UNIVERSITY OF MINNESOTA
This is to certify that I have examined this copy of a master’s thesis by
Mahri Monson
and have found that it is complete and satisfactory in all respects, and that any and all revisions
required by the final examining committee have been made.
Jennifer Kuzma
Name of Faculty Adviser
Signature of Faculty Adviser
Date
Humphrey Institute of Public Affairs
Monson 2
Minnesota Biofuels Policy:
How much can biofuels contribute to carbon dioxide reductions from passenger vehicles?
A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE
UNIVERSITY OF MINNESOTA
BY
Mahri Monson
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF MASTER OF SCIENCE
Jennifer Kuzma, Adviser
August 2008
Monson 3
ABSTRACT
In order to avoid catastrophic and irreversible impacts associated with global climate change,
major and sustainable reductions in greenhouse gas (GHG) emissions must be made in all sectors
relatively quickly. Biofuels have been touted as one significant solution to GHG emissions from
the transportation sector, and policies that encourage the production and use of biofuels have
been pursued at multiple levels of government. The State of Minnesota has had a renewable fuels
standard in the form of a 10% ethanol mandate in place since 1997, and recently applied to the
Environmental Protection Agency for a waiver necessary to increase the standard to 20%
ethanol. MN is additionally considering the pursuit of an alternative biofuels policy, the low
carbon fuel standard. It is important to analyze the potential long term impacts of these two
biofuels policies in order to guide decisions that can maximize GHG reductions from the
transportation sector. This paper utilizes a unique model based on the system dynamics
framework to assess the impact of biofuels consumption in Minnesota on CO2 emissions, a
greenhouse gas that contributes significantly to global warming.
Monson 4
Table of Contents
ABSTRACT vesmsnceuusnomnerm nN ERENT 3
LIST OF FIGURES ...
LIST OF TABLES 0... 0.ccceccscecesscseseeseseessseeescseessscescassessssesessssesucacsecsssesusacseeasseseeasscsacseeeneseeeesesenes® 5
EIST OE ' GRAPHS canna ee ORT 6
CHAPTER 1: INTRODUCTION AND GHG REDUCTION POLICY FRAMEWORK............... ub
CHAPTER 2: TRADITIONAL BIOFUELS POLICY AND TRADITIONAL BIOFUELS......... 12
CHAPTER 3: IMPACTS OF TRADITIONAL BIOFUELS POLICY AND TRADITIONAL
BIQFUELS iessscssssovvssssssessocorensoncessvessnvvavasxsvouwsncesesenessoxeneocunvvassnveuavaccsnsvsncesasonvensavansxitaunsecenesevcsssrss 15
CHAPTER 4: AN ALTERNATIVE TO TRADITIONAL BIOFUELS POLICY: THE LOW
CARBON: FUEL: STANDARD is sscossssevissivsiasscconsssonsnssasvanssvansavevenseccresencovexencasescensevasonssnecsesowesseues 23
CHAPTER 5: SYSTEM DYNAMICS MODELING FRAMEWORK: AN ALTERNATIVE
METHODOLOGY FOR ANALYZING BIOFUELS POLICIES ........0.c:cccscesessssesessesseseseeneseseeee SL
CHAPTER 6: DEVELOPMENT OF A UNIQUE MODEL FOR MN BIOFUELS POLICY
ANALY SIS wressesiserceannarncsrnarennnaanmennranrnayrnsminann eer euanama tan ancaunTeCR Ire 35
CHAPTER 7: MODEL SCENARIOS: BOUNDARIES, ASSUMPTIONS, LIMITATIONS
AND VALIDATION texccazsscserparreenvnacvmennranacnerncmianenenrcneannemanan anne 46
CHAPTER'S: SCENARIO. RESULTS. sssivscsscccnsssosnssaranssvavsavevenseccresennovexenravescewsevavonssnecsesonessouesi 53
CHAPTER 9: RECOMMENDATIONS, FURTHER RESEARCH AND CONCLUSIONS....... 64
REFERENCES ...
Monson 5
List of Figures
Figure 1 Energy Sectors Overlap
Figure 2 Categories of GHG Reduction Policies. ...........ccccsssesesseessessseeesesnessseeesesnessseeneseseee 10
Figure 3. Tons of corn used for ethanol compared to tons of corn exported.
Figure 4 Basic Factors of Corn Grain Ethanol and Cellulosic Ethanol Production .
Figure 5 Comparison of Biomass Energy Options. ...........c.ccscccesseessesessssessssesessssesesnesesreeseeneee 21
Figure.6: itlear Approach swam mcnponnenuamancnn er 31
Figure] Cyclical Approaches saveemssccnncacanvcnvranmannneinnemnnannanannacncecnene 32
Figure8 SDi Modeling Process is ccisissssninnnnaninannannnmnnnnacnaccwenrmvnnarnaswie 33
Higure:9' Model Stricture.) sscssccossssvssvsiovscccnsssoesnssurvanssvansavsvenseccresencovsxenrasescewssvssonssnessesoxesseues 36
Figure 10 Model Structure 2 ..
Figure 11 Model Structure 3
Figure 12 Model Structure 4 ..
Figure 13 Key Emissions Feedback. .............c.ccccsssecsssesessssessssesecsssesssseseesssesssacsesaesesneasseeseseseenssees 40
Figure 14 Key Low Carbon Fuel Standard Feedback.......cccccccecceeeseeeseeeseeeeeeeeeeeeeeeeeeeeeeeeeeee! Al
Figure 15: Quantified Model wes mecanponnenmunmncnn mr 42
Figure 16 Model Sticture4 wsavemsscnnmeananvcnvrammannnecnmnnannanannannceacners 45
List of Tables
Table 1 Abbreviations ...........c.ccccecececsesesecseseessseseesssessssesesssscsscassssssesecsssecassesesasseeaescseeessseneseaeesey 6
Table 2. GHG Reduction Policies by Category and Energy Sector. ..........cccseesseeeseeeeeeseeee 11
Table3 Listof Key Variables sewers waunneneneere aun I 43
Table4: ExXogerious Variables i vscsscinncasanvcnverammannnercenmmnnannanannacnceacner 44
TableS Endogenous Variables swscisisscwninnnnaninannnnnnnnnanvannnnrmcnnrnawel 45
Table'6: Major Model Boundaries ...csssesssssssscscovessosoveusssvsnsvsusvsasssnescnrevnasoveusesnsnavsusvtensenssonsvonaseveus 51
Table 7 Model Validation ..............00...
Table 8 Scenarios relative to Next Generation Energy Act CO Reduction Goal:
Table 9 AFCI under different scenarios relative to LCFS goals. ..........cccceeee
Table 10 Yearly Emissions and VMT Growth Rate. .0.......cccececeseeeeeeeeeeeeneeeneeeneeeteeeneeeneeeseed 61
Table 11, Yearly Emissions atid CAPE; siissssicssnanunmininiucacacauimnininuucauacen 63
Table 12 CO, emissions reductions as a result of key policy changes .........:...cccceceseeesseeeeseseeee 66
List of Gr:
Graph 1
Graph 2
Graph 3
Graph 4
Graph 5
Graph 6
Monson 6
aphs
Total Yearly Emissions .
Average Fuel Economy .
Vehicle: Miles: Traveled ..cssssvssvsscosscccnsssonssssuranenvansavewenssccrevenconexencavescevsevavsussnecsesoxesseuesi 56
Average Fuel Carbon Intensity .......ccecceceseeeseseseseeeseseseeesesesesescsesesestsesseeeseesseseeseaven 58
Yearly Emissions and VMT o00...c.c.cccsseesescseseeeeeeesescseseseseaesesesesesesesesesesesesesesseeaeeeeeees 62
Yearly Emissions and CAFE . .....c.ccccccseeseseseseseseesceeseeseeaeeseeseseseeseeeseeeeeseseaeees 64
Table 1. Abbreviations
AFCI Average fuel carbon intensity
B Billion
Dmnl Dimensionless/no units
Gal Gallon
gCOve Grams of CO) equivalent
LCFS Low carbon fuel standard
LDV Light duty vehicle fleet
MJ Mega joule
MMtCOze Million metric tons of carbon dioxide
equivalent
RFS Renewable fuel standard
VMT Vehicle miles traveled
Monson 7
CHAPTER 1: INTRODUCTION AND GHG REDUCTION POLICY FRAMEWORK
Background
The scientific community agrees global climate change caused by anthropogenic sources of
greenhouse gas emissions (GHG) is occurring, and the projected rise in average global
temperatures as a result of climate change could lead to serious consequences for human health,
economies and the environment. The major global issues that could cause human, economic and
environmental damage include sea level rise, increased prevalence of extreme weather like
hurricanes and tsunamis, changing regional climates and agricultural growth patterns, and loss of
biodiversity. Global warming has recently made it onto the popular radar and the issue currently
receives wide political focus as a result. Policy makers at all levels of government are focusing
on ways to decrease emissions of CO), one of the main contributors to the greenhouse effect.
The transportation sector is a significant source of CO? emissions. In the United States, it
accounts for 28% of GHG emissions, second in proportion only to electricity production, which
makes up 33% (EPA 2006). Because of transportation’s considerable contribution, the federal
government and states have explored and continue to consider ways to lower CO2 emissions
from this sector. Biofuels in particular have received much attention as a way to address climate
change, while also reducing dependence on foreign oil and providing economic opportunities for
American farmers and rural communities. The basic idea is that fuels made from biomass will
produce less GHG emissions than petroleum based fuels.
There are a variety of policy options for encouraging the development and use of biofuels in the
transportation sector. Traditionally, R&D funding, tax breaks, grants, loans and regulatory
mandates have all been used to support the biofuels industry. Two main types of biofuels policies
have received much focus recently for their potential to reduce GHG emissions from the
transportation sector: the renewable fuel standard (RFS) and the low carbon fuels standard
(LCFS). It is important to analyze and compare these two policies with regard to their potential
contribution to the overarching goal of reducing CO? emissions.
The goal of this analysis is to provide useful insight for the debate on biofuels policies in
Minnesota. This paper seeks to add value to this debate in three main ways:
Monson &
1. By detailing the background and consequences of the most widely implemented biofuels
policy to date, the renewable fuel standard, and analyzing the logistics and potential
impacts of an alternative policy, the low carbon fuel standard
2. Proposing a methodology of analysis for biofuels policies utilizing a system dynamics
framework
3. Providing some basic results with regard to future CO2 emissions from different biofuels
scenarios run in a prototype of a model based on the system dynamics framework
The following section discusses a policy framework that provides context for the analysis of
biofuels policies in this paper within the larger issue of GHG reduction policy and climate
change mitigation.
Greenhouse Gas Reduction Policy Framework
Governments, nonprofit organizations, businesses, communities and individuals around the
world are now calling for significant greenhouse gas emissions reductions in order to mitigate
global climate change. Efforts to reduce emissions are mainly targeted at three basic energy
sectors:
1. Electricity
2. Transportation
3. Heating and cooling
Monson 9
Figure 1. Energy Sectors Overlap
There is some overlap within these sectors, as represented in Figure |, but they are generally
treated as distinct categories. Some examples of overlap include plug-in hybrid electric vehicles
which use both electricity and transportation fuel and power plants that produce electricity but
utilize the heated water for heating.
There are also three general categories of policies aimed at dealing with GHG emissions (Taff
2008):
1. Use less
2. Use cleaner
3. Pollute less
All three categories implicitly seek to reduce emissions, but the first two categories utilize an
indirect approach while the third category directly addresses the issue. Figure 2 represents the
relationship of these three categories to each other.
Monson 10
Figure 2. Categories of GHG Reduction Policies
Use . f Use
less cleaner
/ Pollute
\ less
7
ae
Within each of these categories, there are specific types of policies which help illustrate the
distinction in categories:
1. Use less: efficiency and conservation policies
2. Use cleaner: renewable portfolio standards, renewable fuel standards, and low carbon fuel
standards
3. Pollute les
emissions limits and cap and trade programs
Table 2 provides some examples of policies in each of the three energy sectors according to
which general category they fall in. Note the applicability of certain types of policies, like
conservation and efficiency, in all three energy sectors. In the final category of policy
approaches, pollute less, the policy options listed here could be applied in all three energy
sectors, either specific to one sector or as a national policy which encompasses all three sectors.
Table 2. GHG Reduction Policies by Category and Energy Sector
Monson 11
Electricity Transportation Heating and
Cooling
Use Less Conservation Corporate average fuel economy | Conservation
standards
Efficient appliances Efficient appliances
Reduced vehicle miles traveled
Building energy
Public transit efficiency audits
Use Renewable portfolio Renewable fuel standard Incentives for
Cleaner standards renewable options
Low carbon fuel standard
like geothermal
GHG tailpipe emissions heating and cooling
standards (CA Clean Car/ Pavley
Standards)
Pollute Less Emissions limits
Cap and trade
Carbon tax
Within the transportation sector, policies aimed at increased fuel economy and lowering vehicle
miles traveled fall into the “use less” category. Biofuels policies fall into the “use cleaner”
category, whether or not the evidence is clear enough to ensure that biofuels actually are cleaner,
i.e. emit less CO2 on a per energy unit basis. Both the renewable fuels standard and the low
carbon fuel standard belong to this category of using cleaner energy in the transportation sector.
