A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Understanding the Growth of 

the Cellulosic Ethanol Industry 

D. Sandor and R. Wallace 

National Renewable Energy Laboratory 

S. Peterson 

The Peterson Group

Technical Report 

NREL/TP-150-42120 

April 2008 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
National Renewable Energy Laboratory

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Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337

Technical Report 

NREL/TP-150-42120 

April 2008 

Understanding the Growth of 

the Cellulosic Ethanol Industry 

D. Sandor and R. Wallace 

National Renewable Energy Laboratory 

S. Peterson 

The Peterson Group

Prepared under Task No. BZ07.1005 
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List of Acronyms 

CAFE Corporate Average Fuel Economy 

OBP Office of the Biomass Program 

R&D research and development 

BSM Biomass Scenario Model 

CRP Conservation Reserve Program 

CG conventional gasoline 

FFV flexible fuel vehicle 

RFS renewable fuel standard 

VEETC Volumetric Ethanol Excise Tax Credit 

ROI rate of investment 

 iii
Table of Contents 

List of Figures.....................................................................................................................v

List of Tables......................................................................................................................v

Introduction........................................................................................................................1

Purpose and Scope ...........................................................................................................1 

Biomass Scenario Model Description...............................................................................2

Background/Context ........................................................................................................2 

System Dynamics Overview,

...........................................................................................3 

BSM System Dynamics Modeling Tool/Platform...........................................................3 

BSM Conceptual Overview .............................................................................................4 

Overview of BSM Structure ............................................................................................6 

Supply Chain Infrastructure........................................................................................6 

Research and Development.........................................................................................7 

Investment Decisions..................................................................................................8 

Policy Space................................................................................................................9 

External Economy.......................................................................................................9 

Description of the BSM Base Case and Scenarios Evaluated........................................9

BSM Base Case..............................................................................................................10 

Scenarios for Increasing Cellulosic Ethanol Production..............................................12

Operating Cost Reduction Scenarios.............................................................................13 

Capital Cost Reduction Scenario ...................................................................................14 

Combination Operating and Capital Cost Reduction Scenarios....................................14 

Results/Discussion............................................................................................................14

Cellulosic Ethanol Production: Base Case and Five Scenarios.....................................15 

Relative Effectiveness of Government Spending in the Scenarios................................17 

Key Performance Parameters Across the Biomass-to-Biofuels Supply Chain..............19 

Summary/Conclusions.....................................................................................................20

Recommendations/Next Steps.........................................................................................21

Appendix A: An Overview of Symbology Used by the STELLA Modeling Tool......25

Appendix B: Structural Description of the BSM..........................................................27

 iv
List of Figures 

In Text 

Figure 1. Supply Chain Underlying the BSM................................................................4 

Figure 3. Schematic of BSM..........................................................................................6 

Figure 4. Cellulosic Ethanol Production: Base Case and Five Scenarios....................15 

Figure 5. Impact of Cumulative Government Spending on Cellulosic Ethanol 

Production Volume in 2017 (excludes VEETC and R&D Spending)..........18 

Figure 6. Government Policy Impact on Industry Capital Investment.........................19 

Appendices 

Figure A-1. Simple Structure Accounting for Allocation of Land Between 

Switchgrass and Other Uses..........................................................................26 

Figure B-1. The Biomass-to-Biofuels Supply Chain .......................................................27 

Figure B-2. Simplified View Land Allocation and Feedstock Production ......................29 

Figure B-3. Simplified Structure for Tracking Yield Growth in Switchgrass .................31 

Figure B-4. Inputs and Outputs Associated with Feedstock Logistics Module ...............33 

Figure B-5. Simplified View of Logic Determining Attractiveness of EtOH Plant 

Investment.....................................................................................................36 

Figure B-6. Accounting Structure Used to Track Plants and Conversion Yields by 

Scale..............................................................................................................38 

Figure B-7. Representation of Logic Surrounding Pre- and Post-Commercial R&D......39 

Figure B-8. Simplified Representation of Accounting Framework used for BSM 

Biofuels End Use ..........................................................................................41

List of Tables 

In Text 

Table 1. Biomass Program Goals and BSM Objectives by Supply Chain Element .....4

Table 2. Supply Chain Infrastructures within BSM......................................................7

Table 3: Key Base Case Assumptions and Notes .......................................................11

Table 4. Cost of Initiatives—Cumulative Government Spending in 2017.................17

Table 5. Suggested Impact of Policy on BSM Key Performance Parameters Across 

Biomass-to-Biofuels Supply Chain in 2017 .................................................20

Appendices 

Table A-1. Primitives of the system dynamics language as implemented in the 

STELLA software.........................................................................................25

Table B-1. KPIs, Outputs, and Inputs Associated with Feedstock Production Module .28

Table B-2. KPIs, Outputs, and Inputs Associated with Feedstock Logistics Module ....32

Table B-3. KPIs, Outputs, and Inputs Associated with Biofuels Production Module....34

Table B-4. KPIs, Outputs, and Inputs Associated with Biofuels End Use Module........40

 v
Introduction 

In the 2007 State of the Union address, the president announced his "Twenty In Ten" goal 

to cut U.S. gasoline consumption by 20% over the next 10 years. His proposal focuses on 

two key elements1

: 

• An alternative fuels standard that would require the equivalent of 35 billion 

gallons of renewable and alternative fuels in 2017 (15% reduction)

• Tighter Corporate Average Fuel Economy (CAFE) standards to reduce projected 

annual gasoline use by 8.5 billion gallons in 2017 (5% reduction).

A variety of renewable and alternative fuels—including ethanol, natural gas, propane, 

and coal-to-liquids—will be required to meet the 35 billion gallon fuel target. The 

Department of Energy (DOE) Office of the Biomass Program (OBP) has been tasked 

with estimating the role of ethanol in meeting the goal. The corn ethanol industry, which 

produced 4.9 billion gallons of ethanol in 2006,2

 is expected to continue to grow rapidly 

through 2010, contributing an estimated 12 to 20 billion gallons of ethanol by 2016.3,4

OBP is investigating the additional volumes that ethanol from lignocellulosic biomass 

could contribute to the 2017 goal. Deploying large volumes of cellulosic ethanol in this 

timeframe will require significant technology advancements in concert with effective 

government policies to motivate industry transformation across the biomass-to-biofuels 

supply chain. 

