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Feedstocks Used in Biodiesel Production that Influence Biodiesel Price A Research Paper Submitted for the Master of Science in Agriculture and Natural Resources Degree University of Tennessee at Martin Submitted by: Benjamin C. Carlisle December 2013

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  • Feedstocks Used in Biodiesel Production that Influence Biodiesel Price

    A Research Paper Submitted for the Master of Science in

    Agriculture and Natural Resources Degree University of Tennessee at Martin

    Submitted by: Benjamin C. Carlisle

    December 2013

  • ii  

    Abstract

    Biodiesel is one renewable fuel source whose primary feedstock in the U.S. is soybeans

    and poultry fat. Legislation passed by the government is helping to drive the adoption of biofuels

    through usage mandates and subsidies. Very little biodiesel sold in the retail market is pure

    biodiesel; most biodiesel is blended with fossil diesel. Due to engine and technology constraints,

    most blends do not contain more than 20% biodiesel. Price data from March 1, 2011, to

    September 28, 2012, was obtained from OPIS and contained 42 biodiesel blends, 16 biodiesel

    feedstocks, 3 petroleum based products and was surveyed from 8 geographical locations. Using

    a Principal Component Analysis (PCA), variables from the data set were reduced for each

    biodiesel blend. Examining the correlation values from each PCA, it was determined the price of

    diesel, soybean crude degummed, soybean refined, bleached, and deodorized (RBD), and poultry

    fat influenced the price of biodiesel. Diesel affects the price of biodiesel because they are perfect

    substitutes. The feedstocks with the predominant share of input influenced the cost of production

    and final retail price of biodiesel.

  • iii  

    Table of Contents

    Chapter 1 – Introduction...............................................................................................................1

    Chapter II – Literature Review ....................................................................................................4

    Production Capacity .............................................................................................................4

    Diesel Types.........................................................................................................................5

    Price of Feedstocks ..............................................................................................................6

    Land Value and Commodity Market ...................................................................................7

    Policy and Subsidies ............................................................................................................8

    Results of Previous Work ..................................................................................................10

    Chapter III – Materials and Methods ........................................................................................12

    Oil Price Information Service ............................................................................................12

    Feedstocks ..........................................................................................................................13

    ANOVA .............................................................................................................................14

    Correlation Analysis ..........................................................................................................14

    Principal Component Analysis ..........................................................................................18

    Chapter IV – Results ...................................................................................................................20

    Gas Price ............................................................................................................................22

    Diesel Price ........................................................................................................................22

    Brent Crude Oil Futures .....................................................................................................24

    Soybean Crude Degummed Price ......................................................................................25

    Soybean RBD Price ...........................................................................................................25

    Poultry Fat Price ................................................................................................................26

    Chapter V – Discussion ...............................................................................................................28

    Literature Cited ...........................................................................................................................30

  • iv  

    List of Tables

    Page

    Table 1. Biodiesel blend by geographical location. ......................................................................15

    Table 2. Data set name and commodity name ..............................................................................18

    Table 3. Percent of compositions needed to explain biodiesel winter premium blends. ..............20

    Table 4. Weights of compositions 1-8 by day. ..............................................................................21

    Table 5. Percentage of 42 variables correlated with biodiesel price .............................................21

  • 1  

    Chapter 1 – Introduction

    The Renewable Fuel Standard (RFS) of 2005 and the Energy Independence and Security

    Act of 2007 have been passed in the last decade to mandate an increased use of renewable fuels

    (Gustafson, 2010). Biofuel is seen as a renewable and almost perfect substitute to petroleum.

    However, there are several implications that come with its increase in production. The

    conversion of land from food production to feedstock production and resource and technology

    constraints when increasing production of biofuel are some of the implications associated with

    biofuel production (Gardner & Tyner, 2007). Biofuels can help alleviate pressure on the

    petroleum crude oil market when low cost mass production is achieved.

    The first biofuel produced was ethanol in 1826. In the 1880’s, Dr. Rudolph Diesel

    invented the first diesel engine. The engine works on the principal of compression ignition in

    which a fuel is injected into the cylinder inside the engine block which air has been compressed

    into at a high pressure and temperature (History of Biodiesel Fuel, 2013). The engine’s first

    power source was a crude form of biodiesel derived from peanut oil (History of Biodiesel Fuel,

    2013). The first public demonstration was performed at the Paris Exhibition in 1900 (Cankci &

    Sanli, 2008). However, because of the fuel source, engines that ran vegetable oils for extended

    periods of time had several mechanical problems such as deposition of the combustion chamber,

    deterioration of lubricating oil, piston ring sticking, and injector tips cooking (Cankci & Sanli,

    2008). Today, the diesel engine has become the engine of choice for reliability, power, and

    higher fuel economy worldwide.

    As opposed to fossil fuel, biofuel is an energy source derived from organic matter that

    was recently living (Gardner, 2007). Most United States (U.S.) biodiesel is made from soybeans,

    but other countries are using alternative inputs such as palm oil, beef tallow, and sugarcane

  • 2  

    (Fortenbary, 2008). Due to the abundance of rape seed in Europe, the production of biodiesel

    from rape seed dominates the country (Moser, 2009). Animal fat is also used in parts of the

    United States to produce biodiesel. The biomass, also known as stover, remaining in the field

    post-harvest is seen as a source for biofuel production; however, the removal of stover could

    affect the long-term sustainability of the field’s fertility (Baker & Zahniser, 2007). Cellulosic

    biomass is seen as a means to curbing row crop used for the production of biofuel (Baker &

    Zahniser, 2007).

    Subsidies have been a part of the biofuel market since the 1970’s. Subsidies have a

    tendency to generate a net social loss on a global scale in the biofuel market (Gardner & Tyner,

    2007). Biofuel subsidies are important politically because they link agricultural policy with

    biofuels energy policy (Gardner & Tyner, 2007). They are also seen as a means to encourage the

    production of ethanol (Tyner & Taheripour, 2007). Ethanol subsidies began in 1978 and have

    ranged from $0.40 - $0.60 per gallon. They have helped to spur the production of corn as a

    feedstock, reduce greenhouse emissions, and increase national security (Tyner & Taheripour,

    2007).

