# 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 o r implied, is made with respect to the**  **accuracy, completeness, and/or usefulness of inform ation contained in this report.**  **Finally, no liability is assumed with respect to th e use of, or for damages resulting from**  **the use of, any information, method or process disc losed in this report. In accepting this**  **report, the reader agrees to these terms.**

**ACKNOWLEDGEMENT**

**Life Cycle Associates, LLC performed this study wit h 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) W**  **ell-to-Wheel GHG Emissions and Land Use Change Impacts of Biodie**  **sel 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.................................**  **.................................................. 2 6** 

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

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

**2.8.8 Fuel Combustion....................................**  **.................................................. 3 0** 

**3 DIRECT LAND USEEFFECTS.............................**  **.................................................. 3 3** 

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

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

**3.1.2 Malaysian soil types...............................**  **.................................................. 3 4** 

**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 CHANG**  **E**

ERROR! BOOKMARK NOT DEFINED.

### ii | Life Cycle Associates, LLC 

# TABLES

................................................... ................................................... .................................. 6 Table 1. Base Case and Sub-pathway Palm Oil Mill PO ME 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 Pal m 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 In puts ............................................. 28 Table 10. Palm Oil Fatty Acid Profile..............

................................................... ............ 28 Table 11. Biodiesel Transport Parameters...........

................................................... ...... 30 Table 12. Biodiesel Transport Parameters...........

................................................... ...... 31

Table 13. Vehicle CH 4 

### and N 2 

### O 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, A ND FI) (Over 20 years) for Different Management Activities on Cropland ........

................................................... .... 45 Table 16. Carbon release factors and 100 year emiss ions with 2% discount factor...... 49 Table 17 Estimates for Emission Factors vary consid erably for the different land use change types.......................................

................................................... ....................... 59



# FIGURES

Figure 1. Summary of LUC and direct emission result s for Malaysian Palm Biodiesel.... 3 Figure 2. General Approach for counting direct and indirect LUC ................................... 5 Figure 3. System Boundary Diagram for Palm Biodiese l 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 Efflue nt............................................. 25 Figure 11. Transesterification Reaction of Triglyce rides to Methyl Esters..................... 27 Figure 12. AEZ for Peninsular Malaysia.............

................................................... ........ 34 Figure 13 Estimated annual carbon stocks in tropica l Asian and Malaysian forests ..... 35 Figure 14 Malaysian study for SOC measurements wit hin the harvesting and frond file paths representing three sampling depths (a, b and c) ................................................. 38 Figure 12 Conventional pastoral agriculture vs. agr oforestry system such as for palm represent very different carbon cycles.............

................................................... ........... 39

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Figure 13 Panamanian forest study with agroforest a nd pasture land compared.......... 42 Figure 14. Calculating LUC impacts from palm oil ...

................................................... .. 46 Figure 15. Carbon stock emission factors used in th e LCFS assume the same stored carbon from all crops. However, in Malaysia, the bi ofuel 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: Stefa n Unnasch) ................................... 52 Figure 20. Global Oil Production ...................

................................................... ............. 53 Figure 21 Oil palm products ........................

................................................... ............... 54 Figure 22. Direct fuel LCA emissions and LUC emissi ons 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 biodiese l 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 im pacts 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 inclu de the effects associated with the**  **usage of crude palm oil (CPO) feedstock, the co-pro duction 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 t he following:** 

• Methane capture from palm mill **. The decomposition of palm oil mill effluent (POME) results in the production of methane emissio**  **ns if the material is pumped into lagoons, which is currently the predominant pr**  **actice. Malaysia will be requiring the conversion of palm mills to methane c**  **apture. 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 alloca tion approach may be best for**  **the palm kernel. Crude glycerin produced from Mala ysian palm oil is sold for**  **reprocessing as green glycerin. The practice of sel ling crude glycerin is**  **sufficiently common and Malaysian palm oil biodiese l should be treated with a**  **substitution credit for this co-product.**

• Yields **. Both fresh fruit bunch (FFB) and oil extraction y ields are improving due to**  **various technology and agronomy efforts. FFB yield s 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 productivit y increase from 4 to 7.8**  **tonne/ha or almost doubling of oil yield. The doub ling of oil yield would reduce**  **the WTW emissions by almost 50% for farming and tra nsport 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 CO 2**  

### during their **growth cycle. The carbon is stored in the soil, ro ots, above ground biomass, and**  **litter. The net carbon storage needs to be account ed for in the LUC analysis**  **1**



### . **The carbon stored in harvested wood products (HWP) from conversion also**  **requires assessment. Finally, the inclusion of cre dits 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 iLU C 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 ch oice and allocation** 

**method.**

• Time horizon **. Palm plantations require a 25 to 30 year growth c ycle and are**  **costly to develop. A minimum of three cycles are n ecessary to achieve economic**  **viability; so a 100 year time horizon is appropriat e for assessing time varying** 

**GHG releases.**

• iLUC of fungible oils **. Indirect LUC (iLUC) effects from the use of CPO w ould 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 pro ducer and considered the**  **marginal source on the market. Differences in marke tability depend on a wide**  **range of customer preferences, marketing, the fat p rofile, and other parameters.**  **Because palm, soy, and rapeseed are highly substitu table, this creates**  **fractionation in the palm market. Thus, difficult t o 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 outsid e of Malaysia can be substantial**  **as palm productivity in Africa and South America ar e much lower than those in**  **Malaysia.**

**These principles are taken into account in the anal ysis presented in this study. Figure 1**  **shows the LUC results for palm oil combined with th e direct emission effects, which are**  **documented in a separate report. The GHG emission e stimates 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 tailpip e emissions correspond to the direct**  **and upstream fuel cycle emissions for fuel producti on equipment and vehicle operation.** 

**These emissions are constant over time. The next c ategory is farm inputs, fertilizer,** 

**and associated N 2**  

### O emissions, which are based on the inputs for palm **oil production 2** 



### .

**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 feedst ock oils are traded and are largely**  **fungible. The iLUC result here is based on EPA’s a nalysis for soy biodiesel and**  **includes all of the LUC effects except for the farm inputs and N**  **2** 

### O. Finally, the carbon **storage associated with the palm plantation is take n into account. Note that any**  **changes in carbon stocks associated with the produc tion of the CPO feedstock are** 

**taken into account with the iLUC results.**

**2 EPA calculates U.S. and international fertilizer e**  **missions using FASOM and international fertilizer d ata.** 

**Since FASOM is not available to assess the emission s from Malaysian agriculture, the analysis**  **represents the direct and fuel cycle emissions asso ciated with palm oil. The same approach is taken b**  **y ARB under the LCFS; where fertilizer impacts are no**  **t 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** 

**2 0**  **0 5**  **D i**  **e s**  **e l**  **B a**  **s e**  **l i**  **n e** 

**S o**  **y B**  **i o**  **d i**  **e s**  **e l**  **b a**  **s e**  **y i**  **e l**  **d**

**P a**  **l m**  **B i**  **o d**  **i e**  **s e**  **l ,**  **C H**  **4 C**  **a p**  **t u**  **r e** 

**P a**  **l m**  **B i**  **o d**  **i e**  **s e**  **l ,**  **C H**  **4 C**  **a p**  **t u**  **r e**  **, B**  **i o**  **m a**  **s s**  **S a**  **l e**  **s**

**P a**  **l m**  **B i**  **o d**  **i e**  **s e**  **l ,**  **N o**  **C H**  **4 C**  **a p**  **t u**  **r e** 

**P a**  **l m**  **B i**  **o d**  **i e**  **s e**  **l ,**  **P a**  **l m**  **B o**  **l e**  **P r**  **o d**  **u c**  **t s** 

**P a**  **l m**  **B i**  **o d**  **i e**  **s e**  **l M**  **a l**  **a y**  **s i**  **a o**  **n l**  **y L**  **U C** GHG Emissions (g CO2e / 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 Mal aysian Palm Biodiesel**  Source Life Cycle Associates, EPA 2010



### The direct emissions component from the palm planta **tion is not included in EPA’s**

**carbon stock emission factors and is therefore incl uded 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 pal m plantation. The far right bar**  **represents the LUC impacts if all marginal palm wer e grown on Malaysian land that**  **is currently reserved for agriculture with a forest cover**  **3** 

### . Here the carbon storage **from the palm plantation largely offsets any loss o f 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 expand**  **ing 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.** 

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# 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 Sout**  **h America**

**The oil is used for a variety of purposes. Palm st earin (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 exc lusively 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 pa lm oil has soared in the last two**  **decades, first for its use in food or consumer prod ucts and more recently as the raw** 

**material for biofuel.**



## 1.1 Objective

**This document provides detailed calculations, assum ptions, inputs and other necessary**  **information to calculate the energy requirements an d GHG emissions for the palm oil to**  **biodiesel (BD) pathway. This document provides a s ummary 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 contra st to indirect economic effects. The**  **functional unit is a megajoule (MJ) of fuel produce d.** 



## 1.2 Land Use Conversion

**LUC includes the direct emissions (dLUC) associated with land conversion to**  **agricultural production and indirect land use chang e (iLUC) effects associated with land**  **change (locally or elsewhere), including economic i mpacts induced by perturbation of**  **global commodity markets. Estimating this change in cludes estimates of conversion of**  **land between ecosystem types resulting in a new bal ance 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 fr om the expanded production of**  **biofuels, yet the temporal and spatial pattern of t his change is difficult to measure with**  **certainty (Searchinger, Heimlich et al., 2008, Farg ione et al., 2008, Liska et al., 2009,**  **EPA 2010).**

**Two papers facilitated the current iLUC global deba te: Fargione (et al., 2008) and**  **Searchinger (et al., 2008). Fargione stresses the i mportance of nature for carbon**  **storage, and uses the carbon payback time to calcul ate different biofuel scenarios in**  **terms of carbon savings per biofuel (or debt) from avoided fossil fuel combustion.**  **Searchinger examines the indirect effects of land u se impacts from corn ethanol based**  **on agro-economic modeling of the food crop. Previou sly, Wang and Delucchi (2005)**  **included iLUC in the LEM model and a preliminary iL UC factor has been included in**  **GREET. Since these publications, the topic of LUC i n biofuels LCA has been**  **incorporated in the policies and regulations affect ing GHG emissions from biofuels (EPA**  **2010, ARB 2010).**

**.**

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**Inputs from the well to wheel (WTW) or tank to whee l (TTW) reflect a direct causal**  **relationship (Figure 2) between energy inputs and G HG 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 dat asets for carbon stocks to** 

**estimate overall carbon release. The assessment of indirect effects (including iLUC) is**  **often termed a ‘consequential analysis’. Some anal yses aggregate dLUC emissions** 

**with the iLUC emissions or treat all land cover cha nge as iLUC (Tyner et al., 2010).**  **This approach needs to be examined carefully with p alm 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 L UC** 



### Indirect impacts include economic impacts associate **d with this change, and subsequent flux of carbon from production, and are difficult t**  **o quantify with certainty. Using the consequential method requires projections of biofue**  **ls demand over time and debate continues about how to include demand projections f**  **rom current and future legislation that mandate biofuel quantities. Market effects are**  **determined by many factors, including regional supply and demand curves and ela**  **sticity factors (price, yield, etc.). Partial and general equilibrium models are employed**  **to calculate these changes.**

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**Lastly, historical trends of land use patterns fail to represent LUC as a geospatial and**  **non-linear function of output because this is diffi cult 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 ad dress this issue. Studies are**  **beginning to focus on the complexity of relaying us e data to ecosystem cover ((i.e.**  **Farming vs. another activity in grassland vs. fores t) 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 i n 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 Producti on** 



## 1.3 Background

**To understand the complex effects and impacts of bi ofuel production, iLUC has been**  **introduced, with much speculation due to variable r esults 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 biodie sel.**  **With the exception of marginal or abandoned croplan ds, direct land use change (dLUC)**  **results in an initial net loss of sequestered carbo n in the form of both aboveground**  **(plants, litter) or belowground (roots, soil) bioma ss when crops are planted in place of**  **existing land cover (Fargione et al, 2008). The dir ect effects are established in one**  **place in time and include the additional land requi red. 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 ker**  **nel oil, palm kernel expeller and glycerin. How to calculate these as avoided em**  **issions 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 fungibilit y 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 decreas**  **es in each per year?**