OBP is employing a system dynamics approach to understand possible sequences of 

events behind the deployment of biofuels technology. A prototype dynamic system 

model, the Biomass Scenario Model (BSM), has been developed to investigate potential 

market penetration scenarios for cellulosic ethanol and to strategically assess OBP’s 

research and development (R&D) and deployment strategies. 

Purpose and Scope 

This report identifies, outlines, and documents a set of plausible scenarios for producing 

significant quantities of lignocellulosic ethanol in 2017. These scenarios can provide 

guidance for setting government policy and targeting government investment to the areas 

with greatest potential impact. A prototype version of the BSM was used to develop the 

scenarios. The analysis underlying the scenario-generation exercise focuses on 

understanding the impact of two types of proposed government policies on the 

deployment of cellulosic biofuels technologies: 

 

1

 Twenty in Ten: Strengthening America’s Energy Security. http://www.whitehouse.gov/infocus/energy 2 Ethanol Industry Outlook 2007: Building New Horizons. (February 2007). Renewable Fuels Association. 

http://www.ethanolrfa.org/objects/pdf/outlook/RFA_Outlook_2007.pdf 3

 Agricultural Baseline Projections. (February 2007). Economic Research Service, U.S. Department of 

Agriculture. http://www.ers.usda.gov/Briefing/Baseline/ 4

 Ethanol Transportation Backgrounder: Expansion of U.S. Corn-based Ethanol from the Agricultural 

Transportation Perspective. (September 2007). Agricultural Marketing Sector. U.S. Department of 

Agriculture. http://www.ams.usda.gov/tmd/TSB/EthanolTransportationBackgrounder09-17-07.pdf

1
• Policies focused on reducing operating costs associated with cellulosic ethanol 

production. These policies include payments to feedstock producers and subsidies 

associated with production of cellulosic ethanol. 

• Policies focused on reducing capital costs associated with cellulosic ethanol 

production. These policies include capital subsidies for construction of full-scale 

cellulosic ethanol production plants. 

Consistent with the goal of generating a plausible set of scenarios around production of 

lignocellulosic ethanol in 2017, this report is not intended to provide a comprehensive 

analysis of all policy options. To support future, more comprehensive analyses, BSM is 

currently in process of significant modification and revision. 

This report begins with a high-level description of BSM. Then, it discusses the scenarios 

generated and evaluated. Finally, the report summarizes key results and offers 

recommendations for future work. The appendices provide more detailed views into the: 

• System dynamics language elements (Appendix A) 

• Internal logic of BSM (Appendix B). 

Biomass Scenario Model Description 

Background/Context 

The BSM was developed as a prototype tool for investigating the dynamics associated 

with the potential evolutionary trajectories of a biofuels industry in the United States. The 

BSM describes the deployment of biofuels technology in the marketplace across the 

biomass-to-biofuels supply chain. This model tracks the deployment of ethanol given 

development of new technologies (in feedstock collection, conversion, and vehicles) and 

the reaction of the investment community to those technologies in light of the competing 

oil market, vehicle demand for biofuels and various government policies over time. This 

prototype was originally developed for the Role of Biomass in America’s Energy Future

(RBAEF)5 project. It is currently undergoing significant revision in order to provide a 

richer view into feedstock production and logistics, conversion technology, fuel 

infrastructure, and markets. 

The BSM uses the system dynamics framework for its conceptual underpinnings to 

understand the dynamic interactions across the entire biomass-to-biofuels system, or 

supply chain. This supply chain, in turn, provides a broad landscape for considering the 

emergence of a sustainable industry. Additionally, it serves as a focal point for the 

integration of other, more detailed and narrowly-focused studies of various components 

of the supply chain. These other analyses can include, for example: 

 

5 “The Role of Biomass in America’s Energy Future” a joint project initiated in 2003 of Dartmouth 

College, Princeton University, University of Tennessee, Michigan State University, Argonne National 

Laboratory, National Renewable Energy Laboratory, Natural Resources Defense Council and sponsored by 

US DOE and the National Commission on Energy Policy. 

http://engineering.dartmouth.edu/rbaef/index.shtml 

2
• Feedstock logistics analyses 

• Agricultural economics analyses 

• Process models 

• Integrated biorefinery models 

• Life cycle models 

• Economic models 

• Market analyses. 

System Dynamics Overview6,7

System dynamics is a methodology for analyzing the behavior of complex feedback 

systems over time. The methodology and computer simulation modeling techniques 

originated at the Massachusetts Institute of Technology in the 1950s as tools to improve 

understanding of industrial processes. System dynamics has been productively applied to 

a wide range of industrial and organizational, and policy-oriented systems problems over 

the past 40 years.8 

Systems problems tend to share four important characteristics: 

• They involve dynamics—change over time is a defining characteristic of the 

issue-space 

• Multiple players and diverse interests interact to impact system performance 

• Interdependencies among different sub-systems and processes are critical to 

system performance 

• Communication of issues, structure, and leverage points can be very difficult. 

Using these criteria as a filter, the potential evolutionary trajectories of a biofuels industry 

in the United States can be seen as a classic systems problem. The issues involve 

dynamics around developing and sustaining an entire industry. Multiple players are 

involved—ranging from farmers to venture capitalists to automobile manufacturers to 

congressional policy makers. Failure to develop any link within the supply chain can 

thwart development of the industry. And finally, because both detail and dynamic 

complexity is so rich, it can be very difficult to understand and communicate the system 

of interactions from the perspective of a single disciplinary framework. 

BSM System Dynamics Modeling Tool/Platform 

The STELLATM software tool is the platform for the BSM development effort. STELLA 

is a “systems thinking” software package developed specifically for dynamic systems 

modeling by ISEE Systems. The software provides a simple, yet powerful visual 

language for representing dynamic systems and processes, thus making the system 

 

6 “US DOE Introduction to System Dynamics: A Systems Approach to Understanding Complex Policy 

Issues, Version 1” (1997) US Department of Energy http://www.systemdynamics.org/DL-IntroSysDyn/ 

7 System Dynamics Society Website (visited 8-11-07) http://www.systemdynamics.org/ (Updated 4/25/07) 8

 See, for example, Sterman, John. Business Dynamics: Systems Thinking and Modeling for a Complex 

World. Homewood, IL: Irwin/McGraw-Hill, 2000, which provides a thorough treatment of the framework 

and its application areas. 