    This research project was designed to examine the feedstocks that impact the price of

    biodiesel production. Using price of feedstocks and biodiesel, a Principal Component Analysis

    (PCA) was used to determine those significant feedstocks. Biodiesel markets examined are Little

    Rock, AR, Los Angeles, CA, Paducah, KY, Cape Gerardo, MO, Greenville, MS, Trenton, NJ,

    Memphis, TN, and Houston, TX.

    Price data from March 1, 2011 to September 28, 2012 was obtained from OPIS and

    contained 42 biodiesel blends, 16 biodiesel feedstocks, 3 petroleum based products and was

    surveyed from 8 geographical locations. The data was sorted by biodiesel blend and an ANOVA

  • 3  

    was performed on each feedstock. Also, a PCA was conducted on each biodiesel blend and

    results were recorded in a spreadsheet for further analysis. Descriptive statistics were performed

    on the data from each PCA and it was determined the price of gas, diesel, futures contract of

    crude, soybean crude degummed, soybean RBD, and poultry fat influence the price of biodiesel.

    Diesel affects the price of biodiesel because it is an almost perfect substitute.

  • 4  

    Chapter II – Literature Review

    Production Capacity

    Biofuel production began influencing the U.S. farm economy in 2006. Changes made to

    the Farm Bill in 2005 dramatically increased the demand for biofuel through the use of subsidies.

    It also placed tariffs on the import of biodiesel from other countries. In 2007, the U.S. Congress

    passed the Renewable Fuel Standard (RFS) mandate. The RFS requires the blending of an

    amount specified, by the Environmental Protection Agency (EPA), each year of each type of

    biodiesel (Tyner, Taheripour, & Perkis, 2010). It acts as a hidden variable incentive because it

    requires petroleum fuel manufacturers to use a specified amount of biofuel regardless of its price

    or the price of crude oil. Without these subsidies, the biofuel market would not have existed prior

    to 2005 (Taheripour & Tyner, 2008). The demand for biofuel is driven by oil prices and

    government support programs (Pokrivcak & Rajcaniova, 2011). The surge of crude oil and

    gasoline prices in 2006 increased the profits and demand for biofuel manufacturers. This spilled

    over into agriculture commodities, fueling sharp gains in the corn and soybean market

    (Henderson & Gloy, 2009).

    Most diesel/biodiesel blends are made of 80% -95% diesel and 5% - 20% biodiesel

    (About OPIS, 2012). The blend contains 93% of the energy of a regular gallon of diesel

    (Alternative Fuels Data Center, 2013). Biodiesel production averaged 0.728 million gallons in

    2001 increasing to 128.31 million gallons in 2013 (U.S. Energy Information Administration,

    2013). World biofuel production exceeded 26.4 billion gallons in 2009. Of the 26.4 billion

    gallons, an estimated 85% was ethanol. Biodiesel made the remaining 15% (Pokrivcak &

    Rajcaniova, 2011). With the addition of biofuel from the plants currently under construction, the

    U.S. could have a production capacity of 11 billion gallons (Baker & Zahniser, 2007).

  • 5  

    Diesel Types

    Due to government regulations, there are several different types of diesel made.

    Different types of diesel are produced to satisfy tax regulations and government requirements.

    Number (No.). 2 Ultra Low-Sulfur has a sulfur content of less than 15 ppm and must be used to

    supply at least 80% of the nations on road diesel fuel. No. 2 Low Sulfur diesel has sulfur content

    up to 500 ppm and can be used to satisfy 20% of the nations on road diesel fuel sold at the retail

    level. No. 2 High-Sulfur is used as an off-road fuel for equipment such as farm equipment,

    mining equipment, or as home heating oil. No.1 Low-Sulfur is commonly used for “blending”

    on-road fuels. Diesel is blended in the winter months to create a diesel fuel that will not solidify

    or gel in colder temperatures. No. 1 High-Sulfur is used for various off road agricultural and

    industrial purposes. Red dye denotes the fuel is for tax-exempt purposes. Farm equipment, off

    highway trucks, mining equipment, and school boards are just a few examples of what would use

    red dye. There is no difference in the fuel but its cost of production is higher due to the dying

    process and cost of dye.

    Premium diesel has a higher cetane rating than that of regular diesel. Usually premium

    diesel contains a detergent package that helps to clean the engine as the fuel is burned. Winter

    diesel is produced during the winter months to help keep the diesel from solidifying or gelling at

    colder temperatures. It’s produced by taking on road diesel and mixing other diesel fuels or

    chemical additives to create the new blend. CARB diesel is sold for vehicular use in California.

    It must meet a 15 ppm maximum sulfur limit and all of the current low aromatic diesel

    specifications. California’s definition of “vehicular use” includes all highway vehicles and non-

    road vehicles such as agriculture and construction equipment. Low emissions diesel (LED) must

    contain less than 10 percent by volume of aromatic hydrocarbons and must have a cetane number

  • 6  

    of 48 or greater. This fuel is specifically used in 110 counties in East/Central Texas (About

    OPIS, 2012).

    Price of Feedstocks

    The increased demand for biofuel raised concerns about the impact ethanol production

    would have on the price level and volatility of agricultural commodities. The price of the

    nation’s top two crops, corn and soybeans, have been affected in recent years. The popular press

    attributes this to the increased and decreased demand for biofuel (Zhang, Lohr, Escalente, &

    Westestain, 2009). Analysis published by the United States Department of Agriculture (USDA)

    indicates that for every 50 million gallons of biodiesel produced, the price of soybeans rises 1%.

    A $0.30 increase has been recognized at current demand (Alternative Fuels Data Center, 2013).

