI associated with soy biodiesel (21.25 g CO2e/MJ) under the LCFS. 

The second pathway scenario presents the CI results when the mass allocation method 
32 | Life Cycle Associates, LLC 

was applied to palm kernels and wood products and the displacement method was used 

to credit bio-glycerin for displacing petro-glycerin. This method results in a large credit 

for bio-glycerin because the carbon in the petro-glycerin is assumed to be oxidized to 

carbon dioxide at the end of the chemical life cycle; the carbon in bio-glycerin is 

considered biogenic and the net carbon emissions are zero. The CI using these coproduct methods is 1.8 g CO2e/MJ, representing a 98% reduction compared to the 

petroleum baseline. 

Table 14. Malaysian Palm Oil Biodiesel Fuel Cycle Emission Results (g CO2e/MJ) 

Co-Product Methods: 

Malaysian Palm Oil 

Biodiesel CA ULSD 

Palm Kernel 

Mass 

Allocation 

Mass 

Allocation 

Fiber and Palm Products 

Mass 

Allocation 

Mass 

Allocation 

Glycerin 

Energy 

Allocation Displacement 

Farm Equipment 0.9 0.9 Crude Extraction 6.93 

Farm Chemicals 4.5 4.5 

FFB Transport 0.6 0.6 Crude Transport 1.14 

Oil Extraction 0.7 0.7 

POME Methane 3.6 3.6 

Oil Transport 0.3 0.3 

Transesterification 5.2 5.2 Crude Refining 11.41 

BD T&D 3.2 3.2 Diesel T&D 0.33 

Fuel Combustion 4.4 4.4 Fuel Combustion 74.90 

Palm Kernel Credit 

Glycerin Credit -21.5 

Total Fuel Cycle 23.3 1.8 Total Fuel Cycle 94.71 

% Reduction from PetroDiesel 75.4% 98.1% 

% Reduction from 

Petro-Diesel 0.0% 
33 | Life Cycle Associates, LLC 

3 Direct Land Use Effects 

Direct effects from land conversion include impacts to the immediate production system 

within one region at a specific place and time without effects elsewhere. Indirect effects 

are highly uncertain to estimate for a number of reasons as outlined in Section 1. 

However, some methods (Tyner et al 2010) do not separate indirect and direct 

emissions but rather treat all LUC impacts as changes on the margin. This approach 

needs to be carefully examined for perennial crops and trees where the biomass is not 

simply tilled into the soil every year. 

Ideally, the net carbon flux from Malaysian palm operations could also be calculated. 

This section provides a review of the available data to guide such an assessment. 

3.1 Carbon Accounting in Malaysia 

3.1.1 Land Classification in Malaysia 

Malaysia has a total land area of 339,733 km2, consisting of two geographical regions 

(West or Peninsular Malaysia and East Malaysia). These regions are separated by the 

South China Sea. Their climatic and agro-ecological environments are somewhat 

different despite belonging to the same warm humid tropics classification. The two 

regions are therefore described separately. 

In Malaysia, the rainfall is high (> 2,000 mm per year) but not evenly distributed. Thus, 

agro-ecological zones based on rainfall distribution have been recognized. Generally 

there is little competition for moisture between the tree crops and the native forages, 

except when there is a prolonged dry spell. 

Data on crop areas and production in Malaysia are based on classification by state and 

not on the agro-ecological zones (AEZ). Of the 13 states all but Perlis and Kelantan are 

suitable for palm production (FAO 2004). Agro-ecological Zones are assigned for 

Peninsular Malaysia shown in Figure 12. 
34 | Life Cycle Associates, LLC 

Figure 12. AEZ for Peninsular Malaysia

Source: FAO 

3.1.2 Malaysian soil types 

According to Goh (1999) oil palm is now grown on very diverse soil types (primarily 

oxisols and ultisols) that require different treatment for carbon and fertilizer estimates. 

For example, peat soils are either fibric to hemic and may produce a large flush of 

nitrogen from the second year after planting onwards, owing to mineralization of the 

peat, and the nitrogen application should be reduced to avoid N/K imbalance (Corley 

and Tinker, 2003). In coastal soils in West Malaysia, the soil exchangeable Ca and Mg 

are usually high, and no Mg addition is needed (Corley and Tinker, 2003). The last case 

is found within in ultra-basic soils where the application of acidic fertilizers such as 

ammonium sulfate and the use of di-ammonium phosphate as a P and N source appear 
35 | Life Cycle Associates, LLC 

beneficial on a commercial scale although there is no published evidence to support the 

practice. The fertilizer rates were discussed in the direct emissions report. However, the 

continuous supply of recycled palm organic matter such as pruned fronds and applied 

agro-industrial wastes, together with zero tillage in mature plantations, helps to maintain 

continuous and sustained high productivity of aluminum-tolerant tree crops on the highly 

weathered and leached acidic soils of Malaysia (Montagnini and Nair 2004). The carbon 

flux within these soil types requires an understanding of the mineralization and 

subsequent flow of carbon. IPCC identifies defaults for different soil types as well as 

temperature and moisture effects for carbon stock estimates (IPCC 2006). 

3.1.3 Palm Carbon Cycle 

A carbon sink is a carbon pool from which more carbon flows in than out: forests can act 

as sink through the process of tree growth and resultant biological carbon sequestration 

(Brown, 2002). Forests can switch between being a source and a sink of carbon over 

time, with the stock of the forest referring to the absolute quantity of carbon held within a 

forest component at a specified time. Trees often represent the greatest fraction of total 

biomass of a forested area, with other carbon pools only a fraction of the total tree 

biomass. The under storey is estimated to be equivalent to 3% of above-ground tree 

biomass (AGB), dead wood 5-40%, and fine litter only 5% of that in the above-ground 

tree biomass. Below ground biomass (BGB) is more variable, ranging between 4 - 

230%, and can be more than two times greater than that in the above-ground tree 

biomass (Brown, 1997). However, for tropical forests, and for SE Asia specifically, 

Figure 13 shows these estimates vary between several authors. 

0

50

100

150

200

250

300

350

400

450

500

SE AsiaSE AsiaSE AsiaSE Asia

E Africa

SE AsiaSE AsiaSE AsiaSE Asia

Asia

Global

SE AsiaSE Asia

Malaysia

Oil Palm Mature

Rubber

Young

Rubber

Young

cinnamon

(7 yrs)

2- year

fallow

6-year

fallow

(ave.)

Alley

cropping

Jungle

rubber

GmelinacacaoIPCC Asia

Broadleaf

IPCC Wet

Tropical

Cropland

WHRC

Tropical M

Forest

Winrock

Plantation

Winrock

Forest 

Carbon Stock (Mg/ha)

Range

Range (low/high)

Average

Cstock _by_Region.xls

Reported Error

Figure 13. Estimated annual total carbon stocks in tropical Asian and Malaysian forests 
36 | Life Cycle Associates, LLC 

More recent studies and government analyses (EU 2010) have addressed the lack of 

data for tropical systems, particularly for underground carbon stocks, and the need to 

update factors such as the management and land use factors that can alter stock 

estimates. 

The carbon contents of the various components of oil palm biomass were taken from 

multiple studies and compared in the direct emissions report. In addition, the 

conversion factors were estimated. About 10% of empty fruit bunches (EFB) and palm 

oil mill effluent (POME) was assumed to be converted to organic matter upon land 

application. 

FAO estimates carbon stored in forest soils represents 36 percent of the total C soil 

pool to 1 m depth (1 500 Pg) (FAO 2001). However loss of standing biomass may be 

offset by long-term carbon storage, either as harvested material or carbon sequestered 

in soil organic matter. The main harvest wood products (HWP) include harvested logs, 

paper products and fuel wood. 

Current assumptions of land in agricultural production vary indicating a range in the net 

carbon balance for palm plantations. Oil palm stands require replanting every 25-30 

years and this presents the question of calculating all steps of the palm production 

lifecycle including what carbon sinks and emissions occur within each cycle. The next 

few pages discuss the separate carbon stock pools. 

Peatlands

While not a true biome, peatlands represent a special case in the management of the 

global carbon cycle. Peatlands are associated with a range of waterlogged 

environments in which the decomposition of dead plant material and soil carbon is 

extremely slow, resulting in the fossilization of litter inputs and soil with an organic 

carbon content of over 30%. Although some peat soils can be found in productive 

ecosystems such as reed and papyrus swamps and mangroves, peat soils are often 

seen in unproductive environments where plant growth is very slow. Their capacity for 

storage is large; with estimates suggesting that ~550 Gt of C is stored globally in peat 

soils (Sabine et al. 2004), and a worldwide average of 1450 t C per ha (Parish et al. 

2008). 

These areas are globally widespread but cover a small proportion of land area making 

peatland among the most space effective carbon stores of all ecosystems. Uncertainty 

over the degree of carbon losses from drained peatlands has been identified in several 

studies (Parish et al. 2008; Verwer et al. 2008). However, peatlands provide a special 

example of the effects of the water table on carbon storage and release. Hoojier et al. 

(2010) present a linear relationship that implies that for every 10 cm water table 

drawdown will result in an increase in CO2 emission rate of 9.1 t CO2 /ha/y. Their study 

was based on a carbon content of Southeast Asian peat was taken to be 60 kg/m3

(Kanapathy, 1976; Neuzil, 1997; Shimada et al.2001) and this value was applied to all 

areas. 
37 | Life Cycle Associates, LLC 

 Additional studies (Page et al., 2010, Hooijer et al., 2010) also identify peatlands as a 

diminishing carbon sink due to a complex combination of mostly anthropogenic causes. 

However, the water table has much to do with this effect because part of the carbon 

stock in peatlands is below the drainage base and may never be oxidized. However, a 

common observation is that drainage systems in abandoned peatlands continue to draw 

down water levels for decades, because no funding is available for canal blocking 

(Hooijer et al., 2010). 

Roots and SOC

Soil organic matter (SOM) contains more reactive organic C than any other single 

terrestrial pool. Consequently, SOM plays a major role in determining C storage in 

ecosystems and in regulating atmospheric CO2 concentrations. (Nair et al., 2009). 

Based on current understanding, the major factors in biomass allocation to roots appear 

to be latitude, tree type, age, precipitation, temperature, temperature: precipitation ratio 

(T/P) and index of water availability (Brown and Lugo 1982, Carins, 1997). A root 

biomass average of 24% of the AGB estimate for palms above 1 cm diameter at breast 

height (DBH) was developed by Cairns et al. (1997), where several studies were 

analyzed over time to arise at 24% for roots. 

Still, more studies on palm root carbon are needed. The oil palm has an adventitious 

root system (Yahya et al., 2010), with primary roots originating from the base of the 

trunk and either spreading horizontally or descending at varying angles into the soil. The 

primary roots then bear secondary roots, tertiary roots, which in turn bear the 

quaternary roots. Quaternary roots are unlignified and are often assumed to be the main 

absorbing roots (Corley et al., 1976).The total length of tertiary and quaternary roots in 

the soil is the most important root characteristic as they are the absorbing roots that 

affect fertilizer use efficiency and are found mostly in the upper 30 cm from the soil 

surface. Primary roots can grow up to 20 m away from the base of the palm and some 

primary roots could penetrate below the water table at 90 cm from the surface (Ng et al., 

2003). The distribution of roots depends largely on the nature of the soil. 

Oil palm is a monocot and needs a friable soil for root branching because the roots 

cannot expand laterally, forcing their way through impermeable material. The extent of 

the root system may also be limited by the occurrence of a water table. Root elongation 

as well as proliferation is strongly affected by many physical and chemical properties of 

the soil (Yahya et al., 2010). Figure 14 shows an example of sampling locations for 

taking soil measurement. Including effects from trailer tracks (eg. compaction) and 

nutrient application can affect the carbon estimates. Sampling methods are critical for 

SOC and while IPCC recommends 30 cm depth; this is not appropriate for peat systems 

where roots are measured below 2m. 
38 | Life Cycle Associates, LLC 

Figure 14 Malaysian study for SOC measurements within the harvesting and frond file paths 

representing three sampling depths (a, b and c)

Source Yahya 2010 

Estimates of roots within oil palm plantations are not widely variable and often difficult to 

discern from aggregated soil carbon or above ground biomass (which could include 

excavated roots). Another reason for less data available on actual root measurements is 

the high cost of excavation, washing, drying and weighing. 

Although reference soil organic matter (SOM) data exists (Brown et al., 2004, IPCC, 

2006, Houghton et al., 1997, ISRIC World Inventory of Soil Emission (WISE) Potential 

Database, ISRIC, 2009), research findings to date on the forest management impacts 

on soil carbon are highly variable This is due to large differences in carbon impact, 

dependent on the site-specific ratio of mineral to organic soil types, uncertain carbon 

impacts of soil erosion, and long time periods of adjustment after land use changes. 