• **What are current calculations of** carbon stocks for palm plantations
  ** representing the differences in life cycle, HWP pot** **ential 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 indu stry 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 t ree species that is a long term**  **production system (>25 years). Therefore, the carbo n sequestration potential of this**  **system has not yet been adequately addressed in rec ent studies on LUC, particularly**  **for roots and below ground carbon. Palm grown on pe at soil does represent a difference** 

**in emissions and is included in the direct emission s 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 de scriptive model approach; Joint**  **Research Council (JRC) in Europe is also analyzing the palm biodiesel iLUC results;**  **Environmental Protection Agency (EPA) and Californi a Air Resources Board (ARB)**  **have analyzed the LUC effects associated with vario us biofuels including corn and**  **sugarcane ethanol, soy biodiesel, and are also look ing into analyses of palm LUC. The**  **Roundtable on Sustainable Palm Oil (RSPO) has not y et made a decision on inclusion** 

**of LUC or GHG model at this time.**

**Policies around the globe have addressed iLUC diffe rently so far. However, measuring**  **iLUC in practice is fraught with difficulty and the re is no agreed methodology on how to**  **analyze iLUC and dLUC at present. The distinction b etween the impacts is often** 

**blurred.**

**In the U.S., EISA defines lifecycle GHG emissions a s follows: “The term ‘lifecycle**  **greenhouse gas emissions’ means the aggregate quant ity of greenhouse gas emissions**  **(including direct emissions and significant indirec t 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 a nd 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 t he mass values for all greenhouse**  **gases are adjusted to account for their relative gl obal warming potential.”** 



## 1.4 Categorizing Direct and Indirect LUC

**The categorization of dLUC and iLUC emissions provi des opportunities to estimate**  **emissions and yet no clear method has evolved for c ounting these impacts. In the**  **simplest analysis, a grain crop is converted to bio fuel. The grain is a global commodity** 

### 8 | Life Cycle Associates, LLC

**and the new production of the crop could occur anyw here in the world. Furthermore**  **other crops might provide a substitute and other sh ifts in the agricultural system could**  **occur. The GHG emissions associated with changes i n cropping are referred to as**  **iLUC. However, expanding crop emissions may also h ave some effects associated with**  **the crop itself. For example, corn cropping can bu ild up root mass and soil carbon due**  **to no till practice. Similarly switch grass and pa lm 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 examin ed in a modeling system that**  **accounts for all crop changes globally, thus placin g the net carbon flux from the crop**  **into the iLUC category.**

**Detailed modeling of dLUC effects are complicated b y factors such as soil conditions,**  **fate of the biomass and many other factors. For th e purposes of this study, dLUC**  **impacts are treated as an adder 4**  

### 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 f or switch grass and farmed trees. EPA models**  **net GHG flux with the FASOM model, which captures d LUC impacts in the U.S. International emissions**  **are estimated by assuming a switch from one land cl assification 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 a nd processing options is important in**  **terms of establishing pathways under the LCFS. The choice of pathway and**  **aggregation affects the requirements for fuel produ cers to provide data to establish**  **project specific CI values. The following factors affect pathway aggregation:** 

**Farming Inputs**

**Palm oil is grown in plantations throughout peninsu lar Malaysia, Sabah, and Sarawak.**  **The plantations occur over a range of soil conditio ns, age of plantation, transport**  **logistics, and other factors. FFBs are processed i n 400 palm mills in Malaysia. The**  **source of FFBs to the palm mills is not dedicated a nd varies with market conditions.**  **The key LCA inputs for farming are fertilizer input s 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 t he purpose of the pathway, the**  **average Malaysian farming inputs and yields are rec ommended for pathway**  **development. This situation is comparable to the d ata aggregation for corn ethanol and**  **soy biodiesel. Average national statistics are use d to characterize the fuel pathway.** 

**Aggregating crop production by region or farm level is problematic because data**  **collection becomes too difficult for most fuel prod ucers. Furthermore, allowing both**  **country average and regional data creates a situati on where producers with lower inputs**  **will use local data while producers with higher inp uts will choose average or default**  **pathways. Therefore, using Malaysian aggregate sta tistics 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. Esti mates 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 pa lm oil mill effluent (POME)**  **processing options. Methane emissions from POME la goons are a dominant source of**  **fuel pathway emissions when the methane is not capt ured (in addition to agricultural**  **chemical use). A base case and five sub-pathways w ere developed to address**  **methane capture and use. The oil mill base case an d 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 Scen arios** 

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 pro cessing fuel, methanol, and**  **biodiesel, and glycerin yield. Transportation logi stics also affect the fuel pathway**  **depending on whether CPO or biodiesel is shipped to California.** 

**The energy inputs and yields for biodiesel producti on depend on the plant technology.**  **More sophisticated plants will achieve higher level s of methanol recover, higher yields,** 

**and lower fuel use. The CA LCFS defaults for biodie sel 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 co**  **products produced at the oil mill and biodiesel fac ility. 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 he re are allocation and displacement.**  **The allocation method allocates production energy a nd emissions among the products**  **produced based on some property, while the displace ment 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 str eams. 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 tha n energy since kernels are** 

**used as food**

• **Avoids substitution data for palm kernel such as c oconut oil and coconut meal** 

### 11 | Life Cycle Associates, LLC

**In the case of palm oil, the emissions are allocate d to CPO and palm kernels. The**  **share of energy and emissions from agriculture, fee dstock 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 resul ts and represents the co-product** 

**credit.**

**Other fuel LCA studies calculate a substitution cre dit based on the production of other**  **tropical oils. However, this approach is complicat ed 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 biodie sel transport and distribution are**  **not allocated. This approach results in a relative ly low valuation of glycerin. If the**  **glycerin were valued as a chemical substitute displ acing petro-glycerin, the co-product**  **credit would be -21 g/MJ. The soy biodiesel develo pers have also made this**  **observation. We believe that the approach conducte d 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 a pproximately 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 frui t bunches. The kernels contain**  **approximately 55% oil and 8% protein. 5** 



### The agricultural stage for oil palm includes activi **ties related to cultivation of oil palm. The inventory is normalized to 1 hectare oil palm p**  **lantation 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 fre**  **sh fruit bunches (FFB) is applied as the average yield in Malaysia.** 

**Since oil palm is a perennial, three different stag es are considered for the plantation**  **site: immature plantation, mature plantation and a transition period for repositioning of**  **next generation palms. Furthermore, every plantati on has a nursery for sowing the**  **seed. The palms are mature from two years and the e conomically 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 2 5 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 len gth 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 s eedling is approximately three to four**  **months. The seedlings in the small poly bag are ke pt under a cover protecting them for**  **direct sunlight. After that, the seedling is plant ed in a larger poly bag in the main**  **nursery 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 pal **ms per ha on mineral soils (assumed average of 142 palms per ha) and approxima**  **tely 160 palms per ha on peat soils. Before the palms are planted, the soil is sp**  **rayed with pesticides, plowed, compacted and legume cover is sown, typically puera**  **ria. The cover crop prevents erosion and fixes nitrogen from the atmosphere, esp**  **ecially when the palms are young. The oil palms are harvested the first time when the**  **y 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. Curr ent yields are over 4.5 tons of CPO/ha**  **with projections towards 8 tons/ha based on improve ments in cultivation and more**  **productive cultivars. Thus, one average hectare con sists of 8% immature palms and**  **92% mature palms. When the palms become unproducti ve (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 foderv ærdi 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 th e 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 e ffective 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 q uadratic relationship; higher fertilizer**  **input results in higher crop yield over a limited r ange and plateaus thereafter. Higher**  **yields are also attributed to improved tree varieti es, improved cultural practices.**  **Malaysian oil yields are higher than Indonesian yie lds 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 Indo nesia 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 u sed 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 plan tations, 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 cult ivated 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 chemi cal costs and lower yields. Lands**  **with peat soils require much more soil compaction a nd preparation than mineral soils to** 

7 **Schmidt J.H., 2008c. Life cycle assessment of palm oil at United Plantations Berhad, (In press). Telu**  **k Intan, Malaysia: United Plantations Berhad.** 

### 14 | Life Cycle Associates, LLC

**prevent root oxidation and prevent trees from toppl ing 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 so il. Careful planning on peat soil fields**  **is required to ensure suitable water conditions dur ing 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 soi ls require a different fertilizer mix than**  **palms grown in mineral soils (discussed below). Yi elds for oil palms grown on peat soils**  **vary significantly based on the nutrient inputs, wa ter 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 cultiva tion 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 recyc **led 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 yiel d and nutrient balance. FFB**  **compositional data is used to estimate the quantity of each nutrient in the feedstock.**  **The nutrient balance establishes the limiting nutri ent(s) and accounts for nutrients**  **supplied by soil, legumes and nutrient recycling. Palms planted in mineral soil require**  **nitrogen input (primarily ammonium sulfate), phosph orus (as phosphate), potassium**  **(potash) and magnesium (magnesium oxide). Peat soi ls, 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 so ils have a low pH and require**  **addition of lime or other amendments to raise the p H. 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 s upplied to the palms. In particular,**  **retention of potassium in peat soils is very poor a nd the nutrient must be supplemented.** 

**The Base Case life cycle pathway is based on fertil izer data reviewed and converted to**  **input parameters by MPOB. The data is based on sur veys 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 a nd mature palms grown on mineral**  **and peat soil and represents a wide range of planta tions at all stages of development.**  **The MPOB survey data is one of the best sources ava ilable to represent average**  **fertilizer inputs in Malaysia. The data is well do cumented, converted to input**  **parameters transparently and represents a large num ber of producers at one time (3**  **months). However, longitudinal fertilizer data (in puts over the entire life cycle of oil**  **palms) is difficult or impossible to obtain. Ideal ly, the fertilizer inputs for the Malaysian**  **average case should be based on longitudinal data o f a large group of representative**  **plantations. Moreover, new techniques are emerging that may improve nutrient uptake**  **and reduce application requirements. These complex ities make it difficult to develop**  **representative fertilizer input parameters. The in put values are presented in Section**  **5.2. MPOB has also developed recommended nitrogen inputs and standard (average)**  **Malaysian plantation nitrogen inputs by palm age ba sed on FFB yield and palm age**  **data acquired from the MPOB Observation Block Netwo rk for low, medium and high**  **yielding palms.**

**In addition to plantation surveys and nutrient bala nce analysis, fertilizer purchase data**  **can be used to estimate fertilizer inputs. MPOB re viewed 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/to nne FFB units based on the FFB yield**  **(tonne/ha). The data indicates an increase in aver age fertilizer use from 2002 to 2008.**  **Overall, the input parameters derived from import d ata are consistent with the inputs**  **derived from the plantation survey data. The nitrog en input parameter derived from the**  **import data is slightly lower than the similar valu e 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 an d 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 p est management program. Usually**  **that includes planting of beneficial flowering plan ts 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 a re attracted by setting up nesting**  **boxes. Another part of integrated pest management i s 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.** 

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**traps. 10**  

### More complete recovery of all fruits from the gro **und also reduces food supplies**

**for rats.**



## 2.4 Palm Oil Production

**The FFBs are collected and transported to a palm oi l extraction facility where the oil is**  **extracted and co-products separated. Figure 7 show s 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 involve s 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 processi ng.** 

• **Sterilization of the FFB is done batchwise in an a utoclave for 1 hour 40 minutes**  **for the FFB to be completely cooked. The temperatu re 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 o ccurs in this step.** 

• **Crude palm oil extraction and purification: the ho mogenous 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 a t this step. Centrifugal and**  **vacuum driers are used to further purify the oil be fore sending it to a storage**  **tank. The purpose of purification is to boil the m ixture of oil and water to remove**  **water-soluble gums and resins in the oil. The temp erature of oil (60°C) in the**  **storage is maintained with steam coil heating befor e 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 o f the cyclone is used as boiler**  **fuel from which ash is produced after combustion.**

• **Nut cracking: the nuts are cracked in a centrifuga l cracker. After the cracking**  **process, the kernels and shells are separated by cl ay 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 use d as transport fuel in California**  **based on the CA-GREET model methodology in an exter nal analysis framework. The**  **analysis uses life cycle inventory (LCI) data from the CA-GREET in a separate**  **workbook to calculate disaggregated fuel cycle emis sions in g CO**  **2** 