Neither of these policies, nor any policy within the “use cleaner” category, will necessarily result
in lower absolute levels of emissions, because they change the average carbon intensity of the
fuel mix but do not explicitly address the total amount of fuel used and therefore the total
Monson 12
emissions levels. For this reason, it seems reasonable and logical to pursue policies in all 3
categories within the transportation sector. Lee Freidman of UC Berkley pointed out during a
presentation on cap and trade for the transportation sector an analogy between policies in the
electricity sector and policies in the transportation sector. Dr. Friedman said in the electricity
sector, government policies at a variety of levels have been pursued in energy conservation and
efficiency, through efficient appliances and power plant conservation mandates, which fall into
the “use less” category. Many states have also enacted renewable portfolio standards which
mandate greater use of renewable energy, a “use cleaner” type of policy. The numerous regional
cap and trade programs that have sprung up in recent years, as well as federal cap and trade
program proposals at the national legislature belong to the “pollute less” category of policy
(Friedman 2008).
Freidman pointed out that policy makers and regulated industries are generally comfortable with
the prospect that renewable portfolio standards and cap and trade policies specifically are useful
and appropriate for reducing GHG emissions from the transportation sector. The analogy to the
transportation sector is the renewable fuel standard or the low carbon fuel standard, and cap and
trade for the transportation sector (Friedman 2008). I would take Dr. Friedman’s argument one
step further to say that a variety of policies from all three categories are generally accepted as
productive pursuits in the electricity sector; it would be useful to open up a similar discussion
within the transportation sector with regard to climate change mitigation policies.
With the background context of the GHG reduction policy framework addressed above, it is
useful to examine traditional biofuels policies and their impacts on the goals of GHG reductions
and other environmental and social issues.
CHAPTER 2: TRADITIONAL BIOFUELS POLICY AND TRADITIONAL BIOFUELS
The most widely deployed biofuels policy to date is the renewable fuel standard (RFS), and the
most widely produced biofuel is corn grain ethanol. It is important to preface the analysis in this
paper with a look at this traditional biofuels policy and the traditional biofuel that has filled it.
This chapter will address the RFS and the rise of corn grain ethanol as the most prominent
biofuel in order to help guide analysis of this and other policies and biofuels moving forward.
Monson 13
Renewable Fuel Standard
A renewable fuel standard requires that transportation fuel sold in a certain area contain a
specified minimum amount of renewable fuel. This policy falls under the “use cleaner” category
of GHG reduction policies. Generally, any fuels made from plant or animal products or wastes,
as opposed to fossil fuels, are considered renewable fuels under an RFS. The United States has a
national RFS, updated under the Energy Policy Act of 2005, which now requires 7.5 billion
gallons of renewable fuels be blended into the total supply of gasoline sold in the U.S. by 2012.
The U.S. is already on its way to achieving this regulation; in 2006 5.4 billion gallons of
renewable fuels were used by refiners, blenders and importers, an amount almost 25% above the
required volume for that year (EPA 2007). In addition to the national level, eight states have an
RFS in place (Renewable Fuels Association 2008).
In Minnesota, the RFS takes the form of an ethanol requirement. According to the MN RFS
statute, as of 1997 all gasoline sold in MN must have 10% denatured ethanol by volume. The
State Legislature recently passed a more stringent RFS which will require 20% ethanol starting
in August of 2013 (MN Statute 239.791, 2007), pending EPA approval. The vast majority of the
MN RFS, as well as other states’ RFS, are currently filled by corn grain ethanol.
Corn Grain Ethanol
Ethanol, or EtOH, is an ethyl alcohol made from a chemical reaction where sugar is fermented
into a volatile liquid. Ethanol can be combusted as a fuel in internal combustion engines, forming
carbon dioxide and water, similar to the combustion of gasoline. The sugar needed to produce
alcohol can be utilized from starchy materials, like corn grain, which requires a process of
hydrolysis of starch into glucose (Ethanol Information 2008).
The corn ethanol industry is established, financially successful, and growing in the United States.
Over $10 billion has been spent on the industry since the year 2000, resulting in approximately
6.5 billion gallons of ethanol capacity (Koplow 2006). In 2004 3.4 billion gallons of ethanol
were produced, an amount that constituted approximately 2% of the volume of gasoline sold that
year (Farrell et al. 2006). Midwest states, including Minnesota, have gained much from this
industry as big corn producers. While the ethanol industry has historically maintained a high
Monson 14
level of political support over the years, there is now popular controversy over the previously
touted advantages of corn ethanol and some potentially significant disadvantages. Chapter 3
addresses the issues associated with this controversy in more detail.
Federal Ethanol Policy
The ethanol industry has been in the making for almost 30 years. Concerns similar to those of
today, i.e. rising gasoline prices, environmental degradation and dependence on foreign sources
of oil, resulted in a push for development of fossil fuel alternatives 28 years ago. This push came
mostly in the form of government policy. A report put out by the U.S. Department of Agriculture
in 1988 summarizes how the corn ethanol industry came into being: “The fuel-ethanol industry
was created by a mix of Federal and State subsidies, loan programs, and incentives. It continues
to depend on Federal and State subsidies” (Koplow 2006). The first federal policy to favor
ethanol was the 1978 Energy Tax Act, which exempted ethanol from the fuel excise tax of $0.04
per gallon at that time. This act enabled the development of the first commercial ethanol
production of 20 million gallons that year. Since that time, the corn ethanol industry has enjoyed
the support and encouragement of a large array of policies including R&D funding, tax breaks,
grants, loans and regulatory mandates. By 1985, 29 states had implemented some type of ethanol
incentive. The Volumetric Ethanol Excise Tax Credit (VEETC), enacted by the JOBS Act of
2004, is the contemporary version of the fuel excise tax that benefited ethanol for 26 years.
VEETC provides a tax refund of $0.51 per gallon of ethanol blended with gasoline, and
constitutes the largest single ethanol subsidy (Koplow 2006).
Minnesota Ethanol Policy
The state of Minnesota has contributed heavily to the development of corn ethanol, spending
over $300 million since 1982 on subsidies for plant operators (The Great Corn Rush 2006). In
1980 MN implemented an ethanol blenders’ credit for $0.40 per gallon, a credit which was in
place for 16 years until it was phased out in 1997 (Koplow 2006). According to the Minnesota
renewable fuel standard already mentioned, all gasoline in the state of Minnesota today contains
10% ethanol, and even with this high portion, MN still exports over half of the ethanol it
produces to other states (The Great Corn Rush 2006). Minnesota has also established a
Monson 15
renewable fuels mandate that in 2013 will require 20% ethanol content in gasoline sold in the
state (Koplow 2006), if MN can receive EPA approval for this level of blending. There are
currently 17 ethanol plants operating in MN at a total capacity of 680 million gallons per year,
spread throughout the southern half of the state. There are also 4 ethanol plants under
construction representing a potential addition of 400 million gallons (MDA 2008).
Federal and some state governments have historically placed high value on the existence and
success of the corn ethanol industry, as is exemplified by their twenty-eight-plus years of
financial support and various mandates. But organizations and individuals are questioning the
real benefits of the proliferation of this industry that compel us as a society to continue heavily
supporting it. It is useful and appropriate to quantify and qualify these benefits as thoroughly as
possible, in order to evaluate corn ethanol’s contribution to the goals of rural economic
development, energy independence, environmental improvements and global climate change
mitigation, and decide accordingly what the next logical steps are regarding renewable fuels. The
following chapter will address some of these issues in order to guide analysis of the continuation
of current trends and of an alternative biofuels policy.
CHAPTER 3: IMPACTS OF TRADITIONAL BIOFUELS POLICY AND
TRADITIONAL BIOFUELS
The prominence of the RFS as the leading biofuels policy and corn grain ethanol as the leading
biofuel has had some significant economic and environmental impacts, and the continued growth
of both policy and fuel could have even more serious consequences moving forward. This
chapter addresses some of the impacts of traditional biofuels policy and the most widely
produced biofuel to date. Chapter 3 also examines future trends in ethanol, specifically a switch
to cellulosic ethanol. Examination of these consequences and future possibilities provides a
useful starting point for this paper’s analysis of possible future scenarios under the RFS with
corn grain ethanol and cellulosic ethanol.
Energetic Outputs and Environmental Consequences of Corn Ethanol
While rural economic development is an important social and political goal, it is essential to
include in an analysis of corn grain ethanol its contributions with regard to energetic output and
Monson 16
environmental impacts. Hill, Nelson, Tilman, Polasky and Tiffany from the University of
Minnesota conducted a life-cycle accounting of ethanol to determine its net energy output and
overall environmental benefits (2006). To achieve such a complete account, their study included
all the energy and environmental impacts of ethanol production throughout the process,
including the farming of corn, transportation of the feedstock to a processing plant, and
processing the corn into ethanol.
The Hill et al. study finds that corn grain ethanol produces 25% more energy than the energy put
into its production, compared, for example, to biodiesel which has a net yield of 93% (2006).
One essential service that should be provided by alternative energy sources is reduction of
greenhouse gas (GHG) emissions, which fossil fuels emit heavily, and which cause global
warming. The Hill et al. study reveals that corn grain ethanol production and use reduces GHG
emissions by only 12% compared to the petroleum gasoline it replaces (2006). There are a
number of reasons for the relatively low net energy yield and GHG reduction of corn ethanol.
Corn is an intensive agricultural crop, and therefore its cultivation has significant environmental
impacts. Its production requires heavy inputs of nitrogen and phosphorus in fertilizers, the use of
pesticides to keep pests at bay, and the farm equipment used runs on petroleum-based fuel.
Another factor limiting the overall environmental benefits of corn grain ethanol is that many
ethanol plants are powered by conventional, fossil-fuel electricity, the type of ethanol plant that
will be modeled in this analysis. The potential impacts of an alternative to grain ethanol,
cellulosic ethanol, are addressed later in this chapter.
Potential Supply of Corn Ethanol
It is also important to consider the possible scale of corn ethanol production. Corn ethanol’s
potential for displacement of petroleum-based fuel is limited. In 2005, corn grain ethanol
production was equal to 1.72% of U.S. gasoline usage, and this amount was made by utilizing
14.3% of the U.S. corn harvest (Hill et al. 2006). Hill et al. addressed the future potential supply
of corn ethanol and found that corn ethanol could meet at the most 12% of U.S. gasoline
demand, by dedicating all U.S. corn crops to ethanol production. In addition, the 12%
displacement of U.S. gasoline demand would represent only a 2.4% net energy gain because of
the fossil energy currently used to produce corn grain ethanol (Hill et al. 2006). It is unlikely that
Monson 17
we would ever achieve this percentage of petroleum fuel displacement, since corn is an
important food crop. Biofuels in general and corn ethanol in particular have been part of a
contentious international debate referred to as the “food versus fuel” controversy. There are two
main issues at play in this controversy. First, the use of food crops for fuel takes away from the
actual food supply. Second, the demand for crops for fuel typically increases the price of that
crop, which in turn could increase food prices. Figure 3 shows the increase in the amount of the
U.S. corn crop being dedicated to ethanol production since 1980 compared to the total amount of
corn exported. In 2006, the amount of corn used for ethanol is the same as the amount of corn
exported (Earth Policy Institute 2006).
Figure 3. Tons of corn used for ethanol compared to tons of corn exported
(Earth Policy Institute 2006).
80
70 :
. /\ |
im Com Exports ne
© an ral
fat
5 0
= V V
30
20
Corn Used for Fuel Ethanol
10
in} T T T T T
1980 1985 1990 1995 2000 2005 2010
Source: USDA
Monson 18
The Future of Corn Ethanol
Ethanol production is expected to increase from current levels by 50% by 2008 (Koplow 2006).
Corn ethanol is currently the most dominant alternative fuel in the United States; 99% of biofuels
produced in the U.S. come from corn ethanol (Farrell et al. 2006). In light of the recent studies
by Hill et al. and others that illuminate the relatively limited energetic productivity,
environmental benefits and potential supply of corn grain ethanol, it is now appropriate to focus
on developing alternative renewable fuels with greater energetic potential and better
environmental impacts.
Cellulosic Ethanol
Cellulosic ethanol, which can be produced from a variety of feedstocks including plants, trees,
agricultural residues, and municipal waste, may be able to reduce CO2 emissions. Farrell et al.
modeled a potential reduction in GHG emissions associated with cellulosic ethanol of 88%
compared to gasoline (Farrell et al., 2006). Technologies for cellulosic ethanol production are
currently being utilized in pilot and demonstration projects in the public and private sectors in
the U.S. and internationally.
One potential biofuel alternative is cellulosic ethanol. While corn grain ethanol is made by
processing only the starch found in corn kernels, cellulosic ethanol is made by processing
cellulose which means it can be produced using virtually any organic matter from trees and
grasses to agricultural wastes, including the stocks and husks of corn and wheat straw, and even
municipal garbage, which include a lot of organic matter. Figure 4 below outlines the processing
similarities and differences between corn ethanol and cellulosic ethanol, using wheat straw as an
example. A number of companies are pursuing wide scale production of cellulosic ethanol,
although there remain some challenges including high costs of processing cellulose and the risk
to investors of an unproven market.
Monson 19
Figure 4. Basic Factors of Corn Grain Ethanol and Cellulosic Ethanol Production
(DuPont 2006).
Mixed Grasses for Cellulosic Ethanol
Many of the feedstocks that are being pursued for cellulosic ethanol so far are grown as
monoculture crops such as switchgrass, poplar and willow (Mapemba et al. 2006). It is important
to consider the energetic, environmental and economic attributes, and potential supply of various
feedstocks to decide which ones should be prioritized and pursued for use in the production of
cellulosic ethanol. One feedstock with good potential is native prairie grass mixtures. Native
grasslands are claimed to provide marked advantages over monocultures in the following ways:
mixed communities are less subject to being severely damaged by pests or diseases, which tend
to attack one particular species. Diverse mixtures may provide other environmental benefits such
as superior wildlife habitat. One of the most important advantages of diverse mixtures of prairie
grasses over grass monocultures as feedstocks for biofuels is their tendency to produce higher
amounts of total biomass, and therefore more energy (Tilman, Hill and Lehman 2006).