Effects from Forestry Practice

Some soils in tropical agricultural systems are estimated to have lost as much as 20 to 

80 tonnes of carbon per ha, most of which has been released into the atmosphere (Lal 

2004a). According to some estimates, degraded soils represent half of the world’s 

carbon sequestration potential (Lal 2004a) with sequestration rates of smallholder 

agroforestry systems in the tropics being around 1.5–3.5 tonnes of carbon per ha per 

year (Montagnini and Nair 2004). Shown in Figure 15, agroforestry systems such as 

palm (at right) clearly represent a different picture to the majority of annual production 

systems used for most biofuels. Additional studies that calculate various new methods 

and their effects on carbon stocks are needed. 
39 | Life Cycle Associates, LLC 

Figure 15 Conventional pastoral agriculture vs. agroforestry system such as for palm represent 

very different carbon cycles

Source UNEP 2009 

Sequestration Rates

Based on an Indonesian study, with an annual growth rate of 10 /Mg/ha, annual C 

accumulation is approximately 50% of this accumulation at 5 Mg C/ha/y (Lasco 2002). 

This same study estimated that conversion from natural forests to tree plantations in SE 

Asian forests reduces carbon density by 50%. However, this does not explain the 

different carbon stock estimates at different life stages within the palm- and it is often 

the case that numbers for plantations do not represent averages but one growth stage. 

Available estimates of C sequestration potential of agroforestry systems are mostly for 

tropical regions. Based on a preliminary assessment of national and global terrestrial C 

sinks, Dixon (1995) identified two primary beneficial attributes of agroforestry systems in 

terms of C sequestration: (1) direct near-term C storage (decades to centuries) in trees 

and soils; and (2) a potential to offset immediate greenhouse gas emissions associated 

with deforestation and subsequent shifting cultivation. 

Some projections have been made on the role of agroforestry in reducing the C 

emission from tropical deforestation. Based on its project known as ‘Alternatives to 

Slash and Burn’, ICRAF (International Centre for Research in Agroforestry) and its 

collaborators around the humid tropics concluded that the greatest potential for C 

sequestration in the humid tropics is above ground, not in the soil. Through the 

establishment of tree-based systems on degraded pastures, croplands, and grasslands, 

the time-averaged C stocks in the vegetation would increase as much as 50 Mg C/ha in 

20 years, whereas the soil stocks would increase only 5 to 15 Mg C/ha (Palm et al. 

2000). A projection of carbon stocks for smallholder agroforestry systems in the tropics 

indicated C sequestration rates ranging from 1.5 to 3.5 Mg C/ha/yr and a tripling of C 
40 | Life Cycle Associates, LLC 

stocks in a twenty-year period, to 70 Mg C/ha (Watson et al. 2000). The total carbon 

emission from global deforestation, estimated at 17 million ha/yr, is 1.6 Pg/yr. 

3.1.1 Carbon Accounting for Palm 

Methods

Biomes reflect natural geographic differences in soils and climate, and consequently 

different vegetation types (Woodward et al. 2004). Each is different in capacity to 

assimilate and store carbon (De Deyn et al. 2008). The flux of carbon gains through 

growth and losses through respiration, ecosystem carbon balance is regulated by 

several other factors including fire, herbivores, erosion and leaching. In addition, forest 

carbon stocks vary within biomes according to drainage, soil type, land-use history and 

other variables (UNDP 2009). 

However controversial, the most commonly accepted way to estimate forest carbon 

stocks over large areas is to apply carbon values to broad forest classes, the “biomeaverage approach”. This is the approach required by Tier 1 of the IPCC’s National 

Greenhouse Gas Inventories and assumes all carbon is ‘lost’ upon conversion. This 

approach, also used by the UNFCC for carbon accounting under REDD, does not 

currently value long term plantations or harvested wood products (HWP). 

The biomass of palms, unlike broadleaf trees, is more closely related to height than 

diameter and they have highly variable stem density (Rich, 1987) and stem-to-leaf 

biomass (Frangi & Lugo, 1985). Litter should also be counted and for dead trees above 

and is often calculated as ‘downed woody debris’ for forest inventories. Table 15 

identifies the two main approaches to carbon accounting used by IPCC: the biome 

average or inventory/period accounting approach and the activity based approach. 

Table 15 Approaches to Carbon Accounting 

Two approaches to carbon accounting 

Equation 1: Inventory/Periodic Accounting 

Change C = change (Ct2 – Ct1) / (t2 – t1) 

Change C = carbon stock change, tonnes C per year 

Ct1 = carbon stock at time t1, tonnes C 

Ct2 = carbon stock at time t2, tonnes C 

Equation 2: Activity-based/Flux Accounting 

Change C = Change [A change (CI – CL)] 

A = area of land, ha 

CI = rate of gain of carbon, tonnes C per ha per year 

CL = rate of loss of carbon, tonnes C per ha per year 

Source: IPCC, 2006 
41 | Life Cycle Associates, LLC 

In contrast to tree AGB, palm AGB is argued to be more reliably predicted by height 

than DBH, as palms increase biomass through apical growth with little growth in 

diameter (Brown, 1997, 2002). However, some studies (Kirby et al., 2007) follow 

arguments that hold that the tendency for DBH-only models to underestimate palm stem 

biomass will be offset by their tendency to overestimate palm ‘crown’ biomass (Malhi et 

al., 2004). 

To convert AGB to C, AGB can be multiplied by the %C content of the component in 

question. C content for ‘forests’ assumed to be: 45% for litter, 43% for seedlings, 41% 

for grass, and 50% for downed woody debris (Hughes et al., 1999). For trees, palms 

and lianas (including roots) the assumption generally for C content is 47%. However, 

this was the mean C content for palms in a wet forest in Mexico (Hughes et al., 1999), 

and is intermediate to the mean C content for trees of two neotropical studies (46% in 

Elias and Potvin (2003) and 48% in Hughes et al. (1999). 

A recent literature review by Cairns et al. (1997) included more than 160 studies 

covering tropical, temperate, and boreal forests that exported both root biomass and 

aboveground biomass. The mean root-to-shoot ratio (R=S) based on these studies was 

0.26, with a range of 0.18 (lower 25% quartile) to 0.30 (upper 75% quartile). 

Dead wood, both lying and standing, is an important carbon pool in forests and one that 

should be measured in many forestry projects. Dead wood is generally divided (Harmon 

& Sexton 1996). Although coarse dead wood, including standing and lying, is often a 

significant component of forest ecosystems, often accounting for 10{20% of the 

aboveground biomass in mature forests (e.g. Harmon et al., 1993; Delaney et al . 1998), 

it tends to be ignored in many forest-carbon budgets. Methods have been developed for 

this component and have been tested in many forest types and generally require no 

more effort than measuring live trees (Harmon & Sexton 1996; Brown 2002). Figure 16 

shows the importance of representing all carbon pools including effects from practice 

(Kirby et al., 2007). 
42 | Life Cycle Associates, LLC 

Figure 16 Panamanian Forest Study with Agroforest and Pasture Land Compared 

Source: Kirby et al. 2007 

Palm Studies

Several recent studies attempt to evaluate palm plantation carbon stocks using various 

methods and address varying degrees of assumptions for sequestration and emission 

factors from palm plantations (Melling et al. 2006, Wicke et al. (2008), Couwenberg 

(2009), MPOB (2008 for JRC), and European proposed methods (2010). 

The actual carbon storage in the biomass of the oil palm is generally referred to as 50% 

(Hamburg 2000). However, this calculation should be evaluated more closely as 32.3-

44.2% (weighted average is 41.3%) is a range reported in the literature between the 

different plant parts; including tertiary and quaternary roots which estimate between 5.2-

11.2 Mg C/ha young palm (3 year old) to 21.8 and 62.8 Mg C/ha towards the end of the 

oil palm cycle (30 year old stand). The maximum measured in a Sumatra study, for 

example, for AGB was 81.6 Mg C/ha so the sequestration rate was calculated at 3.4 Mg 

C/ha/yr. Approximately 20% of the C is stored below ground for oil palm but the factor of 

26% should be used (as explained above). 
43 | Life Cycle Associates, LLC 

Melling et al. (2006) found on an annual basis, the four to five years old palms on deep 

tropical peat were neither a carbon sink nor source. However, most of the C was 

sequestered in the vegetative components of the palms or exported from the 

agroecosystem as FFB resulting in a negative NBP i.e. a C source. The returns of palm 

biomass such as pruned fronds, empty fruit bunches and palm oil mill effluent to the 

agroecosystem would increase the soil organic C by only 0.3 t/ha/yr, which was lower 

than the oxidation rate of peat soils. In fact, the soil heterotropic respiration would cause 

the peat to subside by about 1.6 cm/yr. 

Root data was the least available for palms. However, Lehman (2003) calculated root 

biomass for different types of trees. The study showed tree crops can have shallow root 

activity and 75% of their total root activity in the first 0.1 m of soil, or very deep root 

activity with more than 90% below 0.1 m. Neither environmental factors (i.e., climate 

and soil properties available from these publications) nor plant species explain 

differences of root activity distributions with depth (Montagnini and Nair (2004). The 

deepest root activity is found for fruit trees such as citrus, guava and mango. Shaded 

crops such as coffee and cacao tend to have shallower root activity than fruit trees. Oil 

palm, Elaeis guianensis in Malaysia (for sandy soil with a k value of 0.939) note above 

22 cm is where 75% of root activity occurs 0.987 IAEA (1975) 

Monocots including oil palm, coconut or banana have root activity that can be both deep 

and shallow. Regional and temporal variations of subsoil root activity for the same tree 

species are significant and generally larger than differences between species. Root 

activity patterns of tree crops appear to be sufficiently flexible to allow for subsoil 

resource use. Consequently, management such as pruning, fertilization, liming and 

irrigation are shown to significantly affect subsoil root activity. 

Soil carbon is distributed in a pattern that is dependent on soil age, soil texture, rainfall, 

temperature patterns and vegetation around the plantation (competition). Spatial 

variation driven by distribution of the fine root biomass and C content is highly depth 

dependent (Syahrinudin 2005). For palm, SOC decreased sharply within 50 cm, and 

then gradually deeper down. See (Mackensen 1998 for low numbers to 95 Mg) One 

study reported SOC of 139.2-260.6 Mg/ha (Sommer et al., 2000). However, much 

higher SOC numbers have been reported (482.4 Mg in Sumatra in a 4 year old oil palm 

plantation). 

Harvested Wood Potential Accounting: 

Where information on the volume of wood stock exists, such as from commercial 

inventories, biomass density can be estimated by expanding the merchantable volume 

of stock, net annual increment or wood removals, to account for biomass of the other 

above-ground components (MPI 2001).To do this, either Biomass Expansion Factors 

(BEFs) or Biomass Conversion and Expansion Factors (BCEFs) are applied. BEFs 

expand dry wood stock volume to account for other, non-merchantable, components of 

the tree. To establish biomass the volume must also be converted to a weight by 
44 | Life Cycle Associates, LLC 

multiplication of the wood density as well as the BEF. In contrast, BCEFs use only a 

single multiplication to transform volume into biomass; this is useful where wood 

densities are not available. Brown (et al., 1999) and Lim et al., (1999) developed three 

approaches to measuring HWP as shown in Table 16, below. Each produces a very 

different result and either favors the producer or consumer. 

Table 16 Three Approaches to measuring stock change/ GHGs from HWP

Stock Change 

Approach (SCA) 

Production 

Approach/ Simple 

Decay approach 

Atmospheric 

Flow Approach 

favor 

producer/penalize 

consumer and not 

compatible with 

AFLOU approach 

How to follow HWP 

in developing world?

favor consumer/ 

penalize 

producer 

Construction 

Furniture 

Products to 

Measure and 

Result Packaging 

Paper/board 

Wood Energy 

SWDS 

High CO2 result Mid range result negative CO2

(benefit) 

Default BEFs and BCEFs reported in the literature can be applied in forest carbon 

accounting. However, unless locally-specific equations exist to convert direct 

measurements of tree height and diameter to volume, regression equations to directly 

estimate biomass from tree diameter are preferable (IPCC, 2003). 

With the tree component of a forest the major fraction of biomass, and so carbon 

(although as discussed, conversion of biomass into carbon and CO2 can vary slightly 

from the usually applied 50% value), the understory is often omitted from accounting. 

LU Factors 

Strategies to reduce GHG emissions often compete with natural impacts. IPCC and the 

LULUCF developed factors within carbon accounting to estimate management practice 

or land use activity. These factors, listed in Table 17, are highly dependent on moisture 

and temperature, and inherently include error. 
45 | Life Cycle Associates, LLC 

Table 17 Relative Stock Change Factors (FLU, FMG, AND FI) (Over 20 years) for Different 

Management Activities on Cropland

Factor 

value 

type Level 

Temperature 

regime 

Moisture 

regime 

IPCC 

defaults Error 

Description 

Temperate/ Dry 0.8 9% (+/-) 

Boreal Moist 0.69 12% (+/-) 

Dry 0.58 61% (+/-) 

Land Tropical Moist/Wet 0.48 46% (+/-) 

use 

(FLU) 

Long term 

cultivated 

Tropical 

montane n/a 0.64 50% (+/-) 

Represents area 

continuously managed 

for >20 yrs, to 

predominately annual 

crops. Input and tillage 

factors are also applied 

to est. C stock 

changes. LU factor est. 

relative to use of full 

tillage and nominal 

('medium') carbon input 

levels. 