3
dynamics framework accessible as a desktop tool for analysts and policy makers. Its 

relatively intuitive nature is well suited to the “what if” scenario building needed to 

understand the possible paths for a new bioindustry. For more information on STELLA, 

see Appendix A. 

BSM Conceptual Overview 

The initial development of the BSM was motivated by a desire to understand “transition 

dynamics” associated with the development of a lignocellulosic ethanol industry. Early in 

the effort, it was recognized that an integrative systems view into the industry supply 

chain was required. Figure 1 provides a conceptual overview of the supply chain for 

lignocellulosic ethanol. 

Feedstock 

Production 

Feedstock 

Logistics 

Biofuels 

Production 

Biofuels 

Distribution 

Biofuels 

End Use 

Figure 1. Supply Chain Underlying the BSM 

The BSM is organized around the five major elements of the biomass-to-biofuels supply 

chain for the future biofuels industry. The OBP’s overarching goal for each element of 

the supply chain (as described in the OBP FY07 Multi-Year Program Plan9) and the 

objective of the analogous BSM module are summarized in Table 1. 

Table 1. Biomass Program Goals and BSM Objectives by Supply Chain Element 

Supply Chain 

Element Biomass Program Goal Current BSM Module Objective 

Feedstock 

Production 

Produce large, sustainable 

supplies of regionally available 

biomass 

Provide a basis for the production of cellulosic 

feedstocks, respecting the constraints imposed by 

the agricultural land base in a manner consistent 

with economics. 

Feedstock 

Logistics 

Implement biomass feedstock 

infrastructure, equipment and 

systems (biomass harvesting, 

collection, storage, 

preprocessing and 

transportation) 

Account for the cost associated with moving 

biomass feedstock from the point of production 

(i.e., farm gate) to the point of conversion (i.e., 

plant gate). (Note: The current BSM focuses on 

feedstock transportation only—it does not 

account for the other logistics areas.)

Biofuels 

Production 

Deploy cost-effective, integrated 

biomass-to-biofuels conversion 

facilities 

Represent biomass-to-ethanol conversion process 

and the logic associated with the decision to add 

new plant to the system. 

 

9

 Biomass Multi-Year Program Plan. (November 2007). Office of the Biomass Program, Office of Energy 

Efficiency and Renewable Energy, U.S. Department of Energy. 

http://www1.eere.energy.gov/biomass/pdfs/biomass_program_mypp.pdf

4
Biofuels 

Distribution 

Implement biofuels distribution 

infrastructure (storage, blending, 

transportation, and dispensing) 

No model structure currently exists around fuel 

transport from point to production to point of use. 

(During development of the BSM, a simplifying 

assumption was made that fuel transportation 

would not limit the development of the biofuels 

industry.) 

Biofuels End Use Expand public availability of 

biofuels-compatible vehicles 

with same performance as 

petroleum fuels 

Represent the consumption of fuel ethanol based 

on the potential demand for ethanol as E10 and 

E85 and the impacts of vehicle mix and 

efficiency. 

From the OBP perspective, this supply chain provides the basic framework to align and 

coordinate the efforts of the large and diverse group of stakeholders that will play a 

critical role in realizing the president’s challenging biofuels goals. In the context of 

systems dynamics, the BSM supply chain framework creates the potential to analyze and 

understand feedback effects between disparate processes as the industry evolves over 

time. A highly simplified representation of key feedbacks captured within the supply 

chain framework is shown in Figure 2. 

Figure 2. Key Feedbacks Captured in the BSM

As suggested in Figure 2, for example, limited availability of feedstock can constrain the 

rate of growth in cellulosic ethanol production capacity. Meanwhile, mismatches between 

available supply and quantity demanded at a given price will tend to drive changes in 

price. 

5
Overview of BSM Structure 

Figure 3 provides a high-level view of the BSM and points to the key features and 

components of the dynamic framework—supply chain, supply infrastructure, R&D, 

investment decision, policy space, and external economy—which are described here. 

Figure 3. Sche tic of BSM 

upply Chain Infrastructure 

he BSM seeks to represent the physical infrastructure 

Feedstock

Production

Feedstock

Logistics

Biofuels

Production

Biofuels

Distribution

Biofuels

End Use

ma

S

At each stage in the supply chain, t

required to capture the dynamics of the emerging industry. Table 2 identifies key supply 

chain infrastructures and describes how they are handled within BSM. 

6
Table 2. Supply Chain Infrastructures within BSM 

Stage in 

Supply Chain Infrastructure Notes 

Feedstock 

Production 

Agricultural Land 

Use 

• Two land bases: Prime and Conservation Reserve Program 

(CRP) land 

• Allocation between dedicated cellulosic crop (switchgrass) and 

other crops 

Residues • Simple price/supply curves for agricultural, forest residues 

Yield Dynamics • Capture yield changes for herbaceous energy crops 

• Distinguish between yield growth at lab/research station and 

yield growth on the farm 

• Yield growth driven by R&D efforts 

Supply/Demand 

Dynamics 

• Endogenous pricing structure resolves supply/demand 

discrepancies (demand driven by cellulose-to-ethanol 

conversion capacity) 

• Cellulosic residues respond to price changes through simple 

supply curve 

Feedstock 

Logistics 

- • Build up of infrastructure not explicitly represented 

• Farm-gate to plant-gate costs driven by distance to plant 

Biofuels 

Production 

Conversion 

Facilities 

• Track design, construction, and on-line facilities (at three scales) 

o Demonstration scale 

o Pioneer scale 

o Full scale 

Biofuels 

Distribution 

- • Infrastructure not currently represented—assumed not to be 

rate-limiting 

Vehicle Mix • Accounting Structure for Multiple Vehicle Types 

o Conventional gasoline (CG) cars 

o CG light trucks 

o Flexible fuel vehicle (FFV) cars 

o FFV light trucks 

o “Other” cars (catch-all category including diesel, off-road 

diesel, plug in hybrids, hydrogen vehicles) 

o “Other” trucks 

• Three age classes for vehicles to capture vehicle vintaging 

• Mix of new vehicles entering system over time captured as userdefined scenario 

Vehicle Efficiency • Tracked by vehicle age and type 

• Efficiency of new vehicles entering system over time captured 

as user-defined scenario 

Biofuels End Use 

Other Vehicle 

Attributes 

• Miles driven per year captured as user-defined scenario 

• Vehicle projected lifetime captured as user-defined scenario 

Research and Development 

As suggested in Figure 3, R&D activities impact both the feedstock production and 

biofuels production stages of the supply chain. BSM explicitly represents R&D efforts 

associated with yield growth in herbaceous perennial cellulosic crops, pre-commercial 

conversion technology, and post-commercial “learning by doing” activities. 