Monson 20
A recent paper from University of Minnesota researchers gives a quantitative analysis of the
potential advantages of biofuels produced from low-input high-diversity (LIHD) grassland
biomass over corn grain ethanol and biofuels produced from monocultures. The paper is based
on experimental plots that were planted in LIDH and monocultures of perennial grassland
species in 1994. The plots were planted on agriculturally marginal land without the use of
fertilizer. Compared to monocultures of various grasses, Tilman et al. found that LIHD grasses
yield 238% more standing biomass, which can be converted into energy. Because LIHD biomass
requires no fertilizer and minimal pesticides, its production is significantly more environmentally
benign than the growth of corn, which requires heavy fertilizer and pesticide application (see
Figure 5). Native prairie grassland mixtures also sequester a significant amount of CO», an
important service in a world faced with global warming as a result of too much CO; in the
atmosphere. Tilman et al. found that LIHD sequestered 160% more CO; than grass
monocultures. LIHD grassland biomass can actually sequester more CO than is emitted from
fossil fuels used in their growth, transportation to plants and production into biofuel, making
LIHD biofuel net carbon negative. According to models of converting LIHD into biofuels, this
implies substantial greenhouse gas (GHG) reductions over corn ethanol (see Figure 5), and an
opportunity to provide energy with no net CO) emissions. (Tilman et al. 2006). Given the
multiple benefits provided by mixed communities over monocultures, policy aimed at feedstock
development for cellulosic ethanol should favor diverse prairie grasses.
Monson 21
Figure 5. Comparison of Biomass Energy Options
A modeled comparison of corn grain ethanol, soybean biodiesel, and three types of energy
production from LIHD biomass in (A) greenhouse gas (GHG) reduction compared to
conventional sources, (B) fertilizer inputs and (C) pesticide inputs (Tilman et al. 2006).
LIHD prairie bioenergy
Current biofuels on degraded soil
A r Vt 1
—~ 12,500 -—
e%
88 10,000 |-
38 7,500 -
6 gs 5,000 }-
Se 2,500 }-
= 0 a —
B
200 ;—
Qn 150 F- Phosphorus
8's
25 100}- i
= g LI Nitrogen
a 50 +
0 —
c
Pesticides
(kg ha!)
‘|
i 1—
0 i — —. —ae
Corn grain Soybean Biomass Biomass Biomass
ethanol biodiesel electricity ethanol synfuel
Potential Supply of Biomass
The growth and harvest of mixed prairie grasses in their native habitats throughout the Corn Belt
could provide a significant source for cellulosic ethanol.
Monson 22
One potential supply for biomass production for cellulosic ethanol is Conservation Reserve
Program (CRP) land. The CRP is a conservation program of the U.S. Department of Agriculture,
which pays rent to farmers and landowners to retire agriculturally marginal and environmentally
sensitive land from production. There is wide consensus that the CRP provides substantial
environmental benefits, including reduction in soil erosion, reduction in pollution associated with
lower fertilizer and pesticide use, and provision of wildlife habitat (Lubowski et al., Tiffany et
al.). Nationally, CRP enrolls a land area almost the size of lowa (Lubowski et al. 2006). Much of
this land is concentrated in the Great Plains region. There are over 5.3 million acres of land
actively enrolled in the Conservation Reserve Program in Minnesota, North Dakota and South
Dakota as of April 2006. Minnesota alone has almost 1.8 million CRP acres (Tiffany et al. 2006),
which constitutes approximately 34% of the CRP land in the three-state area mentioned above
and 5% of the total U.S. CRP land of 34 million acres (Farm Service Agency 2008). CRP land in
the Midwest may be ideal for mixed-grasses biomass production because it contains
agriculturally marginal land and prairie grasses are native species there. Because growth of
native grasses on CRP land would not require foregoing more valuable crops, since the land is
already in retirement, it would provide a relatively cheap source of biomass for cellulosic
ethanol. Tiffany et al. estimate a cost for biomass of $20 to $30 per ton on CRP land, which
includes a CRP rental payment of $20 to $40 per acre, and approximately $50 per ton on non-
CRP land. Ideal locations for cellulosic ethanol processing plants would be in close proximity to
the feedstocks, like the prairie grasslands of the Corn Belt, in order to minimize the costs of
transporting biomass to production plants (Tiffany et al. 2006). Biomass production on CRP land
poses a cheap and environmentally friendly feedstock for cellulosic ethanol. Producing native
prairie grasslands on non-CRP land should also be explored. This is not currently being done
because the rules of CRP at present prohibit harvest, but revision of the rules for multiple-use
including biomass production is being explored (Farm Service Agency 2008).
While cellulosic ethanol produced from various low-input feedstocks poses potential
improvements over corn grain ethanol, the traditional renewable fuel standard does not
necessarily incentivize the development of cellulosic ethanol. The general characteristics of the
RFS limit its impact as a tool for climate change mitigation. The RFS does not explicitly address
GHG emissions reductions, and therefore does not discriminate among different types of biofuels
Monson 23
based on this or any other environmental metric. Different types of biofuels can vary widely in
their emissions characteristics based on feedstock type and processing technologies; the ability of
a particular biofuel to displace GHG emissions relative to petroleum-based fuels depends on the
carbon intensity associated with its production and use across its full life-cycle (Farrell et al.
2007). Given this limitation, it is important to explore alternative biofuels policy options.
CHAPTER 4: ALTERNATIVE TO TRADITIONAL BIOFUELS POLICY: THE LOW
CARBON FUEL STANDARD
The low carbon fuel standard (LCFS) is an alternative biofuels policy which has already been
passed in California, and is being considered by other states, including Minnesota. The LCFS
differs from the RFS in a number of key ways, posing the potential for much different impacts.
This chapter describes the LCFS and lists potential advantages relative to the RFS, as well as
some disadvantages and limitations. An understanding of the LCFS relative to the RFS is
important to the model developed for this analysis, which addresses the impacts of various RFS
scenarios on hypothetical LCFS goals for Minnesota.
The LCFS is a “use cleaner” policy aimed at reducing GHG emissions by regulating an
industry’s emissions on a per-unit of output basis; this type of policy could be used in a variety
of sectors including electricity, industry, and transportation fuels. The LCFS has received focus
and support recently in a range of regions and across different levels of government. Outside the
U.S., a number of governments are considering implementation or have already instituted some
type of LCFS including the European Union, the United Kingdom and British Columbia. U.S.
Senators John McCain and Barack Obama promote enactment of a federal LCFS and several
U.S. states (Arizona, Illinois, Minnesota, New Mexico, Oregon and Washington) are exploring
the possibility as well. California, under executive order from Governor Schwarzenegger in
January of 2007, is currently reviewing their transportation fuels LCFS for implementation in
early 2010 (Holland, Knittel & Hughes 2007).
The CA LCFS serves as a useful example of the basic principles and components of a low
carbon fuel standard for the transportation sector. California is particularly well suited for testing
the effectiveness of this policy, since its transportation sector accounts for 40% of the state’s
Monson 24
total GHG emissions, as opposed to 28% for the national average (Farrell et al. 2007). The CA
tule applies to all liquid fuels sold in the state of California for use in transportation, including
passenger vehicles, freight and off-road applications like construction and agricultural
equipment. Petroleum gasoline and diesel, as well as any biofuels substitutes for these two main
transportation fuels, are regulated by this law. The point of regulation is refiners, blenders and
importers of liquid fuels when finished gas or diesel is first manufactured or imported to CA. In
addition, non-liquid fuels like hydrogen, electricity, natural gas, propane and others are allowed
to participate in the LCFS, and may be required to do so sometime in the future as the number of
non liquid fueled vehicles grows (Farrell et al. 2007).
The LCFS has an intensity standard which requires fuel providers to decrease the carbon
intensity of average annual fuel sales. California is considering emissions goals which are
compatible with what is considered necessary to avoid dangerous climate change: 10% reduction
in average carbon intensity by 2020 and an 80% reduction by 2050, relative to 2005 intensity
(Farrell et al. 2007). The definition of carbon intensity under this regulation is “the average full
fuel-cycle greenhouse gas emissions per unit of transportation fuel energy” (SB 210). The carbon
intensity is based on lifecycle analysis, i.e., it measures carbon emissions across the full life
cycle of the fuel. This cradle to grave approach, referred to as a life cycle analysis (LCA)
includes CO, emissions associated with the production, transport, storage and combustion of
both the feedstock and the fuel itself (Scopec 2007). The CA rule is quite comprehensive and
explicitly includes in the full fuel-cycle “production, extraction, cultivation, transportation, and
storage of feedstock; the production, manufacture, distribution, marketing, transportation, and
storage of fuel; and vehicle operation including refueling, combustion, conversion, and
evaporation.” The full fuel-cycle also encompasses the use and transportation of water, as well
as land use changes associated with feedstock and fuel production (SB 210).
The State Air Resources Board will be responsible for determining values for carbon intensity
(also known as global warming intensity or GWI) based on the best available empirical data for
specific inputs and processes across each fuel’s life cycle. The methodology for determining full
fuel-cycle GHG emissions will be updated periodically to account for new and/or improved
information (SB 210). If fuel providers do not want to use these default values, and they provide
Monson 25
sufficient data, they can certify their particular fuel as having a lower GWI value (Farrell et al.
2007).
There is not yet one standardized way to calculate life cycle emissions. There are a variety of
models that take into account various issues, and both setting the boundaries of these models and
estimating values can be a challenging and contentious issue. There are basically two conceptual
ways to estimate life cycle impacts: well to tank, which documents emissions from production of
a fuel to the gas tank, and well to wheel which adds on the combustion impacts associated with
actual use of the fuel in a vehicle. Resource extraction is the beginning of an LCA estimate, and
this resource extraction can include the impacts of land use change. Recent studies by Fargione
et al. 2008 and Searchinger et al. 2008 have estimated LCA numbers for various biofuels that are
much higher than previous estimates because they incorporate the long term GHG impacts of
converting native ecosystem lands to biomass crops. This is a contentious issue, because it
drastically changes the picture of current biofuels; many individuals and organizations do not
accept the scientific validity behind these analyses. Nonetheless, this type of inclusive analysis,
e.g. far-reaching impacts of land use change, will prove important moving forward as we get
better at articulating the true impact of various fuels and use that knowledge to make better
informed GHG reduction policy decisions.
There are two basic ways a low carbon fuel standard can impact the overall carbon intensity of
the transportation fuel sector: either by driving changes in the portfolio mix of fuels or by driving
changes in the carbon intensity of various fuels across their entire life cycle (Taff 2008). In the
shorter term, it seems likely an LCFS would instigate changes in the portfolio, so blenders and
producers would increase the use of relatively lower carbon fuels and decrease the use of higher
carbon fuels according to current life cycle estimates of carbon intensities for certain types of
fuels. This would be the most immediate way to achieve the required reductions, but in the
longer term, fuels producers would have the incentive to reduce the carbon intensity numbers of
different fuels through changes in production technologies or practices. For example, the carbon
intensity of corn grain ethanol can have large variations depending on the power and heat
sources used in production; an ethanol plant which utilizes corn stover for processing heat and
electricity would emit less carbon over the full life cycle than a plant which uses coal fired
Monson 26
electricity or even natural gas. In addition, changes in the farming practices of the feedstock
could change the carbon intensity of ethanol. Corn grown on agriculturally marginal land, using
low or no till practices, may have a lower carbon footprint than conventionally-raised corn.
Transportation distance can also have a major impact on carbon intensity; corn grain ethanol
produced in the Midwest has a lower carbon intensity when it is used in the same region than
when it is transported to California, for instance. A low carbon fuel standard would likely impact
the overall carbon intensity in both of these ways: by changing the portfolio and by changing the
carbon intensities.
The low carbon fuel standard has a number of general advantages as a biofuels standard; there
are a number of additional potential benefits associated with the specific layout of the California
LCES. Many of these assets draw from and leverage advantage in multiple areas at once, e.g.
biofuels can be both an environmental benefit as far as decreased air pollution and an economic
boon for producers who have a new market for a profitable product. For the sake of simplicity,
the advantages are divided into the three categories of analysis below: environmental, economic
and political. Benefits specifically attributable to CA’s LCFS are explicitly stated as such.
Environmental
The low carbon fuel standard is designed to deal with global climate change, arguably the most
pressing contemporary environmental issue. The most important environmental benefit of the
LCES in this respect is that it explicitly addresses CO2 emissions, the greatest human activity
induced contributor to global warming. By setting a required reduction in the carbon intensity of
the fuel mix, which increases over time, the LCFS should result in reduced CO, emissions
(Farrell et al. 2007, Holland et al. 2007). This gives the LCFS a major advantage over a
renewable fuel standard, which does not necessarily lead to a decrease in GHG emissions. The
LCFS does not commit society to technologies with small or modest GHG benefits. The LCFS
takes an additional step to address climate change issues not directly incorporated into the carbon
intensity for each fuel; there will be an estimated value of global warming impact associated with
crop-based biofuels for direct and indirect land use change (Farrell et al. 2007).
Monson 27
In addition to global warming, there are a variety of other critical environmental issues that are
profoundly impacted by biofuels development and policy. Land use and cultivation methods can
influence soil quality, water quality, wildlife habitat and biodiversity. In order to protect the
environmental quality gains contributed to by land in conservation programs, biofuels produced
on protected lands will not be eligible for inclusion in the LCFS. This policy will further aim to
address environmental issues by expressly dealing with externalities and unintended
consequences associated with biofuels by incorporating these impacts into the market for fuels
(Farrell et al. 2007).