Land 

use 

(FLU) 

Paddy 

Rice 

All 

Dry and 

Moist/Wet 1.1 50% (+/-) 

Long term (>20 year) 

annual cropping of 

wetlands. Can include 

double- cropping with 

non-flooded crops. 

Tillage and input factors 

not used for paddy rice. 

Land 

use 

(FLU) 

Perennial/

Tree 

All 

Dry and 

Moist/Wet 1 50% (+/-) 

Long term perennial 

tree crops such as fruit 

and nut trees, coffee 

and cacao. 

 

Additional factors have recently been issued by the European Commission including the 

LU (FI) to represent different soil types (EU 2010). 

4 LUC Model Approach 

Figure 17 shows the treatment of LUC impacts for palm oil. The use of CPO for 

biodiesel generates additional demand for CPO. This production of CPO and other 

vegetable oils creates a land use response. The land use responds can be met with a 

combination of expansion in area or an increase in yield (extensification and 

intensification). 
46 | Life Cycle Associates, LLC 

Figure 17. Calculating LUC Impacts From Palm Oil.

EPA Carbon Stock Analysis

The land response ultimately results in a change in land use from one category to 

another. For the EPA RFS2 analysis, Winrock incorporated MODIS satellite data with a 

refined 500 x 500 m resolution (from the previous 1x1km resolution). Winrock also 

included reversion rates and forgone sequestration from various land use change 

events such as N20 emissions and fire- in addition to soil carbon estimates under Tiers 

1-2 of the IPCC methodology17

. 

The Winrock results are available in a spreadsheet that enables the lookup of crop 

conversions for regions in the world (typically at the state of government unit level) for 

conversion and reversion between the following land classes: 

Cnvrsn_1 Cnvrsn_2 Rvrsn_1 Rvrsn_2 

Forest Crop Crop Forest 

Grass Grass Grass Grass 

Savanna Savanna Savanna Savanna 

Shrub Perennial Perennial Shrub 

Wetland Wetland 

Perennial Perennial

Mixed Mixed 

Winrock emission factors for Malaysia are separated by state and shown in Appendix A 

with a comparison chart of the carbon stock factors and emission factors to other 

datasets shown in Table A-1. The analysis is based on a conversion of various land 

types to crops or perennials, which does not capture the details of the palm plantation. 

This is particularly the case for peatland emissions where the emission factor for 

peatlands was calculated for annual crop rotations not perennial trees. 

 

17 Winrock actually developed much of the IPCC methodology for measurement of carbon stocks based 

on Harris and Brown published data. See references.
47 | Life Cycle Associates, LLC 

ARB LCFS

ARB combined the analysis from GTAP with a carbon stock analysis from Woods Hole. 

This carbon stock dataset was also used in the Searchinger (2008) analysis and is 

compared to Winrock and other SE Asian datasets in Appendix A18. Carbon stock 

emission factors used in the LCFS assume the same stored carbon from all crops. 

However, in Malaysia, the biofuel crops would contain much higher carbon stocks. A 

similar issue arises with the accounting of the mass of the palm tree, root mass, and 

other biomass as indicated in Figure 18. 

AEZ forestry lost

forestry 

gained unmngland crops livestock

1 USA 760 219 0 18 106

2 CAN 705 434 0 18 196

3 EU27 297 362 0 18 156

4 BRAZIL 388 164 0 18 72

5 JAPAN 574 223 0 18 85

6 CHIHKG 574 223 0 18 196

7 INDIA 574 223 0 18 196

8 LAEEX 388 164 0 18 72

9 RoLAC 388 164 0 18 72

10 EEFSUEX 311 392 0 18 156

11 RoE 297 362 0 18 156

12 MEASTNAEX 152 59 0 18 82

13 SSAEX 305 129 0 18 43

14 RoAFR 305 129 0 18 43

15 SASIAEEX 937 337 0 18 85

16 RoHIA 574 223 0 18 85

17 RoASIA 937 337 0 18 85

18 Oceania 388 198 0 18 98

Emission factors (Mg CO2e/ha)

All at time zero, no discounting

Carbon Stocks for 

Row Crops. Palm 

Trees > 200 

Mg/ha

Figure 18. Carbon Stock Emission Factors Used in the LCFS 

Effect of Carbon Uptake and Storage

The effect of carbon releases for a typical palm plantation is illustrated in Figure 19. 

The initial release reflects a carbon emission factor including above ground carbon and 

soil carbon release. Addition loss of carbon is estimated from avoided aforestation or 

forest cover growth. The factors that are typically omitted from the LUC analysis are the 

uptake from the palm tree, litter and storage from roots. These factors vary 

considerably depending on soil conditions, moisture, use of biomass, and other factors. 

However, a typical net carbon balance can be shown in Table 18. 

 

18 The EPA Winrock dataset was specific to Malaysia and Woods Hole is aggregate data based on 

original datasets including Houghton for South East Asia.
48 | Life Cycle Associates, LLC 

-50

0

50

100

150

200

250

300

350

400

450

500

1

8

15

22

29

36

43

50

57

64

71

78

85

92

99

GHG Release (t CO 2e/ha)

Intial Above Ground

Uptake

Avoided AF

Roots

Figure 19. Carbon release and uptake from Malaysian palm plantations 

Source: Life Cycle Associates, based on EPA 2010 Winrock carbon stocks 

The analysis here takes into account the carbon stored in the roots and the uptake from 

the tree. Most of the carbon absorbed by the tree is released when the plantation is 

replanted; however some of the tree carbon mass remains in the soil. No soil storage 

from the tree mass is assumed. The net change in carbon is summed over a 100 year 

period using the same method that EPA applies in its analysis for the RFS2. 

The situation where more wood products are harvested was also examined. Both 

empty fruit bunches, cleared forest land, and palm tree boles provide a source of wood 

products for many applications. A case where 20% of the initial cleared forest and 20% 

of the palm tree boles was also examined. The removal of wood products can be 

examined several ways. 
49 | Life Cycle Associates, LLC 

Table 18. Carbon release factors and 100 year emissions with 2% discount factor. 

GHG Factor Mg CO2/ha 

Carbon Impact 

 per year 

100 year 

Malaysian 

Forest, 2% 

Discount 

factor 

100 year 

Malaysian 

Forest, 1% 

Discount factor 

100year Malaysian 

Forest, 20% Wood 

Product removal, 

2% discount factor 

Initial Release 433 433 433 433 

Avoided AF 1.8 78 113 78 

Harvested Wood 20% -282 

Uptake -15 -267 -314 -267 

Roots -2.9 -125 -183 -125 

Total 119 50 -163 

GJ/ha 220 220 220 

g CO2e/MJ 0.5 0.2 -.7 

g CO2e/mmBtu 570 238 -780 

The real carbon losses might be significantly underestimated; oil palm carbon losses 

may be underestimated by a factor up to 10 (Couwenberg 2009). Short-term 

greenhouse gas balances of oil palm production, including losses from degrading peat, 

may suggest no or only small carbon losses (Melling et al., 2007). More comprehensive 

lifecycle analyses that address the lifetime of a plantation (usually ~25 years) all arrive 

at clear carbon debits (Germer & Sauerborn, 2007; Pastowski et al., 2007; Fargione et 

al., 2008; Reijnders & Huijbregts, 2008; Wicke et al., 2008; Danielsen et al., 2009). The 

CO2 emissions from peat degradation assumed in these studies range from 18 to 73 t 

CO2/ha/y. The measurements of Melling et al. (2007) indicate emission values from 

oxidizing peat of 50 t CO2 /ha /y or more (Couwenberg et al. 2009). This implies that the 

emission factor of biofuel derived from oil palm grown on tropical peat soil is at least 400 

g CO2-eq/MJ (Wicke et al., 2008; cf. Couwenberg, 2007), which exceeds emission 

factors of common fossil fuels (cf. IPCC, 2006). 
50 | Life Cycle Associates, LLC 

5 LCA Issues 

Accounting Issues 

The accounting of LUC emissions poses definitional and accounting challenges. The 

grouping of direct energy inputs and indirect energy inputs and allocation systems 

provides opportunities for double-counting. 

Double-counting co-products is discussed in the Renewable Energy Directive. If the 

European Commission introduces an iLUC factor, and that factor includes a credit or 

reduced iLUC factor due to the substitution effect of biofuel co-products, the benefit of 

the co-products will be double-counted as co-products are already treated using 

allocation by energy content rather than by substitution. The ARB LCFS avoids this 

particular issue by treating the mass substitution in the direct emission analysis and the 

mass substitution in the iLUC analysis consistently as indicated in Figure 20. The 

analysis is more complex for EPA with several models employed. 

Allocation of co-products presents an issue of mass and energy balance where system 

expansion, on the other hand, accounts for carbon, mass and energy balance within the 

palm production system. New database (Suh et al. 2010) under development for 

standardized LC inputs for system expansion; thus, reducing uncertainty by this 

method. 

Harvested wood potential (HWP) also presents an accounting issue and calculating this 

as a carbon credit is highly dependent on good data but also the method chosen. 

CH4 exchange, erosion and leaching, wildfire, HWP and BVOC are often omitted from 

carbon modeling efforts. In this recent study of a Malaysian forest in Sarawak, local data 

was used for the area and compiled using the VISIT Ecosystem model which takes into 

account the ‘ecosystem’ factors including land use factors that affect the forest and 

product budgets (Sarawak Study). 
51 | Life Cycle Associates, LLC 

Figure 20. VISIT Ecosystem Model 

Source: http://conference.gistda.or.th/2ndgeoforest/templates/techline_de_template-j15-

001/images/Session_8-Linking_observations_satellite_%20in_situ_with_models-A.Ito.pdf

Palm Biomass

The life cycle of the palm is significantly different than that from annual crops. As 

indicated in Figure 21 the plantation goes through a 30 year cycle of growth and 

regrowth. The net carbon flux is complex due to the nature of vegetation, above ground 

biomass and root mass (shown in Figure 22). The effect of the growth and regrowth 

needs too be included in the carbon stock analysis and a double counting of land 

clearing in the dLUC and iLUC must be avoided. With the current modeling practices, a 

net dLUC adder reflecting the overall carbon balance of the palm plantation may be best 

approach until the palm life cycle can be better integrated with LUC models. 
52 | Life Cycle Associates, LLC 

0

20

40

60

80

100

120

0 10 20 30 40

Time (Y)

Above Ground Biomass

Figure 21. Carbon cycle of palm plantation (source: derived from EPA RFS2) Photo credits: 

Stefan Unnasch

Figure 22. Palm tree root mass (Photo credit: Stefan Unnasch)
53 | Life Cycle Associates, LLC 

Assessment of biomass stocks and growth rates are essential for knowledge on carbon 

cycle dynamics. Being a part of an agricultural system, fallows provide two services: 

they contribute to food security and they store carbon. In a peat study in Sarawak, over 

3,000 trees were measured for diameter at breast height (DBH) along a 

chronosequence of fallow ages, and the biomass and carbon stocks of the fallows were 

estimated using published allometric equations. During the first 15 years following 

abandonment, the biomass of the shifting cultivation fallows accumulates following a 

logistic model reaching saturation after 6 years at 47 Mg dry matter (DM) per hectare. 

Using the differential equation of the logistic model to calculate biomass accumulation 

rates shows very high accumulation rates (up to 12.7 Mg carbon per hectare per year) 

when compared to standard calculations of mean annual accumulation rates (Jespen 

2006). Management factors need updating to reflect the different practices on the palm 

plantation as often just fire practice is highlighted as the only management factor. 

Harvested Wood Products (HWP) are often missing. Various methods are available as 

described in Section 3 to calculate HWP. 

Substitution of Vegetable Oils and other Palm Products

Another key issue in the LUC analysis of biofuels is substitutability of different products 

including vegetable oils. Agro-economic models such as FASOM examine numerous 

interactions among crops and foods that have been in the U.S. food system for 

decades. However, the markets for palm oil are still evolving as shown below in Figure 

23. The rapid rise in palm oil is due to many factors excluded from econometric models. 

The understanding of food nutrition and saturated fats is evolving. Uses for palm oil are 

also expanding. Given the closely coupled trends in the uses of vegetable oils, the 

indirect effects of these oils should be considered interchangeable as a primary 

assumption for LUC analysis. 

Figure 23. Global Oil Production.

0

5

10

15

20

25

30

35

40

45

50

2005/06 2006/07 2007/08 2008/09 2009/10

Production (M tonne)

Soybean

Rapeseed

Cottonseed

Sunflowerseed

Peanut

Palm Kernel

Olive

Coconut

Palm
54 | Life Cycle Associates, LLC 

Palm oil has rapidly grown to become the leading vegetable oil produced globally. Palm 

oil competes with other oils in many applications and also dominates certain niches. 