7
• Agricultural R&D is represented simply. Cumulative investment in R&D is 

assumed to drive yields for herbaceous perennial cellulosic feedstocks. The 

specific investment pattern can be defined by the user as a scenario. In the base 

run, herbaceous perennial cellulosic feedstock yield as the research level grows 

from five tons per acre per year to slightly over seven tons per acre per year. A 

one-year dwell time is assumed for translating yield growth at the research level 

into yield growth on the farm. 

• Conversion R&D 

o Pre-commercial R&D is represented in some detail. Two technology tracks 

are represented within the model. Each technology track exhibits a unique 

assumed curve relating cumulative R&D investment against yield. This in 

turn is used to as an input to the model logic around plant capital and 

operating cost metrics. For each track, R&D is represented at fundamental 

and applied R&D and pilot scale operations. Higher levels of cumulative 

investment lead to increased yields, with resultant lowered costs. 

o Post-commercial R&D is captured through learning curve dynamics. As 

cumulative industry output doubles, conversion yields seek an asymptote 

given by an assumed maximum mature technology yield. In the base run, 

the progress ratio for learning curve dynamics is set to an assumed value 

of 0.8. Each doubling in cumulative industry output is associated with 

conversion yield increases of 80% of the gap between prior yield and 

mature technology yield. 

R&D and associated yield improvements for residues are handled as a scenario. The 

initial agricultural residue supply curve taken from POLYSYS, is assumed to shift 

linearly at a rate of 1% per year. 

Investment Decisions 

Investors process multiple cost, risk, and external inputs to determine when and how 

much to invest. The investment decision is central to the operation of the model. Investor 

decision-making is driven by multiple inputs, including: 

• Potential demand for ethanol (as oxygenate in E10, and as E85) 

• Level of capital subsidy 

• Level of fuel subsidy (e.g., VEETC) 

• Level of feedstock subsidy (i.e., grower payment) 

• Relative price of gasoline 

• Plant capital cost (dependent on scale of plant and experience at previous scales) 

• Perceived risk (dependent on industry maturity) 

• Interest rates 

• Investor type (possible to select aggressive, moderate, or risk-averse investor). 

8
Policy Space 

The BSM provides a framework for exploring the impact of government policies on 

industry development. Government policy intervention points can occur along the supply 

chain and within the R&D community. Several policy options are currently being 

considered, including: 

• Feedstock subsidies. These subsidies are aimed at reducing the delivered cost of 

cellulosic feedstock material to the plant gate. Magnitude, timing, and duration of 

subsidies can be varied for particular simulation experiments. 

• Renewable fuel standard (RFS). A RFS can be switched on or off. The specific 

magnitude and timing reflects current legislation, but may be modified to reflect 

different scenarios. 

• Ethanol fuel subsidies. Specific magnitude and timing can be modified to explore 

different policies. Subsidies can be applied to all ethanol fuels, or specifically to 

cellulosic ethanol fuel. 

• Conversion plant capital subsidies. Subsidies can be configured on a scalespecific (i.e., demonstration, pioneer, full scale) basis. Policy levers include the 

total (cumulative) dollar amount available for subsidies, the maximum percent of 

per-project cost to be subsidized, and the maximum dollar amount to subsidize 

per project. 

External Economy 

Inputs associated with the external economy are used to drive different background 

scenarios for model analysis. These inputs include economic factors, such as interest rates 

and scenarios around the price of gasoline. They also include physical limits (such as the 

availability of construction capacity) which may constrain the rate of development of the 

industry. 

For additional information on the BSM, see Appendix B, which describes the BSM in 

some detail from the perspective of: 

• Supply chain module purpose and approach 

• Key module outputs and inputs 

• Structure, logic, and decision rules embedded into the module 

• Policy levers and scenario variables that impact the module. 

Description of the BSM Base Case and Scenarios Evaluated 

This section provides a description of baseline output along with a set of scenarios 

generated with the BSM. In considering the model results described in this report, it is 

important to keep in mind the prototype, provisional nature of the model, which is used to 

generate the output. In particular: 

9
• Because the model is a prototype, the scenarios generated by the model can in no 

way be considered to be definitive. While BSM developers consider the scenarios 

to be plausible, they also consider the model to be very much a work in process. 

Current development efforts are focused on strengthening the structure, 

assumptions, and data used to generate BSM output. It is appropriate to view 

model scenarios here with an attitude of healthy skepticism around assumptions, 

relationships, and data. 

• The model results are scenarios not predictions. A good use of the scenarios 

presented here is to envision, in broad terms, plausible future conditions. It would 

be improper to use the scenarios described here to make point-in-time predictions 

that never come to pass. 

• The feedback loop structure within BSM is rich. In the conversion model in 

particular, multiple positive feedbacks are latent within the model’s structure. 

Under some sets of policy scenarios, positive feedbacks can become activated, 

driving the industry toward self-sustaining growth. It can be useful to assess the 

scenarios by considering the degree to which an initiative moves the industry 

toward self-sustaining growth over time. This is different than viewing output at a 

single point in time. 

BSM Base Case 

The Base Case model used in this analysis is a modified version of the original RBAEF 

prototype model. This Base Case (model bw070829-base-sp.STM) reflects the current 

state of the industry and policy incentives now in place. The key Base Case assumptions 

are summarized in Table 3. Policy incentives represented in the Base Case include: 

• Volumetric Ethanol Excise Tax Credit (VEETC) 

o $0.51 per gallon of ethanol blended through 201010

• Integrated biorefinery capital subsidies 

o $385 million for demonstration plants (50 dry tons per day) awarded in 2005 

(DOE:industry cost share ratio of 40:60)11

o $385 million for pioneer plants (500 dry tons per day) awarded in 2007 

(DOE:industry cost share ratio of 40:60) 12

The Base Case scenario results in the production of 500 million gallons of cellulosic 

ethanol in 2017. 