In order to achieve the reductions in GHG emissions from the transportation sector necessary for
the successful mitigation of global warming, the market needs a significant price or policy signal
to promote substantial investments in new fuel and vehicle technologies soon. The LCFS creates
a stable economic environment for this kind of investment, and also creates a certain and long-
term market for low carbon fuels (Farrell 2007). The market for low carbon fuels may put
enough of a premium on cellulosic crops to incentivize farmers to grow the biomass necessary.
Economic
Fundamentally, there is market failure in transportation fuels in that the negative externalities
associated with GHG emissions are not accounted for within the market. There is currently little
market incentive to develop low carbon alternatives to fossil fuels. The LCFS could address this
issue by creating a market for low carbon biofuels while simultaneously penalizing the use of
high carbon fuels. In theory, the LCFS would provide an economic disincentive for further
development and investment in carbon-intense transportation fuels like heavy oil, tar sands, oil
shale and coal which have higher carbon emissions than traditional petroleum fuels. Because of
the participation of non-liquid transportation fuels, the LCFS should also increase the number of
alternative fuel and hybrid vehicles available and sold in the U.S. (Farrell et al. 2007).
One overall advantage of the low carbon fuel standard is that it is fuel neutral-- fuel providers
can choose which fuels and at what volumes they want to sell. From an economic perspective,
this allows greater freedom for the market to operate unhindered and better chances of finding
the economically efficient allocation of biofuels production and consumption. A volumetric or
Monson 28
proportional mandate for a specific fuel, on the other hand, inhibits the laissez faire function of
the market and, in essence, constitutes “picking winners”. The CA version takes this principle a
step further by allowing fuel providers and fuel producers to use electricity, hydrogen, natural
gas etc. in the carbon intensity standard, e.g. by allowing trading of carbon intensity credits
created by electric vehicles or hydrogen fuel cell powered vehicles (Farrell 2007, Scopec 2007).
Compared to a renewable fuel standard, an LCFS is more flexible because it is not limited to
biofuels only.
The LCFS also allows flexibility in compliance by sanctioning averaging, banking and trading
among the regulated producers and providers. The flexibility of this policy both in fuel type and
compliance options leads to a least cost method of abatement because firms are free to negotiate
among higher and lower cost abaters, allowing a certain level of CO) reduction at a lower cost
than mandating each producer to abate the same amount (Farrell et al. 2007). Both petroleum-
based fuel providers and biofuels producers can benefit under this policy. There are a variety of
ways petro fuel manufacturers can actualize their carbon reducing potential, including blending
higher levels of biofuels, buying low-carbon fuels and emissions credits, making refineries more
efficient and using lower-carbon sources of energy to run refineries. According to the rules under
the CA LCFS which allow for trading, low carbon biofuels firms may have a greater incentive to
innovate because they would be able to recoup some of their investments by selling credits to
higher carbon fuel producers (UC Experts 2007). The rules and details of trading credits within
the LCFS are still under debate and consideration, but allowing trading is one key aspect of
allowing the market to decide which firms can lower their carbon intensities at the least cost.
The formulators of California’s low carbon fuel standard are taking additional steps to specify an
effective policy which is based on sound economic principles. The LCFS will provide an added
incentive for innovation in biofuels by offering “innovation credits” which will grant additional
carbon credits to novel and highly innovative low carbon fuels. A cost analysis will be conducted
on the LCFS at some point in the future, similar to the evaluation of the U.S. Clean Air Act
based on the cost-effectiveness approach. This analysis will protect proprietary information and
acknowledge uncertainty in the estimates of carbon intensities for newer biofuels; it will also
include non-climate related costs and benefits (Farrell et al. 2007).
Monson 29
Political
Politicians, both democratic and republican, have paid particular attention recently to the low
carbon fuel standard. Former democratic Senator Tom Daschle has a very optimistic view of the
LCES: “By mandating that a growing percentage of the market for transportation fuel be set
aside for low-carbon fuels, such programs would unleash a tidal wave of private-sector
investment...the example of these policies could serve as a beacon to the rest of the world and
encourage similar behavior elsewhere, including in China and India” (Daschle 2007).
Disadvantages and Further Economic Analysis
To date, there has only been one published economic analysis of the low carbon fuel standard.
Holland, Knittel and Hughes (2007) analyze the potential impact of the LCFS by using economic
models to simulate the outcomes of a national LCFS with various parameters. This study treats
carbon emissions as a negative externality associated with energy production and for simplicity
uses only two different fuels, a low carbon and a high carbon fuel. The authors model corn grain
ethanol as the low carbon fuel, based on their assumption that corn ethanol is less carbon intense
than gasoline, and gasoline as the high carbon fuel. The findings of this analysis are cause for
some caution in unbridled enthusiasm for the LCFS. More importantly, however, the Holland et
al. study illustrates some key ways this policy can be designed and specified for the best chances
of effective CO reduction in an economically sound way. In addition, the results of this analysis
could change dramatically with the incorporation of a lower carbon fuel than corn ethanol.
Second generation fuels, like cellulosic ethanol, represent significantly lower carbon intensity
than corn grain ethanol and may even be carbon negative over their lifetime. As we look to the
future of biofuels, the truly low or negative carbon fuels will change the nature of the economics
of the LCFS.
In order to comply with the specified level of carbon intensity (the life cycle quantity of CO2
emitted per energy unit of fuel) established by an LCFS, firms will either have to increase
production of low carbon fuels or decrease production of high carbon fuels. Overall, Holland et
al. find that the LCFS causes both an increase in the production of low carbon fuels and a
decrease in the production of high carbon fuels. Therefore, the net impact could be an increase in
Monson 30
emissions if the carbon from increased production of low carbon fuels outweighs the reduction in
carbon from decreased production of high carbon fuel. However, using a variety of parameters in
their model, the authors determine it is unlikely that CO2 emissions would increase under an
LCES. In fact, in calibrating the theoretical model to current U.S. supply and demand conditions
for ethanol and gas, Holland et al. found one simulation for an LCFS that reduces carbon
intensity by 10% that resulted in a 45% decrease in CO? emissions. Note that the 10% reduction
in carbon intensity by 2020 is exactly what the California LCFS calls for (Holland et al. 2007,
Farrell et al. 2007).
Holland et al. articulate economic efficiency problems with the LCFS through high surplus
losses and high average costs for carbon reduction. According to one simulation for an LCFS
which reduces carbon intensities by 10%, the authors estimated $80-760 billion per year in
surplus losses and $307-2272 per ton of CO2, which may be significantly greater than climate
change damage estimates on a per ton of carbon basis. The social welfare impacts depend on
elasticity of supply and demand; a given tax or subsidy has a larger impact on output for more
elastic supply and demand. The relative elasticities determine whether producers, consumers or
both bear the costs of the LCFS through changes in producer and consumer surplus. According
to classical economic theory, consumer surplus is the overall benefit consumers achieve from
paying an actual price for a good that is lower than what they are willing to pay. This amount or
surplus can then be used for economic gain in other areas. Producer surplus is the net revenue
producers receive after subtracting all the opportunity costs of their investment. This analysis
shows, under most standards (except extremely lenient ones) consumer surplus from energy
consumption decreases and producer surplus can decrease or increase (Holland et al. 2007).
According to the Holland et al. economic analysis, the LCFS would be most effective with the
low carbon fuel that has a carbon intensity of zero. Since the authors did not model a carbon
neutral or negative biofuel, it is possible the incorporation of such a fuel would lead to a more
favorable economic analysis of the low carbon fuel standard overall (Holland et al. 2007).
Cellulosic ethanol produced from low input high diversity prairie grasses, addressed above, is
one potentially carbon neutral biofuel that could be modeled.
Monson 31
The economic analyses conducted by Holland et al. provide a useful starting point for addressing
the potential benefits and costs of a low carbon fuel standard. However, these analyses are also
very complicated and reproduction seems limited to highly trained economic practitioners. Since
the overarching goal of this paper is to contribute useful insight to the biofuels policy debate, the
author hopes to build on the existing literature and analysis by applying a different framework to
the issue than those addressed above. Specifically, this paper seeks an alternative method of
analysis for addressing the impacts of biofuels policies that is more transparent and provides
ample opportunity for the user to change assumptions and test different options. The following
chapter outlines one possible alternative framework.
CHAPTER 5: SYSTEM DYNAMICS MODELING FRAMEWORK: AN ALTERNATIVE
METHODOLOGY FOR ANALYZING BIOFUELS POLICIES
System dynamics is an analytical tool which presents some benefits as a framework for
addressing biofuels policy. This chapter provides a general description of the system dynamics
(SD) methodology, addresses some pros and cons, and finally discusses the advantage of
utilizing the SD framework for this analysis of biofuels policy in MN.
System dynamics is an approach to studying the behavior of complex systems over time. The
system dynamics (SD) method contrasts the prevailing linear, cause and effect approach to a
static world represented in Figure 6.
Figure 6. Linear Approach
(Forrester 1991)
Open-loop Impression
of the World
Information
about —— Action — Result
problem
Monson 32
The SD approach, on the other hand, recognizes and asserts that the world actually operates as a
dynamic system within which there is continual feedback and a never ending cycle of
interactions, represented in Figure 7.
Figure 7. Cyclical Approach
(Forrester 1991)
Closed-loop Structure
of the World
Action
Information
about Result
problem
The SD method is based in part on the premise that the structure of a system is as important as
the individual components of that system in influencing and determining behavior. System
dynamics also recognizes that structures are dynamically complex and we need unique
conceptual tools and perspectives to address and understand the true nature of systems. SD
modeling is a rigorous process of building computer simulations that enable us to test the
impacts of various decisions and policies over time and to design more effective structures
(Sterman 2000).
System dynamics has a fundamental basis in scientific methodology; Jay Forrester founded SD
at the MIT Sloan School of Management in the 1950s by applying his knowledge about systems
from a background in electrical engineering to other systems. Forrester predicted wide
applicability of the tool, stating “System dynamics provides a common foundation that can be
applied wherever we want to understand and influence how things change through time” (1991,
p. 5). Indeed, SD has been used in a variety of settings including business and management,
environmental, biological, and ecological issues, economics, engineering, medicine, public
policy and other fields.
Monson 33
The System Dynamics Modeling Process
The process of SD mimics the scientific approach to addressing a problem in that it outlines a
rigorous format of analysis, based on the use of the best available information and
experimentation. The general methodology of system dynamics modeling includes the following
five steps, represented in Figure 8 below, in a non-linear and iterative process: 1. Problem
Articulation (Boundary Selection); 2. Dynamic Hypothesis; 3. Formulation; 4. Testing; and 5.
Policy Formulation and Evaluation. The shape in the middle of Figure 3 represents the
interconnectedness of each step with every other step.
Figure 8. SD Modeling Process
(Sterman 2000)
1. Problem Articulation
1 (Boundary Selection)
5. Policy
Formulation
& Evaluation
\
4. Testing 3. “oua
2. Dynamic
)
Distinctive Features of System Dynamics
The system dynamics process has some important features that distinguish it from other methods
of analysis, and these distinctive features present some advantages for the use of SD modeling in
public policy decision making. One of the main features of SD is its emphasis on system
Monson 34
structure as the determinant of individual and group behavior. Many disciplines, including
economics and politics, have traditionally treated humans as individuals who act rationally in
their own best interest, regardless of how the system is set up. This line of thinking leads to
solutions that focus on individual choices rather than on the system. The SD focus on
understanding the systemic structure makes it a potentially very powerful tool for finding
effective ways via public policy to actualize the behavior you want to see. Anderson et al. argue
that “strategy change happens [only] when structure changes” (2005 p. 268). Solutions that come
out of this approach will likely focus on how designing a different structure can lead to the
desired changes in behavior.
Another important feature of SD compared to other analytical tools is the significance it places
on endogenous causes of behavior in the system. Instead of looking at exogenous shocks as the
main determinants of system problems, SD seeks to model the issues within the system.
Economics, for example, tends to attribute many “market failures” to outside causes, asserting
that the normal state of the economy is close to equilibrium most of the time. Business
sometimes blames government regulation as the main driver of system failure, rather than
looking at internal structures as a possible cause. SD practitioners have found over the years that
unwanted behavior within a system is often caused by endogenous factors (Forrester 1991). For
issues where this is the case, SD offers a valuable tool to find the true causes of problems
internal to the system, and has the added benefit of giving power to the policy designers to affect
change, instead of just relying on external forces.
System dynamics also distinguishes itself from some approaches by looking at issues in the
context of a long-term timeframe; SD incorporates historical experience and its influence on
current behavior, and then examines how decisions made today would likely impact the future.
Thinking in longer terms is very likely the only way society can solve complex problems with
delayed and far-reaching impacts like global warming.
The issue of greenhouse gas emissions reductions from biofuels in the transportation sector is a
complex problem with long term consequences. The system dynamics framework offers a unique
advantage for addressing MN biofuels policies and their potential impact on CO>. This analysis
applies an SD methodology in order to provide insights to the biofuels policy debate.
Monson 35
CHAPTER 6: DEVELOPMENT OF A UNIQUE MODEL FOR MN BIOFUELS POLICY
ANALYSIS
In order to analyze the impacts of MN biofuels policies on CO, emissions from the
transportation sector, a unique model based on a system dynamics framework was constructed.
The goal of this model is to capture a few of the major issues associated with biofuels policy in
Minnesota in order to both provide a transparent representation of the structure of the overall
issue and to project future CO2 emissions from the MN passenger transportation sector under a
limited number of policy scenarios. Although the model is simple, it provides an example of an
alternate framing of the biofuels debate and a template for future analyses that add complexity.