The interactions among the vegetable oils and substitutability are very complex. 

Numerous markets exist for vegetable oils such as shortening, food oil, frying oil, and 

many others. Each oil has unique properties that affect its marketability and limits 

complete substitution. Nonetheless, the opportunities for substituting one oil for another 

or hydrotreated soybean oil for palm oil suggest that the oils are fungible on the margin 

and that the indirect LUC for play oil and soybean oil should be the same. 

Other complexity with palm products is the fate of palm kernels. Many palm products 

are marketed as shown in Figure 24. Identifying a substitute value for palm kernel oil is 

challenging because it has a higher value than crude palm oil. Other tropical oils could 

serve as substitutes. The life cycle analysis and indirect land use impacts complicate 

the analysis further. Therefore, an allocation treatment of the palm kernel oil is most 

straightforward. 

 

Figure 24 Oil Palm Products

http://cmc-indo.blogspot.com/2010/04/utilization-biomass-oil-palm.html

The trends in uses of palm biomass are difficult to follow as ‘biomass’ is often 

aggregated into waste or general biomass statistics. For example, 15 million tones per 

year of total biomass collected in Malaysia each year (FRIM 2009) how much of this is 

palm. There is potential for oil palm trunks (OPT) and numerous palms are in the 

process of decay and ready for felling/replanting. However, biomass is in high demand 

for co-firing projects and data could not be found on current trends in biomass sales. 
55 | Life Cycle Associates, LLC 

Demand Induced Yield

The concept of yield improvements due to biofuel programs with a portion of the 

additional demand on biofuels met by and further yield improvements in considered in 

some modeling approaches. Most notably, GTAP includes models these yield 

improvements through an elasticity factor. The estimation of such factors is difficult 

because historical data doe not provide a simple set of price versus yield relationships. 

Typically demand for crops, prices, and yields are all changing. Figure 25 below shows 

the variation of palm oil prices over time. Thus the demand or price induced yield is 

often estimated with econometric estimates. Estimating the yield effects from palm are 

as complicated as those from other crops19. While Malaysian palm practices are 

relatively mature, the technology continues to improve. Higher demand and higher 

prices would stimulate even more precise fertilizer application and fruit much collection 

practices. Even more significant, the yield of palm in Africa and South America could 

experience significant price induced yield, this affecting the iLUC for all vegetable oils. 

Figure 25. MPOB Palm Oil Price Data 2009 

 

19 The E4Tech preliminary LUC study assigns a zero price induced yield to palm oil and very high factor to 

rapeseed oil because the palm tree are not replaced annually. 
56 | Life Cycle Associates, LLC 

6 iLUC Results 

Estimates of ILUC results based on EPA’s analysis for the RFS2 are shown below. The 

LUC results for palm oil combined with the direct emission effects are documented in a 

separate report. The GHG emission estimates are consistent with EPA’s analysis of soy 

biodiesel under the RFS2 (EPA 2010). The analysis is presented over a 100 year time 

horizon to be consistent with the long commercial life of a palm plantation. The 

emissions summed over the time horizon using a 2% discount factor. The fuel 

production, transport, and tailpipe emissions correspond to the direct and upstream fuel 

cycle emissions for fuel production equipment and vehicle operation. These emissions 

are constant over time. The next category is farm inputs, fertilizer, and associated N2O 

emissions, which are based on the inputs for palm oil production20

. 

The far right bar represents the LUC impacts if all marginal palm were grown on 

Malaysian land that is currently reserved for agriculture with a forest cover21. Here the 

carbon storage from the palm plantation largely offsets any loss of carbon from forest 

vegetation. Even this scenario represents a high emission case as palm could also 

expand onto non forested lands. The result here shows only modest LUC impacts for 

the conversion of forest covered land to palm due to the long commercial life of the 

palm plantation and the high CO2 uptake and storage from the palm plantation. The 

latter factor appears to be simplified in the Winrock analysis for EPA, using carbon 

uptake factors for generic crops. 

 

20 EPA calculates U.S. and international fertilizer emissions using FASOM and international fertilizer data. 

Since FASOM is not available to assess the emissions from Malaysian agriculture, the analysis 

represents the direct and fuel cycle emissions associated with palm oil. The same approach is taken by 

ARB under the LCFS, where fertilizer impacts are not included in the LUC analysis.

21 This scenario is comparable to corn ethanol expanding onto CRP land in the U.S. indirect effects are not relevant 

if the expansion is onto land that may not be otherwise available for agriculture. 
57 | Life Cycle Associates, LLC 

-2,000,000

-1,000,000

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

2005 Diesel Baseline

Soy Biodiesel base yield

Palm

Biodiesel, CH4 Capture

Palm Biodiesel, CH4 Capture, Biomass Sales

Palm Biodiesel, No CH4 Capture

Palm Biodiesel, Palm Bole Products

Palm Biodiesel Malaysia only LUC

GHG Emissions (g CO2 e / mmBtu)

Fuel Production

International Farm Inputs and

Fert N2O

U.S. Farm Inputs and Fert

N2O

Fuel & Feed Transport

Tailpipe

International Land Use

Change

Palm Net dLUC

International Rice Methane

International Livestock

U.S. Livestock

U.S. Soil Carbon

U.S. Rice Methane

50% Reduction Threshold

Net Emissions

100 year time horizon

2% Discount Factor

Figure 26. Direct fuel LCA emissions and LUC emissions compared to soy biodiesel and 

petroleum diesel (100 year time horizon)
58 | Life Cycle Associates, LLC 

7 Discussion 

Various scenarios for palm biodiesel production effects on land use change have been 

discussed, including direct and indirect effects. The following conclusions are drawn 

from this study: 

Carbon stock 

Regression models to estimate carbon stocks should not be used beyond their range of 

validity. The majority of carbon stock models for AGB are designed for broadleaf trees. 

While equations and models are improving with factors to accommodate palms and 

other plantation species, different models should hold for conifers, palms, and lianas 

(Chave 2005). 

Carbon assessments clearly require further analysis. New local and regional data 

requires analysis and comparison to existing datasets. This is particularly important to 

capture variability in land class and types, improved management practice on stocks, 

sequestration values and separate estimates for each plant part and phase of decay 

within the plantation. 

The IPCC Tier 1 method assumes LUC has no carbon stock storage for below ground 

biomass and therefore no GHG effect. We know from several studies presented here 

this is not the case. Carbon reporting for palm – beyond the default IPCC Tier 1 valuesat the regional level (IPCC Tier 2 or 3) will require the following steps: 

Identification of national carbon stock key categories, develop country specific sampling 

design and stratification, implement field sample plots to derive tallometric data for 

biomass conversion and expansion, carbon fraction values considering country-specific 

stratification, disturbance matrices tracking transfers between carbon pools. Analysis of 

HWP and evaluation of age of palm stands on a net basis to ascertain HWP values 

In addition to the balance between carbon gains through growth and losses through 

respiration, ecosystem carbon balance is also regulated by several other factors 

including fire, herbivores, erosion and leaching. 

Land Use Management Factor (s)

Approximately 4% of the global forest area is represented by plantations (FAO 2006). 

They supply a substantial proportion of the demand for timber products. Plantations can 

sequester significant amounts of carbon and are generally considered to be carbon 

sinks, unless they replace natural forests, which are usually richer in carbon. The 

largest potential carbon gains for plantations are on marginal agricultural land and 

degraded soils (Lal 2004b). However, in some cases plantations deplete soil carbon 

stocks and careful management is therefore necessary. By increasing the rotation 
59 | Life Cycle Associates, LLC 

period for cutting and implementing site improvement strategies, soil carbon stocks can 

be replenished and more carbon sequestered by the vegetation. 

Therefore, agroforestry practice improvements require estimation and inclusion into the 

LU FLU factor. This includes analysis of improved practices on carbon stock values and 

monitoring techniques to capture new data. 

Land Use Types

Further analysis of palm grown on new land is grown for long term (e.g. for 100 years) 

and use of degraded land or shifting use (rubber to palm plantation). Emission factors 

need to take into account for the shifts in land use but also should take into account the 

age of the plantation. Table 5 shows the variability in these emission factors for the 

various conversions based on the Couwenberg (2009) and IPCC (2006) method. This 

highlights the key problem in LUC: lack of harmonization. 

Table 19 Emission Factor Estimates for Different Land Use Change Types 

Activity Source 

t CO2-C/ha/yr 

(range) 

Tropical Agroforestry IPCC 2006 1.36 (0.82-3.82) 

Tropical Agroforestry 

Couwenberg 

2009 11 (8-13.5) 

Cropland or Grassland IPCC 2006 20 ± 90% 

Cropland or Grassland 

Couwenberg 

2009 11 (8-13.5) 

Peat mining IPCC 2006 2.0 (0.06-7.0) 

Peat mining 

Couwenberg 

2009 8 

Allocation- Marginal yield and effects on other uses of palm oil

Allocation of co-products are particularly challenging for palm oil. Valuing PKO presents 

the challenge of finding a substitute tropical oil and its associated LUC. The treatment 

of biomass products is also challenging as the potential quantity of biomass harvested 

exceeds that of the CPO. Future palm oil plantations may produce much more 

products. 
60 | Life Cycle Associates, LLC 

Time Horizon

The time horizon for palm oil plantations is very long as the trees take 25 years to 

mature. Given the high investment costs for establishing palm plantations, economic 

viability is only possible if three or four growth cycles are assured. Therefore, a longer 

100 year time horizon approach is appropriate for palm oil biofuels. 

. 

Carbon Sequestration

Forest growth does not accumulate biomass linearly, despite the assumption of linearity 

used in many accounting approaches. Young, growing forests accrue carbon more 

quickly than mature forests, and the use of time-averaged biomass increment does not 

fully capture this growth over short accounting timescales. Biomass uptake through 

growth is also dependent on tree species and site conditions, whether trees are planted 

or naturally regenerating, and the presence, absence and frequency of disturbances. 

Forests may continue to sequester carbon at highly variable rates for centuries without 

observable changes in the forest area; there is no easy way to assess whether forests 

are growing or have reached carbon equilibrium (Houghton, 2005). 

How to Measure Uncertainty

Uncertainty is defined by IPCC (2006) as ‘lack of knowledge of the true value of a 

variable that can be described as a probability density function1 characterizing the 

range and likelihood of possible values’. The establishment of uncertainty is important 

whether accounting is for forest carbon stock, emissions or project emission reductions. 

Quantification of individual variable uncertainty permits identification of the main 

sources of uncertainty, as well as enabling the prioritization of data collection and focus 

of efforts to improve future forest carbon accounting. For the total inventory, uncertainty 

quantification lends confidence and acceptance which is more likely to lead to inclusion 

of forestry in climate change policy. Tiers 2 and 3 better, lower uncertainty. 

Furthermore, it is unclear what will happen under a changing climate and CO2

concentration. Default growth and biomass accumulation rates are based on past 

observations and so do not take into account global changes in the future. CO2

fertilization will increase plant growth and so carbon uptake, particularly in the tropics 

(Ciais et al., 2005). 
61 | Life Cycle Associates, LLC 

8 References 

Bignell, D.E., Eggleton, P., Nunes, L. and Thomas, K.L. (1997) ‘Termites as mediators of carbon 

fluxes in tropical forest: budgets for carbon dioxide and methane emissions’, in A.D. Watt, N.E. 

Stork and M.D. Hunter (eds) Forests and Insects, Chapman and Hall, London, pp109-134. 

Brown, J.K., Roussopoulos, P.J. (1974) Eliminating biases in the planar intersect 

method for estimating volumes of small fuels. Forest Sci. 20, 350–356. 

Brown, S., Sathaye, J., Cannel, M. (1996) Management of forests for mitigation of greenhouse 

gas emissions, Chapter 24 in Climate Change 1995: Impacts, Adaptations, and Mitigation of 

Climate Change: Scientific-Technical Analyses, Contribution of Working Group II to the Second 

Assessment Report of the Intergovernmental Panel on Climate Change (eds. Watson, R. T., 

Zinyowera, M. C., Moss, R. H.), Cambridge: Cambridge University Press, 1996, 775 797 . 

Brown, P., Cabarle, B., Livernash, R. (1997) Carbon Counts: Estimating Climate Change 

mitigation in Forestry Projects. World Resources Institute, Washington DC. 

Brown, S. (2002) Measuring carbon in forests: current status and future challenges. 

Environmental Pollution, 116, 363-372. 

Brown, S., (1993) Tropical forests and the global carbon cycle: the need for sustainable landuse systems, Agriculture, Ecosystems and Environment, 1993, 46: 31 44. 

Brown, S., Iverson, L. I., Prassad A. (1993) Geographical distribution of carbon in biomass and 

soils of tropical Asian forests,Geocarto International, 4: 45 59. 