    A significant area of land has been needed to accommodate the increase in demand of

    biodiesel. In 2007/08 on a global scale, the food versus fuel trade-off crisis developed in the

    global commodity market (Zhang, Lohr, Escalente, & Westestain, 2009). Biofuels will

    eventually compete for renewable and nonrenewable resources, impacting its sustainability and

    that of food (Zhang, Lohr, Escalente, & Westestain, 2009). Eventually, land-use will shift to

    allow for increased cultivation of crop for the production of biofuels instead of food. Ethanol and

    biodiesel not only impact the price level of corn and soybeans, but can also impact their price

    volatility (Zhang, Lohr, Escalente, & Westestain, 2009).

    The price of biofuel products reflects the price volatility of current as well as the future

    value of inventory, consumption, and production demand (Zhang, Lohr, Escalente, &

    Westestain, 2009). The total cost of producing biodiesel (pre-subsidy) is relatively high when

    compared to fossil fuels (Larson, 2008). However, new production practices and technologies are

    helping decrease the cost of production. When considering the cost of production for a gallon of

  • 7  

    biodiesel, both the cost of the feedstock and the total volume output of the plant should be

    considered. The cost of a feedstock for biodiesel could run as high as 80% of the total cost of

    production (Cankci & Sanli, 2008). Another key factor boosting the demand for biofuel is high

    oil prices. Higher oil prices have helped to sustain opportunities for efficiency gains, generated

    increased supply from alternative and traditional energy sources, and stimulated worldwide

    conservation of energy (Coyle, 2013). However, high oil prices make intensive corn production

    less attractive because of the increased cost of inputs such as fertilizer (Baker, 2007).

    The U.S. dependency on foreign oil has been a major topic in recent years. Importing

    two-thirds of our oil supply, half coming from unstable sources, is seen as a national security

    risk. Biofuels are almost a perfect substitute to fossil fuel and market price is strongly dependent

    on market price of gasoline and diesel (Pokrivcak & Rajcaniova, 2011). The conversion rate of

    soybean to biodiesel is 1.5 gal/bu (Cankci & Sanli, 2008).

    Land Value and Commodity Market

    The rising demand for agriculture crops can increase farmland values, which are

    calculated from the discounted earnings of what the land is expected to return (Henderson &

    Gloy, 2009). If a large local demand impacts local basis patterns, increased land value gains in

    close proximity to biofuel plants are possible (Henderson & Gloy, 2009). An analysis of basis

    patterns around 12 ethanol plants, opening between 2000 and 2003, found on average an increase

    of $0.059/bu in basis values within a 150 sq. mile area around the plant. However, biofuel plants

    may not be the only factor affecting land price. Competition among agriculture producers could

    also drive land values (Henderson & Gloy, 2009).

    The growing demand for feedstocks by biofuel producers could be satisfied through

    higher feedstock output. In 2009, production of soybeans was 3.4 billion bushels averaging 44

  • 8  

    bushels per acre for 77.4 million acres (Cropwatch: Bioenergy, 2013). The increased production

    of palm oil in the EU has led to deforestation of the local rain forest (Saraf & Thomas, 2007).

    Establishing a crop rotation within a field will also help to increase yields (Baker & Zahniser,

    2007). Care must be exercised to ensure the land for food is not being converted to land for

    biofuel production, unless there is a food surplus (Cankci & Sanli, 2008).

    If U.S. corn exports are reduced by the demand for ethanol, other geographical areas may

    be affected due to higher prices. Japan and Taiwan are two foreign buyers that would be least

    responsive to a rise in corn. Mexico would also be greatly affected. From June 2006 to June

    2007, the price of tortillas rose 50% due to a greater tightness in the global corn market (Baker &

    Zahniser, 2007). Canada, Egypt, Central America, and the Caribbean region would see the

    largest impact (Baker & Zahniser, 2007). The same could be said for any feedstock used in the

    production of biodiesel.

    Policy and Subsidies

    The U.S., Brazil, and the European Union use an array of instruments to support the

    production of biofuels. They all use excise-tax exemptions, mandatory blending, import tariffs,

    subsidies for feedstocks, and subsidies for research and development of new technology in the

    biofuel industry to help promote growth of the industry (Pokrivcak & Rajcaniova, 2011). The

    U.S. has subsidized ethanol production since 1978 and biodiesel has been subsidized in the last

    decade (Tyner & Taheripour, 2007). Historically, subsidies have ranged from $0.40 to $0.60 per

    gallon. The most recent ethanol subsidy is $0.51/gal. Biodiesel made from recycled material was

    $0.50/gal and biodiesel made from oilseed crops was $1.00/gal (Tyner & Taheripour, 2007). The

    RFS mandates increased use of biofuels, but currently the industry is experiencing over capacity

    (Zhang, Lohr, Escalente, & Westestain, 2009). The 2009 RFS will require a 10.22% blend of

  • 9  

    ethanol to gasoline to ensure at least 11.1 billion gallons of renewable fuels are used. Most of the

    renewable fuels will be supplied by ethanol, which should help the industry recover (Zhang,

    Lohr, Escalente, & Westestain, 2009).

    Biofuel subsidies have four main objectives: increase farm income, achieve

    environmental gains, increase national security, and reduce greenhouse gas emissions (Tyner &

    Taheripour, 2007). Part of the subsidy program to promote U.S. biofuel production is a $0.54/gal

    import tariff is placed on any foreign ethanol sold in the U.S. (Zhang, Lohr, Escalente, &

    Westestain, 2009). This helps decrease competition in domestic markets to help the industry

    become more stabilized. Subsidies are designed to keep market volatility at a minimum and

    lessen the risk exposure to producers and biofuel refineries (Zhang, Lohr, Escalente, &

    Westestain, 2009). With the assistance of subsidies, biofuel production has increased, resulting in

    an increased capacity available to the market. Due to the increase in biofuel capacity, large price

    increases in the U.S. oil refinery industry were limited because biofuel helped relieve an industry

    running at near capacity (Du & Hayes, 2008). If not for ethanol, the crude oil refining capacity

    may be larger today (Du & Hayes, 2008). In 2004, crude oil prices climbed about $70 bbl while

    subsidies remained fixed. This helped drive a boom in the construction of new ethanol plants

    (Tyner & Taheripour, 2007).