### e/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 stud y examines the impact on co**  **product credits for palm kernels and glycerin using the two different methods: allocation**  **and substitution. This approach illustrates the dif ferences in biodiesel carbon intensity**  **when emissions are allocated to co-products and whe n co-products are instead credited**  **as substituting for other products. The analysis e xamines 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 cor respond 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 consump tion 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 CO 2**  

### equivalent per unit of fuel (g

**CO 2**  

### e/MJ), and are referred to as the carbon intensity **(CI). The GHGs considered in the analysis are CO**  **2** 

### , N **2** 

### O, CH **4** 

### , CO and volatile organic compounds (VOC). GHG **emissions from TTW portion of the analysis arise fr om the carbon content of the fuel (g**  **C/MJ fuel) converted to CO 2**  

### (44 g CO **2** 

### /12 g C), plus vehicle emissions of CH **4** 

### (evaporative and exhaust) and N **2** 

### O. Global warming potentials (GWP) (g

**CO 2**  

### e/g constituent) for CH **4** 

### and N **2** 

### O 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 converte d to CO**  **2** 

### in the atmosphere, and **thus have GWPs of 1 when expressed as CO 2**  

### (fully oxidized form). The analysis **excludes the climate impact of secondary and higher order atmospheric species that**  **arise from direct emissions, including ozone, oxide s of nitrogen (NO** 

**x** 

### ) and aerosols.

2.5.3 System Boundary

**The Malaysian palm oil to biodiesel fuel pathway pr esented in this report includes oil**  **palm farming, fresh fruit bunch (FFB) transport, pa lm oil production at the oil mill, oil**  **transport, biodiesel production, transport and dist ribution (T&D) and use of biodiesel in**  **an internal combustion engine. Figure 1.1 shows th e discrete components that form the**  **biodiesel pathway including farming, biodiesel prod uction, 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 analys is and a co-product credit is**  **assessed for whole palm kernels. Palm oil producti on 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 Cali fornia GREET for the individual**  **process fuels, chemical inputs and transport segmen ts contained in the fuel pathway.** 

**These data vectors were assembled in an off-model m atrix format (discussed in Section**  **4.3) that uses the input energy for each process fu el 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 prod uction and transesterification, to “U.S.**  **average” for biodiesel transport by ocean tanker an d 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 emi ssion factors for methane (CH**  **4** 

### ) **and nitrous oxide (N 2**  

### O). The following two subsections describe LCI dat **a 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 consumpti on in agricultural machinery used**  **in nursery, maintenance, harvesting and collection procedures. It has not been possible**  **to determine the specific diesel consumption per si ngle field work process per hectare.**  **Data has only been available as the total diesel co nsumption per year on the estate**  **level. Therefore, diesel consumption in machinery i n 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 O bligation estimates diesel**  **consumption for the oil palm farming of approximate ly 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:**



### 35.8 MJ 70.0 l ha 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 ene rgy divided by output energy. The**  **process specific energy is converted to a net speci fic energy (MJ diesel/MJ biodiesel)**  **by multiplying it by the feedstock use factor. Thi s is accomplished with a matrix**  **calculation and is done for most life cycle compone nts in the analysis and will not be**  **explicitly shown for each pathway step.**



### 1.04 tonne CPO kg BD 125.4 MJ 4.01 tonne FFB tonne
tonne FFB tonne CPO tonne BD 1,000 kg 37.9 M J

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, pesti cides and herbicides. The LCI chemical**  **data includes transportation of each product from p lant 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 t he 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 Fue**  **l Obligation (RTFO). Carbon and Sustainability Reporting Within the Renewable Transport Fuel Oblig**  **ation Requirements and Guidance Draft**

**Government Recommendation to RTFO Administrator. Lo ndon. Referenced [30.1.2008].**  **Available:www.dft.gov.uk/roads/RTFO**

### 20 | Life Cycle Associates, LLC

**and zinc. Although other trace inputs are often us ed, these are excluded for lack of**  **data and these inputs have a negligible impact on f uel 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 (P 2**  **O 5**  **) 56.0 2.8** 
**K (K 2**  **O) 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 artifici **al synthetic fertilizers, biomass residuals (pruned fronds, EFB, and POME), decomposition from**  **atmosphere or decrease from soil nitrogen pool. In this study it was assumed t**  **hat nutrient requirements are fulfilled by using synthetic fertilizers. Ammonium sulfate i**  **s 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 amm**  **onium sulfate was constructed for the analysis based on these precursor chemical resu**  **lts from GREET. MPOB survey data representative of oil palm cultivation in Mala**  **ysia 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 pa lm and results in annual application**  **of 2.59 kg herbicide/ha, 0.58 kg pesticide/ha (Schm idt, 2007). These values are similar**  **to the value given (3 kg /**  

### ha year) in the RTFO methodology documentation (RTF **O, 2008), where no differentiation between herbicide a**  **nd pesticide has been done. The typical herbicide used for Malaysian oil palm c**  **ultivation is glyphosate, but this**

**herbicide is not included in the GREET model. The c losest herbicide in applicability that**  **is included in GREET and also used on oil palm plan tations is atrazine, which is**  **assumed as herbicide in this analysis.**

Table 3 **Herbicide and Pesticide Consumption on Palm Oil Pl antations.** 

**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 N 2 

### O Emissions

**The analysis includes the direct field and downstre am N**  **2** 

### O emissions resulting from **conversion of nitrogen in the fertilizer input and above and below ground biomass.**  **Agricultural N 2**  

### O emissions result from conversion of fixed (natura **l and anthropogenic) nitrogen in the soil. Fixed nitrogen applied to fie**  **ld crops is either extracted by the crop**

### 21 | Life Cycle Associates, LLC

**as a nutrient, absorbed (chemically bound) into org anic 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 N 2**  

### O, volatilized as nitrate or ammonia, or leached ou **t as nitrate. Field and downstream inputs are significant components of agr**  **icultural emissions associated with Oil palm cultivation. GREET assumes 1.3% of fertil**  **izer-N is ultimately converted to**

**N 2**  

### O, including 1.1% conversion on the field and 0.2% **conversion downstream. The**

**calculation of downstream N 2**  

### O emissions is presented below:



# ( ) 2 2

2 

### 44 g N 72.65 g N 3,490 g N 1.3% tonne FFB 28 g N tonne FFB O O    

=

       



### The calculation uses the mass ratio of N **2** 

### O to N **2** 

### (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, yiel ding 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 tr ansport 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 transport ed product to energy and emissions**  **per MJ of biodiesel produced.**

Table 4. **Herbicide and Pesticide Consumption on Palm Oil Pl antations.** 

**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 inp ut parameters. The data were**  **published by Subramaniam in 2008 and provides a goo d representation of palm oil mills**  **in Malaysia (Vijaya Subramaniam, 2008). These pal m 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 ran ging 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 p**  **eriod of three months from each mill, which consisted of raw material usage as well as th**  **e emissions of solid, liquid and gaseous wastes. The weighted average of data value**  **s 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 fu el 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 fossi l fuel inputs. The Base Case oil mill** 

**scenario assumes that 80% of the POME methane emiss ions 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 c aptured emissions are shown below:** 

3

4 4

3 

### 777 g CH 49.9 MJ CH 1 kg 55.38 m 1 tonne 1.7 MJ/kg CPO tonne CPO kg kg 1000 g

m        

 

=        

           

**The usual extraction rate for Malaysian palm oil mi lls 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 p alm oil. During processing,**  **significant amount of palm kernels (PK) are produce d. Processing of 5 tonnes FFB**  **produces approximately 0.41 tonnes palm kernels. Ke rnels are used for extraction of**  **palm kernel oil (PKO) and the production of palm ke rnel meal. Oil mills also produce**  **fiber in excess of that used as process fuel and wo ody co-products. The next section**  **discusses the co-products produced and co-product l ife 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 produ ction 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

**CH 4**  **form POME pond**  **(m 3**  **) 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

**CO 2**  **form POME pond**  **(m 3**  **) 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

**SO**

**x (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

**NO**

**x (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 Mu**  **lching Mulching Mulching Mulching Mulching**

**POME Treated Treated Treated Treated Treated Treated Tre**  **ated Treated Treated Treated Treated Treated Treate d** 



### There are three main sources of POME in the palm oi **l mill: sterilizer condensate (36% of total POME), clarification waste water (60% of t**  **otal POME) and hydro cyclone waste water from nut and fiber separation (4% of total PO**  **ME) (Department of Environment 1999)**  **12** 

### .After recovering residual oil in the waste water u **sing 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 Proce sses & the Environment (Handbook No. 3) Crude**  **Palm Oil Industry. Department of Environment, Malay sia** 

### 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 t o reduce GHG emissions.** 

**Digested POME 3.04 MT**

**CH 4**  **55.38 m 3** 

**CO 2**  **29.82 m 3**  **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**

**CH 4**  **55.38 m 3** 

**CO 2**  **29.82 m 3**  **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 pond s during the anaerobic digestion**  **is the biogas which consists of methane, carbon dio xide and traces of hydrogen sulfide.**  **An example of a POME biogas capture plants is a fac ility completed in Perak, Malaysia**  **in 2006, which produce carbon credits under a CDM-E B approved methodologies, but**  **this is still the exemption. 13** 

**13 (Aquarius, 2008), An Integrated POME Methane Recov**  **ery - Effluent Treatment System and CDM Financing, Aquarius, Systems (Malaysia) Sdn Bhd, 40**  **150 Shah Alam, Selangor, Malaysia, 2008.**

### 25 | Life Cycle Associates, LLC **© Life Cycle Associates © Life Cycle Associates**  

### Biomass Boiler at Palm Oil Mill Untreated POME Effl **uent**

Figure 10. 

### Biomass Boiler and Untreated POME Effluent.

2.7.6 Palm Oil Mill Co-Products

**This study considers the following co-products exit ing the oil mill: palm kernels, fiber**  **and palm products. Co-product credits are calculati ng using the hybrid allocation**  **method utilized by the California Air Resources Boa rd (ARB) under the Low Carbon**  **Fuel Standard for soybean-based biodiesel. This hy brid 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, a nd all mass-based allocation factors** 

**used are shown below in Table 7. The allocation fa ctors 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 whe re fiber and biomass considered co**  **products. Fiber and biomass could be used to gener ate electricity and some biomass** 

**could be used as lumber. The second allocation met hod 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 appro ach effectively credits fiber and**  **palm products in addition to CPO and PK based on th e mass of all of the products.**  **The second method is displacement, in which palm ke rnels are credited as displacing**  **feed corn on a mass basis (based on the LCI data fo r feed corn). In general, the**  **displacement method is the preferred approach for c o-products when there is a small**  **number of displaced products, the displacement fact or is well characterized (kg product**  **displaced/kg co-product), and the assumed displacem ent is economically justified.**  **Although the LCI data for displaced feed is availab le, using the displacement method to**  **credit palm kernels is difficult because of the dif ficulty in developing a good**  **displacement metric. Ideally, feed displacement sh ould 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 curren tly available. Other metrics include** 

### 26 | Life Cycle Associates, LLC

**feed mass, calories and protein content, although n one of these are necessarily**  **correlated to animal weight gain in the same way ac ross feed products. The**  **displacement factor for displaced glycerin is strai ghtforward, but this approach becomes**  **less justifiable when the glycerin market is satura ted 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 a nd 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 ani**  **mal feed respectively. Production statistics show that 1 tonne of kernel delivers app**  **roximately 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 0.21 32 t of PKM (0.41 tonne**  **kernels/tonne CPO x 0.52 tonne PKC/tonne kernel) ca n be produced. This analysis**  **considers only the palm kernels as a co-product; th e 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 p roduced 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 f uel 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 t he presence of a catalyst (acid or**  **base) to produce fatty acid methyl esters (FAMEs) a nd co-product glycerin. A base**  **catalyst (sodium hydroxide) is typically used to ca talyze transesterification by removing**  **a proton from the alcohol, making the alcohol enhan ces the ion more reactive. Sodium**  **methoxide is also used as a strong base to help fac ilitate the reaction; methoxide, the**  **negative ion formed when sodium methoxide dissociat es, is the conjugate base on**  **methanol. The mixture of FAMEs is the biodiesel pr oduct. Figure 5.3 illustrates this**  **process, in which R in the figure represents the fa tty acid hydrocarbon chain in the**  **triglycerides comprising the oil. As the figure sh ows, 1 mole of oil triglycerides reacts**  **with 3 moles of alcohol to yield 3 moles of FAME an d 1 mole of glycerol (glycerin) in the** 

**transesterification reaction.**

Figure 11. 