 

10 Volumetric Ethanol Excise Tax Credit as per EPAct; http://thomas.loc.gov/cgibin/query/D?c110:1:./temp/~c110GQLElc::

11 April 2002 solicitation for "Biomass Research and Development for the Production of Fuels, Power, 

Chemicals and other Economical and Sustainable Products" projects. Note that these projects were actually 

smaller scale pilot, rather than demonstration scale, efforts. 

12 EPAct 2005 Section 932 Solicitation for 700 tons/day cellulosic ethanol production plants 

10
Table 3: Key Base Case Assumptions and Notes 

Stage in 

Supply 

Chain 

Topic Assumption/note 

Agricultural residues • Supply curve taken from ORNL Analysis $50/ton 

priceÆ110M tons per year

Forest residues • Supply curve taken from ORNL Analysis $50/ton price Æ

57M tons per year 

Herbaceous cellulosic 

energy crop supply 

• Consistent with University of Tennessee POLYSYS land 

competition analysis for switchgrass on prime cropland 

Herbaceous cellulosic 

energy crop yield 

(research setting) 

• Initially 5 tons/acre per year, rising 1.5% per year to 13 

tons/acre per year in 2050 

Herbaceous cellulosic 

energy crop yield 

(farm/commercial 

setting) 

• One year dwell time between yield increases in research 

setting and yield increases in farm/commercial settings. 

NOTE: In the Base Case, less than 10% of 2017 total 

cellulosic biomass comes from herbaceous cellulosic energy 

crop 

CRP land • Base run allows CRP land to be converted to herbaceous 

cellulosic energy crop production. In base run, majority of 

2017 cellulosic energy crop production comes from CRP 

land. 

Feedstock 

Production 

Corn market • Initial corn price set at $1.90/bu 

• Non-EtOH demand for corn assumed to increase linearly at 

0.8% of initial value each year 

• EtOH production from corn grows to max value of 10 

billion gal/yr in 2010 

• Corn yield assumed to grow linearly from 160 to 180 bu/A 

over the period of 2005 to 2017 

• Corn price adjusts to resolve supply/demand imbalances 

Feedstock 

Logistics 

Delivery mechanism • Truck delivery up to 50 miles to plant 

• No assumed constraints (e.g., highway capacity) to delivery 

of feedstocks from point of production to plant gate 

Conversion yields • Maximum potential conversion yield conversion yield : 105 

gal/dry ton biomass 

Plants • All plants constructed successfully come on line 

• Three year plant planning, permitting, construction time 

• Commercial-scale plant: 5000 TPD 

Target price • $1.07 per gallon ethanol nominal price target in 201213 at 

pilot scale 

Biofuels 

Production 

Investor financing 

detail 

• Required ROI for equity investor for “new” industry = 30% 

• Required ROI for equity investor for “mature” industry = 

10% 

• Aggressive equity investor assumed: Industry moves from 

“new” to “mature” as annual production capacity moves 

from 0 to 100M gal/year 

• Capital cost growth factor of 2.22x for “new” industry; 

declines to 1.0x as annual production capacity moves from 0 

to 500M gal/yr 

• Debt:Equity Mix = 40:60 

 

13 Note: Ethanol price target in 2002$ (Upcoming BSM revision will update ethanol price target to 2007$ to 

match target in FY07 MYPP). 

11
Venture Capital 

Detail 

• VC investment enabled 

• VC Investment in Demonstration-scale plants triggered 

when: 

o Sufficient pilot scale effort has been logged 

o Expected EtOH price from full scale facilities < 

expected gasoline wholesale price at wholesale on BTU 

equivalency basis 

o Industry capacity < 20M gal/yr 

Biofuels 

Distribution 

• No modeling done. Assumes no barriers to distribution and 

refueling stations (i.e., assumes infrastructure development 

not rate-limiting) 

Biofuels End 

Use 

Flexible-Fuel 

Vehicles (FFV) 

• Influx of new FFV vehicles ramps to 25% of total new 

vehicle production by 2012 

• Total FFV vehicles in use grow from ~1.5M in 2006 to 

~30M in 2017 

Fuel Market • Oil price based on “AEO 2006 High Oil Price Projection” 

• ORNL refinery model analysis used to predict ethanol 

blending value as function of demand 

Scenarios for Increasing Cellulosic Ethanol Production 

Technical and financial risk are significant barriers to the development of a commercial 

cellulosic ethanol industry. To reduce this risk and facilitate initial investments while the 

industry is in its infancy, significant federal funding will be required. The scenarios 

developed for this analysis take a “conversion facility-centric” approach to motivating 

cellulosic ethanol production, recognizing that to enable greatly increased production in 

2017 and beyond, it is necessary to improve substantially the economic proposition 

associated with building new or first-of-a-kind facilities. Government can improve the 

economic proposition in two ways: (1) by lowering operating costs and (2) by reducing 

the capital cost of these first facilities. As the first facilities are built and come on-line 

(the model assumes successful investments), risk can be reduced sufficiently to enable 

the industry to grow of its own accord. The ultimate goal of government policies, then, is 

to make this transition or tipping point occur as quickly as possible. 

In its current prototype state, BSM can be used to explore potential impacts of policy 

incentives on cellulosic ethanol production, including those that focus on influencing the 

biomass production, biomass transport and biofuels production elements of the biomassto-biofuels supply chain. Currently, however, BSM is not set up to address policies aimed 

at influencing the biofuels distribution and biofuels use stages in the supply chain. BSM 

is currently undergoing significant revision. As a more robust version of the model is 

developed, initiatives along the entire supply chain will be explored. 

The BSM assesses the impact of a variety of factors on the attractiveness of building 

cellulosic ethanol production facilities. Factors such as cost, risk, feedstock availability, 

and demand for ethanol are considered in the calculations to determine when and how 

much to invest in these facilities (as summarized in the previous Investment Decisions

discussion and detailed in Appendix B). Essentially, the “attractiveness to build” is a 

reflection of the minimum selling price of ethanol necessary to satisfy the required return 

12
on investment. The attractiveness of investing in new cellulosic ethanol is calculated by 

comparing the minimum ethanol selling price with an appropriate market price of the 

competing product. Initially, ethanol (both corn ethanol and cellulosic ethanol) is 

assumed to fill the oxygenate market, and then the competing product is considered to be 

motor gasoline. 