    New rules proposed by the EPA in 2014 have set large mandated volumes for the biofuel

    industry. However, when compared to the RFS, mandated volumes have been significantly

    reduced from 1.75 billion gallons to 17 million gallons for cellulosic production (Meyer &

    Johansson, 2013). Mandated levels for biodiesel for 2014 have been set to 1.92 million gallons

    and ethanol blend wall has been set to 13,021 million gallons. The EPA is currently seeking

    comment on these mandated volumes.

  • 10  

    Results of Previous Work

    Studies have been conducted in various fields of the biofuel market. Studies have

    examined the impact of biofuel feedstocks on food prices, the relationship between biofuel and

    crude oil prices, the relationship of biofuel production on feedstocks, and the effect of higher

    corn production on soybean price. Findings vary with geographical location and local markets.

    Zhang (2007) indicated in recent years there are no long-run relationships among

    agriculture commodity price (soybeans and corn) and fuel (gasoline, ethanol, and oil) price. With

    the significance of the reversal of fuel prices directly influencing commodity markets, a debate

    of the food versus fuel security issue started (Zhang, Lohr, Escalente, & Westestain, 2009). For

    the time period before 2000, results indicated through variance-decomposition and impulse

    response curves, ethanol price only caused a small, short-run impact on soybean prices (Zhang,

    Lohr, Escalente, & Westestain, 2009). Also, any shock the ethanol market may feel from corn

    prices is not lasting. During the ethanol boom (2000-2007), ethanol prices influenced soybean

    prices. However, the relationship was almost insignificant. Only 0.2% of the price variation of

    soybeans could be related to ethanol price variations (Zhang, Lohr, Escalente, & Westestain,

    2009). The results of the study do not support the hypothesis that much of the increases in

    commodity prices are due to the increased demand for ethanol (Zhang, Lohr, Escalente, &

    Westestain, 2009).

    Eidman’s research views ethanol as a fuel extender. The results from his research found a

    strong positive correlation between ethanol and gasoline prices (Eidman, 2005). O’Brien (2009)

    also found a relationship between ethanol and gasoline price. Correlation amounted to 83%

    between the oil price series and the ethanol price series (2007-2009). A 10% increase in

    Midwest gasoline prices caused a 6.59% increase in Iowa ethanol prices (O'Brien, 2009).

  • 11  

    Pokrivcak (2011) found a high positive correlation (83.56%) between gasoline and ethanol. The

    results were drawn from weekly data between January 2000 to October 2009 (Pokrivcak &

    Rajcaniova, 2011). It is important to note that of the three studies, no researcher used the same

    model to arrive at their results. Also, while the research was conducted for ethanol, the same

    principals could be implied for biodiesel.

    Du (2008) conducted a study of the U.S. and regional gasoline prices and on the

    profitability of the U.S oil refinery industry. The refinery product markets, set forth by the

    Petroleum Administration for Defense Districts (PADDS), were used to define the market of the

    study. The markets (East Coast, Midwest, Gulf Coast, Rocky Mtn., and West Coast) were

    defined during World War II for the purpose of administrating oil allocations (Du & Hayes,

    2008). The five regions are different in oil and petroleum characteristics, oil-related pipeline

    infrastructure, economic conditions, and social product supply and demand conditions (Du &

    Hayes, 2008). Whole spot commodity markets were examined from the Gulf Coast, Los

    Angeles, and New York areas for the effects of market structure on the pattern of price

    adjustment (Du & Hayes, 2008). Findings indicate the market concentration has an asymmetric

    effect on gasoline prices responding to crude oil shocks. From 1995 to 2007, results indicated,

    ethanol production had a negative impact on retail gas prices of $0.29/gal to $0.40/gal (Du &

    Hayes, 2008). The reduction in gasoline price came at the expense of oil refineries profits (Du &

    Hayes, 2008).

  • 12  

    Chapter III -Materials and Methods

    Price data from the Oil Price Information Service (OPIS) was used to conduct multiple

    ANOVA’s and Principal Component Analysis (PCA). The data was purchased by the

    Agriculture, Geosciences, and Natural Resource department at the University of Tennessee at

    Martin in October 2012. The data set contains price points form March 1, 2011 to September 28,

    2012 for 42 biodiesel blends, 16 biodiesel feedstocks, 3 petroleum based products, and is

    surveyed from 8 geographical locations.

    Oil Price Information Service

    OPIS provides one of the world’s most comprehensive sources of petroleum pricing and

    news information (About OPIS, 2012). In 1977, the company started providing a newsletter on

    price data and petroleum market information. By 1980, they had pioneered the collection of

    “rack” pricing from wholesalers and in 1981, started offering spot price assessments for refined

    products. OPIS began providing retail fuel data to its members in 1999. Today, they provide up

    to 130,000 rack prices per day from 1500 terminals in over 400 world market locations. These

    prices are obtained through exclusive relationships with credit card companies, survey methods,

    and direct feeds.

    OPIS was used because they can provide several different types of price data across

    multiple products and locations. To ensure the accuracy of the data, OPIS filters the data by

    means of computer programs to ensure the reported price is current and doesn’t contain any

    nonfuel related purchases. For analysis, the price data was sorted by biodiesel type which

    allowed for multiple locations (Table 1) for each type of biodiesel to be included in the ANOVA

    and PCA analysis. The data was sorted by day of the week and organized in an Excel

    spreadsheet. In table 2, all feedstocks used as part of the analysis are listed by variable name

    from the data set and commodity name.

  • 13  

    The data used in this research was observed on a daily basis for some variables

    and weekly basis for others. Therefore, the daily observations required aggregation to

    correspond with the weekly observations. In theory, there should be no difference in

    results if a mean of daily observations of price variables is reported on Monday or Friday,

    but in this case, there were some instances where Friday values were not observed likely

    due to holidays. To maximize observations, we chose the day of the week with the

    maximum number of observations, Monday.