### Transesterification Reaction of Triglycerides to Me **thyl Esters.**

**The inputs driving the transesterification energy a nd emissions calculation include the**  **direct esterification energy inputs (natural gas an d 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. N atural gas is used for heating and**  **electricity is used for mixing and peripheral proce sses. The analysis includes the direct**  **energy content of the chemicals used in the process , but omits upstream energy and**  **emissions associated with chemical production, sinc e the GREET model does not**  **model the life cycles of these chemicals. Therefor e, the GREET calculations**  **underestimate the total energy and emissions associ ated with oil transesterification.**  **The transesterification electricity input is 0.03 k Wh/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 transesterif ication. The glycerin yield is**  **calculated in this analysis based on ideal stoichio metric yield and the CPO triglyceride**  **profile. The typical fatty acid profile for palm o il is shown below in Table 10, based on**  **palm oil composition presented in the Renewable Ene rgy Group report.**  **15** 

### As the table **shows, the weighted average molecular weight (MW) f or 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 c **alculated based on the FAME weighted average molecular weight, the molar mass o**  **f glycerin and glycerin molar yield. The MW for glycerin is 92.1 g/mol, indicating a gly**  **cerin-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 a nimal feed, soap production,**  **energy, chemical synthesis and many applications in the food and pharmaceutical**  **industries. Glycerin can be used as a feedstock fo r many chemical syntheses yielding**  **high-value products because it functions as a versa tile prochiral building block. Life** 

15 **Sanford, S., et al., 2009, “Feedstock and Biodiese l Characteristics Report”, Renewable Energy Group,**  **available at www.regfuel.com**

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**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. Ener gy allocation is used (rather than mass**  **based allocation) and the displacement method assum es petro-glycerin displacement**  **by bio-glycerin. The energy allocation method repr esents glycerin used as fuel, which is**  **a conservative approach that credits based on the l owest value use of glycerin. The**  **calculation for the glycerin energy allocation fact or is based on the glycerin yield and**  **energy content; the energy allocation factor calcul ation is shown below:** 



# ( ) (37.9 MJ/kg BD) = 95.0% 37.9 MJ/kg BD + (0.108 kg glycerin/kg BD)(18.6 MJ/k g glycerin)



### The heating value for palm oil biodiesel in the abo **ve 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, F**  **FB 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 r**  **esults.**

**The displacement method represents a more liberal c redit because it assumes that the**  **bio-glycerin produced displaces glycerin produced f rom petroleum, which has a**  **relatively high carbon intensity. This assumption is valid only when petro-glycerin is the**  **dominant glycerin source, the bio-glycerin displace s 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 s traightforward, but this approach**  **becomes less justifiable when the glycerin market i s saturated with bio-glycerin (as has**  **happen in recent years). This analysis assumes dis placement 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 sta tions. 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 r eturn empty after transporting** 

16 **Mehta, P., et al., 2009, “Estimation of a Lower Hea ting Value of Vegetable Oil and Biodiesel Fuel,”**  Energy and Fuels **, 23, 3893 – 3898.**

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**biodiesel; ocean tankers, by contrast, are assumed to carry fuel or chemicals during the**  **backhaul, and therefore energy and emissions associ ated with the ocean tanker back**  **haul are not counted in the analysis. The energy v alues are converted from Btu/ton**  **mile to total energy as follows: Btu/ton-mile input s are converted to Btu/ton, which is**  **converted to Btu/mmBtu fuel for both legs of the tr ip. 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 biodies **el transport are calculated the same way as the transport energy. The general form**  **ula 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 (M J biodiesel), in contrast to the**  **feedstock (FFB and CPO) transport calculations, whi ch are based on mass-based (ton**  **cargo) LCI data.**

2.7.10 Fuel Combustion

**The tank-to-wheel emissions refer to vehicle tailpi pe 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 mai n parts: tailpipe CO**  **2** 

### calculated **based on the fuel carbon content and fuel heating v alue and combustion CH**  **4** 

### and N **2** 

### O, **which depend on the emission factors of the specifi c equipment and vehicle energy use.**  **Emission factors (g/km) from the EMFAC model are us ed to represent biodiesel** 

**combustion CH 4**  

### and N **2** 

### O emissions and the vehicle energy use (MJ/km).

Vehicle CO 2 

### (Carbon in Fuel)

**This life cycle analysis considers only the fossil carbon in fuel (expressed as fully**  **oxidized, g CO 2**  

### /mmBtu fuel), since biologically derived fuel carbo **n originates from the atmosphere and the net greenhouse gas impact is neu**  **tral. This approach is consistent with the IPCC guidelines. The only fossil carbon i**  **n biodiesel originates from the methanol (produced from natural gas) used in palm o**  **il transesterification. Table 12 shows the parameters need to calculate fossil CO**  **2** 

### 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 t he production of bio glycerin.** 

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**Providing a substitution credit for the glycerin mo re accurately reflects the**  **environmental impact of this co-product. Thus foss il 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%**

**CO 2**  **/C Mass Ratio (wt%) 44.0095/** 

**12.011 Fossil Carbon in Fuel (gCO**  **2 e/MJ)**  3.67



### Vehicle CH

4 

### and N 2 

### O emissions

**The CH 4**  

### and N **2** 

### O emissions are assumed to be the same as ULSD. UL **SD emission factors for heavy duty trucks are shown in Table ba**  **sed on EMFAC factors used by the Air Resources Board. The vehicle energy use, N**  **2** 

### O and CH **4** 

### emission rates and final

**emissions are shown in Table 13.**

Table 13. **Vehicle CH 4**  

### and N **2** 

### O Emissions.

Parameter 2010

Emissions factor (g/km) GWP GHG (gCO 2 e/MJ)

**N 2**  **O 0.030 298 0.692**

**CH 4**  **0.022 25 0.042 Energy Use (MJ/km) 12.8**  0.73



### The total tailpipe fossil carbon CO **2** 

### plus combustion CH **4** 

### and N **2** 

### O is 4.40 g CO **2** 

### e/MJ **BD. For comparison, the combustion fossil carbon i n conventional diesel (ULSD) is** 

**74.95 CO 2**  

### e/MJ, for total TTW emissions of 75.68 g CO **2** 

### e/MJ including combustion CH **4** 

### and N **2** 

### O.



## 2.8 Well-to-Wheel Results

**The direct CI results for palm oil biodiesel are pr esented below in Table 14. The table**  **shows greenhouse gas emissions disaggregated by pat hway stage for two different sets**  **of co-product methods and the reference petroleum U ltra-Low Sulfur Diesel (ULSD)**  **results. In the first case, which corresponds to th e co-product treatment used by ARB**  **for soy biodiesel, mass allocation was used to acco unt for production of palm kernels**  **and wood products (fiber and palm) and the energy a llocation method was used to**  **credit the bio-glycerin produced. The CI results fo r this scenario are 23.3 g CO**  **2** 

### e/MJ, **representing a 75% emission reduction compared to t he petroleum baseline. The CI is**  **very similar to the CI associated with soy biodiese l (21.25 g CO**  **2** 

### e/MJ) under the LCFS. **The second pathway scenario presents the CI results when the mass allocation method** 

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**was applied to palm kernels and wood products and t he displacement method was used**  **to credit bio-glycerin for displacing petro-glyceri n. This method results in a large credit**  **for bio-glycerin because the carbon in the petro-gl ycerin is assumed to be oxidized to**  **carbon dioxide at the end of the chemical life cycl e; the carbon in bio-glycerin is**  **considered biogenic and the net carbon emissions ar e zero. The CI using these co**  **product methods is 1.8 g CO 2**  

### e/MJ, representing a 98% reduction compared to the **petroleum baseline.**

Table 14. **Malaysian Palm Oil Biodiesel Fuel Cycle Emission R esults (g CO**  **2** 

### e/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%** 

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# 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 with out effects elsewhere. Indirect effects**  **are highly uncertain to estimate for a number of re asons as outlined in Section 1.**  **However, some methods (Tyner et al 2010) do not sep arate indirect and direct**  **emissions but rather treat all LUC impacts as chang es 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 op erations could also be calculated.**  **This section provides a review of the available dat a 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 km 2**  

### , consisting of two geographical regions **(West or Peninsular Malaysia and East Malaysia). Th ese 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 y ear) but not evenly distributed. Thus,**  **agro-ecological zones based on rainfall distributio n have been recognized. Generally**  **there is little competition for moisture between th e tree crops and the native forages,**  **except when there is a prolonged dry spell.**

**Data on crop areas and production in Malaysia are b ased on classification by state and**  **not on the agro-ecological zones (AEZ). Of the 13 s tates all but Perlis and Kelantan are**  **suitable for palm production (FAO 2004). Agro-ecolo gical Zones are assigned for**  **Peninsular Malaysia shown in Figure 12.**

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Figure 12. **AEZ for Peninsular Malaysia**

**Source: FAO**



### 3.1.2 Malaysian soil types

**According to Goh (1999) oil palm is now grown on ve ry diverse soil types (primarily**  **oxisols and ultisols) that require different treatm ent 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 onward s, owing to mineralization of the**  **peat, and the nitrogen application should be reduce d to avoid N/K imbalance (Corley**  **and Tinker, 2003). In coastal soils in West Malaysi a, the soil exchangeable Ca and Mg**  **are usually high, and no Mg addition is needed (Cor ley and Tinker, 2003). The last case**  **is found within in ultra-basic soils where the appl ication of acidic fertilizers such as**  **ammonium sulfate and the use of di-ammonium phospha te as a P and N source appear** 

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**beneficial on a commercial scale although there is no published evidence to support the**  **practice. The fertilizer rates were discussed in th e direct emissions report. However, the**  **continuous supply of recycled palm organic matter s uch as pruned fronds and applied**  **agro-industrial wastes, together with zero tillage in mature plantations, helps to maintain**  **continuous and sustained high productivity of alumi num-tolerant tree crops on the highly**  **weathered and leached acidic soils of Malaysia (Mon tagnini and Nair 2004). The carbon**  **flux within these soil types requires an understand ing 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 e stimates (IPCC 2006).** 

3.1.3 Palm Carbon Cycle

**A carbon sink is a carbon pool from which more carb on flows in than out: forests can act**  **as sink through the process of tree growth and resu ltant biological carbon sequestration**  **(Brown, 2002). Forests can switch between being a s ource 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 r epresent 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 equiva lent to 3% of above-ground tree**  **biomass (AGB), dead wood 5-40%, and fine litter onl y 5% of that in the above-ground**  **tree biomass. Below ground biomass (BGB) is more va riable, ranging between 4 -**  **230%, and can be more than two times greater than t hat in the above-ground tree**  **biomass (Brown, 1997). However, for tropical forest s, and for SE Asia specifically,**  **Figure 13 shows these estimates vary between severa l authors.** 

**0 50**  **100 150**  **200 250**  **300 350**  **400 450**  **500 SE Asia** **SE Asia SE Asia** **SE Asia E Africa** **SE Asia SE Asia** **SE Asia SE Asia** **Asia Global** **SE Asia SE Asia** **Malaysia**

**Oil Palm Mature**

**Rubber Young** 

**Rubber Young** 

**cinnamon**

**(7 yrs) 2- year** 

**fallow 6-year** 

**fallow**

**(ave.) Alley** 

**cropping Jungle** 

**rubber Gmelina** 

**cacao IPCC 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 A sian and Malaysian forests** 

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**More recent studies and government analyses (EU 201 0) have addressed the lack of**  **data for tropical systems, particularly for undergr ound 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 oi l palm biomass were taken from**  **multiple studies and compared in the direct emissio ns report. In addition, the**  **conversion factors were estimated. About 10% of emp ty fruit bunches (EFB) and palm**  **oil mill effluent (POME) was assumed to be converte d to organic matter upon land**  **application.**

**FAO estimates carbon stored in forest soils represe nts 36 percent of the total C soil**  **pool to 1 m depth (1 500 Pg) (FAO 2001). However lo ss of standing biomass may be**  **offset by long-term carbon storage, either as harve sted material or carbon sequestered**  **in soil organic matter. The main harvest wood produ cts (HWP) include harvested logs,**  **paper products and fuel wood.**