To generate scenarios, we explored three federal policy options that have the potential to 

increase the production of cellulosic ethanol. As indicated, these policy options either 

reduce operating costs or reduce required capital investment: 

• Lowering the operating cost 

o Feedstock grower payment 

o ($20/ton; 2007-2017) 

o Cellulosic ethanol production subsidy 

o ($2/gallon up to 500M gallon cellulosic ethanol produced; 2009–until 

production volume met) 

• Lowering the capital investment 

o Capital subsidy for commercial-scale (5000 tpd) cellulosic ethanol 

production plants 

– (40% per project up to a total government expenditure of $1.5 

billion; 2009–until funds depleted) 

These policy options were used individually and in combination, by enabling specific 

policy levers in the BSM, to generate five distinct scenarios. The scenarios were then 

evaluated and compared with the Base Case model. In all five scenarios, the current 

policy incentives are maintained and all assumptions match the Base Case except as 

described below. 

Operating Cost Reduction Scenarios 

Grower Payment (model bw070829-Feedstock.STM). Targeted at the initial stage in the 

supply chain, a grower payment subsidy aims to reduce the cost of feedstocks for 

producers at the plant gate while providing an incentive for producers to provide 

feedstock. By providing a subsidy to growers in the form of a grower payment, this lever 

reduces the amount that conversion facilities will need to pay to acquire feedstock for 

their operations. The grower payment scenario examines the impact of feedstock 

subsidies in the form of a $20/dry ton (unadjusted for inflation) grower payment through 

2017. In the BSM, this is controlled via the Government Policy Input page; the feedstock 

subsidy per ton variable is set at $20/ton through 2017. This variable is used to calculate 

the subsidized feedstock cost per ton variable, which feeds into the attractiveness to build 

calculations. 

Production Subsidy (model bw070829-EtOH Subsidy.STM). Production subsidies 

directly target the conversion processes by providing a payment for the output of 

facilities. The production subsidy scenario looks at the impact of cellulosic ethanol 

13
production subsidies in the form of a $2/gallon payment (unadjusted for inflation) for the 

first 500M gallons of cellulosic ethanol produced beginning in 2009. In the BSM, this is 

controlled via the Government Policy Input page; the cellulosic ethanol subsidy variable 

jumps from $0 to $2, for the first 500M gallons of cellulosic ethanol produced, beginning 

in 2009. For each gallon of cellulosic ethanol produced, the cellulosic ethanol subsidy is 

used to calculate the cellulosic ethanol subsidy actual variable, which feeds into 

attractiveness to build calculations. 

Capital Cost Reduction Scenario 

Capital Cost Reduction (model bw070829-Capital.STM). Capital cost reduction 

subsidies target substantial capital cost associated with the development of a conversion 

facility. By funding directly a portion of the capital cost, an initiative can reduce the risk 

of investing in a new industry. The capital cost reduction scenario looks at the impact of a 

capital subsidy for full scale facilities of 40% per project up to a total expenditure by the 

government of $1.5 billion, with funds available beginning in 2009. In the BSM, this is 

controlled via the Government Policy Input page; the Ann Gov Cap $$M $$ yr (for full 

scale plants) variable jumps from $0 to $1.5 billion in 2009. This variable is used to 

calculate the Gov Project Capital Subsidy (for full scale plants) variable, which feeds into 

the attractiveness to build calculations. 

Combination Operating and Capital Cost Reduction Scenarios 

Grower Payment + Capital Cost Reduction (model # bw070829-Capital plus 

Feedstock.STM). This combination scenario explores the joint impact of a feedstock 

subsidy of $20/ton in combination with a capital subsidy for full scale plants (40% per 

project; total government expenditure of $1.5 billion. The model is modified through the 

Government Policy Input page as described above for Feedstock and Capital Subsidies.

Production Subsidy + Capital Cost Reduction (model bw070829-Capital plus 

EtOH.STM). This combination scenario explores the joint impact of a cellulosic ethanol 

production feedstock subsidy of $2/gal for the first 500M gallons of cellulosic ethanol 

produced in combination with a capital subsidy for full scale plants (40% per project; 

total government expenditure of $1.5 billion). The model is modified through the 

Government Policy Input page as described above for Scenarios Cellulosic EtOH and 

Capital Subsidies.

Results/Discussion 

In the generation of the scenarios presented here, the BSM provides the analyst with an 

experimental laboratory of sorts. Against a fixed backdrop of assumed relationships, it is 

possible to conduct controlled experiments in order to better understand how specific 

policies might facilitate the growth of the industry. Even though the BSM is only in 

prototype form, analysis can help to clarify issues, identify trade-offs, uncover 

weaknesses in assumptions, and inform the policy debate. In this case, analysis of 

scenarios presented here can help in understanding potential growth trajectories and 

likely volumes of cellulosic ethanol that could be produced in 2017, as a result of various 

government initiatives. Additionally, analysis can help to identify potential outcomes 

14
around biomass feedstock supply and conversion infrastructure. Key performance metrics 

relevant to this analysis include: 

• Potential cellulosic ethanol production 

o Cellulosic ethanol production volume 

o Cellulosic biomass feedstock requirement 

o Full scale cellulosic ethanol facilities on line 

• Comparison of policy effectiveness 

o Cumulative government spending 

o Impact of government spending on cellulosic ethanol production 

o Impact of government spending on industry capital investment.14

Cellulosic Ethanol Production: Base Case and Five Scenarios 

The 2017 cellulosic ethanol production potential ranges from 1.6 to 7.2 billion gallons for 

the set of policy options evaluated—a significant increase from the 500M gallons 

produced in the Base Case. The growth of this capacity through 2017 is shown in Figure 

4. The combination of the production subsidy plus capital cost reduction appears to be the 

most effective option in driving industry growth. The least effective option appears to be 

the grower payment alone. 

Cellulosic Ethanol Production

500

1,677

3,189

1,604

4,553

7,175

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Year

Million Gallons/year

Base Case Capital Cost Reduction

Production Subsidy Grower Payment

Grower Payment + Capital Cost Reduction Production Subsidy + Capital Cost Reduction

Figure 4. Cellulosic Ethanol Production: Base Case and Five Scenarios 

 

14 The only capital costs currently calculated by the BSM are the capital costs of plants at the 

demonstration, pioneer, and full scales. The current version of the BSM does not explicitly calculate the 

costs of feedstock harvesting, storage, and delivery infrastructure nor the costs associated with fuel 

distribution and infrastructure. See Appendix B for description of model structure. 

