The Effect of Oil Price Variability on Biofuel Production


Over the past thirty years, biofuels have been developing as a renewable fuel resource in the United States, as oil prices have also grown more volatile. In this paper, we explore the relationship between oil prices and the opportunity to produce biofuels. More specifically, we evaluate the extent to which variability in oil prices affects the production or output of biofuel. We approach this primary objective by exploring three distinct objectives: to evaluate trends in oil prices between 1985 and 2014, to assess trends in biofuel production between 1985 and 2014, and finally to determine the effect that oil price volatility has on bioethanol production in the Unites States. We eliminate biodiesel from our evaluation of biofuels due to a lack of data, using bioethanol as our measure of the American market for biofuels. We develop trendlines that describe the growth of oil prices and bioethanol production, and determine that, for both variables, growth was linear in the last fifteen years of the twentieth century, but developed at an exponential rate during the first fifteen years of the twenty-first century. For the third objective of measuring the relationship between oil price variability and biofuel production, we create a linear regression function describing the effect of the volatility of oil prices on ethanol production, using the stepwise regression technique. We also account for other influences on ethanol production by including corn and soybean prices, government policy, and a dummy variable that describes whether or not there is a recession. We determine that variability in oil prices has a significant positive impact on bioethanol production.

Ethanol Biorefineries in the United States

The maps below indicate the locations of ethanol biorefineries in the United States in 2002 and 2013. Over time, the concentration of producers remains strongest in the northern central portion of the country, although the number and spread of the plants increases between 2002 and 2013.

Biorefinery Locations, 2002
Biorefinery Locations, 2013




As concerns about the availability of oil for petroleum use have been brought to the forefront of energy discussions in recent times, renewable energy sources have also come to the attention of policy-makers, energy firms, renewable energy activists, and consumers. Today, developed countries are becoming even more dependent on energy, and the rapid growth of developing regions also contributes to the ever-increasing demand for energy, drawing the issues of energy and sustainability to the center of attention not only in the United States but on the world stage. One of the most particularly recognizable sources of renewable and sustainable energy is biofuel.


Oil and biofuels have had a close relationship since biomass was first considered as a potential source of mass energy in the United States. In 1973, Israel was engaged in the Yom Kippur War with surrounding nations in one of the most oil-rich regions on the globe. The Organization of the Petroleum Exporting Countries (OPEC) retaliated against nations providing support to Israel by making drastic cuts in production, which caused global oil prices to quadruple within the next two years. The United States government, feeling the pressure of the boycott, instituted Project Independence to develop methods of energy self-sufficiency, in order to reduce dependence on (and therefore vulnerability to) the whims of foreign oil producers. Policy-makers took interest in the nearly nonexistent bioethanol industry, offering loans to plants producing ethanol from biomass. Ethanol's role as a gasoline additive further reinforced the immutable bond between oil and biofuels.

The late 1970s brought about more political unrest in the Middle East, this time between Iraq and Iran. The conflict led to a 10% decrease in the world's oil supply, and prices skyrocketed, hitting approximately $35/barrel in 1981. When oil prices finally settled back down later in the decade, numerous economic studies were developed to evaluate the efficiency of biofuels in the market. It was determined that ethanol could remain a competitive fuel option as long as the blending fuel tax exemption remained in place. Without this support, biofuels could never compete with the dominating power of big oil in the energy market. Furthermore, ethanol production was limited by the knowledge that its growth would result in definite increases in corn and grain prices. Pre-existing market structures and networks, in addition to cost disadvantages, emphasized the market-entry barriers that biofuels (and renewable energy technologies in general) faced during the time period, and continue to encounter today.