**Current assumptions of land in agricultural product ion vary indicating a range in the net**  **carbon balance for palm plantations. Oil palm stan ds 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 speci al case in the management of the**  **global carbon cycle. Peatlands are associated with a range of waterlogged**  **environments in which the decomposition of dead pla nt material and soil carbon is**  **extremely slow, resulting in the fossilization of l itter inputs and soil with an organic**  **carbon content of over 30%. Although some peat soil s can be found in productive**  **ecosystems such as reed and papyrus swamps and mang roves, peat soils are often**  **seen in unproductive environments where plant growt h is very slow. Their capacity for**  **storage is large; with estimates suggesting that ~5 50 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 sma ll proportion of land area making**  **peatland among the most space effective carbon stor es of all ecosystems. Uncertainty**  **over the degree of carbon losses from drained peatl ands has been identified in several**  **studies (Parish et al. 2008; Verwer et al. 2008). H owever, 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 t hat for every 10 cm water table** 

**drawdown will result in an increase in CO 2**  

### emission rate of 9.1 t CO **2** 

### /ha/y. **Their study was based on a carbon content of Southeast Asian pe**  **at was taken to be 60 kg/m 3** 


### (Kanapathy, 1976; Neuzil, 1997; Shimada et al.2001) **and this value was applied to all**

**areas.**

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**Additional studies (Page et al., 2010, Hooijer et al., 2010) also identify peatlands as a**  **diminishing carbon sink due to a complex combinatio n of mostly anthropogenic causes.** 
**However, the water table has much to do with this e ffect because part of the carbon**  **stock in peatlands is below the drainage base and m ay never be oxidized. However, a**  **common observation is that drainage systems in aban doned peatlands continue to draw**  **down water levels for decades, because no funding i s available for canal blocking**  **(Hooijer et al., 2010).**

**Roots and SOC**

**Soil organic matter (SOM) contains more reactive or ganic C than any other single**  **terrestrial pool. Consequently, SOM plays a major r ole in determining C storage in**  **ecosystems and in regulating atmospheric CO 2**  

### concentrations. (Nair et al., 2009).

**Based on current understanding, the major factors i n biomass allocation to roots appear**  **to be latitude, tree type, age, precipitation, temp erature, temperature: precipitation ratio**  **(T/P) and index of water availability (Brown and Lu go 1982, Carins, 1997). A root**  **biomass average of 24% of the AGB estimate for palm s 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 root s originating from the base of the**  **trunk and either spreading horizontally or descendi ng at varying angles into the soil. The**  **primary roots then bear secondary roots, tertiary r oots, 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 len gth 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 most ly in the upper 30 cm from the soil**  **surface. Primary roots can grow up to 20 m away fro m 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 occurren ce of a water table. Root elongation**  **as well as proliferation is strongly affected by ma ny physical and chemical properties of**  **the soil (Yahya et al., 2010). Figure 14 shows an e xample of sampling locations for**  **taking soil measurement. Including effects from tra iler tracks (eg. compaction) and**  **nutrient application can affect the carbon estimate s. 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 har vesting 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 avai**  **lable on actual root measurements is the high cost of excavation, washing, drying and we**  **ighing.**

**Although reference soil organic matter (SOM) data e xists (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 o n the forest management impacts**  **on soil carbon are highly variable This is due to l arge 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 a djustment after land use changes.** 

**Effects from Forestry Practice**

**Some soils in tropical agricultural systems are est imated 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 represe**  **nt half of the world’s carbon sequestration potential (Lal 2004a) with seq**  **uestration 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 pictu**  **re 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.**

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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 accumulat ion at 5 Mg C/ha/y (Lasco 2002).**  **This same study estimated that conversion from natu ral forests to tree plantations in SE**  **Asian forests reduces carbon density by 50%. Howeve r, 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 repres ent 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 benefici al attributes of agroforestry systems in**  **terms of C sequestration: (1) direct near-term C st orage (decades to centuries) in trees**  **and soils; and (2) a potential to offset immediate greenhouse gas emissions associated**  **with deforestation and subsequent shifting cultivat ion.** 

**Some projections have been made on the role of agro forestry in reducing the C**  **emission from tropical deforestation. Based on its project known as ‘Alternatives to**  **Slash and Burn’, ICRAF (International Centre for Re search in Agroforestry) and its**  **collaborators around the humid tropics concluded th at 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 pas tures, 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 on ly 5 to 15 Mg C/ha (Palm et al.**  **2000). A projection of carbon stocks for smallholde r 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** 

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**stocks in a twenty-year period, to 70 Mg C/ha (Wats on 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 so ils 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 ca rbon balance is regulated by**  **several other factors including fire, herbivores, e rosion and leaching. In addition, forest**  **carbon stocks vary within biomes according to drain age, soil type, land-use history and**  **other variables (UNDP 2009).**

**However controversial, the most commonly accepted w ay to estimate forest carbon**  **stocks over large areas is to apply carbon values t o broad forest classes, the “biome**  **average approach”. This is the approach required by Tier 1 of the IPCC’s National** 

**Greenhouse Gas Inventories and assumes all carbon i s ‘lost’ upon conversion. This**  **approach, also used by the UNFCC for carbon account ing under REDD, does not**  **currently value long term plantations or harvested wood products (HWP).** 

**The biomass of palms, unlike broadleaf trees, is mo re 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’ fo r forest inventories. Table 15**  **identifies the two main approaches to carbon accoun ting 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 ye ar**  **CL = rate of loss of carbon, tonnes C per ha per ye ar**  **Source: IPCC, 2006**

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**In contrast to tree AGB, palm AGB is argued to be m ore 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 overest imate 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 general ly for C content is 47%. However,**  **this was the mean C content for palms in a wet fore st 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. (1 999).** 

**A recent literature review by Cairns et al. (1997) included more than 160 studies**  **covering tropical, temperate, and boreal forests th at 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, includin g standing and lying, is often a**  **significant component of forest ecosystems, often a ccounting 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 budget s. 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 & Sex ton 1996; Brown 2002). Figure 16**  **shows the importance of representing all carbon poo ls including effects from practice**  **(Kirby et al., 2007).**

### 42 | Life Cycle Associates, LLC

Figure 16 **Panamanian Forest Study with Agroforest and Pastur e Land Compared**  **Source: Kirby et al. 2007**



### Palm Studies

**Several recent studies attempt to evaluate palm pla ntation 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 report ed in the literature between the**  **different plant parts; including tertiary and quate rnary 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 mea sured in a Sumatra study, for**  **example, for AGB was 81.6 Mg C/ha so the sequestrat ion 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 pal ms 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. Howeve r, Lehman (2003) calculated root**  **biomass for different types of trees. The study sho wed tree crops can have shallow root**  **activity and 75% of their total root activity in th e first 0.1 m of soil, or very deep root**  **activity with more than 90% below 0.1 m. Neither en vironmental factors (i.e., climate**  **and soil properties available from these publicatio ns) nor plant species explain**  **differences of root activity distributions with dep th (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 shallow er root activity than fruit trees. Oil**  **palm,** Elaeis guianensis **in Malaysia (for sandy soil with a k value of 0.93 9) note above**  **22 cm is where 75% of root activity occurs 0.987 IA EA (1975)** 

**Monocots including oil palm, coconut or banana have root activity that can be both deep**  **and shallow. Regional and temporal variations of su bsoil root activity for the same tree**  **species are significant and generally larger than d ifferences between species. Root**  **activity patterns of tree crops appear to be suffic iently flexible to allow for subsoil**  **resource use. Consequently, management such as prun ing, fertilization, liming and**  **irrigation are shown to significantly affect subsoi l root activity.** 

**Soil carbon is distributed in a pattern that is dep endent on soil age, soil texture, rainfall,**  **temperature patterns and vegetation around the plan tation (competition). Spatial**  **variation driven by distribution of the fine root b iomass and C content is highly depth**  **dependent (Syahrinudin 2005). For palm, SOC decreas ed 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 exist s, such as from commercial**  **inventories, biomass density can be estimated by ex panding 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, eith er 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 B EF. In contrast, BCEFs use only a**  **single multiplication to transform volume into biom ass; this is useful where wood**  **densities are not available. Brown (et al., 1999) a nd Lim et al., (1999) developed three**  **approaches to measuring HWP as shown in Table 16, b elow. Each produces a very**  **different result and either favors the producer or consumer.** 

Table 16 **Three Approaches to measuring stock change/ GHGs fr om 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 CO**  **2** 

### result Mid range result **negative CO 2**  

### (benefit)



### Default BEFs and BCEFs reported in the literature c **an be applied in forest carbon accounting. However, unless locally-specific equati**  **ons 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 fract**  **ion 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 wi th natural impacts. IPCC and the**  **LULUCF developed factors within carbon accounting t o estimate management practice**  **or land use activity. These factors, listed in Tabl e 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) (O ver 20 years) for Different**  **Management Activities on Cropland**

Factor

value type Level Temperature regime Moisture

regime IPCC

defaults Error Description

**Dry 0.8 9% (+/-) Temperate/** 

**Boreal Moist 0.69 12% (+/-)** 

**Dry 0.58 61% (+/-)**

**Tropical Moist/Wet 0.48 46% (+/-) Land** 

**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 pa lm oil. The use of CPO for**  **biodiesel generates additional demand for CPO. Thi s production of CPO and other**  **vegetable oils creates a land use response. The la nd 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 incorp orated MODIS satellite data with a**  **refined 500 x 500 m resolution (from the previous 1 x1km 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 methodology 17** 



### .

**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 captur**  **e the details of the palm plantation. This is particularly the case for peatland emission**  **s where the emission factor for peatlands was calculated for annual crop rotations**  **not perennial trees.**

17 **Winrock actually developed much of the IPCC methodo logy 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 s tock analysis from Woods Hole.** 

**This carbon stock dataset was also used in the Sear chinger (2008) analysis and is**  **compared to Winrock and other SE Asian datasets in Appendix A**  **18** 

### . Carbon stock

**emission factors used in the LCFS assume the same s tored carbon from all crops.**  **However, in Malaysia, the biofuel crops would conta in much higher carbon stocks. A**  **similar issue arises with the accounting of the mas s 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 pl antation is illustrated in Figure 19.** 

**The initial release reflects a carbon emission fact or including above ground carbon and** 

**soil carbon release. Addition loss of carbon is es timated from avoided aforestation or**  **forest cover growth. The factors that are typicall y 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 an d 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**  **8 5**  **92 99** GHG Release (t CO2e/ha)



### Intial Above Ground

**Uptake**

**Avoided AF**

**Roots**



### Figure 19. **Carbon release and uptake from Malaysian palm plan tations**  Source: Life Cycle Associates, based on EPA 2010 Wi nrock carbon stocks



### The analysis here takes into account the carbon sto **red 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 rem**  **ains in the soil. No soil storage from the tree mass is assumed. The net change in ca**  **rbon is summed over a 100 year period using the same method that EPA applies in it**  **s analysis for the RFS2.**

**The situation where more wood products are harveste d 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% o f the initial cleared forest and 20%**  **of the palm tree boles was also examined. The remo val 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 CO 2**  

### /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 CO 2**  

### e/MJ 0.5 0.2 -.7

**g CO 2**  

### e/mmBtu 570 238 -780



### The real carbon losses might be significantly under **estimated; oil palm carbon losses may be underestimated by a factor up to 10 (Couwenb**  **erg 2009). Short-term greenhouse gas balances of oil palm production, inc**  **luding 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 p**  **lantation (usually ~25 years) all arrive at clear carbon debits (Germer & Sauerborn, 2007; P**  **astowski et al., 2007; Fargione et al., 2008; Reijnders & Huijbregts, 2008; Wicke et a**  **l., 2008; Danielsen et al., 2009). The**

**CO 2**  

### emissions from peat degradation assumed in these s **tudies range from 18 to 73 t**

**CO 2**  

### /ha/y. The measurements of Melling et al. (2007) in **dicate emission values from oxidizing peat of 50 t CO**  **2** 

### /ha /y or more (Couwenberg et al. 2009). This impl **ies that the emission factor of biofuel derived from oil palm gr**  **own on tropical peat soil is at least 400 g CO**  **2** 

### -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 energ y inputs and allocation systems**  **provides opportunities for double-counting.**

**Double-counting co-products is discussed in the Ren ewable 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-produc ts are already treated using**  **allocation by energy content rather than by substit ution. 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 model s 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. 20 10) under development for**  **standardized LC inputs for system expansion; thus, reducing uncertainty by this** 