15
These curves provide some potential insight into the role of different government 

initiatives in facilitating growth in the cellulosic ethanol industry over the period of 2007 

to 2017. In Figure 4, there are several important things to notice: 

• All scenarios lead to higher cellulosic ethanol production in 2017 than in the Base 

Case. 

• Viewed in isolation, the production subsidy initiative yields higher 2017 

cellulosic ethanol production than either the capital cost reduction initiative or the 

grower payment initiative. 

• The capital cost reduction scenario, in isolation, appears to yield a higher 

compounded rate of growth in production in the years 2015-2017 than either the 

production subsidy initiative or the grower payment initiative. 

• The increment in production (relative to the Base Case) from the grower payment 

+ capital cost combination initiative is larger than the sum of the incremental 

production from grower payment + incremental production from capital cost 

reduction, suggesting leverage from the combination initiative. 

• The incremental production (relative to the Base Case) from the production 

subsidy + capital cost reduction initiative is larger than the sum of the incremental 

production from production subsidy + incremental production from capital cost 

reduction, again suggesting leverage from this combination initiative. 

Note that the Base Case and each scenario appear to generate nonlinear behavior. Latent 

within the model are positive feedbacks that in isolation would tend to underwrite 

exponential growth in cellulosic production capacity. The rate of growth in the industry 

can be constrained by investment risk as well as operating costs. By ameliorating these 

constraints to varying degrees, the scenario initiatives enable growth in production 

relative to the Base Case. 

Generating large quantities of cellulosic ethanol will require significant volumes of 

biomass feedstocks to supply the substrate for conversion along with construction of 

many large-scale conversion facilities to produce the ethanol, as summarized below: 

• Cellulosic Biomass Feedstock Production Volume. In the current BSM, 

cellulosic biomass feedstock resources include agricultural and forestry residues 

and switchgrass.15 Potential biomass feedstock production volumes to support the 

cellulosic ethanol production volumes range from 30 to 146 million dry tons in 

2017 (for the grower payment and production subsidy + capital cost reduction 

scenarios, respectively); 26 million dry tons are required in the Base Case. The 

maximum total biomass requirement suggested by the BSM is well beyond the 

 

15 Note: Because the feedstock production logic in the BSM is currently undergoing significant 

modification, the breakout of feedstock type is not included in this analysis. 

16
upper bound of availability of 250 million dry tons by 2017.16 (Details for all 

scenarios are presented at the end of this section in Table 3.) 

• Full Scale Cellulosic Ethanol Facilities On-Line. In 2017, the number of full 

scale (5,000 dry tons/day) cellulosic production facilities on-line ranges from 8 

for the grower payment scenario to 37 for the production subsidy + capital cost 

reduction scenario, in comparison to just two plants for the Base Case. (Details 

for all scenarios are presented at the end of this section in Table 3.) 

Relative Effectiveness of Government Spending in the Scenarios 

To evaluate the effectiveness of different initiatives, it is helpful to (1) compare the cost 

of initiatives, then (2) compare the impact relative to cost across initiatives. This latter 

view provides a consistent metric for evaluation. The cumulative government spending 

by 2017 associated with the initiatives in the scenarios (excluding VEETC and R&D 

spending, which is unchanged across all scenarios) is summarized in Table 4. Not 

surprisingly, the combination scenarios are the most costly; individually, the three 

subsidies under evaluation require roughly equivalent amounts of government funds. 

Table 4. Cost of Initiatives—Cumulative Government Spending in 2017 

Scenario Cost of Initiative 

(Cumulative Government 

Spending in 2017) (Million $) 

Base Case $439 

Grower Payment $1,552 

Production Subsidy $1,568 

Capital Cost Reduction $1,671 

Production Subsidy + Capital Cost Reduction $2,913 

Grower Payment + Capital Cost Reduction $4,384 

Figure 5 shows the effectiveness, relative to government outlay, across scenarios. As this 

figure illustrates, by 2017 the cumulative government cost for the three policy options 

considered ranges from $1.6 billion for just the Grower Payment to $4.4 billion for the 

Grower Payment + Capital Cost Reduction combination. Figure 5 also shows the 

cellulosic ethanol produced as a result of each policy option. Note that although the 

government outlay is greatest for the Grower Payment + Capital Cost Reduction 

combination, it does not yield the highest volume of cellulosic ethanol. Comparing the 

ratios of cellulosic ethanol production capacity (gallons) to cumulative government 

spending in 2017 for each scenario indicates that the combination of the Production 

Subsidy + Capital Cost Reduction offers the highest return on government investment, 

with a ratio of almost 2.5:1 (cellulosic ethanol capacity in gallons:cumulative government 

dollars spent). Second best is the Production Subsidy alone with a ratio of 2:1. The Base 

Case and other three scenarios are all similar with a ratio of around 1:1. 

 

16 Biomass Multi-Year Program Plan. (November 2007). Office of the Biomass Program, Office of Energy 

Efficiency and Renewable Energy, U.S. Department of Energy. 

http://www1.eere.energy.gov/biomass/pdfs/biomass_program_mypp.pdf

17
Impact of Government Policy Spending on Cellulosic 

Ethanol Production Volume by 2017

(excludes VEETC and R&D Spending)

Production 

Subsidy + Capital 

Cost Reduction

Base Case

Production 

Subsidy

Grower 

Payment + 

Capital Cost 

Reduction

Capital Cost 

Reduction Grower Payment

0

1000

2000

3000

4000

5000

6000

7000

8000

$0 $1,000 $2,000 $3,000 $4,000 $5,000

Cumulative Government Spending by 2017 (Million $)

Cellulosic Ethanol Production in 

2017 (Million Gallons/year)

Figure 5. Impact of Cumulative Government Spending on Cellulosic Ethanol Production 

Volume in 2017 (excludes VEETC and R&D Spending) 

Another way to view the effectiveness of government initiatives is to consider their 

effectiveness in stimulating private investment. Figure 6 compares government 

investment (in operating cost reduction and in capital cost reduction) against cumulative 

private investment across all scenarios. As Figure 6 suggests, the high return on 

government investment for the Production Subsidy + Capital Cost Reduction 

combination is reflected in the increased private investment that this scenario appears to 

motivate. Model results indicate that the Production Subsidy + Capital Cost Reduction 

motivates private capital investment in cellulosic ethanol production facilities (includes 

investment for all three scales—demonstration, pioneer, and full scale) more effectively 

than the other options considered here. For this scenario, each government dollar invested 

motivates private investment of over $9 by 2017. 