    Feedstocks

    The data contained 16 feedstocks and 3 petroleum based products. These products can be

    summed into 3 categories: oils, fats, and crude oil. The production process turns oils and

    fats into chemicals called long-chain mono alkyl esters, which are also known as fatty

    acid methyl esters. There are four conversion processes used in the production of

    biodiesel, but the most widely used is transesterification. In this process, the oil and fat

    are reacted with a short chain alcohol, usually methanol, in the presence of a catalyst

    (Alternative Fuels Data Center, 2013).

    Each feedstock is different and the conversion process is slightly different based

    on the feedstock, but the basic process remains the same. This allows for biodiesel

    production to change from one feedstock to another. The main production difference

    between oil and fat is oil must be extracted from the feedstock and fat must be heated and

    turned into a liquid form for the reaction process. Depending on the feedstock, different

    catalysts are used to help with the reaction process (Alternative Fuels Data Center, 2013).

    Crude oil products are not considered feedstocks but they are complimentary and

    substitutes to each other. With the exception of B100 (100% biodiesel), all biodiesel

  • 14  

    blends contain fossil diesel. The ratio of the blend will determine the amount of biodiesel

    in the blend.

    ANOVA

    The ANOVA is an arithmetic process used for partitioning a total sum of squares

    into components associated with recognized sources or variation (Darroch, 2012,

    personal communication). An ANOVA partitions sums of squares into two components:

    treatment and error. It measures the impact independent variables have on the dependent

    variables (Y). Typically, an ANOVA is used for observational studies and designed

    experiments. A limitation of an ANOVA that applies to this data set is all observations

    of Y are to be independent. For the data set we are using, Y is not independent of each

    other. They are related from day-to-day. The formula (1) used for the ANOVA were

    sums of squares total equals the mean square for treatments or sums of squares between

    groups plus the mean square of error or sums of squares within a group. However, excel

    was used to complete the ANOVA.

    ∑ ∑ yij 2 = ∑ j 2+ ∑ yij j 2 1

    Correlation Analysis

    A correlation analysis was performed on relevant feedstocks. Due to the ANOVA’s

    limitations, the t-test inputs could be flawed such as the standard error output from the ANOVA.

    The inputs to the ANOVA were not truly dependent of one another. It is also important to note

    the correlation analysis does not measure causation. Therefore, we cannot determine how the

    price fluctuation of the dependent variable influences the price of the independent variable.

    T= / 2

    Where = sample mean, = hypothesis, s2 = variance, and n= sample size

  • 15  

    Biodiesel B

    11 SM

    E U

    ltra Low

    Sul N

    o2

    Biodiesel B

    100 SM

    E w

    /RIN

    Biodiesel B

    100 SM

    E w

    /o R

    IN

    Biodiesel B

    10 SM

    E U

    ltra LS

    No2 D

    ye

    Biodiesel B

    10 SM

    E U

    ltra Low

    Sul N

    o2

    Biodiesel B

    10 SM

    E U

    LS#2

    Winter Prem

    ium R

    ed Dye

    Biodiesel B

    10 SM

    E U

    LS#2

    Winter Prem

    ium

    Biodiesel B

    10 SM

    E U

    LS

    Prem

    ium D

    ye

    Biodiesel B

    10 SM

    E U

    LS

    Premium

    Biodiesel B

    10 SM

    E H

    S#2

    Biodiesel B

    0-5 SM

    E U

    ltra LS

    No2 D

    ye

    Biodiesel B

    0-5 SM

    E U

    ltra Low

    Sul N

    o2

    Biodiesel B

    0-5 SM

    E C

    arb U

    LS D

    ye

    Biodiesel B

    0-5 SM

    E C

    arb U

    LS Biodiesel B

    lend

    Table 1. Biodiesel B

    lend by Geographical Location

    X X X

    Little R

    ock, AR

    Geographical Location

    X X X X

    Los A

    ngeles, C

    A

    Paducah,

    KY

    X

    Cape

    Gerardo, M

    O

    X X

    Greenville,

    MS

    X X X X X X X

    Trenton, N

    J

    X X

    Mem

    phis, TN

    X

    Houston,

    TX

     

  • 16  

    Biodiesel B

    20 SM

    E U

    ltra Low

    Sul N

    o2

    Biodiesel B

    20 SM

    E U

    LS#2

    Winter Prem

    ium R

    ed Dye

    Biodiesel B

    20 SM

    E U

    LS#2

    Winter Prem

    ium

    Biodiesel B

    20 SM

    E U

    LS

    Prem

    ium D

    ye

    Biodiesel B

    20 SM

    E U

    LS

    Premium

    Biodiesel B

    20 SM

    E H

    S#2

    Biodiesel B

    2 SM

    E U

    ltra LS N

    o2 Dye

    Biodiesel B

    2 SM

    E U

    ltra Low

    Sul N

    o2

    Biodiesel B

    2 SM

    E U

    LS#2

    Winter Prem

    ium R

    ed Dye

    Biodiesel B

    2 SM

    E U

    LS#2

    Winter Prem

    ium

    Biodiesel B

    2 SM

    E U

    LS#2

    Winter Prem

    ium R

    ed Dye

    Biodiesel B

    2 SM

    E U

    LS

    Prem

    ium D

    ye

    Biodiesel P

    remium

    B2 S

    ME

    U

    LS

    Biodiesel B

    2 SM

    E H

    S #2

    Biodiesel B

    11 SM

    E U

    ltra LS

    No2 D

    ye

    Biodiesel B

    lend

    Table 1 Cont. B

    iodiesel Blend by G

    eographical Location

    X X X X

    Little R

    ock, AR

    Geographical Location

    Los A

    ngeles, C

    A

    Paducah,

    KY

    X X

    Cape

    Gerardo, M

    O

    X

    Greenville,

    MS

    X X X X X X X X X X X X

    Trenton, N

    J

    Mem

    phis, TN

    Houston,

    TX

     

     