The nineties brought a time of relief for the oil market, as prices remained relatively low and stable for the period. The biofuel market continued to experience slow and steady growth, encouraged by government policies. In the early 2000s, biofuel growth was spurred forward by the introduction of the Renewable Fuels Standard, which was established by the 2005 Energy Policy Act. The EPAct decreed requirements for the use of ethanol in reformulated gasoline, and set targets for biofuel production and consumption. As a result of so many supportive developments in the biofuel industry during this decade, biofuel production increased by over ten times between 2000 and 2010. Oil prices also experienced considerable growth during this decade, particularly between 2004 and 2008, a period of persistent increases not only in oil prices, but in volatility of oil prices as well. Crude oil prices have long influenced biofuel development, as the two products have a clear connection in the energy market.


Research Question and Objectives

We deduce that there is a relationship between oil prices and the opportunity to produce biofuels in the US. The research question we seek to answer is this: To what extent does the volatility in oil prices affect the production or output of biofuels? In the measurement of biofuel diffusion, we focus our attention on bioethanol due to greater availability of detailed and extensive secondary data on bioethanol production than on biodiesel production.

The primary objective of the research is to evaluate the relationship between the variability of oil prices and biofuel production opportunities. The specific objectives are as follows:

  • 1. Analyze the trend in oil prices over the past three decades.
  • 2. Evaluate the trend in biofuel production over the last three decades.
  • 3. Estimate the growth in bioethanol production in the U.S. and determine the effect of volatility of oil prices on bioethanol production.




We used statistical methods and linear regression models to address each of our objectives. We approached Objectives 1 and 2 by first graphing our data onto scatter plots to analyze the overall trends of the variables over time. We dichotomized the data sets into two distinct periods: the last fifteen years of the twentieth century, and the first fifteen years of the twenty-first century. We used statistical methods and trend analysis to determine the differences between the two periods and evaluate the changes that occurred with time.

We addressed Objective 3 using an ordinary least square (OLS) linear regression model that explores the effect of oil price volatility and a number of control variables on bioethanol production. Biofuel production is expressed as a function of oil price variability. To create a more accurate model, we account for changes in input prices, and government policy to control for changes in bioethanol production that resulted from factors external to oil price variability. The control variables we use are corn prices, soybean prices, and blenders' fuel tax credits.


Our main source of data is the U.S. Energy Information Administration (EIA), a collector of independent energy data for the understanding of energy's connection to the economy and the environment. From the EIA, we collect secondary data on biofuel production and crude oil prices. We measure biofuel production in millions of gallons of bioethanol produced monthly by United States Oxygenate Plants. Due to a lack of data on biodiesel production in the United States, we limit our analysis of biofuels to bioethanol, which is the most widely produced biofuel in the United States as well as globally. Pokrivcak and Rajcanoiva estimate that ethanol makes up 85% of biofuel production, with biodiesel consuming the remaining 15% (2011).


From the EIA, we also evaluate the long-term changes in imported crude oil prices (in dollars per gallon) between 1985 and 2014 by dividing the data into two fifteen-year time periods. The first period extends from 1985 to 1999, while the second period begins at the turn of the century and lasts from 2000 to 2014. We evaluate both nominal and real oil prices, and the real oil prices are measured in terms of the May 2015 dollar.


We also reference the University of Illinois's Farm Decision Outreach Central (Farmdoc), a project which aims to create a comprehensive agricultural information system for decision-makers in agriculture. We use U.S. monthly average corn prices and soybean prices (in dollars per bushel) as proxies for the input prices of ethanol, and to account for the market demand for food.


For data on government biofuel policies, we cite the Purdue Extension, an educational network and provider of research-based knowledge related to agriculture, human services, and community development. The blenders' fuel tax credit was first introduced as a gasoline excise tax exemption in 1978, and converted to a blenders' fuel tax credit in 2004. The credit was offered to producers in dollars per gallon of ethanol.


Finally, we refer to the National Bureau of Economic Research, a nonprofit research body that accumulates data on the American macroeconomy and focuses on long-term studies of macro development. We also want to evaluate the months when there were recessions in the United States. On our timeline of interest, there were three periods of recession. The first two recessions lasted nine months, from July of 1990 to March of 1991, and from March to November of 2001. The third and most recent recession persisted for nineteen months, from December of 2007 until June of 2009.