This chapter outlines the structure and assumptions of the model constructed for this analysis.
For the sake of simplicity and greatest impact, this analysis focuses only on the light duty vehicle
(LDV) fleet, which consists of passenger car vehicles and light trucks in MN. In 2004, emissions
from the LDV fleet accounted for approximately 63% of total MN transportation sector GHG
emissions (MPCA 2007). The model was constructed using system dynamics modeling software
called Vensim. SD models are made up of different kinds of variables that are connected by
causal arrows. Variables enclosed in a box represent stocks, or things that accumulate over time
as a result of changes in flows, which are variables perpendicular to a stock which control the
rate of change in that stock. The changing stocks in a model provide the dynamic change over
time captured in the system. The variables are numbered simply for quick identification; the
numbers have no significance of their own. Equations, values and sources for each variable are
listed at the end of this chapter.
The structure of the model is based on a simple relationship where total LDV transportation fuel
consumed is determined by vehicle miles traveled (VMT) and average fuel economy, shown in
Figure 9 below. Total Yearly LDV VMT in MN is a stock which changes over time based on an
initial VMT level and a rate of change which accounts for the growth rate in LDV VMT.
Within the LDV fleet only gasoline engines are addressed, since diesel engine vehicles account
for only 2% of the LDV fleet (UMCTS 2008). The model is limited to three fuels that can be
used in gasoline engines: gasoline, corn grain ethanol and cellulosic ethanol. Figure 9 also shows
Monson 36
the next level of the model, which divides total LDV transportation fuel into these three fuel
types.
The portion of fuel used from each type is driven in the model by a biofuels policy: the
renewable fuels standard. Figure 9 shows the impact of the RFS on the fraction of ethanol
consumed, and the fraction of gasoline is the fraction left over. Within Vensim, a variable that
has already been defined in one place can be copied into another location as a shadow variable;
shadow variables are denoted in grey font and with brackets, like “21. fraction of transportation
fuel from corn grain ethanol” below.
Figure 9. Model Structure 1
18, vohme cellulosic
16. volume gasoline ethanol consumed (gal)
cone 17. volume com grain
ethanol consumed (gal)
22. faction of
20. faction of
siisportaton fuel i 19. totalkiv transportation fuel from
—. " transportation fuel celilosc ethanol
~~ 4 eee 21. fraction of
1 fraction I ‘transportation fuel fom
SE in = ‘com grain ethanol
“ee 24, LDV AVERAGE FUEL
ECONOMY IN MN Z
25. Total Yearly (MILEGAL) 28 mew fl
26. net rate ofchange in. | Lev Vit In Mn stan
ly mt (mie/year) (ile)
27. kv vt
growth rate
28. 1990 INITIAL LDV
VMT IN MN (MILE)
Ethanol and gasoline have different energy contents on a volumetric basis, so the model next
converts the volume of each fuel consumed, in gallons, to the energy of that fuel consumed, in
mega joules, based on the energy content of each fuel; see Figure 10 below.
Since one goal of the model is to estimate GHG emissions from the LDV fleet in the MN
transportation sector, the next portion of the model calculates the total yearly emissions from
each fuel based on a life cycle analysis carbon intensity estimate for each fuel type, based on the
intensity estimates used in analyses of the CA low carbon fuel standard. Figure 10 shows the
total yearly emissions from each fuel as a function of the total energy of that fuel consumed and
the per unit of energy carbon intensity, expressed in grams of carbon dioxide equivalent gas
(gCOze/MJ).
Monson 37
Finally, in order to capture the cumulative GHG emissions from the use of these three fuels, they
are summed to total yearly emissions in gCOze and then converted to million metric tons of
carbon dioxide equivalent (MMtCOe) in order to compare the value to other estimates and
analyses.
Figure 10. Model Structure 2
3. total yearly emissions
fiom gasoline (gco2e)
7. CARBON INTENSITY
‘OF GASOLINE
(GCO2EMI)
13. ENERGY CONTENT SosUped i)
8. CARBON INTENSITY OF
enersy gasoline CORN GRAIN ETHANOL
10 ay easokne (GCO2E/MJ)
1. total yearly emissions
from idv (mmtco2e)
6. CONVERSION FROM
‘GRAMS TO MMT
2. total yearly emissions (GCO2EMMTCO2E)
from Idv (gco2e)
~~
5. total yearly emissions
from celbsic ethanol
4. total yearly emissions (gco2e)
fom com grain ethanol
(gco2e)
9. CARBON INTENSITY OF
CELLULOSIC ETHANOL
(Gegeent) 12. energy cellulosic
ethanol consumed (mj)
OF GASOLINE ee een
(MJIGAL) 14. ENERGY CONTENT — “tno! consumed (mi 15. ENERGY CONTENT OF 18. volume cellulosic
16. vokime gasoline OF CORN ETHANOL CELLULOSIC ETHANOL ethanol consumed (gab)
consumed (gab (MUIGAL) (MUGAL)
20. fraction of
‘transportation fuel from
‘gasoline
transportation fuel
17. volume corn grain
ethanol consumed (gal)
22. fraction of
transportation fuel from
cellulosic ethanol
19. total kv
‘consumed (gal)
f 24. LDV AVERAGE FUEL
21. fraction of
transportation fuel from
i , com grain ethanol
ECONOMY IN MN
25, Total Yearly (MILEIGAL) 23. renewable fuel
26, net rate of change in| Ldv Viet In Mn standard
Lv vn (mieiyear) (wile
27. dv vt
growth rate
28. 1990 INITIAL LDV
\VMT IN MN (MILE)
Finally, in order to see the impacts of biofuels use over time on the rules of a low carbon fuel
standard, the average fuel carbon intensity (AFCI), which is the parameter measured in the
LCES, was added to the model and is determined by the weighted average of the carbon
intensities of the three different fuels, depending on the portion of each fuel used. Figure 6 shows
the AFCI and the model variables that determine it. This portion is represented as a separate page
in the model, but it is part of the model. The variables in brackets and grey font are the variables
from the main model view.
Monson 38
Figure 11. Model Structure 3
<7. CARBON INTENSITY 29. average fuel carbon 22 hab BE
es : ae e \ ———______ a
OF GASOLINE intensity (gco2e/mj) transportation fuel from
(GCO2E/M))> ae oe cellulosic ethanol>
<20. fraction of <9. CARBON IN SITY
transportation fuel from OF CELLULOSIC ETHANOL,
gasoline> (GCO2E/MJ)>
<8. CARBON INTENSITY <21. fraction of
OF CORN GRAIN transportation fuel from corn
ETHANOL (GCO2E/MJ)> grain ethanob>
Figure 12 shows the entire model as one view with the average fuel carbon intensity included in
the main view. The effect on the model is the same; this is just an alternate view to make explicit
all the causal relationships.
Monson 39
Figure 12. Model Structure 4
29, average fel carbon 1 total yearty emong
intensity (goo2e/ni) from Kv (nmtco2e)
3. total yearly emissions
fiom gasoline (gco2e)
4. total yearly emissions
fom com grain ethanol
! (gco2e)
CARBON INTENSITY
OF GASOLINE
(GCO2EM)) 8, CARBON INTENSITY OF
‘CORN GRAIN ETHANOL
(GCO2EMI)
12. energy cellulosic
ethanol consumed (mj)
10. energy gasoline
11. energy com grain
14. ENERGY CONTENT > Sthnol consumed (mi)
15. BNERGY CONTENTOF 18, volume cel
16. volume gasoline OF CORN ETHANOL a
ULOSIC ETHANOL ethanol consume} (a)
‘consumed (gal) (MI/GAL) (MI/GAL)
eS 17. vohime com grain
a conan Gab
2. fetim of
Yo. faci of
eae 19. anspor fl Fom
fac fal a ek etal
consumed (gal) 21. fraction of
transportation fuel from
‘com grain ethanol
24. LDV AVERAGE FUEL
ECONOMY IN MN
(MILEIGAL)
25. Total Yearly
26. net rate of 9 Lv Vort In Mn
ly vine (mie/ye’ (miley
27. a vt
growth rate
28. 1990 INITIAL
MT IN MN (MILE)
One key concept in a system dynamics model is feedback. Figure 13 shows the key feedbac
from total yearly emissions from the light duty vehicle fleet identified in the model. As policy
makers, organizations and constituents assess the trend in emissions, they will make different
decisions about the variables which they have some policy control over. For example, a sharp
rise in emissions may cause concerned policy makers to pass a more stringent renewable fuel
standard, or tighter CAFE standards that result in higher miles per gallon, or efforts to slow the
LDV vehicle miles traveled growth rate. All of these policy changes would then in turn lead to
further changes in the total yearly emissions.
Monson 40
Figure 13. Key Emissions Feedback
Grverage fil carbon 1 ial yearly ems
intensty (goo2e/ni) from kv mmtco2e)
3. total yearly emissions
fiom gasoline (gco2e)
4. total yearly emissions
fom com grain ethanol
(gco2e)
|
CARBON INTENSITY
OF GASOLINE
(GCO2EM)) 8, CARBON INTENSITY OF
‘CORN GRAIN ETHANOL
(GCO2EMI)
12. energy cellulosic
10. energy gasoline
ethanol consumed (mj)
__-e consumed (mj)
13, ENERGY CONTENT.
11. energy com grain
(M\GAL) 14, ENERGY CONTENT —® Stare! corsumed (my) ENERGY CONTENT OF 18. volume cellilosic
16. volume gasoline OF CORN ETHANOL ELKULOSIC ETHANOL ethanol consumel! (gal)
consumed (gal) (MIIGAL) (MIIGAL)
17. volume corn grain
‘ethanol consumed (gal)
22. fraction.
siasportaton fa 19. total div transportation fg from
manepoition Gel fv transportation fuel, inol
pale consumed (gal)
21. fraction of
transportation fuel from
‘com grain ethanol
24. LDV AVERAGE FUEL
ECONOMY IN MN
25. Total Yearly (MILEIGAL)
Lav Vint In Mn
(miley
7 ivy at —
poste
28.1990 NTTIAL
MT IN MN (MILE)
Similarly, the results of a low carbon fuel standard, measured as the average fuel carbon
intensity, may lead to policy changes in the carbon intensity of all three fuels modeled here or in
the renewable fuel standard. Figure 14 shows the key feedback from average fuel carbon
intensity.
Monson 41
Figure 14. Key Low Carbon Fuel Standard Feedback
‘average fuel carbon’
intensity (gco2e/mj)
al yearly emissions
fiole gasoline (gco2e)
4. total yearly emissions
fom com grain ethanol
(gco2e)
|
CARBON INTENSITY
OF GASOLINE
(GCO2EM))
8, CARBON INTENSITY OF
10.energy/gasolng CORN GRAIN ETHANOL
(GCO2EMI)
13, ENERGY CONTENT Somsumed (my)
11. energy com grain
| saenenay contre —e et! consumed (nj)
16. vohine gasoline OF CORN ETHANOL
consumed (gt (MIGAL)
17. volume corn grain
ethanol consumed (gal)
19. toalkiv a
transportation fue rom transportation fel ;
gasoline consumed (gal)
21. fraction of
transportation fuel from
‘com grain ethanol
24, lv average fuel
25, Total Yearly ‘economy in rm (mie/gal) 23,Aenewable ful
Lv Vort In Mn standard
(miley
growth rate
28. 1990 INITIAL
VMT IN MN (MILE)
While the feedback relationships articulated above are extremely important to the biofuels policy
system and debate, these relationships are also very difficult to quantify. In order to have a
working model, all relationships must be quantified in some way. Therefore, in order to
demonstrate the ability to run scenarios and make some estimates of CO2 emissions from the
LDV fleet, the model was quantified without the feedback shown above. Chapter 9,
Recommendations, further research and conclusions, suggests some possible approaches to
quantifying these policy feedbacks in order to be able to include them in future analyses.
Quantitative Relationships
Within the model, there are exogenous and endogenous variables. Exogenous variables are
defined and determined outside the model. For example, the energy content of each fuel is an
Monson 42
exogenous variable, taken from MIT information; the model does not determine or change this
variable. An endogenous variable is one which is determined within the model, by relationships
articulated by the model structure. Total LDV transportation fuel consumed, for example, is an
endogenous variable which is a function of average fuel economy and yearly VMT. This model
contains 29 variables in total. 11 of these variables are exogenous, and one of the exogenous
variables is a policy variable (23. renewable fuel standard) which is set by the author at different
levels to test different policies. The remaining 18 variables are endogenous. In order to easily
follow the types of variables contained in the model, policy parameter is denoted in red and all
other exogenous variables are in yellow. See Figure 15 below.
Figure 15. Quantified Model
1 total yearly emissions
fiom iv (mnco2e)
|. eee
|
i
4. toma yearly emissions
fiom com grain ethanol
(geo2e)
10, energy gasoline 12. energy celubsic
anol consumed (1)
Ler comuned ai)
11, energy com gain XY
dtano| censured (a) soe scenes
16. voli gasoline a‘ ethan conse (ga
we ae
17. volume com grain
ethanol consumed (gal)
22. faction of
transportation fel from
20. feton of ‘cellulosic ethanol
ransportaton fuel from 19. total idv
% ‘consumed (gal)
f % 21. fraction of
‘wansportaton fuel from
‘comm grain ethanol
25. Total Yearly
26, net rate of change in_ | Lilv Viet In Mn
lv vee (mile/year) (nike)
com_/ ae
Table 3 lists key endogenous and exogenous variables included in the model, as well as some
key variables that were excluded from this model. Some of these excluded variables would be
useful for future research; for example, fuel prices and fuel availability would likely have some
interesting feedback with renewable fuel standards and low carbon fuel standards. For the base
Monson 43
case in the model, Table 4 below lists the exogenous variables, their values and the sources of
those values. Table 5 shows the endogenous variables and their equations.