  • 17  

    Biodiesel B

    99 SM

    E w

    /RIN

    Biodiesel B

    99 SM

    E w

    /o RIN

    Biodiesel B

    5 SM

    E U

    ltra LS N

    o2 Dye

    Biodiesel B

    5 SM

    E U

    ltra Low

    Sul N

    o2

    Biodiesel B

    5 SM

    E U

    LS#2

    Winter Prem

    ium R

    ed Dye

    Biodiesel B

    5 SM

    E U

    LS#2

    Winter Prem

    ium

    Biodiesel B

    5 SM

    E U

    LS#2

    Winter

    Biodiesel B

    5 SM

    E U

    LS#2

    Red D

    ye LED

    Biodiesel B

    5 SM

    E U

    LS#2

    LED

    Biodiesel B

    5 SM

    E U

    LS

    Prem

    ium D

    ye

    Biodiesel B

    5 SM

    E U

    LS

    Premium

    Biodiesel B

    5 SM

    E H

    S #2

    Biodiesel B

    20 SM

    E U

    ltra LS

    No2 D

    ye

    Biodiesel B

    lend

    Table 1 Cont. B

    iodiesel Blend by G

    eographical Location

    X X X

    Little R

    ock, AR

    Geographical Location

    X X

    Los A

    ngeles, C

    A

    X X

    Paducah,

    KY

    X X X X

    Cape

    Gerardo, M

    O

    X X

    Greenville,

    MS

    X X X X X X X X X X

    Trenton, N

    J

    X X

    Mem

    phis, TN

    X X X X X

    Houston,

    TX

     

     

  • 18  

    Table 2. Data set name and commodity name Variable Name Commodity Name

    Price_g Price of Gas Price_d Price of Diesel

    Palmolein Palmolein Crude Degummed Soybean Crude Degummed

    RBD Soybean RBD Canola Oil we Canola Oil West Coast Canola Oil mw Canola Oil Midwest

    Beef Tallow Beef Tallow Choice Whitegrass Choice Whitegrass

    Poultry Fat Low FFA Poultry Fat Low (Free Fatty Acid) Yellow Grease Yellow Grease Soymeal Hipro Soymeal High Protein

    Corn Last Corn Future Soybean Last Soybean Future

    Crude Glycerin 80 Crude Glycerin (80%) NG_last Natural Gas Future

    Crude Last Brent Crude Oil Futures (WTI) Cattle Last Cattle Futures

    Principal Component Analysis

    A principal component analysis (PCA) is a variable reduction procedure. It is useful

    when there is a data set containing a large number of variables that is believed to have some

    redundancy in the variables. Redundancy means there may be some variables that are correlated

    with each other, possibly because they are measuring the same idea. Due to the redundancy, it

    should be possible to reduce the observed variables into a smaller number of principal

    components that will account for most of the variance in the data set.

    The principal component analysis is meant to reduce the complexity of the multivariate data.

    The first principal component that explains most of the variation in the original variables should

    be chosen (Chapter 6.2 - Principal Component Analysis, 2001). It is thought of as revealing the

    internal structure of the data in a way that best explains the variance in the data. The number of

  • 19  

    components will be no more than the number of variables entered into the analysis. Three criteria

    should be followed when selecting the number of principal components (Chapter 6.2 - Principal

    Component Analysis, 2001).

    1. Create a scree plot. This helps provide a visual component that will allow the less

    important components to be identified.

    2. Exclude principal components with eigenvalues below the average.

    3. Include enough components that explain some random amount (typically 80%) of the

    variance.

    A PCA was conducted by biodiesel blend using Stata Version 12 for Friday price data. It

    was determined Monday had the maximum number of observations and a PCA was conducted

    for each biodiesel blend. A spreadsheet in Excel was built organizing biodiesel blend in columns

    and feedstocks in rows. The correlation table was analyzed for each PCA, recording all

    significant interactions (α= 0.05, α= 0.01) of feedstock and biodiesel in the spreadsheet. The

    number of components needed to describe the data and the weight of each component was also

    recorded in the spreadsheet. The purpose of the spreadsheet was to sum all results in one

    location for further analysis. Using the data analysis tool pack in excel, descriptive statistics were

    conducted on the correlation value for each feedstock. From this output came the mean, standard

    deviation, and the range. The percentage of each feedstock having a significant interaction with

    all biodiesel blends was also calculated and recorded.

  • 20  

    Chapter IV – Results

    The biodiesel blend designated as “winter premium” were evaluated across all types of biodiesel

    products. These specific blends typically took a higher percent of composition to adequately

    explain the data as depicted in Table 3. These winter blends all have the geographical location

    Trenton, NJ, in common. This may be because the data set had more types of biodiesel

    associated with Trenton.

    The average weight given to each composition by day is listed in Table 4. The feedstock

    variables for both days were better explained by composition one because of the higher weight

    assigned to the composition. Biodiesel price, as it relates to time follows normal market trends.

    Few, single day price spikes were observed. This is important to note because erratic price

    variation for biodiesel or the feedstock could affect the compositions needed to explain the data.

    The correlation matrix from the output indicated an interaction (P < 0.05) of biodiesel price with

    all feedstocks, but not all biodiesel blends (Table 5). From the feedstocks, the top 30% of

    biodiesel price and feedstock interactions will be considered as having an effect on biodiesel

    price.

    Table 3. Percent of compositions needed to explain biodiesel winter premium blends. Biodiesel Blend Monday Friday

    Biodiesel B10 SME ULS #2 Winter Premium 67 67

    Biodiesel B10 SME ULS #2 Winter Premium Red Dye 67 33

    Biodiesel B2 SME ULS #2 Winter Premium 50 67

    Biodiesel B2 SME ULS #2 Winter Premium Red Dye 50 67

    Biodiesel B20 SME ULS #2 Winter Premium 67 67

    Biodiesel B20 SME ULS #2 Winter Premium Red Dye 67 67

    Biodiesel B5 SME ULS #2 Winter Premium Red Dye 40 60

  • 21  

    Table 4. Weights of compositions 1-8 by day. Composition Monday Friday

    1 0.443 0.432 2 0.241 0.228 3 0.123 0.105 4 0.069 0.069 5 0.055 0.049 6 0.031 0.033 7 0.021 0.027 8 0.02

    Table 5. Percentage of 42 variables correlated with biodiesel price. Feedstock Monday Friday