**method.**

**Harvested wood potential (HWP) also presents an acc ounting issue and calculating this**  **as a carbon credit is highly dependent on good data but also the method chosen.** 

**CH 4**  

### exchange, erosion and leaching, wildfire, HWP and B **VOC 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/templat es/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 differe nt 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 ef fect 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. Wit h the current modeling practices, a**  **net dLUC adder reflecting the overall carbon balanc e of the palm plantation may be best**  **approach until the palm life cycle can be better in tegrated 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 f rom 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 e ssential for knowledge on carbon**  **cycle dynamics. Being a part of an agricultural sys tem, fallows provide two services:**  **they contribute to food security and they store car bon. In a peat study in Sarawak, over**  **3,000 trees were measured for diameter at breast he ight (DBH) along a**  **chronosequence of fallow ages, and the biomass and carbon stocks of the fallows were**  **estimated using published allometric equations. Dur ing the first 15 years following**  **abandonment, the biomass of the shifting cultivatio n 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 mod el 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 annu al accumulation rates (Jespen**  **2006). Management factors need updating to reflect the different practices on the palm**  **plantation as often just fire practice is highlight ed as the only management factor.** 

**Harvested Wood Products (HWP) are often missing. Va rious methods are available as** 

**described in Section 3 to calculate HWP.**

**Substitution of Vegetable Oils and other Palm Produ cts** 

**Another key issue in the LUC analysis of biofuels i s substitutability of different products**  **including vegetable oils. Agro-economic models such as FASOM examine numerous**  **interactions among crops and foods that have been i n the U.S. food system for**  **decades. However, the markets for palm oil are sti ll evolving as shown below in Figure**  **23. The rapid rise in palm oil is due to many fact ors excluded from econometric models.**  **The understanding of food nutrition and saturated f ats is evolving. Uses for palm oil are**  **also expanding. Given the closely coupled trends i n 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 ve getable oil produced globally. Palm**  **oil competes with other oils in many applications a nd also dominates certain niches.**  **The interactions among the vegetable oils and subst itutability are very complex.**  **Numerous markets exist for vegetable oils such as s hortening, food oil, frying oil, and**  **many others. Each oil has unique properties that a ffect its marketability and limits**  **complete substitution. Nonetheless, the opportunit ies for substituting one oil for another**  **or hydrotreated soybean oil for palm oil suggest th at 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 crud e 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 tre atment of the palm kernel oil is most**  **straightforward.**

Figure 24 **Oil Palm Products** 

### http://cmc-indo.blogspot.com/2010/04/utilization-bi **omass-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 ye ar (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 pr ograms with a portion of the**  **additional demand on biofuels met by and further yi eld improvements in considered in**  **some modeling approaches. Most notably, GTAP inclu des models these yield**  **improvements through an elasticity factor. The est imation of such factors is difficult**  **because historical data doe not provide a simple se t 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 th e demand or price induced yield is** 

**often estimated with econometric estimates. Estima ting the yield effects from palm are**  **as complicated as those from other crops 19**  

### . While Malaysian palm practices are **relatively mature, the technology continues to impr ove. Higher demand and higher**  **prices would stimulate even more precise fertilizer application and fruit much collection**  **practices. Even more significant, the yield of pal m in Africa and South America could**  **experience significant price induced yield, this af fecting the iLUC for all vegetable oils.** 

Figure 25. 

### MPOB Palm Oil Price Data 2009

19 The E4Tech preliminary LUC study assigns a zero pr ice 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 f or the RFS2 are shown below. The**  **LUC results for palm oil combined with the direct e mission effects are documented in a**  **separate report. The GHG emission estimates are con sistent with EPA’s analysis of soy**  **biodiesel under the RFS2 (EPA 2010). The analysis i s presented over a 100 year time**  **horizon to be consistent with the long commercial l ife of a palm plantation. The**  **emissions summed over the time horizon using a 2% d iscount factor. The fuel**  **production, transport, and tailpipe emissions corre spond to the direct and upstream fuel**  **cycle emissions for fuel production equipment and v ehicle operation. These emissions**  **are constant over time. The next category is farm inputs, fertilizer, and associated N**  **2** 

### O **emissions, which are based on the inputs for palm o il production**  **20**



### .

**The far right bar represents the LUC impacts if all marginal palm were grown on**  **Malaysian land that is currently reserved for agric ulture with a forest cover**  **21** 

### . Here the **carbon storage from the palm plantation largely off sets any loss of carbon from forest**  **vegetation. Even this scenario represents a high e mission case as palm could also**  **expand onto non forested lands. The result here sh ows only modest LUC impacts for**  **the conversion of forest covered land to palm due t o the long commercial life of the**  **palm plantation and the high CO 2**  

### uptake and storage from the palm plantation. The **latter factor appears to be simplified in the Winro ck analysis for EPA, using carbon**  **uptake factors for generic crops.**

20 **EPA calculates U.S. and international fertilizer em issions using FASOM and international fertilizer da**  **ta.**

**Since FASOM is not available to assess the emission s from Malaysian agriculture, the analysis**  **represents the direct and fuel cycle emissions asso ciated with palm oil. The same approach is taken b**  **y ARB under the LCFS, where fertilizer impacts are no**  **t included in the LUC analysis.**

21 This scenario is comparable to corn ethanol expand ing onto CRP land in the U.S. indirect effects are not relevant if the expansion is onto land that may not be other wise 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** 

**2 0**  **0 5**  **D i**  **e s**  **e l**  **B a**  **s e**  **l i**  **n e** 

**S o**  **y B**  **i o**  **d i**  **e s**  **e l**  **b a**  **s e**  **y i**  **e l**  **d**

**P a**  **l m**  **B i**  **o d**  **i e**  **s e**  **l ,**  **C H**  **4 C**  **a p**  **t u**  **r e** 

**P a**  **l m**  **B i**  **o d**  **i e**  **s e**  **l ,**  **C H**  **4 C**  **a p**  **t u**  **r e**  **, B**  **i o**  **m a**  **s s**  **S a**  **l e**  **s**

**P a**  **l m**  **B i**  **o d**  **i e**  **s e**  **l ,**  **N o**  **C H**  **4 C**  **a p**  **t u**  **r e** 

**P a**  **l m**  **B i**  **o d**  **i e**  **s e**  **l ,**  **P a**  **l m**  **B o**  **l e**  **P r**  **o d**  **u c**  **t s** 

**P a**  **l m**  **B i**  **o d**  **i e**  **s e**  **l M**  **a l**  **a y**  **s i**  **a o**  **n l**  **y L**  **U C** GHG Emissions (g CO2e / 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 compar ed to soy biodiesel and**  **petroleum diesel (100 year time horizon)**

### 58 | Life Cycle Associates, LLC



# 7 Discussion

**Various scenarios for palm biodiesel production eff ects on land use change have been**  **discussed, including direct and indirect effects. T he 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 A GB are designed for broadleaf trees.**  **While equations and models are improving with facto rs to accommodate palms and**  **other plantation species, different models should h old for conifers, palms, and lianas**  **(Chave 2005).**

**Carbon assessments clearly require further analysis . New local and regional data**  **requires analysis and comparison to existing datase ts. This is particularly important to**  **capture variability in land class and types, improv ed management practice on stocks,**  **sequestration values and separate estimates for eac h plant part and phase of decay**  **within the plantation.**

**The IPCC Tier 1 method assumes LUC has no carbon st ock storage for below ground** 

**biomass and therefore no GHG effect. We know from s everal studies presented here**  **this is not the case. Carbon reporting for palm – b eyond the default IPCC Tier 1 values**  **at the regional level (IPCC Tier 2 or 3) will requi re the following steps:** 

**Identification of national carbon stock key categor ies, develop country specific sampling**  **design and stratification, implement field sample plots to derive tallometric data for**  **biomass conversion and expansion, carbon fraction v alues considering country-specific**  **stratification, disturbance matrices tracking trans fers between carbon pools. Analysis of**  **HWP and evaluation of age of palm stands on a net b asis to ascertain HWP values** 

**In addition to the balance between carbon gains thr ough growth and losses through**  **respiration, ecosystem carbon balance is also regul ated by several other factors**  **including fire, herbivores, erosion and leaching.**

**Land Use Management Factor (s)**

**Approximately 4% of the global forest area is repre sented by plantations (FAO 2006).**  **They supply a substantial proportion of the demand for timber products. Plantations can**  **sequester significant amounts of carbon and are gen erally considered to be carbon**  **sinks, unless they replace natural forests, which a re 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 necessar y. By increasing the rotation** 

### 59 | Life Cycle Associates, LLC

**period for cutting and implementing site improvemen t strategies, soil carbon stocks can**  **be replenished and more carbon sequestered by the v egetation.** 

**Therefore, agroforestry practice improvements requi re estimation and inclusion into the**  **LU FLU factor. This includes analysis of improved p ractices 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 us e but also should take into account the**  **age of the plantation. Table 5 shows the variabilit y 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 harmoniz ation.** 

Table 19 **Emission Factor Estimates for Different Land Use C hange Types** 

Activity Source t CO 2 

### -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 use **s of palm oil**

**Allocation of co-products are particularly challeng ing 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 pote ntial quantity of biomass harvested**  **exceeds that of the CPO. Future palm oil plantatio ns may produce much more**  **products.**

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**Time Horizon**

**The time horizon for palm oil plantations is very l ong as the trees take 25 years to**  **mature. Given the high investment costs for establi shing 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 p alm 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-av eraged biomass increment does not**  **fully capture this growth over short accounting tim escales. Biomass uptake through**  **growth is also dependent on tree species and site c onditions, whether trees are planted**  **or naturally regenerating, and the presence, absenc e 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 (Hou ghton, 2005).** 

**How to Measure Uncertainty**

**Uncertainty is defined by IPCC (2006) as ‘lack of k nowledge of the true value of a**  **variable that can be described as a probability den sity function1 characterizing the**  **range and likelihood of possible values’. The establishment of uncertainty is important**  **whether accounting is for forest carbon stock, emis sions or project emission reductions.**  **Quantification of individual variable uncertainty p ermits identification of the main**  **sources of uncertainty, as well as enabling the pri oritization of data collection and focus**  **of efforts to improve future forest carbon accounti ng. For the total inventory, uncertainty**  **quantification lends confidence and acceptance whic h 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 CO**  **2** 

### concentration. Default growth and biomass accumulat **ion rates are based on past observations and so do not take into account global**  **changes in the future. CO 2**  

### fertilization will increase plant growth and so car **bon uptake, particularly in the tropics (Ciais et al., 2005).** 

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### cloud above of efflux measurements so upscaling to

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# 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 Asia** **Peatlands Asia-Peat** **removed Asia Humid** **Palm Asia-Wet** **Tropical Asia- Dry** **Forest Low Asia** **Tropical MF Asia Open** **Forest Asia** **Tropical SF SE Dry** **Subtropical SE Tropical** **Equatorial SE Tropical** **Dry Malaysia** **Johor 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 CO**  **2 ha**  **-1 )**  **Lost forest**

**sequestration (t CO**  **2 ha**  **-1 y**  **-1 )**  **Soil C before**

**(t C ha -1**  **) Soil C after** 

**(t C ha -1**  **) Annual** 

**soil C flux (t CO**  **2 ha**  **-1 y**  **-1 )**  **Annual peat**

**emissions (t CO**  **2 e 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 CO**  **2 ha**  **-1 y**  **-1 )**  **CH 4** 

**emissions (t CO**  **2 ha**  **-1 )**  **N 2**  **O**

**emissions (t CO**  **2 ha**  **-1 )**  **Total fire**

**emissions (t CO**  **2 ha**  **-1 )**  **Yr 0**

**Emissions (t CO**  **2 ha**  **-1 y**  **-1 )**  **Yr 1-19**

**Emissions (t CO**  **2 ha**  **-1 y**  **-1 )**  **Yr 20-80**

**Emissions (t CO**  **2 ha**  **-1 y**  **-1 )**  **30-yr**

**Emissions (t CO**  **2 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 (P 2**  **O 5**  **) 2.80** 

**K (K 2**  **O) 11.50** 

**CaCO 3**  **0.00**

**Mg (MgO) 0.90** 
**Cu (CuSO 4**  **) 0.00** 

**Zn (ZnSO 4**  **) 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 N 2**  **O 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/P K 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 CH**  **4 /kg CPO** 