Table 3. List of Key Variables
Endogenous
Exogenous
Excluded
Yearly VMT
Initial VMT in 1990
Fuel prices
Total transportation fuel
consumed
VMT growth rate
Fuel availability
Gasoline consumed
Fuel economy
Feedstock availability
Corn grain ethanol consumed
Carbon intensity of gasoline
Diesel and biodiesel
Cellulosic ethanol consumed
Carbon intensity of corn grain
ethanol
Heavy duty and off-road
vehicles, aviation
Total yearly GHG emissions
Carbon intensity of cellulosic
ethanol
Land use patterns
GHG emissions from gasoline
Energy content of gasoline
Public transit use
GHG emissions from corn
grain ethanol
Energy content of corn grain
ethanol
Vehicle type and numbers
GHG emissions from
cellulosic ethanol
Energy content of cellulosic
ethanol
Other GHG reduction policies:
feebates, taxes, tailpipe GHG
standards, cap and trade,
carbon tax
Average fuel carbon intensity
Renewable fuel standard
Population growth
Table 4. Exogenous Variables
Monson 44
Variable Name Variable Units Source
Value or
Equation
Conversion from 1x 10” gCOxe/MMtCOye | MIT 2007
grams to MMT
Carbon intensity of | 92 gCOze/MJ Farrell & Sperling 2007
gasoline
Carbon intensity of | 72 gCO,e/MJ Farrell & Sperling 2007
corn grain ethanol
Carbon intensity of | 0 gCOze/MJ Author’s decision based on Tilman
LIHD cellulosic et al. 2006 which estimates
ethanol negative carbon intensity for LIHD
cellulosic ethanol.
Energy content of 121.3 M3J/gal MIT 2007
gasoline
Energy content of 80.2 MJ/gal MIT 2007 (assume same energy
corn grain ethanol content for ethanol regardless of
source)
Energy content of 80.2 MJ/gal MIT 2007 (assume same energy
LIHD cellulosic content for ethanol regardless of
ethanol source)
Renewable fuel -1 10%) dmnl MN State Legislature (assuming the
standard stated 10% standard is fully met!)
LDV average fuel 19.38+STEP( | mile/gal UMCTS 2008 (average mpg from
economy in MN 15.62, 2020) years 1990-2004. Only slight
variations occur from year to year.
The Energy Independence and
Security Act of 2007 sets new
CAFE standards of 35 mpg by
2020, so the model steps up MN
fuel economy to 35 in 2020)
VMT growth rate 0.0237+STEP | dmnl MNDOT 2008 (average growth in
(-0.0147, VMT from years 1990-2006, then
2007) the model steps up according to
MNDOT projection of .9% growth
starting in 2007)
1990 initial VMT in | 3.575x 10" | mile UMCTS 2008 (total passenger car
MN
VMT (24,456,052,236) plus total
light truck VMT (11,296,141,350).)
' UMCTS 2008 reports fuel ethanol content ramping up from 1% in 1990 to 6% in 1996, and
then 9% in 1997, reaching 10% in 2003.
Table 5. Endogenous Variables
Monson 45
Variable Name Units Equation
(including units)
Net rate of change in mile/year | = “25. total yearly Idv vmt in MN (mile)"*"27. LDV
LDV VMT VMT growth rate")
Total yearly LDV VMT | mile = INTEG ("26. net rate of change in LDV VMT
in MN (mile/year)", initial value: "28. 1990 initial LDV VMT in
MN (mile)"
Total LDV gal = "25. total yearly Idv vmt in MN (mile)"/"24. LDV
transportation fuel average fuel economy in MN (mile/gal)"
consumed
Fraction of dmnl = 1-"22. fraction of transportation fuel from cellulosic
transportation fuel from ethanol"-"21. fraction of transportation fuel from corn
gasoline grain ethanol"
Fraction of Dmnl = "23. Renewable Fuel Standard"*1
transportation fuel from
corn grain ethanol
Fraction of Dmnl = "23. Renewable Fuel Standard"*0
transportation fuel from
cellulosic ethanol
volume gasoline Gal = "19. total transportation fuel consumed (gal)"*"20.
consumed fraction of transportation fuel from gasoline"
volume corn grain Gal = "19. total transportation fuel consumed (gal)"*"21.
ethanol consumed fraction of transportation fuel from corn grain ethanol"
volume cellulosic Gal = "19. total transportation fuel consumed (gal)"*"22.
ethanol consumed fraction of transportation fuel from cellulosic ethanol"
energy gasoline MJ = "16. volume gasoline consumed (gal)"*"13. energy
consumed content of gasoline (mj/gal)"
energy corn grain MJ = "17. volume corn grain ethanol consumed (gal)"*" 14.
ethanol consumed energy content of corn ethanol (mj/gal)"
energy cellulosic MJ = "18. volume cellulosic ethanol consumed (gal)"*"15.
ethanol consumed
energy content of cellulosic ethanol (mj/gal)"
total yearly emissions
gCOve
="10. energy gasoline consumed (mj)"*"7. carbon
Monson 46
from gasoline intensity of gasoline (gco2e/mj)"
total yearly emissions gCOve = "11. energy corn grain ethanol consumed (mj)"*"8.
from corn grain ethanol carbon intensity of corn grain ethanol (gco2e/mj)"
total yearly emissions gCOve = "12. energy cellulosic ethanol consumed (mj)"*"9.
from cellulosic ethanol carbon intensity of cellulosic ethanol (gco2e/mj)"
total yearly emissions gCOve = "3. total yearly emissions from gasoline (gco2e)"+"4.
from LDV total yearly emissions from corn grain ethanol
(gco2e)"+"5. total yearly emissions from cellulosic
ethanol (gco2e)"
Total yearly emissions | MMtCO, | ="2. total yearly emissions from Idv (gco2e)"/"6.
from LDV e conversion from grams to mmt (gco2e/mmtco2e)"
average fuel carbon gCOze/M | ="20. fraction of transportation fuel from gasoline"*"7.
intensity J carbon intensity of gasoline (gco2e/mj)" +"21. fraction of
transportation fuel from corn grain ethanol"*"8. carbon
intensity of corn grain ethanol (gco2e/mj)"+"22. fraction
of transportation fuel from cellulosic ethanol"*"9. carbon
intensity of cellulosic ethanol (gco2e/mj)"
Once relationships were defined, the model was set up to run a series of scenarios within a given
timeframe.
CHAPTER 7: MODEL SCENARIOS: BOUNDARIES, ASSUMPTIONS AND
VALIDATION
As previously stated, one goal of this analysis is to project CO) emissions from the transportation
sector. This chapter details the scenarios programmed into the model and discusses the model
boundaries, assumptions and validation.
Time horizon of model
Dynamic modeling is designed to both help understand problematic behavior that has been
experienced, and to help guide policies that may lead to the improvement of problematic
behavior. For this reason, the selection of a time horizon is best if it extends far enough in history
to capture some emergence of the problem. The timeline should also extend far enough into the
future to account for any delayed or indirect consequences of policy changes (Sterman 2000).
Monson 47
The model used in this analysis has a time horizon of 1990 through 2050. The problem of
increasing GHG emissions from the transportation sector goes back many decades, but since this
analysis seeks mainly to address the impacts of biofuels standards, it does not attempt to model
the emergence of the GHG emissions problem. 1990 was chosen as the initial year for this
model because it precedes the onset of the renewable fuel standard by a few years; the model can
therefore capture some of the impacts of this main policy on GHG emissions. The year 2050 was
selected because it is important to look at the long term impacts of biofuels policy changes on
GHG emissions which may take decades. This year also lines up nicely with Minnesota’s 2007
Next Generation Energy Act, which set a goal of reducing State GHG emissions by 80%, relative
to 2005 levels, by the year 2050. By projecting out to the year 2050, the model results can be
analyzed for their impact on achieving the State GHG reduction goals. One possible dynamic
modeling rule of thumb suggests modeling back approximately half the amount of time you plan
to model forward (Wheat lecture 2008).
Scenarios
In order to examine the potential implications of popular biofuels standards, this analysis outlines
four general scenarios for the state of MN that address some aspects of the renewable fuel
standard and the low carbon fuel standard:
1. Base Case: E10 filled by corn grain ethanol
2. E20 filled by corn grain ethanol
3. E20 filled half by corn grain ethanol and half by cellulosic ethanol
4. E20 filled by cellulosic ethanol
Scenario | is the business as usual scenario, assuming we let present trends continue. This is a
useful scenario to serve as a base case against which other scenarios can be compared. The
current MN state RFS is a 10% ethanol mandate which began in 1997. The model goes from zero
ethanol to 10% ethanol starting in 1997; this is an approximation based on data from the
University of Minnesota Center for Transportation Studies that fuel ethanol content ramped up
from 1% in 1990 to 6% in 1996, and then 9% in 1997, reaching 10% in 2003 (UMCTS 2008).
Monson 48
The State has applied for a waiver from the EPA to certify E20 as gasoline, a waiver necessary
in order to ramp up to a 20% ethanol blend by 2013, and the base case addresses the possible
future should this waiver not be granted. Scenario 2 examines the future if the waiver is granted
and MN achieves a 20% ethanol blend by 2013. With cellulosic ethanol technology developing
rapidly and becoming more economical, some policy makers and organizations are predicting a
greater prominence of cellulosic ethanol within the ethanol mix. The third scenario examines this
prospect by projecting the impacts of a renewable fuel standard of 20% that is filled half by corn
grain ethanol and half by cellulosic ethanol from native prairie grass, which could be a carbon
negative biofuel (Tilman et al. 2006). In order to see how significant the difference between corn
grain ethanol and cellulosic ethanol is with regard to CO, emissions under a 20% ethanol
mandate, the final scenario analyzes the impact of a 20% ethanol blend filled fully with
cellulosic ethanol. Ethanol blending beyond 20% is not modeled because car engines would need
modification in order to handle large blends (UMCTS 2008).
Low Carbon Fuel Standard
The four scenarios above explicitly address differences in a renewable fuel standard. Since this
analysis addresses the RFS relative to an alternative biofuels policy, the LCFS, the results of
each scenario with regard to a hypothetical LCFS are also calculated in the model. In order to
analyze these impacts, the average fuel carbon intensity (AFCI) is calculated for each of the four
scenarios. The AFCI is the metric for measuring compliance with LCFS goals; the average fuel
carbon intensity reflects the average life cycle CO, emissions associated with the entire
transportation fuel supply per unit of energy contained in that supply. While the CA LCFS uses
2004 to calculate a baseline AFCI by which to measure the achievement of LCFS goals, the
UMCTS report models the impacts of a LCFS in Minnesota based on 2007 as the baseline AFCI
year (2008). In order to make a comparison of simulated LCFS results to the UMCTS report, this
model also uses 2007 as the baseline year for AFCI. The UMCTS analysis used the Stockholm
Environment Institute’s Long-range Energy Alternatives Planning system (LEAP) model to
project emissions and AFC] outcomes. The LEAP model calculated an AFCI for 2007 of 89
gCO2,e/MJ (UMCTS 2008), which is very close to the value calculated by the base case in this
model of 90 gCO2e/MJ.
Monson 49
AFCI
Farrell and Sperling articulate two main ways to calculate the average fuel carbon intensity
value:
1. AFCT pen = Tiisveay sCarbonEmissions(gCO,e)
This Year'sFuelSales(MJ)
2. AFCI _ ThisYear' sCarbonEmissions(gCO,e)
historical
BaseYear' sFuelSales(MJ)
The CA LCFS analysis calculates AFCI using method 1. based on current values of carbon
emissions and fuel sales. A formal economic analysis by Holland et al. uses the second method,
which addresses each year’s carbon emissions relative to the fuel sold in the base year. Farrell
and Sperling point out that if the amount of fuel sold increases over time, it will be more difficult
to achieve a LCFS goal that is based on method 1, AFCTeurrent. Conversely, if fuel sales are
increasing, the 2" method, AFCIhistoricals represents a more stringent policy (Farrell & Sperling
2007 II, p. 24).
The analysis of LCFS goals in this model utilizes the 1" method, and calculates the AFCI each
year based on that year’s total carbon emissions divided by the amount of fuel consumed in that
same year. Since the carbon intensities for gasoline, corn grain ethanol and cellulosic ethanol do
not change throughout the time horizon of the model, this AFCI calculation is approximated by
multiplying the proportion of total fuel from each specific by the carbon intensity of that fuel for
each of the three fuels, and then adding those values together for each year:
AFCI = (FractionFuel,
+ (FractionFuel
*CarbonIntensity caine )+ (Fraction uel ¢ymtjian * Carbonlntensity consinaot )
Gasoline
* ity
cetosietitanat * CATBONINEENSILY coysceimot )
In this analysis, the AFCI for 3 different years using both methods was calculated, and both
methods yielded values that were less than one gCOe/MJ apart from each other.