Brown, S., Gillespie, A. J. R., Lugo, A. E., (1991) Biomass of tropical forests of South and 

Southeast Asia, Can. For Res.,21: 111 117. 

Canadell, J. G., Le Qúeré, C., Raupach, M. R., Field, C. B., Buitenhuis, E. T., Ciais, P., Conway, 

T. J., Gillett, N. P., Houghton, R. A., Marland, G. (2007) Contributions to accelerating 

atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural 

sinks. Proceedings of the National Academy of Science, 104, 18,866–18,870 

Cairns, M.A., Brown, S., Helmer, E.H., Baumgardner, G.A. (1997) Root biomass allocation in 

the world’s upland forests. Oecologia 111, 1–11. 

Chave, J., Condit, R., Lao, S., Caspersen, J.P., Foster, R.B., Hubbell, S.P. (2003) Spatial and 

temporal variation of biomass. J. Ecol. 91, 240–252. 

Chave, J., Andalo, C., Brown, S., Cairns, M.A., Chambers, J.Q., Eamus, D., Folster, H., 

Fromard, F., Higuchi, N., Kira, T., Lescure, J.-P., Nelson, B.W., Ogawa, H., Puig, H., Riera, B., 

Yamakura, T.(2005) Tree allometry and improved estimation of carbon stocks and balance in 

tropical forests. 
62 | Life Cycle Associates, LLC 

Oecologia 145, 87–99. 

FRIM (2009) Malaysian Oil Palm Biomass Data 

http://gec.jp/gec/en/Activities/FY2009/ietc/wab/wab_day2-3.pdf 

Chen, C.P. and O. Harun. (1994) Cattle rearing in oil palm plantation of RISDA ESPEK. Proc. 

International Congr. On Quality Veterinary Services for 21st Century, Kuala Lumpur. P.221-225. 

Chen, C.P. and A.B. Shamsuddin (1991) Limitation of forage availability on cattle productivity 

under oil palm. Proc. 2nd Meeting of FAO Regional Forage Working Group on Grazing and 

Feed Resources of Southeast Asia.1991. Philippines p.139-147. 

Chin, F.Y. (1991) Some aspects of management and utilization of ground vegetation under 

rubber and oil palm for animal production. Proc. of 2nd Meeting of Regional Working Group on 

Grazing and Feed Resources of Southeast Asia. 1991 p.121-128. 

Corley, R.H.V., J.J. Hardon and B.J. Wood (1976) Oil Palm Research: Developments in Crop 

Science (1). Elsevier Scientific Publishing Company, Holland, ISBN: 0444414711, pp: 511. 

DeFries, R.F., Richard A. Houghton, Matthew C. Hansen, Christopher B. Field, David Skole, 

and John Townshend (2002) Carbon emissions from tropical deforestation and regrowth based 

on satellite observations for the 1980s and 1990s. 14256–14261 PNAS October 29, 2002 vol. 

99 no. 22 

E4Tech iLUC Study (2010) Malaysian Palm iLUC Draft Results 

http://www.ilucstudy.com/files%5CPalm_AnalysisV2.pdf

ENSUS for JRC on iLUC 

http://re.jrc.ec.europa.eu/biof/pdf/luc_bioenergy_policies_paris/ensus_iluc_modelling.pdf

Fargione J, Hill J, Tilman D, Polasky S and Hawthorne P (2008) Land clearning and the biofuel 

carbon debt Science 319 1235–8 

FAPRI (2009) U.S. and World Agricultural Outlook 

Food and Agriculture Organisation (FAO) (2001 ) Soili Carbon Sequestration for Improved Land 

Management World Soils Resources Report No. 96. Based on Work by Michel Robert, Paris, 

France. 

Grayston, S.J., Vaughan, D., Jones, D. (1997) Rhizosphere carbon flow in trees, in comparison 

with annual plants—the importance of root exudation and its impact on microbial activity and 

nutrient availability. Appl. Soil Ecol. 5, 29–56. 

Harris, NL, S. Grimland and S. Brown (2009) Land Use Change and Emission Factors: Updates 

since Proposed RFS Rule. Report submitted to EPA. 
63 | Life Cycle Associates, LLC 

Houghton, J. T., Meira Filho, L. G., Lim, B., Treanton, K., Mamaty, I., Bonduki, Y., Griggs, D. J., 

Callander, B. A. (1997) Revised 1996 Guidelines for National Greenhouse Gas Inventories. 

IPCC/OECD/IEA. 

Houghton, J. T., Meira Filho, L. G., Callander, B. A. et al. eds., Climate Change (1996) The 

Science of Climate Change,Cambridge, New York: Cambridge University Press, 572. 

IPCC 2006. Agriculture, forestry and other land use. In: Eggleston, H.S., Buendia, L., Miwa, K., 

Ngara, T., Tanabe, K. (Eds.), IPCC Guidelines for National Greenhouse Gas Inventories, 

Prepared by the National Greenhouse Gas Inventories Programme. IGES, Japan. 

ISRIC World Inventory of Soil Emission (WISE) Potential Database, ISRIC ( 2009) [Online] 

Available at: http://www.isric.org/ 

Ito, A., Minaco Adachi and YoshikiYamagata (2009) Preliminary report on forest carbon 

mapping by utilizing PALSAR and ecosystem model 2ndGEO Forest Monitoring Symposium: 

From Research to Operations July 1-3,2009Chiang Rai, Thailand 

http://conference.gistda.or.th/2ndgeoforest/templates/techline_de_template-j15-

001/images/Session_8-Linking_observations_satellite_%20in_situ_with_models-A.Ito.pdf 

Jespen, (2006) Above-ground carbon stocks in tropical fallows, Sarawak, Malaysia Forest 

Ecology and Management Volume 225, Issues 1-3, 15 April 2006, Pages 287-295 

Kirby, K.R. and C. Potvin (2007) Variation in carbon storage among tree species: Implications 

for the management of a small-scale carbon sink project Forest Ecology and Management 246 

208–221 

Lal, R., Bruce, J. (1999) The Potential of World Cropland to Sequester Carbon and Mitigate the 

Greenhouse Effect. Environnental Science and Policy, 2, 177–185. 

Lamade, E. N. Djegui2 and P. Leterme (1996) Estimation of carbon allocation to the roots from 

soil respiration measurements of oil palm in Plant and SoilVolume 181, Number 2 / April, 1996 

http://www.springerlink.com/content/k43rkk7355k35605/ 

Lasco, Rodel D. (2002) Forest carbon budgets in Southeast Asia following harvesting and land 

cover change. Science in China Series C 45: pp 55-64. 

Lehmann, J. (2003) Subsoil root activity in tree-based cropping systems Plant and Soil 255: 

319–331, 2003. 

Montagnini, F. and P. K. R. Nair (2010) Carbon sequestration: An underexploited environmental 

benefit of agroforestry systems 

MPOB. Economics and industry development division— statistics. Malaysian Palm Oil Board, 

(2006) (cross-reference from Wicke et al., 2009) 
64 | Life Cycle Associates, LLC 

Murdiyarso, D. (2000) Land-use change and terrestrial carbon stocks: capacity building, impacts 

assessment, and policy support in South and Southeast Asia. Final Activity Report, Bogor: 

Global Change Impacts Center for Southeast Asia, 2000. 

Nair, P.K. Ramachandran, Vimala D. Nairb, B. Mohan Kumarc and Solomon G. Hailea (2009) 

Soil carbon sequestration in tropical agroforestry systems: a feasibility appraisal

Environmental Science & Policy Volume 12, Issue 8, December 2009, Pages 1099-1111 

Noordwijk, M., Hairiah, K., Sitompul, S. M. (2000) Reducing uncertainties in the assessment at 

national scale of C stock impacts of land use change, in Proc. IGES/NIES Workshop on GHG 

Inventories for Asia-Pacific Region (ed. Macandog, D. B.), Hayama, Japan: Institute for Global 

Environmental Strategies (IGES), 2000, 150 163. 

Page, S., Wüst, R. and C. Banks (2010) Past and present carbon accumulation and loss in 

Southeast Asian peatlands http://www.pages-igbp.org/products/newsletters/2010-

1/Special%20Section/Page_2010-1(25-27).pdf PAGES news • Vol 18 • No 1 • April 2010 

Phillips, O.L., Malhi, Y., Higuchi, N., Laurance, W.F., Núñez, P.V., Vásquez, R.M., Laurance, 

S.G., Ferreira, L.V., Stern, M., Brown, S., Grace, J. (1998) Changes in the carbon balance of 

tropical forests: evidence from long-term plots. Science, 282, 439–442. 

Pinard, M. A., Putz., F. E. (1996) Retaining forest biomass by reduced impact logging damage. 

Biotropica 28, 278-295. 

Rees, A.R. and P. B. H. Tinker (1963) “Dry matter production and Nutrient Content of Plantation 

Oil Palms in Nigeria: 1: Grown and Dry Matter Production” Journal of Plant and Soil XIX No. 1. 

Syahrinudin. In: Vlek PLG, Denich M, Martius C, Rodgers C, van de Giesen N, editors (2005) 

The potential of oil palm and forest plantations for carbon sequestration on degraded land in 

Indonesia. Ecology and development series no. 28. Go ̈ ttingen, Germany: Cuvillier Verlag; 

2005. 

UNEP (year? 2009) Forest Carbon Accounting: Overview and Principles 

http://www.undp.org/climatechange/carbonfinance/Docs/Forest%20Carbon%20Accounting%20-%20Overview%20&%20Principles.pdf 

UNEP (2009) The Natural Fix? The Role of Ecosystems in Climate Mitigation GRID Arendal 

UNEP Rapid Response Assessments. http://www.grida.no/publications/rr/naturalfix/page/3725.aspx 

Wahab, Hassan A. (1991) Forages in Oil Palm and Rubber Plantations in Malaysia. 

Proceedings from : 7th Meeting of the Regional Working Group on Grazing and Feed 

Resources. By Malaysian Agricultural Research and Development Institute (MARDI) Accessed 

from FAO (March 2010) 

http://www.fao.org/ag/AGP/AGPC/doc/Proceedings/manado/contents.htm 
65 | Life Cycle Associates, LLC 

Wicke B, Dornburg V, Junginger M, Faaij, A. (2008) Different palm oil production systems for 

energypurposes and their greenhouse gas implications. Biomass and Bioenergy 

Yahya, Zuraidah, Husin, A., J. Talib, J. Othman, O.H., Ahmed and M.B. Jalloh (2010) Oil Palm 

(Elaeis guineensis) Roots Response to Mechanization in Bernam Series Soil. American Journal 

of Applied Sciences 7 (3): 343-348, 2010 ISSN 1546-9239 

Additional References- Bibliography 

Carbon Stock/ Soil Carbon 

Farrell A E, Plevin R J, Turner B T, Jones A D, O'Hare M and Kammen D M (2006) Ethanol can 

contribute to energy and environmental goals Science 311 506–8 

Gibbs, H.K., M. Johnston, J. A. Foley, T. Holloway. C. Monfreda, N. Ramankutty, and D. Zaks 

(2008) Carbon payback times for crop-based biofuel expansion in the tropics: the effects of 

changing yield and technology. Environmental Research Letters 3 034001 Accessed: 

http://www.iop.org/EJ/article/1748-9326/3/3/034001/erl8_3_034001.html 

Gibbs H K, Brown S, Niles J O and Foley J A (2007) Monitoring and estimating tropical forest 

carbon stocks: making REDD a reality Environ. Res. Lett. 2 045023 

Hooijer et al (2006) study observes a CO2 cloud above of efflux measurements so upscaling to

results noted as ‘uncertain’ 

Hooier, A., Silvius, M., Wösten, H., Page, S. (2006) Peat-CO2 – Assessment of CO2 emissions 

from drained peatlands in Southeast Asia, Delft Hydraulics. 

JCCE (Joint Commission on Carbon Emissions) Malaysia-Netherlands. Link to Waginingen 

University and Malaysian work on Palm: ‘CarbonPeat 

News’http://www.geog.le.ac.uk/carbopeat/media/pdf/news7feb2009.pdf 

Melling, Lulie, K.J. Goh and R. Hatano Short term effect of Urea on CH4 flux under the oil palm 

(Elaies guineensisSoil Science and Plant Nutrition (2006) 52, 788–792 

Porter J R, Chirinda N, Felby C and Olesen J E 2008 Biofuels: putting current practices in 

perspective Science 320 1421–2 

Reijnders, L. and M.A.J. (2006) Huijbregts Palm Oil and the emission of carbon based 

greenhouse gases, Journal of Cleaner Production, Vol. 16, Issue 4, March 2008, pp 477-482, 

Elsevier Science. 