Below, Table 1 describes the summary statistics of the data set.


For all variables, there was a statistically significant difference between the means of the two fifteen-year periods.



Results and Discussion

Objective 1

Objective 1 sought to assess the trend in oil prices over the period of analysis - from 1985 to 2014. Figure 1 below shows nominal imported crude oil prices for the period from 1985 to 2014, with recession months shaded in gray. Overall, this trend is exponential, with an average monthly growth rate of 0.58%.

Figure 1: Nominal Imported Crude Oil Prices


Figure 2 shows the trend for oil prices over Period A, from 1985 to 1999. This period was an era of relatively low volatility, as prices remained stable. Initially, the oil market was still recovering from the political conflict between Iraq and Iran. Up until 1986, OPEC struggled to slow the fall in prices, but prices continued to fall until stabilizing at about $18 per barrel, where they remained for the remainder of the century. The only exception to this stability occurred in late 1990, when the beginning of the Gulf War triggered a brief price surge, but the escalation was short-lived. The trend in this period is slightly negative, with an average growth rate of -0.1%.

Figure 2: Imported Crude Oil Prices for Period A


Figure 3 shows the trend for oil prices for Period B, from 2000 to 2014. In the second period, the oil market experienced radical changes in crude oil prices that were triggered by an OPEC-orchestrated production cut in 1999 with the intention of increasing prices, an upturn that would continue to grow exponentially and reach historic levels. The beginning of the second Gulf War in 2003 added to the momentum of the soaring prices, as did growing Asian demand for oil. Oil prices increased dramatically until peaking at $145/barrel in July 2008, before dropping radically to $35/barrel by the end of the year. After bottoming out, oil prices continued to increase and finally settled at about $100 per barrel, but with a great amount of variability. Overall, in this period, there was a strong trend of exponential growth, with an average growth rate of 0.97% per month.

Figure 3: Imported Crude Oil Prices for Period B


In general, oil prices changed at a much faster rate in the first fifteen years of the twenty-first century than they did in the last fifteen years of the twentieth century.

Objective 2

Objective 2 sought to assess trends in bioethanol production between 1985 and 2014. Figure 4 shows the trend of U.S. Plant Production of Fuel Ethanol for that period, with recession months shaded gray. Production of ethanol in the United States developed slowly at first, but gained momentum in the early 2000s, developing into an exponential growth that resulted in the production of over a billion gallons per month by 2010. Since the turn of the decade, production has leveled off, hovering at about 1200 million gallons produced per month. Overall, the curve has an exponential shape that levels off and creates an S-shape curve. The average growth rate of bioethanol production for the period from 1985 to 2014 was 0.98% per month.

Figure 4: Bioethanol Production


Figure 5 shows the changes in production during Period A, from 1985 to 1999. We describe production expansion in this period with an average monthly growth rate of 0.46%.

Figure 5: Bioethanol Production for Period A


Figure 6 displays the development that occurs in Period B, from 2000 to 2014. In this period, the average growth rate of ethanol production was 1.48% per month.

Figure 6: Bioethanol Production for Period B


Much like oil prices, bioethanol production grew much faster in the first fifteen years of the twenty-first century than in the last fifteen years of the twentieth century.

Objective 3

We start the discussion of this objective with a specification of our model:

Equation 1

Oil price variability was defined in this research as the percentage change in the price of oil in period t. This is equivalent to taking the natural logarithm of prices, as presented in Equation 2:

Equation 2

The model is run using two different measures of prices: (1) nominal prices; and (2) real prices. Real prices were based on prices in May 2015.

The specific model used to conduct this analysis is presented in Equation 3:

Equation 3

The regression model was estimated using a stepwise regression technique, with variables allowed entry into the model as long as the level of their statistical significance is no greater than 5%. This approach is efficient in a number of situations:

  • 1. When there is little theory to guide the selection of variables to include in the model;
  • 2. When there is a need to explore which exogenous variables provide a good fit to the model; and
  • 3. When the model's prediction performance needs to be improved by reducing the variance resulting from including unnecessary terms.