    Gas Price 83.3% 90.5% Diesel Price 85.7% 95.2%

    Natural Gas Last 11.9% 11.9% Crude Last 92.9% 83.3% Corn Last 26.2% 64.3% Soy Last 9.5% 11.9%

    Cattle Last 11.9% 9.5% Palmolien 50.0% 31.0%

    Crude Degummed 85.7% 85.7% RBD 92.9% 88.1%

    Canola Oil WC 59.5% 45.2% Canola Oil MW 28.6% 23.8%

    Beef Tallow 33.3% 23.8% Choice White Grass 59.5% 42.9%

    Poultry Fat 85.7% 76.2% Yellow Grease 45.2% 35.7% Soymeal Hipro 16.7% 7.1%

    Crude Glycerin 80 9.5% 7.1%

  • 22  

    Gas Price

    The price of gas and biodiesel consistently demonstrated a significant, positive

    correlation. When the price of gas increases, the price of all fuel-related products is expected to

    increase, and thereby influence the price of biodiesel. We see a positive correlation for 83% of

    the 42 biodiesel products studied. The range of correlations was 0.488 to 0.8907, with a 0.5811

    mean, and standard deviation of 0.2806. The minimum correlation value was found to be

    statistically different from zero at an alpha of 0.01.

    In the PCA analysis of the many factors associated with biodiesel, the price of gas was a

    significant factor in the relevant components. The correlation range indicates a medium (0.3 -0.5)

    to a strong (0.5 – 1.0) relationship of the two prices. The standard deviation of the correlations is

    the second largest of all relevant feedstock. Gas and biodiesel price are related, but the

    relationship may not be the strongest due to the variability in the two prices. An alpha of 0.01

    indicates a confidence interval of 99%. The two prices are positively related. The results of our

    preliminary ANOVA tests also echo these results for the relationship between these two

    variables.

    The variability of gas and biodiesel prices can be caused by multiple factors. The

    feedstock price for each product can play a significant role in the retail price. The price of

    biodiesel and gasoline are closely related due to their linkages to consumers in the output market.

    They are also tied together from the perspectives of inputs to production. Gasoline and petroleum

    products are a large input cost in the production of biodiesel and its various feedstocks.

    Diesel Price

    The price of diesel and biodiesel consistently demonstrated a significant, positive

    correlation. This is expected due the substitute relationship between these two goods. When the

    price of traditional diesel increases, the demand for biodiesel will increase, and thereby drive the

  • 23  

    price of biodiesel. A positive correlation was seen across 86% of the 42 biodiesel products

    studied. The range of correlations was 0.5738 to 0.8691, with a mean of 0.6437, and a standard

    deviation of 0.2986. The minimum correlation value was found to be statistically different from

    zero at the 0.01 level.

    In the PCA analysis of the many factors associated with biodiesel, the price of diesel was

    found to be an important factor in the relevant components. Again, this is expected because the

    purpose of biodiesel is to act as a renewable substitute good for diesel. The correlation range

    indicates a strong relationship between the two prices. The standard deviation is the largest of all

    relevant variables. Diesel and biodiesel price are related, but the relationship may not be the

    strongest due to the large amount of variability of price in each biodiesel product. Higher

    correlation values are associated with “Winter Premium” blends and “B100 SME” blends. An

    alpha of 0.01 denotes a confident interval of 99% that the two prices are related. The results of

    our preliminary ANOVA tests also echo these results for the relationship between these two

    variables.

    Biodiesel has the potential of supplementing petroleum diesel as an engine fuel.

    However, a few factors can hinder petroleum diesel as a perfect substitute. Not all engines are

    able to run 100 percent biodiesel due to the manufacture’s recommendation. However, most

    engines can run a blend of up to 20 percent without any engine modifications (Coyle, 2013)

    Also, the poorer cold flow properties of biodiesel, when compared to petroleum diesel, can result

    in cold startup problems in engines. One such problem is the plugging of the fuel injection

    system during winter months (Ng, Ng, & Gan, 2010). Production practices and subsidies can also

    influence retail price of biodiesel in the marketplace. This could either influence or hinder the

  • 24  

    substitution, based on price, of biodiesel (Ng, Ng, & Gan, 2010). Also, the blending of diesel

    into biodiesel may cause the price of fossil diesel and biodiesel to be highly correlated.

    Brent Crude Oil Futures

    The price of biodiesel and crude last demonstrated a significant, positive correlation for

    93% of the 42 biodiesel types. This is expected due to the relationship between these two goods.

    When the price of crude oil increases, the price of diesel will rise thus causing the price of

    biodiesel to rise. The range of correlations was 0.4356 to 0.9477, with a mean of 0.7202, and

    standard deviation of 0.1468. The minimum correlation value was found to be statistically

    different from zero at the 0.01 level.

    In the PCA analysis of the many factors associated with biodiesel, the price of crude oil

    was found to be an important factor in the relevant components. The correlation range indicates a

    medium to strong relationship between the prices of the two commodities. The standard

    deviation, when compared to other relevant feedstocks, indicates a medium range of variability

    in price. An alpha of 0.01 indicates a confidence interval of 99%. Again, this is expected

    because of the relationship between crude oil and biodiesel.

    High crude oil prices are one factor boosting the competitiveness of alternative fuels.

    The higher prices have provided the alternative fuel market with opportunities for efficiency

    gains, stimulated energy conservation, and generated an increased supply (Coyle, 2013). The

    energy content of biodiesel when compared to petroleum derived fuels is 93 percent, helping to

    make biodiesel a substitute to crude oil fuels. The relationship of biodiesel to crude oil can also

    be justified by the relationship of petroleum diesel and crude oil. They may be indirectly related,

    but they are still related (Coyle, 2013).

  • 25  

    Soybean Crude Degummed Price

    The price of diesel and soybean crude degummed demonstrated a significant, positive

    correlation for 86% of the 42 biodiesel types. This is expected due to crude degummed being a

    primary unrefined feedstock for biodiesel production. When the price of soybean crude

    degummed increases the price of biodiesel will increase. The range of correlations was 0.4681 to

    0.9251, with a mean of 0.6337, and standard deviation of 0.1086. The minimum correlation

    value was found to be statistically different from zero at the 0.01 level.