Searchinger T, Heimlich R, Houghton R A, Dong F X, Elobeid A, Fabiosa J, Tokgoz S, Hayes D 

and Yu T H 2008 Use of US croplands for biofuels increases greenhouse gases through 

emissions from land-use change Science 319 1238–40 

United Nations (2007) Sustainable Bioenergy: A Framework for Decision Makers 

vanNoordwijk M, Cerri C, Woomer P L, Nugroho K and Bernoux M (1997) Soil carbon dynamics 

in the humid tropical forest zone Geoderma 79 187–225 

Verwer, Caspar, P. van der Meer, and G-J Nabuurs (2008) Review of carbon flux estimates 

and other greenhouse gas emissions from oil palm cultivation on tropical peatlands- Identifying 

the gaps in knowledge. Waginingen, Alterra, Alterra-rapport 1731. 44 pp. Accessed: 

http://www.geog.le.ac.uk/carbopeat/media/pdf/pub_alterra_rapport.pdf 

Notes high emissions in peatlands could be due to drainage effects and tests from virgin 

peatforest (citing Melling et al 2005 study w/ high CO2 flux) 

Watanabe, A., Perwanto, B.H., Ando, H. and F-S Jong (2009) Methane and CO2 fluxes from an 

Indonesian peatland used for sago palm (Metroxylon sagu Rottb.) cultivation: Effects of fertilizer 
66 | Life Cycle Associates, LLC 

and groundwater management in: Agriculture Ecosystem and Environment, Volume 134 Issues 

1-2, pp. 14-18. November 2009. Elsevier Science. 

Land Use Modeling 

Datuk, Tan Sri, Dr. Y Barsiron and Dr. Y. F. Kheong (2009) MPOC The Potential of Palm Oil for 

Developing Countries and Its role in the Food and Fuel Debate 

FAO Inventory of Malaysian land (2010) 

FAO. (2004). ‘Global Forest Resources Assessment Update 2005’, Food and 

Agriculture Organisation of the United Nations, Rome, pp 191-194 

FAPRI (2008). Biofuels Impact of Selected Farm Bill Provisions and other Biofuel Policy 

Options. FAPRI-MU Report #06-08. Accessed: www.fapri.missouri.edu 

GTAP https://www.gtap.agecon.purdue.edu/resources/download/3674.pdf 

Gibbs, H.K. and S. Brown. 2007. Carbon pools in the forests of tropical Southeast Asia: An 

updated database using the GLC2000 Land Cover Product. NDP-017b. ORNL-CDIAC. 

Henson I.E. (2004). Modeling Carbon Sequestration and Emissions Related to Oil Palm 

Cultivation and Associated Land Use Change in Malaysia, MPOB Technology, Malaysian Palm 

Oil Board, Bangi. 

ICF International (2009) Lifecycle Greenhouse Gas Emissions Due to Increased Biofuel 

Production: Model Linkage Peer Review Report 31 July 2009. 

IPCC (2007) Good Guidance Practice for Land Use, Land-Use Change and Forestry. IPCC, 

Japan 

Mattsson, B., Cederberg, C., and Blix, L. (2000) Agricultural Land Use in Life Cycle Assessment 

(LCA), Case Studies of Three Vegetable Oil Crops, Journal of Cleaner Production, 8, pp. 283-

292. 

Palm oil substitution Credit (quoted Fargione 2008) Rapeseed vs. Soy (see page 3) 

Panabokke, C.R. 1979. Agro-ecological zones of South and South-East Asia. FAO Regional 

Office, Bangkok, Thailand. 

Ramli M.N. and Wong L.J. 1989. Rainfed agriculture in Malaysia. In: Rainfed Agriculture in Asia 

and the Pacific Region. RAPA, FAO Publication. pp. 139-144. 

Schmidt J H (2008), Development of LCIA characterisation factors for land use impacts on 

biodiversity. Journal of Cleaner Production 16 (2008), pp. 1929-1942. Accessed: 

http://dx.doi.org/10.1016/j.jclepro.2008.01.004> 

Wahid, B.O., A.A. Nordiana and A.M. Tarmizi (2005) MPOB Information Series ISSN 1511-

7871.Satellite Mapping of Oil Palm Land Use 

Winrock International (2009) White Paper on GHG Implications of Biofuels. Accessed: 

http://www.scribd.com/doc/14532977/Winrocks-White-Paper-on-GHG-Implications-Biofuel 
67 | Life Cycle Associates, LLC 

Appendix A. Carbon Stock Data Tables 

Carbon Stock Data by Study and Region 

0

100

200

300

400

500

600

700

800

900

1000

Asia-Low

Tropics

AsiaPeatlands

Asia-Peat

removed

Asia Humid

Palm

Asia-Wet

Tropical

Asia- Dry

Forest Low

Asia

Tropical MF

Asia Open

Forest

AsiaTropical SF

SE Dry

Subtropical

SE Tropical

Equatorial

SE Tropical

Dry

MalaysiaJohor

Sarawak

(high)

Selangor

(low)

Fargione

(2008)

IPPC Tier1

(2006)

ARB LCFS

(Woods Hole)

Gibbs and

Brown (2007) 

EPA RFS

(Winrock)

Carbon Stock (tonne C/ha)

Soil

Root

Above

cstock_LUC

Note: Data represent carbon 

stocks, not GHG release 

factors

Carbon Stock Data by Country 

Carbon Stock (t C ha-1 Country )

Forest Soil Shrubland Savanna Grassland Cropland

Brazil 104 49 37 20 11 5

Argentina 49 38 22 11 6 5

China 53 36 26 14 8 5

India 111 32 26 14 8 5

Indonesia 179 80 42 14 8 5

Malaysia 176 66 42 14 8 5

Philippines 122 38 42 14 8 5

Nigeria 55 26 46 52 8 5

South Africa 29 21 46 21 8 5

Mexico 76 45 14 7 4 5

Russia 44 89 14 7 4 5

EU 57 81 22 12 6 5

USA 61 50 14 7 4 5
68 | Life Cycle Associates, LLC 

Carbon Emission Factors for Malaysia (Winrock/IPCC Method for EPA RFS 2) 

Admin Unit 

Change in 

 biomass C stocks 

(t CO2 ha-1) 

Lost forest 

sequestration 

(t CO2 ha-1 y-1) 

Soil C before 

(t C ha-1) 

Soil C after 

(t C ha-1) 

Annual 

soil C flux 

(t CO2 ha-1 y-1) 

Annual peat 

emissions 

(t CO2e ha-1 y-1) 

Johor 395 1.8 157 76 3.5 10 

Kedah 359 1.8 144 69 3.7 0 

Kelantan 478 1.8 168 81 4.4 0 

Melaka -18 1.8 165 79 4.3 0 

Negeri 

Sembilan 459 1.8 142 68 3.7 0 

Pahang 437 1.8 152 73 3.7 4 

Perak 379 1.8 161 77 4.0 4 

Perlis 371 1.8 158 76 4.1 0 

Pulau 

Pinang -18 1.8 160 77 4.2 0 

Sabah 718 1.8 169 81 4.3 1 

Sarawak 625 1.8 165 79 3.6 12 

Selangor 274 1.8 170 81 3.4 16 

Trengganu 554 1.8 155 74 4.0 1 

Total change 

in soil C 

(t CO2 ha-1 y-1)

CH4

emissions 

(t CO2 ha-1)

N2O 

emissions 

(t CO2 ha-1)

Total fire 

emissions 

(t CO2 ha-1)

Yr 0 

Emissions 

(t CO2 ha-1 y-1)

Yr 1-19 

Emissions 

(t CO2 ha-1 y-1)

Yr 20-80 

Emissions 

(t CO2 ha-1 y-1)

30-yr 

Emissions 

(t CO2 ha-1)

Johor 13.4 27 12 38 448 15 12 853 

Kedah 3.7 25 11 35 400 6 2 523 

Kelantan 4.4 32 14 46 530 6 2 666 

Melaka 4.3 0 0 0 -12 6 2 121 

Negeri 

Sembilan 3.7 31 13 44 509 5 2 631 

Pahang 8.2 30 13 42 489 10 6 741 

Perak 7.8 26 11 37 426 10 6 664 

Perlis 4.1 25 11 36 413 6 2 543 

Pulau 

Pinang 4.2 0 0 0 -12 6 2 119 

Sabah 5.6 48 21 69 795 7 3 966 

Sarawak 15.5 42 18 60 702 17 14 1169 

Selangor 19.5 19 8 27 322 21 18 905 

Trengganu 4.7 37 16 53 614 7 3 763 
69 | Life Cycle Associates, LLC 

Appendix B. Life Cycle Calculation Materials 

Palm Oil Biodiesel Input Sheet 

Oil Palm Cultivation and Nursery

Feedstock Region Malaysia Average

Farming Scenario MPOB

Energy Inputs and Yield (FFB Basis) Parameter Units MJ/tonne

Diesel 3.50 Liters/tonne 125.4

Gasoline 0.00 Liters/tonne 0.0

Natural gas 0.00 m

3

/tonne 0.0

LPG 0.00 Liters/tonne 0.0

Total 125.4

FFB yield 20.00 Tonne FFB/ha

Oil yield 3.94 Tonne CPO/ha

Electricity Input Parameter Units MJ/tonne

Electricity 0.00 kWh/tonne 0.0

Chemical Inputs (kg Nutrient/tonne FFB) Parameter Ammonia Urea Ammonium Nitrate Ammonium Sulfate

N 3.49 0.0% 0.0% 0.0% 100.0%

P (P2O5) 2.80

K (K2O) 11.50

CaCO3 0.00

Mg (MgO) 0.90

Cu (CuSO4) 0.00

Zn (ZnSO4) 0.00 Atrazine Metolachlor Acetochlor Cyanazine

Herbicide 0.13 100.0% 0.0% 0.0% 0.0%

Insecticide 0.03

Soil N Emissions (Kg/tonne FFB)

N content of above and below ground biomass 0.0

N in N2O as % of N in fertilizer and biomass 1.3%

Fruit Bunch Transport to Fuel Plant

Fresh Fruit Bunches Mode Capacity (tonnes) Distance (km) Share

FFB to Palm Mill Medium Duty Truck 11 27 100.0%

EFB to Field Medium Duty Truck 11 27 50.0%

EFB to Mill 50.0%

Palm Oil Extraction

Oil Extraction Region Malaysia Average

Palm Products

FFB Usage 5.07 kg FFB/kg CPO

Overall Usage 5.27 kg FFB/kg BD

EFB Yield 0.69 kg EFB/kg FFB

Thermal Energy Shares (CPO Basis) Parameter Units MJ/kg CPO MJ/MJ CPO

Natural gas 0.00 m

3

/tonne 0.0

Diesel 2.48 liters/tonne 0.1

Shells 0.16 dry tonne/tonne 2.4

Mesocarp Fiber 0.53 dry tonne/tonne 6.3

Captured Effluent Methane 1.7

Solvent 0.00 liters/tonne 0.0

Total 10.5

Vehicles at Mill (CPO Basis) Parameter Units MJ/kg CPO

Diesel 2.00 liters/tonne 0.1

Electricity Input (CPO Basis) Parameter Units MJ/tonne CPO

Electricity 0.00 kWh/tonne 0.0

Product Ouput Mass (kg/tonne FFB) Yield (kg/kg CPO) Mass Fraction CPO/PK Share

CPO 197 1.0 63.4% 70.9%

Palm kernels 81 0.4 26.0% 29.1%

Fiber 11 0.1 3.5%

Palm tree products 22 0.1 7.1%

Total biomass products 311 100.0% 100.0%
70 | Life Cycle Associates, LLC 

Palm Oil Mill Effluent Parameter Units g CH4/kg CPO

Palm oil mill effluent 0.5 kg/kg FFB

Select methane emission factor reference Plant Survey Data

Effluent methane emission factor 17.2 g CH4/kg Effluent 43.0

Captured Methane Fate

Process fuel 80.0% % 34.4

Flare 0.0% % 0.0 Electricity Efficiency Distribution Loss

Electricity generation 0.0% % 0.0 33.0% 8.1%

Emitted Methane 20.0% % 8.6

Palm Oil Transport

CPO Mode Capacity (tonnes) Distance (km) Share

CPO to biodiesel plant Heavy Duty Truck 11 80 100.0%

CPO from Malaysia to California Ocean Tanker 226,796 14,484 0.0%

Transesterification

Oil Transesterification Region Malaysia Average

Biodiesel Production Factors Parameter Energy Share

Oil use (kg CPO/kg BD) 1.04 95.0% Biodiesel

Glycerin yield (kg/kg BD) 0.108 5.0% Glycerin

Oil Transesterification Thermal Energy MJ/kg BD

Total Energy 4.8

Transesterification Process Fuel and Chemical Energy Shares Percentage MJ/kg BD kg/kg BD