The regression results are presented in Table 2. The overall model was statistically significant at the 1 percent level. The coefficient of variation or R-square is 93.01 percent, implying that the variability of the exogenous variables in the model explain about 93 percent of the variability in the endogenous variable. Table 2 shows that all the variables were statistically significant.

Table 2: Regression Results

*** significance at the 1% level


The results of the stepwise regression show that a 1 percent increase in the nominal crude oil price can be expected to lead to a 186.05 million gallon increase in ethanol production. As oil prices and oil price variability increase, ethanol production also increases. This result indicates that the substitution relationship between oil and ethanol is stronger than the complementary relationship created by ethanol-gasoline blending.

Secondly, the model implies that a one-dollar increase in the U.S. monthly average soybean price leads to a 62.13 million gallon increase in ethanol production.

A one-dollar increase in the per-gallon value of the blenders' fuel tax credit is associated with a 3113.30 million gallon decrease in ethanol production. Although counterintuitive, the indirect relationship between tax credits and ethanol production can be explained by the fact that the value of the tax credit has strictly decreased since the 1990s, while ethanol production has been mainly increasing since that time. At the beginning of our time period of interest and in the years prior, we do see concurrent increases in the value of the tax exemption and ethanol production, indicating that the effects of tax exemption increases may only be consequential for products early in development, or when production is low.

The coefficient of the Recession Month dummy variable expresses that a month of recession is expected to result in a decrease in ethanol production of 58.10 million gallons. Following economic logic, production is limited during times of nationwide economic crisis.

Finally, the model suggests that a one-dollar increase in the U.S. monthly average price of corn leads to a 35.79 million gallon decrease in ethanol production. As corn is a major input for ethanol production, increases in input prices inhibit production.


In this study, we sought to analyze the relationship between oil price variability and biofuel production opportunities in the United States. Our specific objectives were to evaluate the trends in oil prices and biofuel production between 1985 and 2014, and to use those observations to describe the relationship between the volatility of oil prices and the production of bioethanol. We separated our data for oil prices and bioethanol production into two fifteen-year periods, the last fifteen years of the twentieth century and the first fifteen years of the twentieth century, and found significant increases in both oil prices and ethanol production over the two periods. We also describe the growth trends in the two periods, finding that growth between 1985 and 1999 was linear for both variables, but exponential for the period from 2000 to 2014.

To address our final objective of defining the relationship between oil price variability and bioethanol production, we used a stepwise regression technique to develop a linear regression. We also accounted for further influences on bioethanol production by introducing other variables to the model, including corn and soybean prices, the value of the blenders' fuel tax credit, and a dummy variable that describes whether or not there is a recession in the United States. We found that there is a significant positive correlation between variability of oil prices and ethanol production. As oil prices become more volatile, production of bioethanol increases.

This model is useful because it not only explains past changes in biofuel development, but can also be used for estimating the future of biofuels in the United States.



About Maggie

I am currently a junior at the University at Buffalo. I am pursuing a B.S. in Applied Math and a B.A. in Economics. I will be graduating in the spring of 2017.


During the school year, I work as a mentor at a Buffalo high school, helping with a long-term research project on graphene and filtration. At home, I enjoy working as a teller at a local credit union.

I was born and raised in Syracuse, New York.


My hobbies include camping, knitting, and spending time with my family.





  • Advisor: Dr. Vincent Amanor-Boadu, Department of Agricultural Economics, Kansas State University
  • Mentor: Frank Nti, Department of Agricultural Economics, Kansas State University

This material is based upon work supported by National Science Foundation Grant: "REU Site: Summer Academy in Sustainable Bioenergy; NSF Award No.: SMA-1062895," awarded to Kansas State University.



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