    The correlation range indicates a medium to strong price relationship between the

    feedstocks. The standard deviation of the correlations is the lowest of all relevant feedstocks

    indicating the least amount of variability in price data. With an alpha of 0.01, we can say with a

    99% confident interval the prices are related. Once again, this is expected because soybean crude

    degummed is the primary unrefined feedstock of biodiesel.

    Soybean oil contains a high amount of phosphorus that could hinder the action of the

    catalyst during transesterification (Fan, Burton, Austic, & Greg, 2010). The degumming process

    removes most of the phosphorus in the soybean oil, allowing it to be used as a feedstock to

    produce soybean RBD in biodiesel. The price relationship can be confirmed because the

    soybean is the primary feedstock in the production of biodiesel for the U.S. (Bajpai & Tyagi,

    2006). A refiner may choose to purchase soybean crude degummed and finish the conversion to

    biodiesel to ensure a quality product.

    Soybean RBD Price

    The price of soybean RBD and biodiesel demonstrated a significant, positive correlation

    for 93% of biodiesel types. It is important to note RBD had a significant, negative correlation for

    Friday data. This is expected due to soybean RBD being the primary refined feedstock of

  • 26  

    soybean crude degummed used in biodiesel production. When the price of soybean crude

    degummed increases, the price of RBD will increase, thus causing the price of biodiesel to

    increase. The range of correlations was 0.4559 to 0.9656, with a mean of 0.6090, and standard

    deviation of 0.1303. The minimum correlation value was found to be statistically different from

    zero at the 0.01 level for Monday data. However, it was only significant at the 0.05 level for

    Friday data.

    The correlation range indicates a medium to strong relationship between the two

    feedstock prices. The median of the correlations is 0.5881, indicating half of the correlations are

    between 0.4559 and 0.5881. This would suggest over half the correlations are more closely

    related to having a medium relationship. According to the standard deviation, there is some

    variability in the price data for Monday but more for Friday (SD 0.2308).

    Soybean RBD is the product of completely refined soybean crude degummed oil. This is

    usually accomplished through the trasterification method of production. The relationship

    between soybean RBD and biodiesel is expected because it is the refined soybean crude

    degummed feedstock used in the production of biodiesel when soybean oil is used as the original

    source. A refiner typically purchases crude degummed and finishes the process to final product

    (soybean RBD) or purchases RBD as the finished product to create a blend (U.S. Energy

    Information Administration, 2013)

    Poultry Fat Price

    The price of poultry fat and biodiesel demonstrated a significant, positive correlation for

    86% of biodiesel types. This is anticipated due to poultry fat being a viable substitute to soybean

    RBD. It is the secondary renewable product used in the production of biodiesel. Due to the

    relationship of these two products, when the price of poultry fat increases, the price of biodiesel

  • 27  

    will increase. The range of correlations was 0.411 to 0.7355, with a mean of 0.5173, and a

    standard deviation of 0.2036. The minimum correlation value was found to be statistically

    different from zero at the 0.01 level for Monday data. However, it was only significant at the

    0.05 level for Friday data.

    The correlation range was the lowest of all feedstocks with a medium to low strong

    relationship. The standard deviation of the correlations indicates some variability in the price

    data. Monday has a 99% confidence interval while Friday has a confidence interval of 95%.

    This could be due to the missing price data on Friday.

    Poultry fat is the primary animal related feedstock in the production of biodiesel. It is

    relatively low cost and high yielding when compared to other fatty feedstocks (Babcock,

    Clausen, Popp, & Schulte). In the U.S. alone, 1.3 billion pounds are produced from the

    processing of poultry. Poultry fat is expected to have a relationship with biodiesel because it is

    the leading fatty feedstock in biodiesel production (Babcock, Clausen, Popp, & Schulte)

  • 28  

    Chapter V – Discussion

    Renewable fuel sources are a topic of much discussion. The depletion of the

    world’s supply of fossil fuel is of great concern due to its high demand. The U.S.

    government has passed legislation mandating the increased use of renewable fuel sources

    in the coming years. The mandates have the potential to affect the price of feedstocks

    thus, influencing the price of biodiesel. However, at present, only a few feedstocks

    influence the price of biodiesel.

    Soybeans are the primary feedstock used in the production of U.S. biodiesel (U.S.

    Energy Information Administration, 2013). Through genetic gains and better farming

    practices, the average yield of an acre of soybeans has risen. However, the food verse fuel

    debate is still active. Soybean crude degummed and soybean RBD are two products

    derived from soybeans. Both influence biodiesel price due to their biodiesel production

    relationship. High commodity prices make biodiesel production with soybeans less

    desirable. Low commodity prices should cause an increase in the production of biodiesel

    using soybeans due to the price of biodiesel and soybeans being correlated.

    Poultry fat is not the leading animal feedstock used in biodiesel production.

    However, it does influence the price of biodiesel. Poultry fat is a cheaper alternative to

    soybeans and other animal fats. It could be blended with soybean RBD to create a

    biodiesel blend (pre fossil diesel blend). Due to the correlation of the two, if poultry fat

    price increases so should the price of biodiesel. The production ratio is roughly 1:1 for

    poultry fat. With the low cost of poultry fat it may not influence biodiesel price much.

    Two limiting factors in the adoption of poultry fat could be supply and transportation cost

    and logistics.

  • 29  

    The future price of petroleum crude oil does influence the price of biodiesel

    because of the blending of diesel and biodiesel. Depending on the blend, the price of

    diesel could influence the price of biodiesel. Further analysis should be conducted to

    verify this. Gas influences the price of biodiesel due to the fuel market relationship of the

    two.

    Feedstocks are just one factor influencing the price of biodiesel. Production

    constraints, taxes, transportation costs, and market demand are just a few factors that

    could influence the price of biodiesel. As the demand for renewable fuel sources increase,

    so should the research.

     

     

     

  • 30  

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