Natural gas 42.9% 2.1

Coal 0.0% 0.0

Methanol 41.8% 2.01 0.10

Sodium hydroxide 2.0% 0.1

Sodium methoxide 10.1% 0.5

Hydrochloric acid 3.1% 0.1

Total 100.0% 4.8

Transesterification Electricity Units MJ/kg BD

Net electricity (kWh/tonne BD) 30.1 kWh/tonne 0.1083

Biodiesel Transport and Distribution

Fuel Transport Mode Capacity (tonnes) Distance (km) Share

BD from BD plant to Malaysian port Heavy Duty Truck 23 241 100.0%

BD from Malaysian p Ocean Tanker 226,796 14,484 100.0%

Heavy duty truck Heavy Duty Truck 23 80 100.0%

Fuel Distribution

Heavy duty truck Heavy Duty Truck 23 145 100.0%

Fuel Combustion

Vehicle Energy Use HD Vehicle (MJ/km)

Vehicle total energy use 12.8

Exhaust Emissions EF (g/km) Emissions (g/MJ)

CH4 0.022 0.002

N2O 0.030 0.002

CO2 75.1

Fossil Derived CO2 3.6

TTW Emissions (g CO2e/MJ) 4.4
71 | Life Cycle Associates, LLC 

Palm Oil Biodiesel Life Cycle Matrix Calculations 

Pathway Component

Process Diesel Gasoline Natural Gas LPG Electricity

Nitrogen

(N)

Field N2O 

Emissions

Phosphate

(P2O5)

Potash

(K2O)

Lime

(CaCO3)

Process Loss Factor

Cumulative Loss Factor per Fuel Pathway Component 1.00016 1.00016 1.00016 1.00016 1.00016 1.00016 1.00016 1.00016 1.00016 1.00

Feedstock Use Factor 5.27 5.27 5.27 5.27 5.27 5.27 5.27 5.27 5.27 

Units kg FFB/kg BD kg FFB/kg BD kg FFB/kg BD kg FFB/kg BD kg FFB/kg BD kg FFB/kg BD kg FFB/kg BD kg FFB/kg BD kg FFB/kg BD kg FFB/kg 

Process Specific Energy 125 0 0 0 0 3.49 1.00 2.80 11.50 

Units

MJ Diesel/tonne 

FFB

MJ 

Gasoline/tonne 

MJ NG/tonne 

FFB

MJ LPG/tonne 

FFB MJe/tonne FFB kg N/tonne FFB FFB FFB 

Specific Energy 0.0174 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0

Units Biodiesel Biodiesel Biodiesel Biodiesel Biodiesel Biodiesel Biodiesel tonne P2O5/MJ tonne K2O/MJ CaCO

Fuel Cycle Energy (J) Total 20,267 0 0 0 0 46,846 4,492 14,009 

Fossil fuels 2,780 0 0 0 0 46,421 4,339 13,319 

Coal 400 0 0 0 0 1,922 692 3,129 

Natural gas 1,079 0 0 0 0 42,324 3,058 6,601 

Petroleum 18,754 0 0 0 0 2,175 589 3,589 

CH4 0.002 0.000 0.000 0.000 0.000 0.002 0.000 0.001 0

N2O 0.000 0.000 0.000 0.000 0.000 0.000 0.010 0.000 0.000 0CO2 1.5 0.0 0.0 0.0 0.0 2.6 0.0 0.3 1.0 

CO2 (Incl. VOC and CO) 1.5 0.0 0.0 0.0 0.0 2.6 0.3 1.0 

Total Fuel Cycle GHG Emissions (g/MJ) 1.5 0.0 0.0 0.0 0.0 2.7 3.0 0.3 1.0 

Allocated Emission Results: CPO, PK (g/MJ) 1.0 0.0 0.0 0.0 0.0 1.8 2.0 0.2 0.7 

Allocated Emission Results: CPO, PK, fiber, biomass (g/MJ) 0.9 0.0 0.0 0.0 0.0 1.6 1.8 0.2 0.6 

Displacement Emissions Results (g/MJ) 1.5 0.0 0.0 0.0 0.0 2.7 3.0 0.3 1.0 

Conversion to Metric Units

Numerator Unit Conversion 1.0000 1.0000 1.0000 1.0000 1.0000 1055.06 1055.06 1055.06 1055.06 1055.06 

Denominator Unit Conversion 0.0009 0.0009 0.0009 0.0009 0.0009 1.10 1.10 1.10 1.10 1.10 

Fuel Pathway Component Diesel Tractor Gasoline Tractor Natural Gas LPG Electricity N Fertilizer N2O EmissionsPhosphate FertilizerPotash Fertilizer Lime Fertili

Denominator Units MJ Diesel MJ Gasoline MJ NG MJ LPG MJe tonne N tonne FFB tonne P2O5 tonne K2O tonne CaC

Fuel Cycle Energy (J) Total 1,161,266 1,194,446 1,069,713 1,107,040 2,735,823 96,414,375,981 11,524,194,185 8,750,179,989 8,617,165

Fossil fuels 159,316 192,162 69,385 105,526 2,511,475 95,540,135,531 11,129,765,310 8,318,696,348 8,185,863

Coal 22,906 28,626 1,528 14,795 1,002,262 3,956,475,057 1,774,976,145 1,954,280,556 1,952,505

Natural gas 61,799 74,961 1,063,645 543,955 1,419,250 87,107,423,919 7,843,560,796 4,123,038,557 4,117,754

Petroleum 1,074,612 1,088,574 4,212 546,777 89,964 4,476,236,555 1,511,228,370 2,241,377,236 2,115,604

Fuel Cycle Emissions (g) VOC 0.073 0.124 0.045 0.009 0.021 12,230.946 193.011 103.414 93

CO 0.360 0.533 0.336 0.032 0.157 11,804.903 705.851 538.432 515

NOx 0.678 0.485 1.158 0.096 0.215 4,677.940 3,145.196 1,183.924 902

PM10 0.066 0.032 0.006 0.008 0.177 933.121 1,541.188 433.115 409

PM2.5 0.056 0.025 0.006 0.005 0.050 402.155 1,014.161 173.425 155

SOx 0.017 0.013 0.011 0.008 0.022 930.188 61,225.305 166.487 85

CH4 0.093 0.100 0.472 0.091 0.289 4094.419 1236.725 882.985 871

N2O 0.001 0.001 0.001 0.005 0.004 49.268 72.654 10.664 9.551 9

CO2 (Incl. VOC and CO) 85 86 60 73 190 5,415,458 741,080 601,736 591

LCI Data VectorsFuel Pathway Component Diesel Tractor Gasoline Tractor Natural Gas Reciprocating EngineLPG Electricity N Fertilizer N2O EmissionsPhosphate FertilizerPotash Fertilizer Lime Fertili

Denominator Units mmBtu Diesel mmBtu Gasoline mmBtu NG mmBtu LPG mmBtue ton N ton FFB ton P2O5 ton K2O ton CaCO

Fuel Cycle Energy (Btu) Total 1,161,266 1,194,446 1,069,713 1,107,040 2,735,823 82,901,441 9,909,023 7,523,801 7,409

Fossil fuels 159,316 192,162 69,385 105,526 2,511,475 82,149,730 9,569,876 7,152,791 7,038

Coal 22,906 28,626 1,528 14,795 1,002,262 3,401,956 1,526,205 1,680,379 1,678

Natural gas 61,799 74,961 1,063,645 543,955 1,419,250 74,898,903 6,744,248 3,545,175 3,540

Petroleum 1,074,612 1,088,574 4,212 546,777 89,964 3,848,871 1,299,423 1,927,238 1,819

Fuel Cycle Emissions (g) VOC 77.307 130.740 47.401 9.838 22.497 11,095.728 175.097 93.815 84

CO 379.943 562.316 353.976 33.240 165.176 10,709.228 640.337 488.458 467

NOx 714.918 512.160 1,221.898 101.584 226.930 4,243.755 2,853.274 1,074.038 819

PM10 69.215 33.398 6.234 8.026 186.314 846.513 1,398.142 392.915 371

PM2.5 58.604 26.462 6.005 5.405 52.724 364.829 920.032 157.328 141

SOx 18.431 13.435 11.113 8.761 22.872 843.852 55,542.663 151.034 77

CH4 98.559 105.285 497.771 95.571 304.861 3714.394 1121.938 801.030 790

N2O 1.079 1.279 1.567 4.979 3.794 44.696 65.911 9.674 8.665 8

CO2 (Incl. VOC and CO) 89,824 90,766 62,908 76,792 200,352 4,912,821 672,296 545,886 536

LCI Vector Region: Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast A

Oil Palm Farming Energy Fertilizer Inputs
72 | Life Cycle Associates, LLC 

Pathway Component

Process Herbicide Insecticide

Medium Duty 

Truck

Medium Duty 

Truck

Natural Gas 

Boiler Biomass Boiler Diesel Boiler

POME Methane 

Boiler Diesel Vehicles Electricity

Extraction 

Solvent

EsM

Process Loss Factor 1.00010 1.00010 1.00000 1.00000

Cumulative Loss Factor per Fuel Pathway Component 1.00016 1.00016 1.00006 1.00006 1.00006 1.00006 1.00006 1.00006 1.00006 1.00006 1.00006 

Feedstock Use Factor 5.27 5.27 5.27 3.63 1.04 1.04 1.04 1.04 1.04 1.04 1.04 

Units kg FFB/kg BD kg FFB/kg BD kg FFB/kg BD kg EFB/kg BD kg CPO/kg BD kg CPO/kg BD kg CPO/kg BD kg CPO/kg BD kg CPO/kg BD kg CPO/kg BD kg CPO/kg BD kg C

Process Specific Energy 0.13 0.03 27 14 0.0 8.7 0.1 1.7 0.1 0.0 0.0 

Units

Kg 

Herbicide/tonne 

Kg 

Insecticide/tonn km km MJ NG/kg CPO

MJ Biomass/kg 

CPO

MJ Diesel/kg 

CPO MJ CH4/kg CPO

MJ Diesel/kg 

CPO

kWh 

Electricity/kg 

MJ Hexane/kg 

CPO 

Specific Energy 0.0000 0.0000 0.0038 0.0013 0.0000 0.2381 0.0024 0.0471 0.0020 0.0000 0.0000 

Units Herbicide/MJ Insecticide/MJ FFB*km/MJ BD EFB*km/MJ Biodiesel Biodiesel Biodiesel MJ CH4/MJ BD Biodiesel Biodiesel Biodiesel 

Fuel Cycle Energy (J) Total 4,070 1,344 9,373 3,229 0 238,157 2,831 47,116 2,283 0 0 

Fossil fuels 3,956 1,305 9,357 3,223 0 0 388 0 313 0 0 

Coal 532 183 185 64 0 0 56 0 45 0 0 

Natural gas 1,512 503 499 172 0 0 151 0 121 0 0 

Petroleum 1,912 620 8,674 2,988 0 0 2,620 0 2,113 0 0 

CH4 0.000 0.000 0.001 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 

N2O 0.000 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000 CO2 0.3 0.1 0.7 0.2 0.0 0.0 0.2 0.0 0.2 0.0 0.0 

CO2 (Incl. VOC and CO) 0.3 0.1 0.7 0.2 0.0 0.0 0.2 0.0 0.2 0.0 0.0 

Total Fuel Cycle GHG Emissions (g/MJ) 0.3 0.1 0.7 0.2 0.0 0.8 0.2 0.0 0.2 0.0 0.0 

Allocated Emission Results: CPO, PK (g/MJ) 0.2 0.1 0.5 0.2 0.0 0.5 0.1 0.0 0.1 0.0 0.0 

Allocated Emission Results: CPO, PK, fiber, biomass (g/MJ) 0.2 0.1 0.4 0.1 0.0 0.5 0.1 0.0 0.1 0.0 0.0 

Displacement Emissions Results (g/MJ) 0.3 0.1 0.7 0.2 0.0 0.8 0.2 0.0 0.2 0.0 0.0 

Conversion to Metric Units

Numerator Unit Conversion 1055.06 1055.06 1055.06 1055.06 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

Denominator Unit Conversion 1.10 1.10 0.68 0.68 0.0009 0.0009 0.0009 0.0009 0.0009 0.0009 0.0009 

Fuel Pathway Component Herbicide Insecticide Medium Duty TruckMedium Duty TruckNatural Gas Boiler Biomass Boiler Diesel Boiler CH4 Boiler Diesel Tractor Electricity n-hexaneEffluent Es

Denominator Units tonne Herbicide

tonne 

Insecticide tonne*km tonne*km MJ Natural Gas MJ Biomass MJ Diesel MJ CH4 MJ Diesel MJe MJ LPG k

Fuel Cycle Energy (J) Total ############# ############# 2,461,062 2,461,062 1,069,713 1,000,000 1,161,266 1,000,000 1,161,266 2,735,823 1,107,040 

Fossil fuels ############# ############# 2,456,930 2,456,930 69,385 0 159,316 0 159,316 2,511,475 105,526 

Coal 29,489,771,350 45,246,118,400 48,544 48,544 1,528 0 22,906 0 22,906 1,002,262 14,795 

Natural gas 83,854,096,570 ############# 130,970 130,970 1,063,645 0 61,799 0 61,799 1,419,250 43,955 

Petroleum ############# ############# 2,277,416 2,277,416 4,212 0 1,074,612 0 1,074,612 89,964 1,046,777 