**Palm oil mill effluent 0.5 kg/kg FFB** 

**Select methane emission factor reference Plant Surve y Data** 

**Effluent methane emission factor 17.2**  **g CH 4**  **/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 Energ y 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)**

**CH 4**  **0.022 0.002**

**N 2**  **O 0.030 0.002** 

**CO 2**  **75.1**

**Fossil Derived CO 2**  **3.6**

**TTW Emissions (g CO 2**  **e/MJ) 4.4** **71**|Life Cycle Associates, LLCPalm Oil Biodiesel Life Cycle Matrix Calculations**Pathway Component Process** **Diesel Gasoline Natural Gas LPG Electricity Nitrogen** **(N) Field N** **2 O** **Emissions Phosphate** **(P 2** **O 5** **) Potash** **(K 2** **O) Lime** **(CaCO 3** **) Mined Minerals** **(Mg, Cu, Zn) Process Loss Factor** **Cumulative Loss Factor per Fuel Pathway Component 1. 00016 1.00016 1.00016 1.00016 1.00016 1.00016 1.00016 1.00** **016 1.00016 1.00016 1.00016 Feedstock Use Factor 5.27 5.27 5.27 5.27 5.27 5.27 5.27 5.2** **7 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 k g FFB/kg BD kg FFB/kg BD kg FFB/kg BD** **Process Specific Energy 125 0 0 0 0 3.49 1.00 2.80 11.50 0.00 0.90** **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 FFB Kg** **mineral/tonne Specific Energy** **0.0174 0.0000** **0.0000 0.0000** **0.0000 0.0000** **0.0001 0.0000** **0.0000 0.0000** **0.0000 Units** **Biodiesel Biodiesel** **Biodiesel Biodiesel** **Biodiesel Biodiesel** **Biodiesel tonne P** **2 O** **5 /MJ** **tonne K 2** **O/MJ CaCO** **3 /MJ** **Biodiesel Fuel Cycle Energy (J)** **Total 20,267** **0 0** **0 0** **46,846 4,492** **14,009 0** **1,096 Fossil fuels** **2,780 0** **0 0** **0 46,421** **4,339 13,319** **0 1,042** **Coal 400** **0 0** **0 0** **1,922 692** **3,129 0** **245 Natural gas** **1,079 0** **0 0** **0 42,324** **3,058 6,601** **0 517** **Petroleum 18,754** **0 0** **0 0** **2,175 589** **3,589 0** **281 CH** **4 0.002** **0.000 0.000** **0.000 0.000** **0.002 0.000** **0.001 0.000** **0.000 N** **2 O** **0.000 0.000** **0.000 0.000** **0.000 0.000** **0.010 0.000** **0.000 0.000** **0.000 CO** **2 1.5** **0.0 0.0** **0.0 0.0** **2.6 0.0** **0.3 1.0** **0.0 0.1** **CO 2** **(Incl. VOC and CO) 1.5** **0.0 0.0** **0.0 0.0** **2.6 0.3** **1.0 0.0** **0.1 Total Fuel Cycle GHG Emissions (g/MJ)** 1.50.00.00.00.02.73.00.31.00.00.1Allocated Emission Results:**CPO, PK (g/MJ)**1.00.00.00.00.01.82.00.20.70.00.1Allocated Emission Results:**CPO, PK, fiber, biomass (g/MJ)**0.90.00.00.00.01.61.80.20.60.00.0Displacement Emissions Results**(g/MJ)**1.50.00.00.00.02.73.00.31.00.00.1Conversion 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 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 1.10** **Fuel Pathway Component Diesel Tractor Gasoline Tracto r Natural Gas LPG Electricity N Fertilizer** **N 2** **O Emissions Phosphate Fertilizer** **Potash Fertilizer Lime Fertilizer Minerals Denominator Units** **MJ Diesel MJ Gasoline MJ NG MJ LPG MJe tonne N tonne FFB tonne P** **2 O** **5 tonne K** **2 O tonne CaCO** **3 tonne** **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** **8,617,165,505 8,750,179,989** **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 8,185,863,943** **8,318,696,348 Coal** **22,906 28,626** **1,528 14,795** **1,002,262 3,956,475,057** **1,774,976,145 1,954,280,556** **1,952,505,024 1,954,280,556** **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 4,117,754,871** **4,123,038,557 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** **2,115,604,048 2,241,377,236** **Fuel Cycle Emissions (g) VOC** **0.073 0.124** **0.045 0.009** **0.021 12,230.946** **193.011 103.414** **93.357 103.414** **CO 0.360** **0.533 0.336** **0.032 0.157** **11,804.903 705.851** **538.432 515.070** **538.432 NO** **x 0.678** **0.485 1.158** **0.096 0.215** **4,677.940 3,145.196** **1,183.924 902.971** **1,183.924 PM10** **0.066 0.032** **0.006 0.008** **0.177 933.121** **1,541.188 433.115** **409.294 433.115** **PM2.5 0.056** **0.025 0.006** **0.005 0.050** **402.155 1,014.161** **173.425 155.725** **173.425 SO** **x 0.017** **0.013 0.011** **0.008 0.022** **930.188 61,225.305** **166.487 85.035** **166.487 CH** **4 0.093** **0.100 0.472** **0.091 0.289** **4094.419 1236.725** **882.985 871.700** **882.985 N** **2 O** **0.001 0.001** **0.001 0.005** **0.004 49.268** **72.654 10.664** **9.551 9.310** **9.551 CO** **2 (Incl. VOC and CO)** **85 86** **60 73** **190 5,415,458** **741,080 601,736** **591,041 601,736** LCI Data Vectors**Fuel Pathway Component Diesel Tractor Gasoline Tractor** **Natural Gas Reciprocating Engine LPG Electricity N Fertilizer** **N 2** **O Emissions Phosphate Fertilizer** **Potash Fertilizer Lime Fertilizer Minerals Denominator Units** **mmBtu Diesel mmBtu Gasoline mmBtu NG mmBtu LPG mmBtue to n N ton FFB** **ton P 2** **O 5** **ton K 2** **O ton CaCO 3** **ton 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 7,409,429** **7,523,801 Fossil fuels** **159,316 192,162** **69,385 105,526** **2,511,475 82,149,730** **9,569,876 7,152,791** **7,038,576 7,152,791** **Coal 22,906** **28,626 1,528** **14,795 1,002,262** **3,401,956 1,526,205** **1,680,379 1,678,852** **1,680,379 Natural gas** **61,799 74,961** **1,063,645 543,955** **1,419,250 74,898,903** **6,744,248 3,545,175** **3,540,632 3,545,175** **Petroleum 1,074,612** **1,088,574 4,212** **546,777 89,964** **3,848,871 1,299,423** **1,927,238 1,819,092** **1,927,238 Fuel Cycle Emissions (g)** **VOC 77.307** **130.740 47.401** **9.838 22.497** **11,095.728 175.097** **93.815 84.692** **93.815 CO** **379.943 562.316** **353.976 33.240** **165.176 10,709.228** **640.337 488.458** **467.263 488.458** **NO x** **714.918 512.160** **1,221.898 101.584** **226.930 4,243.755** **2,853.274 1,074.038** **819.162 1,074.038** **PM10 69.215** **33.398 6.234** **8.026 186.314** **846.513 1,398.142** **392.915 371.305** **392.915 PM2.5** **58.604 26.462** **6.005 5.405** **52.724 364.829** **920.032 157.328** **141.271 157.328** **SO x** **18.431 13.435** **11.113 8.761** **22.872 843.852** **55,542.663 151.034** **77.143 151.034** **CH 4** **98.559 105.285** **497.771 95.571** **304.861 3714.394** **1121.938 801.030** **790.793 801.030** **N 2** **O 1.079** **1.279 1.567** **4.979 3.794** **44.696 65.911** **9.674 8.665** **8.446 8.665** **CO 2** **(Incl. VOC and CO) 89,824** **90,766 62,908** **76,792 200,352** **4,912,821 672,296** **545,886 536,183** **545,886 LCI Vector Region: Southeast Asia Southeast Asia South** **east Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast** **Asia**Oil Palm Farming EnergyFertilizer 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 Escaped** **Methane Methane Flared Methane Boiler** **for Electricity Electricity Credit 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 1.00 006 1.00006 1.00006 1.00006 1.00000 1.00000 1.00000 1.0000** **0 Feedstock Use Factor 5.27 5.27 5.27 3.63 1.04 1.04 1.04 1.0** **4 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 k g CPO/kg BD kg CPO/kg BD kg CPO/kg BD kg CPO/kg BD kg C** **PO/kg BD kg CPO/kg BD kg CPO/kg BD Process Specific Energy 0.13 0.03 27 14 0.0 8.7 0.1 1.7 0.1 0** **.0 0.0 1 0.0 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 CH 4** **/kg CPO MJ Diesel/kg** **CPO kWh** **Electricity/kg MJ Hexane/kg** **CPO MJ CH** **4 /kg CPO MJ CH** **4 /kg CPO** **MJe/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 0.0275** **0.0000 0.0000** **0.0000 Units** **Herbicide/MJ Insecticide/MJ** **FFB*km/MJ BD****EFB*km/MJ****Biodiesel Biodiesel** **Biodiesel MJ CH** **4 /MJ BD** **Biodiesel Biodiesel** **Biodiesel Biodiesel** **MJ CH 4** **/MJ MJ CH** **4 /MJ** **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** **0 0** **0 0** **Fossil fuels 3,956** **1,305 9,357** **3,223 0** **0 388** **0 313** **0 0** **0 0** **0 0** **Coal 532** **183 185** **64 0** **0 56** **0 45** **0 0** **0 0** **0 0** **Natural gas 1,512** **503 499** **172 0** **0 151** **0 121** **0 0** **0 0** **0 0** **Petroleum 1,912** **620 8,674** **2,988 0** **0 2,620** **0 2,113** **0 0** **0 0** **0 0** **CH 4** **0.000 0.000** **0.001 0.000** **0.000 0.001** **0.000 0.000** **0.000 0.000** **0.000 0.236** **0.000 0.000** **0.000 N** **2 O** **0.000 0.000** **0.000 0.000** **0.000 0.002** **0.000 0.000** **0.000 0.000** **0.000 0.000** **0.000 0.000** **0.000 CO** **2 0.3** **0.1 0.7** **0.2 0.0** **0.0 0.2** **0.0 0.2** **0.0 0.0** **0.0 0.0** **0.0 0.0** **CO 2** **(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** **0.0 0.0** **0.0 0.0** **Total Fuel Cycle GHG Emissions (g/MJ)**0.30.10.70.20.00.80.20.00.20.00.05.90.00.00.0Allocated Emission Results:**CPO, PK (g/MJ)**0.20.10.50.20.00.50.10.00.10.00.04.00.00.00.0Allocated Emission Results:**CPO, PK, fiber, biomass (g/MJ)**0.20.10.40.10.00.50.10.00.10.00.03.60.00.00.0Displacement Emissions Results**(g/MJ)**0.30.10.70.20.00.80.20.00.20.00.05.90.00.00.0Conversion 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 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 0.0009 0.0009 0.000 9** **Fuel Pathway Component Herbicide Insecticide Medium Duty Truck** **Medium Duty Truck Natural Gas Boiler** **Biomass Boiler Diesel Boiler CH** **4 Boiler** **Diesel Tractor Electricity n-hexane Effluent Escaped Methane** **Methane Flared Methane Electricity Prod.