Monson 50
Model Boundaries and Assumptions
Within these four scenarios, distinct boundaries have been set and a number of simplifying
assumptions have been made in order to produce a very basic analysis. Internal combustion
engine vehicles within the passenger fleet are modeled, but diesel engines are not. In reality,
some biofuels are currently used in non-passenger vehicles, mainly from biodiesel in agricultural
and other diesel engine fleets, and there is potential for wide application of biofuels in other non-
passenger vehicles like off-road vehicles including lawnmowers and snowmobiles. Biodiesel can
be and is used by some diesel engine cars in the passenger vehicle fleet. However, diesel engines
currently make up only 2% of the passenger fleet in MN. While these other current and potential
uses of biofuels are important, for the sake of simplicity and maximum impact, this analysis is
limited to a focus on internal combustion engines in the passenger vehicle fleet. In the model,
fuel economy is assumed to follow the historic average of 19.38 mpg from years 1990-2004,
with only slight variations from year to year. The Federal Energy Independence and Security Act
of 2007 set new CAFE standards for 35 mpg by 2020. The new CAFE standards do not specify
any plan or rule for how the standards will be phased in. Therefore, this model assumes MN will
comply with that standard without predicting how we will get there. The model therefore steps
up from 19.38 to 35 mpg in 2020. A smoother transition in reality is likely. The average growth
in VMT in Minnesota from 1990-2006 is 2.37% per year, so the model has a yearly growth rate
to reflect this from 1990 through 2006 (MNDOT 2008). However, the MNDOT is planning for
.9% growth moving forward, based on their analyses and projections, so in 2007 this value in the
model is stepped down to .009 for the remainder of the time (MNDOT 2008). Table 6 below
articulates three major boundaries of the model, the assumptions that go along with these
boundaries, as well as some justification for those assumptions.
Table 6. Major Model Boundaries
Monson 51
Boundaries Simplifying Assumptions Reason for Boundary Selection
MN only Biofuels consumed in MN are | Policy decisions regarding biofuels
produced in MN and climate change mitigation are
made at the State level
Light Duty Vehicle Use average CAFE standards | In 2004, LDVs contributed the
(LDV) fleet only rather than breaking down by | majority of transportation GHG
vehicle type
emissions at 63% (UMCTS from
MPCA 2007)
Gasoline engines only
3 fuels: gasoline, corn grain
ethanol, cellulosic ethanol
Diesel powered vehicles in MN make
up only 2% of the size of the LDV
fleet: diesel=10° and LDV=4.2x10°
(UMCTS 2008)
Limitations
This modeling approach and analysis has some notable limitations. The narrow scope of the
model itself, looking only at gasoline engines in the light duty vehicle fleet in the State of
Minnesota, limits its ability to accurately account for other important factors in MN’s
transportation sector. A model which addresses a wider set of issues in the transportation sector,
including diesel engines, large engines and trucks, off-road vehicles and aviation, would provide
valuable insights to the issue of CO) emissions. This analysi:
's also limited in that it treats only
two possible biofuels policies, the renewable fuel standard and the low carbon fuel standard.
There are other policies available to address GHG emissions from the transportation sector
including feebates, tailpipe CO2 standards (like that passed in California), fuel economy
standards, fuel and vehicle taxes, cap and trade, carbon tax, land use and public transit planning
and others. It would be useful to develop a model which analyzes a variety of possible GHG
reduction policies with regard to their impact on emissions levels, economic development and
other environmental issues.
Monson 52
Validation
In order to check the model’s accuracy at modeling GHG emissions from the LDV fleet in the
transportation sector before moving forward with additional scenarios, model output from the
base case scenario was compared to actual historic data. In order for a model to be useful, the
value of a number produced by the model should be within the same order of magnitude as its
counterpart in collected data. Since the model built here is an extremely simplified representation
of the real world, and does not include feedback, it is not expected to produce results that
perfectly or even closely match historic data. The inclusion of a variety of additional variables
into the model would likely result in more accurate model output. This simple model, however,
with values that are within the same order of magnitude as collected data, can provide a useful
basis for comparison of relative values and changes between different scenarios and
assumptions. It can yield insight about the relative extent to which different policy decisions can
impact GHG emissions from the transportation sector. Four key parameters endogenously
calculated in the model are compared to historic data from the year 2000 in order to check to see
whether the model reasonably approximates actual trends; see Table 7 below. The 2000 model
values are relatively close to historic values for the same year. The discrepancy between model-
derived and historic values suggests that the model could be improved by the inclusion of
feedback, slightly different exogenous variable values, improved model structure via the
inclusion of other important variables like fuel prices, or other changes. However, for the
purposes of this model, the relative similarity in model and historic values lends credibility to the
use of this model for comparing the results of various scenarios. The results of the scenarios run
in this model can yield insights about the relative changes in CO» emissions and average fuel
carbon intensity that may result from different policy decisions.
Monson 53
Table 7. Model Validation
Comparison of Base Case Model Output to Historic Values for Key Parameters
Variable Name 2000 Model Value | 2000 Historic Value | Source of Historic
Value
Total yearly emissions 24.8 MMtCOze 22.8 MMtCOze UMCTS 2008
from LDV
Total LDV transportation | 2.337 B gal 2.493 B gal UMCTS 2008
fuel consumed
Volume corn grain 233.73 M gal 205.65 M gal UMCTS 2008
ethanol consumed
Total yearly LDV VMT__ | 45.29 B miles 48.6 B miles UMCTS 20087
Average fuel carbon 90 gCOze/MJ 89 gCOze/MJ (MN _ | UMCTS 2008
intensity (2007) 2007)
CHAPTER 8: SCENARIO RESULTS
After validating the base case model output, all four scenarios were run in the model:
1. Base Case: E10 filled by corn grain ethanol
2. E20 filled by corn grain ethanol
3. E20 filled half by corn grain ethanol and half by cellulosic ethanol
4. E20 filled by cellulosic ethanol
Greenhouse Gas Emissions and the Next Generation Energy Act GHG Reduction Goals
Graph 1 below shows the model results of the four scenarios with respect to total CO emissions
from the LDV transportation sector in MN. Note the initial, slight drop in emissions in 1997,
2 Passenger cars, 11,378,748 tons COze, plus light trucks, 11,399,746 tons COze, = 22,778,494
tons CO.e=22.8MMt COze
2 Passenger cars, 27,739,403,072 miles, plus light trucks, 20,862,610,622 miles, =
48,602,013,694 miles = 48.6 B miles
Monson 54
when the 10% ethanol blend is instituted. As a result of increasing VMT, however, this decrease
in emissions is brief. For scenarios 2, 3 and 4, when a 20% ethanol blend is mandated beginning
in 2013; the decline in total emissions can again be seen to varying degrees, depending on the
portion of cellulosic ethanol consumed. For all four scenarios, the improved CAFE standards
starting in 2020 have a huge impact on emissions. However, after the initial onset, those
emissions again rise at the previous rates based on increases in VMT.
Graph 1. Total Yearly Emissions
1. total yearly emissions from Idv (mmtco2e)
40
30
Total yearly
emissions 29
(MMtco;e)
10
199919931996 19992002
“L total yearly emissions from kv (mmtco2e)’
1. toal yearly emissions from dv (mae
L total yearly emissions from Mv (mmc
1. total yearly emsions from Ilv (mntco2e)’
2005-2008 2011-2018 2017
Time (Year)
2047-2050
Base Case
Mnico2e
20 Corn Grain Ethanol
£20 Half Com Ethanol, Half Cellubsic Ethanol
E20 AllCellbsic Ethanol
Mnico2e
Mnxco2e
Mnico2e
Graph 2 shows the average fuel economy over time; the rapid increase in 2020 to meet the CAFE
standard of 35 mpg is the cause of decreased GHG emissions in that year.
Average
fuel
economy
(miles/gal)
Graph 2. Average Fuel Economy
Monson 55
24, ldv average fuel economy in mn (mile/gal)
40
10
199919931996 1999 2002 2005 2008 2011-2014 2017 2020
2023 2026 2029-2032 +~«2035~—«2038 «208 ~—«2044 «2047-2080.
Tine (Year)
24. bly average fue economy in mn (mie/ga)" : Base Case
24. Ml average fuel economy in mn (mie/ga)" E20 Corn Grain Ethanol
24. Ml average fel economy in mn (mie/ga)" E20 Half Com Eihanol, Half elliosic Ethanol
ga
24. Ml average fel economy in mm (mie/gab” E20 All Cellulosic Ethanol
nile/gal
Graph 4 shows the growth in VMT over time; this parameter is the same for all four scenarios.
The shape of VMT is similar to the shape of emi
ions in times that are free of shocks like
improved fuel economy and blending onsets, demonstrating visually that VMT is the main driver
of emissions growth in all four scenarios.
Monson 56
Graph 3. Vehicle Miles Traveled
25. Total Yearly Ldv Vint In Mn (mile)
808
op
LDV VMT
(miles) Ane
208
0
199919931996 199920022005 2008 201-214 0172020 2003 2026 2029 20322055 2038 _2oH1 2084 2047 2050
Tine (Yea
"25. Total Yearly Lav Vit in Mn (nie): Base Case ie
2. Total Year La Vat In Mn (nie) E20 Com Grain Bana} tie
°25. Teal Yeatly Lav Vi Tn Ma (le): E20 Half Com Eshano, Half Celuose Etanol tie
2. Toal Yearly Lav Vint tn Mn nie)" 20 All Clic Ethanol tik
Minnesota’s Next Generation Energy Act CO) reduction goals of 15% by 2015, 30% by 2025
and 80% by 2050 serve as a useful basis for comparison of the total GHG emissions from
different scenarios in the model. Table 1 shows the CO emissions for all four scenarios in key
Next Generation Energy Act years in order to see how these scenarios might contribute to the
reduction goals. The emissions levels necessary to achieve the reduction goals are calculated
based on the model’s 2005 baseline emissions level of 27.94 (e.g. 27.94-(27.94*.15)=23.75). The
units for all values are MMtCO2e.
Monson 57
Table 8. Scenarios relative to Next Generation Energy Act CO2 Reduction Goals
2005 CO2z
emissions is
27.94
MMtCOxe in
all scenarios
2015 CO 2 emissions
Level necessary to
achieve Next Gen Act
15% reduction: 23.75
2025 CO, emissions
Level necessary to
achieve Next Gen Act
30% reduction: 19.56
2050 CO 2 emissions
Level necessary to
achieve Next Gen Act
80% reduction: 5.59
SCENARIO
Model
level
Reduction
goal
achieved?
Model
level
Reduction
goal
achieved?
Model
level
Reduction
goal
achieved?
Base Case:
E10 filled by
corn grain
ethanol
31.48 No
19.07 Yes
23.88 No
E20 filled by
corn grain
ethanol
29.88 No
18.1 Yes
22.67 No
E20 filled
half by corn
grain ethanol
and half by
cellulosic
ethanol
28.17 No
17.06 Yes
21.37 No
E20 filled by
cellulosic
ethanol
26.46 No
16.03 Yes
20.07 No
For all four scenarios, the Next Generation reduction goals are only met in 2025. While the
model is far from reaching the 15% reduction in 2015, with the onset of the improved fuel
economy in 2020, the GHG emissions in the model do achieve the 30% reduction in 2025.
However, emissions begin to grow again slowly heading out to 2050, so the actual emissions are
much greater than the level that would be necessary to achieve the 80% reduction goal. The
difference between the total GHG emissions under each of the four scenarios is most notable in
the year 2015. By the time the model reaches 2050, without any other policy changes, the
difference in total emissions between each scenario is minor.
Average
fuel carbon
intensity
(gCO,e/Mj)
Monson 58
Average Fuel Carbon Intensity and Low Carbon Fuel Standard
Since this model does not account for any adjustments in the carbon intensity of gasoline, corn
grain ethanol or cellulosic ethanol over time, changes in the average fuel carbon intensity are
driven by blending rates. Graph 4 shows the AFCI over time; it is static except for times when
ethanol blends increase. In 1997, the 10% RFS causes a slight reduction in the AFCI as more
corn grain ethanol is used. Scenarios 3 and 4, which utilize cellulosic ethanol with a carbon
intensity of 0, significantly reduce the AFCI. The 20% ethanol blend that begins in the year 2013
has a significant impact on the AFCI, as can be seen in the significant drop in that year.
Graph 4. Average Fuel Carbon Intensity
29. average fuel carbon intensity (gco2e/mj)
100
90
70
60
199919931996 1999 20022005 2008 2011 2014 2017-2020 2028-2026 —-2029+~«2032+=«2035~—«2038 «2041 «2044 ~—-2047~—«2050
Time (Year)
29. average fuel carbon intensity (gco2e/ny)": Base Case
E20 Com Grain Ethanol
E20 Half Comm Etlanol, Half Cellulosic Ethanol
E20 AllCellbsic Ethanol
Table 2 below shows the AFCI values under different scenarios and compares them to
hypothetical LCFS goals for the state of MN, based on CA standards. The LCFS sets reductions
goals for average carbon intensity. i.e., for a 10 % reduction goal, the average fuel carbon
intensity in 2015 would need to be 81 gCO2e/MJ or less because that represents a 10% reduction
from the baseline AFCI of 90 gCOe/MJ. The LCFS reduction goals are calculated based on the
model’s 2007 baseline AFCI of 90 gCOze/MJ and the CA LCFS goals of 10% reduction by 2020
and 12% by 2025. The units for all values are gCO2e/MJ.
Monson 59
Table 9: AFCI under different scenarios relative to LCFS goals
2007 AFCT is 90 2015 AFCI 2025 AFCI
gCOze/MJ for all
scenarios AFCI necessary to achieve AFCI necessary to achieve LCFS
LCFS 10% reduction goal: 81 | 12% reduction goal: 79.2
SCENARIO Model level | Reduction goal | Model level | Reduction goal
achieved? achieved?