Fuel Cycle Emissions (g) VOC 2,231.655 4,160.371 0.088 0.088 0.007 0.000 0.009 0.000 0.073 0.021 0.008 

CO 10,877.826 19,494.134 0.266 0.266 0.026 0.000 0.032 0.000 0.360 0.157 0.014 

NOx 23,761.634 37,265.852 0.931 0.931 0.075 0.104 0.106 0.055 0.678 0.215 0.028 

PM10 10,155.431 13,779.327 0.033 0.033 0.004 0.012 0.047 0.003 0.066 0.177 0.005 

PM2.5 5,212.234 7,442.472 0.023 0.023 0.003 0.006 0.039 0.003 0.056 0.050 0.002 

SOx 11,639.187 3,913.247 0.031 0.031 0.011 0.004 0.017 0.000 0.017 0.022 0.008 

CH4 21588.055 31792.832 0.200 0.200 0.123 0.004 0.093 0.001 0.093 0.289 0.090 

N2O 175.247 285.209 0.006 0.006 0.001 0.010 0.001 0.001 0.001 0.004 0.000 

CO2 (Incl. VOC and CO) 16,004,645 23,114,192 182 182 61 0 86 0 85 190 8 

LCI Data VectorsFuel Pathway Component Herbicide Insecticide Medium Duty TruckMedium Duty TruckNatural Gas Boiler Biomass Boiler Diesel Boiler CH4 Boiler Diesel Mill Tractor Grid Electricity n-hexane 

Denominator Units ton Herbicide ton Insecticide ton*mi ton*mi

mmBtu Natural 

Gas mmBtu Biomass mmBtu Diesel mmBtu CH4 mmBtu Diesel

mmBtu 

Electricity mmBtu LPG 

Fuel Cycle Energy (Btu) Total 194,115,774 286,295,110 3,406 3,406 1,069,713 1,000,000 1,161,266 1,000,000 1,161,266 2,735,823 1,107,040 

Fossil fuels 188,648,947 277,967,310 3,400 3,400 69,385 0 159,316 0 159,316 2,511,475 105,526 

Coal 25,356,639 38,904,659 67 67 1,528 0 22,906 0 22,906 1,002,262 14,795 

Natural gas 72,101,545 107,122,124 181 181 1,063,645 0 61,799 0 61,799 1,419,250 43,955 

Petroleum 91,190,763 131,940,526 3,151 3,151 4,212 0 1,074,612 0 1,074,612 89,964 1,046,777 

Fuel Cycle Emissions (g) VOC 2,024.523 3,774.225 0.129 0.129 7.838 0.000 9.235 0.000 77.307 22.497 7.948 

CO 9,868.198 17,684.781 0.388 0.388 27.950 0.000 33.429 0.000 379.943 165.176 14.840 

NOx 21,556.192 33,807.012 1.360 1.360 79.505 110.000 112.182 57.607 714.918 226.930 29.965 

PM10 9,212.852 12,500.396 0.048 0.048 3.910 12.661 49.427 3.206 69.215 186.314 4.783 

PM2.5 4,728.459 6,751.697 0.034 0.034 3.681 6.331 40.928 3.206 58.604 52.724 2.162 

SOx 10,558.892 3,550.038 0.046 0.046 11.113 4.100 18.431 0.269 18.431 22.872 8.761 

CH4 19584.354 28841.972 0.292 0.292 129.931 3.834 98.109 1.100 98.559 304.861 94.491 

N2O 158.982 258.737 0.009 0.009 1.167 11.000 0.549 1.100 1.079 3.794 0.119 

CO2 (Incl. VOC and CO) 14,519,170 20,968,842 266 266 64,556 0 90,582 0 89,824 200,352 8,769 

LCI Vector Region: Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia 

Pesticide Inputs FFB and EFB Transport Oil Mill 
73 | Life Cycle Associates, LLC 

Pathway Component

Process

Heavy Duty 

Truck Ocean Tanker

Natural Gas 

Boiler Coal Boiler Electricity Methanol

Sodium 

Hydroxide

Sodium 

Methoxide

Hydrochloric 

Acid

Palm Kernels 

Displacing Corn

Glycerin Displacing 

Petro-Glycerin

Truck

Diesel Ocean Ta

Process Loss Factor 1.00004 1.00000 1.00000 1.00000 1.00000 1.00001 1.

Cumulative Loss Factor per Fuel Pathway Component 1.00002 1.00002 1.00002 1.00002 1.00002 1.00002 1.00002 1.00002 1.00002 1.00000 1.00000 1.00000 1.

Feedstock Use Factor 1.04 1.04 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.04 1.00 1.00 

Units kg CPO/kg BD kg CPO/kg BD 1.00 1.00

Process Specific Energy 80 0 2.1 0.0 0.1 2.0 0.1 0.5 0.1 -0.410 -0.108 241 1

Units km km MJ NG/kg BD MJ Diesel/kg BD MJe/kg BD

MJ MeOH/kg 

BD MJ NaOH/kg BD

MJ NaMeO/kg 

BD MJ HCl/kg BD

tonne PK/tonne 

CPO

tonne 

glycerin/tonne BD km 

Specific Energy 0.0022 0.0000 0.0546 0.0000 0.0029 0.0531 0.0026 0.0129 0.0039 -0.00001 -0.000003 241 

Units Biodiesel Biodiesel Biodiesel Biodiesel Biodiesel Biodiesel Biodiesel Biodiesel Biodiesel Biodiesel Biodiesel km 

Fuel Cycle Energy (J) Total 3,810 0 58,377 0 7,821 83,105 2,599 12,864 3,898 -23,639 -236,954 16,355 

Fossil fuels 3,804 0 3,787 0 7,179 29,942 0 0 0 -23,205 -206,705 16,321 

Coal 75 0 54,656 0 2,865 108 0 0 0 -2,051 -106,554 287 

Natural gas 203 0 58,046 0 4,057 81,030 0 0 0 -12,805 -72,320 583 

Petroleum 3,526 0 230 0 257 1,946 0 0 0 -8,349 -27,827 15,451 

CH4 0.000 0.000 0.007 0.000 0.001 0.010 0.000 0.000 0.000 -0.002 -0.040 0.001 

N2O 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.006 0.000 0.000 CO2 0.3 0.0 3.3 0.0 0.5 1.1 0.0 0.0 0.0 -1.9 -20.4 0.8 

CO2 (Incl. VOC and CO) 0.3 0.0 3.3 0.0 0.5 1.1 0.0 0.0 0.0 -1.9 -20.4 0.8 

Total Fuel Cycle GHG Emissions (g/MJ) 0.3 0.0 3.5 0.0 0.6 1.3 0.0 0.0 0.0 -3.6 -21.5 0.8 

Allocated Emission Results: CPO, PK (g/MJ) 0.3 0.0 3.3 0.0 0.5 1.3 0.0 0.0 0.0 0.8 

Allocated Emission Results: CPO, PK, fiber, biomass (g/MJ) 0.3 0.0 3.3 0.0 0.5 1.3 0.0 0.0 0.0 0.8 

Displacement Emissions Results (g/MJ) 0.3 0.0 3.5 0.0 0.6 1.3 0.0 0.0 0.0 -3.6 -21.5 0.8 

Conversion to Metric Units

Numerator Unit Conversion 1055.06 1055.06 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1055.06 1055.06 1.0000 1.00 

Denominator Unit Conversion 0.68 0.68 0.0009 0.0009 0.0009 0.0009 1.0000 1.0000 1.0000 1.10 1.10 0.0006 0.000

Fuel Pathway ComponentMedium Duty Truck Ocean Tanker Natural Gas Boiler Coal Boiler Electricity Methanol Sodium HydroxideSodium Methoxide HCl Displaced Feed CornDisplaced Petro-glycerinHeavy Duty Truck Ocean Ta

Denominator Units tonne*km tonne*km MJ Natural Gas MJ Coal MJe MJ MeOH - - - tonne corn tonne glycerin MJ*km BD MJ*km 

Fuel Cycle Energy (J) Total 1,724,712 48,961 1,069,713 1,010,576 2,735,823 1,563,788 1,000,000 1,000,000 1,000,000 2,100,043,624 83,265,978,333 68 

Fossil fuels 1,721,817 48,857 69,385 10,249 2,511,475 563,417 2,061,412,845 72,636,543,858 68 

Coal 34,019 859 1,001,528 1,001,720 1,002,262 2,041 182,209,218 37,443,383,487 1 

Natural gas 91,784 1,744 1,063,645 2,350 1,419,250 1,524,749 1,137,530,506 25,413,552,281 2 

Petroleum 1,596,013 46,254 4,212 6,179 89,964 36,626 741,673,122 9,778,299,820 64 

Fuel Cycle Emissions (g) VOC 0.054 0.004 0.007 0.008 0.021 0.024 195.273 627.832 0.000 

CO 0.239 0.009 0.026 0.096 0.157 0.032 1,763.860 2,627.690 0.000 

NOx 0.677 0.103 0.075 0.108 0.215 0.083 798.496 8,255.518 0.000 

PM10 0.022 0.009 0.004 0.170 0.177 0.016 71.775 5,743.703 0.000 

PM2.5 0.015 0.006 0.003 0.044 0.050 0.015 41.947 1,438.604 0.000 

SOx 0.022 0.030 0.011 0.224 0.022 0.024 384.667 12,967.678 0.000 

CH4 0.140 0.004 0.123 0.114 0.289 0.181 207.646 13933.920 0.000 

N2O 0.003 0.000 0.001 0.001 0.004 0.000 489.340 66.694 0.000 

CO2 (Incl. VOC and CO) 128 4 61 103 190 21 167,903 7,172,476 0 

LCI Data VectorsFuel Pathway Component Heavy Duty Truck Ocean Tanker Natural Gas Boiler Coal Boiler Grid Electricity Methanol Sodium HydroxideSodium Methoxide HCl Displaced Feed CornDisplaced Petro-glycerinHeavy Duty Truck Ocean Ta

Denominator Units ton*mi ton*mi

mmBtu Natural 

Gas mmBtu Coal

mmBtu 

Electricity mmBtu MeOH tonne BD tonne BD tonne BD ton corn ton glycerin mmBtu*mi BD mmBtu*m

Fuel Cycle Energy (Btu) Total 2,387 68 1,069,713 1,010,576 2,735,823 1,563,788 1,000,000 1,000,000 1,000,000 1,990,457 78,920,920 68 

Fossil fuels 2,383 68 69,385 10,249 2,511,475 563,417 1,953,842 68,846,160 68 

Coal 47 1 1,001,528 1,001,720 1,002,262 2,041 172,701 35,489,480 1 

Natural gas 127 2 1,063,645 2,350 1,419,250 1,524,749 1,078,171 24,087,400 2 

Petroleum 2,209 64 4,212 6,179 89,964 36,626 702,970 9,268,040 64 

Fuel Cycle Emissions (g) VOC 0.078 0.005 7.838 8.446 22.497 25.640 177.148 569.560 0.005 

CO 0.349 0.013 27.950 101.763 165.176 33.412 1,600.147 2,383.800 0.013 

NOx 0.988 0.151 79.505 113.597 226.930 87.696 724.383 7,489.280 0.151 

PM10 0.031 0.013 3.910 178.964 186.314 16.456 65.113 5,210.600 0.013 

PM2.5 0.022 0.009 3.681 46.843 52.724 15.460 38.054 1,305.080 0.009 

SOx 0.032 0.044 11.113 236.163 22.872 25.553 348.964 11,764.080 0.044 

CH4 0.204 0.006 129.931 120.041 304.861 191.378 188.373 12640.640 0.006 

N2O 0.005 0.000 1.167 1.077 3.794 0.188 443.922 60.504 0.000 

CO2 (Incl. VOC and CO) 186 6 64,556 109,167 200,352 21,793 152,319 6,506,761 6 

LCI Vector Region: Southeast Asia U.S. Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia U.S

CPO Transport Fuel Production Co-Product Credits (Displacement) F
74 | Life Cycle Associates, LLC 

Terms 

AFOLU agriculture, forestry and other land use 

AGB above-ground biomass 

AR afforestation/reforestation 

BGB below-ground biomass 

CDM clean development mechanism 

CH4 methane 

CO2 carbon dioxide 

CO2e carbon dioxide equivalent 

COP conference of the parties 

DNA designated national authority 

DOM dead organic matter 

FAO Food and Agriculture Organisation of the United Nations 

FRA forest resources assessment 

GHG greenhouse gas 

HWP harvested wood product 

IPCC Intergovernmental Panel on Climate Change 

lCER long-term certified emission reduction 

LULUCF land use, land-use change and forestry 

N2O nitrous oxide 

RMUs removal units 

RS root-to-shoot ratio 

SOM soil organic matter 

tonne metric ton, 1000 kg 

UNDP United Nations Development Programme 

UNFCCC United Nations Framework Convention on Climate Change 

UN-REDD United Nations – Reduced Emissions from Degradation and Deforestation