** **Electricity Displaced Denominator Units** **tonne Herbicide tonne** **Insecticide tonne*km tonne*km MJ Natural Gas MJ Biomass****MJ Diesel MJ CH** **4 MJ Diesel MJe MJ LPG kg CPO** **MJ CH 4** **MJ CH 4** **MJe 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** **0 1,000,000** **2,735,823 Fossil fuels** **############# #############** **2,456,930 2,456,930** **69,385 0** **159,316 0** **159,316 2,511,475** **105,526 0** **0 2,511,475** **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** **0 0** **1,002,262 Natural gas** **83,854,096,570 #############** **130,970 130,970** **1,063,645 0** **61,799 0** **61,799 1,419,250** **43,955 0** **0 1,419,250** **Petroleum #############** **############# 2,277,416** **2,277,416 4,212** **0 1,074,612** **0 1,074,612** **89,964 1,046,777** **0 0** **89,964 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** **0.000 0.000** **0.021 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 0.000** **0.000 0.157** **NO x** **23,761.634 37,265.852** **0.931 0.931** **0.075 0.104** **0.106 0.055** **0.678 0.215** **0.028 0.046** **0.055 0.215** **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** **0.004 0.003** **0.177 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 0.004** **0.003 0.050** **SO x** **11,639.187 3,913.247** **0.031 0.031** **0.011 0.004** **0.017 0.000** **0.017 0.022** **0.008 0.000** **0.000 0.022** **CH 4** **21588.055 31792.832** **0.200 0.200** **0.123 0.004** **0.093 0.001** **0.093 0.289** **0.090 8.605** **0.046 0.001** **0.289 N** **2 O** **175.247 285.209** **0.006 0.006** **0.001 0.010** **0.001 0.001** **0.001 0.004** **0.000 0.001** **0.001 0.004** **CO 2** **(Incl. VOC and CO) 16,004,645** **23,114,192 182** **182 61** **0 86** **0 85** **190 8** **0 0** **190**LCI Data Vectors**Fuel Pathway Component Herbicide Insecticide** **Medium Duty Truck Medium Duty Truck** **Natural Gas Boiler Biomass Boiler Diesel Boiler CH4 Boiler** **Diesel Mill Tractor Grid Electricity n-hexane Methane Flared** **Methane Electricity Prod. Electricity Displaced** **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** **mmBtu CH 4** **mmBtu CH 4** **mmBtue 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 1,1** **07,040 0 1,000,000 2,735,823 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 0** **0 2,511,475** **Coal 25,356,639** **38,904,659 67** **67 1,528** **0 22,906** **0 22,906** **1,002,262 14,795** **0 0** **1,002,262 Natural gas** **72,101,545 107,122,124** **181 181** **1,063,645 0** **61,799 0** **61,799 1,419,250** **43,955 0** **0 1,419,250** **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** **0 0** **89,964 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** **0.000 0.000** **22.497 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 0.000** **0.000 165.176** **NO x** **21,556.192 33,807.012** **1.360 1.360** **79.505 110.000** **112.182 57.607** **714.918 226.930** **29.965 48.900** **57.607 226.930** **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 3.700 3.206 186.314** **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** **3.700 3.206** **52.724 SO** **x 10,558.892** **3,550.038 0.046** **0.046 11.113** **4.100 18.431** **0.269 18.431** **22.872 8.761** **0.269 0.269** **22.872 CH** **4 19584.354** **28841.972 0.292** **0.292 129.931** **3.834 98.109** **1.100 98.559** **304.861 94.491** **49.000 1.100** **304.861 N** **2 O** **158.982 258.737** **0.009 0.009** **1.167 11.000** **0.549 1.100** **1.079 3.794** **0.119 1.100** **1.100 3.794** **CO 2** **(Incl. VOC and CO) 14,519,170** **20,968,842 266** **266 64,556** **0 90,582** **0 89,824** **200,352 8,769** **0 0** **200,352 LCI Vector Region: Southeast Asia Southeast Asia South** **east Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast** **Asia Southeast Asia Southeast Asia Southeast Asia Sout heast Asia** Oil MillPesticide Inputs FFB and EFB TransportPalm Oil Mill Effluent**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 Tanker** **Truck Diesel** **Truck Diesel** **Combustion (TTW)** **Combustion CO** **2 Credit** **Process Loss Factor 1.00004 1.00000 1.00000 1.00000 1.00000 1.00001 1.00000 1.00001 1.00001 1.00000 1.** **00000 Cumulative Loss Factor per Fuel Pathway Component 1.** **00002 1.00002 1.00002 1.00002 1.00002 1.00002 1.00002 1.00 002 1.00002 1.00000 1.00000 1.00000 1.00000 1.00000 1.0000** **0 1.00000 1.00000 Feedstock Use Factor 1.04 1.04 1.00 1.00 1.00 1.00 1.00 1.0** **0 1.00 1.04 1.00 1.00 1.00 1.00 1.00 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 -0.4** **10 -0.108 241 14,484 80 145 1 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 km km 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 14,484 80 145 1 1 Units** **Biodiesel Biodiesel** **Biodiesel Biodiesel** **Biodiesel Biodiesel** **Biodiesel Biodiesel** **Biodiesel Biodiesel** **Biodiesel km** **km km** **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 18** **6,224 11,203** **1,000,000 0** **Fossil fuels 3,804** **0 3,787** **0 7,179** **29,942 0** **0 0** **-23,205 -206,705** **16,321 18** **6,218 11,193** **0 0** **Coal 75** **0 54,656** **0 2,865** **108 0** **0 0** **-2,051 -106,554** **287 0** **82 148** **0 0** **Natural gas 203 0 58,046 0 4,057 81,030 0 0 0 -12,805 -72,320 5 83 1 641 1,153 0 0** **Petroleum 3,526** **0 230** **0 257** **1,946 0** **0 0** **-8,349 -27,827** **15,451 17** **5,495 9,891** **0 0** **CH 4** **0.000 0.000** **0.007 0.000** **0.001 0.010** **0.000 0.000** **0.000 -0.002** **-0.040 0.001** **0.002 0.000** **0.001 0.002** **0.000 N** **2 O** **0.000 0.000** **0.000 0.000** **0.000 0.000** **0.000 0.000** **0.000 -0.006** **0.000 0.000** **0.000 0.000** **0.000 0.002** **0.000 CO** **2 0.3** **0.0 3.3** **0.0 0.5** **1.1 0.0** **0.0 0.0** **-1.9 -20.4** **0.8 1.5** **0.3 0.5** **75.1 -71.5** **CO 2** **(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 1.5** **0.3 0.5** **75.1 -71.5** **Total Fuel Cycle GHG Emissions (g/MJ)**0.30.03.50.00.61.30.00.00.0-3.6-21.50.81.50.30.575.8-71.5Allocated Emission Results:**CPO, PK (g/MJ)**0.30.03.30.00.51.30.00.00.00.81.50.30.575.8-71.5Allocated Emission Results:**CPO, PK, fiber, biomass (g/MJ)**0.30.03.30.00.51.30.00.00.00.81.50.30.575.8-71.5Displacement Emissions Results**(g/MJ)**0.30.03.50.00.61.30.00.00.0-3.6-21.50.81.50.30.575.8-71.5Conversion 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 1.0000 1.0000 Denominator Unit Conversion 0.68 0.68 0.0009 0.0009 0.00** **09 0.0009 1.0000 1.0000 1.0000 1.10 1.10 0.0006 0.0006 0.000 6 0.0006** **Fuel Pathway Component Medium Duty Truck** **Ocean Tanker Natural Gas Boiler** **Coal Boiler Electricity Methanol Sodium Hydroxide** **Sodium Methoxide HCl** **Displaced Feed Corn Displaced Petro-glycerin** **Heavy Duty Truck Ocean Tanker** **Heavy Duty Truck Heavy Duty Truck** **Biodiesel Combustion Biodiesel Combustion** **Denominator Units****tonne*km tonne*km MJ Natural Gas MJ Coal MJe MJ MeOH - - - t****onne corn tonne glycerin MJ*km BD MJ*km BD MJ*km BD MJ*k****m BD MJ BD MJ BD 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 0** **77 77** **1,000,000 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 0** **77 77** **Coal 34,019** **859 1,001,528** **1,001,720 1,002,262** **2,041 182,209,218** **37,443,383,487 1** **0 1** **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 0** **8 8** **Petroleum 1,596,013** **46,254 4,212** **6,179 89,964** **36,626 741,673,122** **9,778,299,820 64** **0 68** **68 Fuel Cycle Emissions (g)** **VOC 0.054** **0.004 0.007** **0.008 0.021** **0.024 195.273** **627.832 0.000** **0.000 0.000** **0.000 CO 0.239 0.009 0.026 0.096 0.157 0.032** **1,763.860 2,627.690 0.000 0.000 0.000 0.000 NO** **x 0.677** **0.103 0.075** **0.108 0.215** **0.083 798.496** **8,255.518 0.000** **0.000 0.000** **0.000 PM10** **0.022 0.009** **0.004 0.170** **0.177 0.016** **71.775 5,743.703** **0.000 0.000** **0.000 0.000** **PM2.5 0.015 0.006 0.003 0.044 0.050 0.015 41.947 1,438.604 0.000 0.000 0.000 0.000** **SO x** **0.022 0.030** **0.011 0.224** **0.022 0.024** **384.667 12,967.678** **0.000 0.000** **0.000 0.000** **CH 4** **0.140 0.004** **0.123 0.114** **0.289 0.181** **207.646 13933.920** **0.000 0.000** **0.000 0.000** **0.002 N** **2 O** **0.003 0.000** **0.001 0.001** **0.004 0.000** **489.340 66.694** **0.000 0.000** **0.000 0.000** **0.002 CO** **2 (Incl. VOC and CO)** **128 4** **61 103** **190 21** **167,903 7,172,476** **0 0** **0 0** **75 -71** LCI Data Vectors**Fuel Pathway Component Heavy Duty Truck** **Ocean Tanker Natural Gas Boiler** **Coal Boiler Grid Electricity Methanol Sodium Hydroxide** **Sodium Methoxide HCl** **Displaced Feed Corn Displaced Petro-glycerin** **Heavy Duty Truck Ocean Tanker** **Heavy Duty Truck Heavy Duty Truck** **Biodiesel Combustion Biodiesel Combustion** **Denominator Units****ton*mi ton*mi****mmBtu Natural Gas mmBtu Coal** **mmBtu Electricity mmBtu MeOH tonne BD tonne BD tonne BD ton co** **rn ton glycerin mmBtu*mi BD mmBtu*mi BD mmBtu*mi BD mmBt****u*mi BD mmBtu BD mmBtu BD****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** **2 77** **77 Fossil fuels** **2,383 68** **69,385 10,249** **2,511,475 563,417** **1,953,842 68,846,160** **68 2** **77 77** **Coal 47 1 1,001,528 1,001,720 1,002,262 2,041 172,701 35,489,480 1 0 1 1** **Natural gas 127** **2 1,063,645** **2,350 1,419,250** **1,524,749 1,078,171** **24,087,400 2** **0 8** **8 Petroleum** **2,209 64** **4,212 6,179** **89,964 36,626** **702,970 9,268,040** **64 2** **68 68** **Fuel Cycle Emissions (g) VOC** **0.078 0.005** **7.838 8.446** **22.497 25.640** **177.148 569.560** **0.005 0.000** **0.003 0.003** **CO 0.349** **0.013 27.950** **101.763 165.176** **33.412 1,600.147** **2,383.800 0.013** **0.000 0.011** **0.011 NO** **x 0.988** **0.151 79.505** **113.597 226.930** **87.696 724.383** **7,489.280 0.151** **0.005 0.033** **0.033 PM10** **0.031 0.013** **3.910 178.964** **186.314 16.456** **65.113 5,210.600** **0.013 0.000** **0.001 0.001** **PM2.5 0.022** **0.009 3.681** **46.843 52.724** **15.460 38.054** **1,305.080 0.009** **0.000 0.001** **0.001 SO** **x 0.032** **0.044 11.113** **236.163 22.872** **25.553 348.964** **11,764.080 0.044** **0.001 0.001** **0.001 CH** **4 0.204** **0.006 129.931** **120.041 304.861** **191.378 188.373** **12640.640 0.006** **0.000 0.007** **0.007 N** **2 O** **0.005 0.000** **1.167 1.077** **3.794 0.188** **443.922 60.504** **0.000 0.000** **0.000 0.000** **CO 2** **(Incl. VOC and CO) 186** **6 64,556** **109,167 200,352** **21,793 152,319** **6,506,761 6** **0 6** **6 LCI Vector Region: Southeast Asia U.S. Southeast Asia S** **outheast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia Southeast Asia South** **east Asia Southeast Asia U.S. U.S. U.S.**Fuel CombustionCo-Product Credits (Displacement)CPO TransportFuel TransportFuel Production

### 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**

**CH 4**  

### methane

**CO 2**  

### carbon dioxide

**CO 2**  

### e carbon dioxide equivalent **COP conference of the parties DNA designated national authority**  **DOM dead organic matter FAO Food and Agriculture Organisation of the Unit**  **ed 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** 

**N 2**  

### O 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 Clim ate Change** 

**UN-REDD United Nations – Reduced Emissions from De gradation and Deforestation**