Base Case: E10 filled | 90 No 90 No
by corn grain ethanol
E20 filled by corn 88 No 88 No
grain ethanol
E20 filled half by 80.8 Yes 80.8 No
corn grain ethanol
and half by cellulosic
ethanol
E20 filled by 73.6 Yes 73.6 Yes
cellulosic ethanol
In the base case scenario, an increased use of a lower carbon fuel is represented by the switch to
10% corn grain ethanol in 1997. Since corn grain ethanol has slightly lower carbon intensity than
gasoline, this provided some improvement of the AFCI relative to 1996 and earlier. Because corn
grain ethanol is blended at a 10% rate throughout the remainder of time in this scenario, there is
no improvement of the AFCI relative to 2007 levels. Therefore, under the base case, low carbon
fuel standard requirements would not be met for any year.
For all scenarios, there are no policy changes made after 2013, and no changes in carbon
intensity of various fuels. Therefore, the AFCI for 2015 in each scenario is the same as the 2025
and 2050 AFCI value for that same scenario. The first two scenarios do not result in the
achievement of the LCFS reduction goals. The third scenario, in which half of a 20% ethanol
blend is filled by corn grain and half by cellulosic, results in achievement of the 10% AFCI
reduction goal by 2015, but not in the 12% reduction goal by 2025, again because the AFCI stays
the same from 2013 on. The fourth scenario, in which a 20% ethanol requirement is filled fully
Monson 60
by cellulosic ethanol, is the only scenario which represents an achievement of the 12% reduction
in AFCI by the year 2025 (and, therefore, the achievement of the 10% reduction by 2015).
Sensitivity Analyses
In order to test the sensitivity of CO) emissions from the LDV transportation sector to changes in
the growth rate in vehicle miles traveled, the analysis was run with a low (0%) and high (2.37%)
growth rate in addition to the original growth rate (.9%), which is the value projected by the
MNDOT. All changes in growth rate relative to the historic value (2.37%) are initiated in the
model starting in 2007. Particularly projecting far into the future, a 0% growth rate in VMT
would significantly reduce 2050 GHG emissions relative to the base case, and a 2.37% increase
in VMT each year would nearly double 2050 GHG emissions from the LDV fleet. Table 3 shows
that the Next Generation Act GHG reduction goals could be achieved in 2025 with a 0 or .9%
growth rate in VMT, but the reduction goals would not be achieved for 2015 or 2050 under any
of the VMT growth rates modeled here.
Table 10: Yearly Emissions and VMT Growth Rate
Monson 61
2015 CO; emissions 2025 2050
(MMtCO;ze)
VMT growth rate VMT growth rate
VMT growth rate
(starting in 2007)
Scenario (0% | .9% 2.37% |0% | 9% 2.37% |0% | 9% 2.37%
(default) (default) (default)
Base 29.29 | 31.48 35.4 16.22 | 19.07 24.83 16.22 | 23.88 44.88
Case: E10
filled by
corn
grain
ethanol
Level necessary to Level necessary to Level necessary to
achieve Next Gen Act achieve Next Gen Act achieve Next Gen Act
15% reduction: 23.75 30% reduction: 19.56 80% reduction: 5.59
Reduction | No No No Yes | Yes No No No No
Goal
Achieved?
Graph 6 shows yearly LDV CO? emissions under the 3 different VMT growth rates addressed by
this sensitivity analysis.
Monson 62
Graph 5. Yearly Emissions and VMT
1. total yearly emissions from Idv (mmtco2e)
60
4s
Total yearly
emissions
(MMtcO2e)
0
1990 1993 1996 1999-2002 2005-2008 -—«2011.-«2014-—«2017-—«2020:~=«2023-«2026 +—«2029:=«2032-~=«2035-~=«2038-—«2041_~«2044:~«2047-~—«22050
Time (Year)
"L.total yearly emissions from liv (mic Manico2e
"1. total yearly emissions from liv ( Manico2e
"L.total yearly emissions from kdv (mmico2e)" : 2.37% growth in VMT Mantco2e
The model was also tested for sensitivity to changes in the average fuel economy in Minnesota.
Table 4 shows the change in GHG emission in 2025 and 2050 as a result of different CAFE
standards including none, the default value of 35 mpg, and 45 mpg. Under CAFE standards of 35
mpg and 45 mpg starting in 2020, the Next Generation reduction goal for 2025 could be reached,
according to this model.
Table 11: Yearly Emissions and CAFE
Monson 63
2025 CO, emissions (MMtCQze)
CAFE improvements (starting in
2050
CAFE improvements
2020)
SCENARIO | None 35 mpg 45 mpg None 35 mpg 45 mpg
(default) (default)
Base Case: 34.44 19.07 14.83 43.13 23.88 18.57
E10 filled by
corn grain
ethanol
Level necessary to achieve Next Level necessary to achieve Next
Gen Act 30% reduction: 19.56 Gen Act 80% reduction: 5.59
Reduction No Yes Yes No No No
Goal
Achieved?
Graph 7 shows CO? emissions with the three different CAFE improvements modeled here.
Monson 64
Graph 6. Yearly Emissions and CAFE
1. total yearly emissions from Idv (mmtco2e)
60
45
Total yearly
emissions
(MMtCo;e)
0
1990 1993 1996 1999 2002 2005 2008 —«2011+«2014 «2017 ~—«2020:~«2023-~«2026~=«2029+=«2032~=«203S «2038 ~—«04:~=«04S:—«204T~—«2050
Time (Year)
"L total yearly emissions from klv (mmtco2e)" : Base Case
"L total yearly emissions from klv (mmtco2e)" : No CAFE standard
"L total yearly emissions from klv (mmico2e)" : CAFE 45 mpg
Mamtco2e
CHAPTER 9: RECOMMENDATIONS, FURTHER RESEARCH AND CONCLUSIONS
This analysis was designed to test possible CO2 implications associated with various RFS
scenarios deemed technologically possible and politically feasible in the relatively near future.
Historically, policy makers have tended to pursue biofuels policies as singular approaches to
emissions reductions in the transportation sector, i.e. separate from other policy options aimed at
lowering CO2 emissions from passenger vehicles. Within this singular approach, there are some
recommendations that may improve the ability of biofuels policies to contribute to CO.
reductions. For example, fundamentally, we should not be measuring a renewable fuel standard
in gallons or as a percentage of total fuel. Since the energy content of different types of fuels
varies significantly, it would be more useful to articulate an RFS based on the amount of energy
in MJ or BTUs coming from fossil fuels versus renewable fuels. This minor distinction would be
one relatively easy way to clarify our goals and means for biofuels and GHG reduction policies.
One important piece of the CA LCFS legislation is the requirement of regular reports by the
State Energy Resources Conservation and Development Commission on the impacts of the
Monson 65
LCFS. Starting in 2013, and every 3 years after, the report will be updated to incorporate new
scientific information, findings, and methodologies of quantifying carbon intensity. The report
will analyze the emissions impacts, based on full fuel-cycles, associated with fuels used to
comply with the LCFS in comparison to historic and projected emissions associated with
petroleum-based transportation fuels. The report will also address a variety of environmental
impacts, including species, biodiversity, land use, air and water quality, and production and
access to food (SB 210). This information should be extremely valuable to policy makers and the
state board as they make future decisions about this and other policies. Should MN decide to
pursue a LCFS, it would be wise to incorporate a similar evaluation requirement as a way to
ensure and promote the achievement of our goals as a state while minimizing any negative
impacts associated with this policy.
While it is important to address potential improvements in biofuels policies, it is also essential to
analyze how biofuels policies fit into the larger context of CO emissions reduction goals in the
transportation sector.
Based on the policy scenarios run in this model and their underlying assumptions, biofuels alone,
given current and projected carbon intensity estimates and supply, will not drastically reduce
CO emissions from the transportation sector. In this analysis, CAFE standard improvements had
the most significant impact on yearly CO2 emissions; the 2020 increase in average fuel economy
to 35 mpg resulted in 44% decreases in annual CO? emissions in all four scenarios. Prior to the
onset of the CAFE standards, even the most aggressive biofuels policy modeled in this analysis,
a 20% RFS filled fully by carbon neutral cellulosic ethanol starting in the year 2013, resulted in a
only a 15% decrease in CO? emissions from the light duty fleet. Table 12 shows the reductions in
CO) emissions resulting from key policy changes in the model. Note that in the first policy
change, E10, the results are the same for all four scenarios because it predates any changes in
CAFE or E20. Given the limited potential of biofuels policies alone and the greater potential of
other types of emissions reductions policies modeled in this analysis, in the future, biofuels
policies should be pursued not by themselves but in conjunction with other policies aimed at
drastically reducing CO, emissions from the transportation sector in MN, like CAFE standards
and VMT reductions.
Table 12. CO: emissions reductions as a result of key policy changes
Monson 66
POLICY CHANGE
E10 E20 CAFE
CO, emissions CO, emissions CO, emissions
(MMtCOze) (MMtCOze) (MMtCOrxe)
Pre Post | Percent | Pre Post Percent | Pre Post | Percent
policy | policy | reduction | policy | policy | reduction | policy | policy | reduction
SCENARIO | (1996) | (1997) (2012) | (2013) (2019) | (2020)
Base Case: | 23.72 | 23.12 | 2.6% 30.64 | 30.92 | -0.9% 32.63 | 18.23 | 44.1%
E10 filled
by corn
grain
ethanol
E20 filled 23.72 | 23.12 | 2.6% 30.64 | 29.35 | 4.2% 30.98 | 17.31 | 44.1%
by corn
grain
ethanol
E20 filled 23.72 | 23.12 | 2.6% 30.64 | 27.67 | 9.7% 29.20 | 16.31 | 44.1%
half by corn
grain
ethanol and
half by
cellulosic
ethanol
E20 filled 23.72 | 23.12 | 2.6% 30.64 | 25.99 | 15.2% 2743 | 15.32 | 44.1%
by cellulosic
ethanol
Further Research
There are a number of specific and general recommendations for further research that could
contribute better insights and analytical bases for both the biofuels policy debate specifically and
the CO, emissions reduction challenge overall.
Monson 67
This analysis provides one possible methodology for analysis of the impacts of various RFS
scenarios. It serves as a starting point for analysis of biofuels policies generally; further work
would benefit from the inclusion of a variety of issues not included here, and from greatly
expanded detail that was beyond the scope of this analysis.
It is important in future research and analysis to find a way to model the impacts of technology
improvements that may change the carbon intensity values of different fuels. It would also be
useful to develop a rigorous model capable of capturing the interconnections and influences of
different policy options that might impact CO, emissions from the transportation sector,
including changes in VMT and CAFE.
Future research should look at models of all three factors of emissions: fuels consumption,
activity and carbon content, as this model only looks at biofuels policies aimed at carbon content,
or policies coming from the general emissions reduction policy category of “use cleaner”. In
addition, analyses that look at the impacts of changing carbon intensities would add value to
understanding the issue, as this model looks only at the impact of changing portfolios on carbon
content.
In Chapter 6, figures 13 and 14 show potentially important policy feedback relationships from
yearly CO? emissions and average fuel carbon intensity to policy decisions in biofuels options
like the RFS and LCFS, as well as VMT and fuel economy. While this analysis did not quantify
these feedbacks, there are some possible approaches to articulating these relationships. One
option would be to conduct a survey of policy makers and citizens with a number of hypothetical
future possibilities for CO2 emissions levels and potential adjustments in RFS or LCFS
standards. An estimated relationship between emissions levels and policy adjustments could then
be incorporated into the model. Another possibility would be to look at historic evidence from a
different environmental issue, and use the resulting pollution level/policy stringency
relationships as a proxy for the CO2 emissions/biofuels policy feedback in future models. For
example, yearly changes in water quality measures in Minnesota could be plotted against water
quality policy changes over the same period of time. A third way to quantify the policy feedback
Monson 68
relationships would be to solicit expert opinion about what levels of emissions would impact
policy decisions and to what extent. A nonlinear relationship could be set up in the model so that
minor increases in emissions levels would have no impact on policy changes, but larger increases
in CO; emissions would result in some increase in the stringency of biofuels and other policies.
The model could also be set up so the user has the opportunity to adjust this relationship based
on their own experience and assumptions about the policy feedback.
Steve Taff of the University of Minnesota and others are working on a project to analyze policy
interactions within the transportation sector of a variety of policies that could impact CO,
emissions, including CAFE, RFS, LCFS, taxes on fuels, taxes on vehicles and cap and trade for
the transportation sector. Portions of this model and analysis will be used to further this dynamic
modeling of various policies, in order to yield insights about which policies and/or sets of
policies should be pursued at the state level to achieve GHG reduction goals while being
sensitive to other economic and environmental issues.
There is new research on the CO. impacts of land use change and conversion of native
ecosystems to cultivated land; this area needs more research. A dynamic model that can account
for the cycles in this complex process would be extremely useful.
The purpose of this analysis is to illustrate the use of techniques and methodologies that allow
for better analysis. There is a balance and tradeoff when building and using models between
complexity and usefulness. A simple model can often be more insightful than a very complex
model where it is easy to get bogged down in the details. It is therefore valuable from both a
theoretical and practical standpoint to address and test whether a simple model tells the same
story as a more complex model.
It is important to examine how different policies interact together. If we continue to pursue
policies as singular and independent drivers of behavior, we are very likely to miss the bigger
picture and therefore unlikely to achieve our environmental, social and economic goals.
Fundamentally, these diverse goals must also be taken into account in a way that incorporates
them and allows us to examine tools for achieving multiple goals at once, or at least achieve
Monson 69
goals in one area without causing harm to another area we value. Dynamic and holistic modeling
approaches provide one beneficial methodology for being able to address multiple goals and
issues in a single framework in order to help illuminate ways to actualize the changes we want to
see as a society.
Monson 70
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