18
Government Policy Impact on Industry Capital 

Investment in 2017 

0

5,000

10,000

15,000

20,000

25,000

30,000

Base Case Capital

Cost

Reduction

Grower

Payment

Grower

Payment +

Capital

Cost

Reduction

Production

Subsidy

Production

Subsidy +

Capital

Cost

Reduction

Million $

Cum Govt Spending Per Policy Option Cum Private Investment

Figure 6. Government Policy Impact on Industry Capital Investment 

Key Performance Parameters Across the Biomass-to-Biofuels Supply 

Chain 

For completeness, the suggested impacts of these proposed government actions in 2017 

are summarized for the biomass-to-biofuels supply chain in Table 5. These data are not 

provided as definitive projections, but rather as preliminary insight into the relative 

impact of each scenario on the key performance metrics of the supply chain. 

19
Table 5. Suggested Impact of Policy on BSM Key Performance Parameters Across 

Biomass-to-Biofuels Supply Chain in 2017 

Base Case

Grower 

Payment

Production 

Subsidy

Capital Cost 

Reduction

Grower 

Payment + 

Capital Cost 

Reduction

Production 

Subsidy + 

Capital Cost 

Reduction

Agricultural Residues (Million Dry 

Tons/year) 23 27 52 38 72 106

Forest Residues (Million Dry 

Tons/year) 0 0 6 2 14 25

Switchgrass (Million Dry Tons/year) 3 3 8 4 914

Total Biomass Production (Million 

Dry Tons/year) 26 30 66 44 95 146

Feedstock Costs ($/dry ton) $20 $22 $31 $28 $34 $39

Cellulosic Ethanol Production 

(Million Gallons/year) 500 1,604 3,189 1,677 4,553 7,175

Number of Full Scale Plants On Line 2 815923 37

Operating Costs ($/gallon) $0.18 $0.18 $0.18 $0.18 $0.18 $0.18

Minimum Ethanol Selling Price 

($/gallon) $0.90 $0.90 $0.90 $0.90 $0.90 $0.90

Cumulative Private Capital 

Spending (Million $)* $4,296 $7,789 $12,746 $9,162 $17,840 $27,025

Maximum Potential Ethanol 

Consumption as E10 in 

Conventional Gasoline Vehicles 

(Million Gallons/year) 17,260 17,260 17,260 17,260 17,260 17,260

Maximum Potential Ethanol 

Consumption used as E85 in FFVs 

(Million Gallons/year) 27,446 27,446 27,446 27,446 27,446 27,446

Total FFVs (Million) 32.7 32.7 32.7 32.7 32.7 32.7

Cumulative Government Spending 

(Million $)** $24,688 $25,802 $25,817 $25,920 $28,634 $24,389

Cumulative Government Spending--

excluding VEETC and R&D (Million 

$) $439 $1,552 $1,568 $1,671 $4,384 $2,913

Model Number

bw070829-basesp.STMbw070829-

Feedstock.STM

bw070829-EtOH 

Subsidy.STM

bw070829-

Capital.STM

Capital plus 

Feedstock.STM

Capital plus 

EtOH.STM

Government Investment

**Includes spending on current policies (including VEETC) and R&D spending

Biomass Feedstock Production and Logistics 

Biofuels Production

Biofuels Distribution and End Use

*Capital Investment in all plant sizes--demonstration, pioneer and full scale 

Summary/Conclusions 

It is critical to remember that the BSM is a prototype model that is still being developed 

and, as such, its results must be interpreted and applied with caution. The analysis results 

presented here are not predictions, but plausible scenarios that can help policy makers to 

envision in broad terms potential future conditions.

The scenarios here focused on a set of government initiatives, in isolation and in 

combination, targeted at reduction in operating costs and/or reduction in capital cost. The 

analysis viewed the five resultant scenarios in terms of their performance through 2017. 

All options were evaluated in the context of influencing the investment decision to build 

full-scale cellulosic ethanol production facilities. The analysis suggests that the policies 

considered here—feedstock grower payment, cellulosic ethanol production subsidy, and 

20
capital subsidy for construction of full-scale cellulosic ethanol production facilities—can 

motivate production of between 1.6 and 7.2 billion gallons of cellulosic ethanol 

production by 2017. 

To produce these volumes of cellulosic ethanol, the BSM results indicate that a 

cumulative government investment on the order of $1.5 to $4.4 billion will be needed. 

(Note that this level of investment is dwarfed by the current VEETC, which the model 

suggests will cost taxpayers over $23 billion by the time it expires in 2010.) 

The scenarios presented here suggests that government initiatives can be used to “prime 

the pump,” reducing investment risk and initial operating cost, thus enabling the new 

industry to grow to the point at which it has become self-sustaining. 

For this analysis, which focuses on the deployment of cellulosic ethanol production 

facilities, the preferred policies and incentives are those with the highest leverage to close 

the gap between the investor required rate of investment (ROI) and the expected ROI (for 

commercial plants) until industrial experience catches up. The synergies of combining the 

capital and operating cost subsidies appear to provide the highest leverage in triggering 

the beginnings of a sustainable bioindustry. 

Recommendations/Next Steps 

The current Base Case BSM should be recognized as a preliminary model. The model 

structure and data were reviewed in October 2006 by experts in agriculture, ethanol 

processing, investment, and vehicle manufacturing.17 The next steps are focused on 

developing a more robust representation of the dynamics at play in the transition to a 

future cellulosic ethanol industry. 

In the short term, modifications and refinements in response to the BSM review are 

already in progress, with a new base case—BSM v.2—scheduled for completion in 2008. 

Once the revised model is finalized and vetted with the standing BSM review committee 

(established as part of the October 2006 workshop), the new base case will be 

documented in detail. In addition, the policy scenarios investigated in this analysis will be 

revisited to determine the impacts, if any, on the model results. The analysis could then 

be expanded to investigate the optimum timing of different types of subsidies, the 

sensitivities around the magnitude of each type of subsidy (along with evaluation of other 

policy options under consideration including crop insurance programs for cellulosic 

ethanol producers), loan guarantees for commercial-scale cellulosic ethanol production 

facilities, and carbon-based incentives for renewable fuels production among others. 

The following enhancements to the model are planned for completion over the next two 

years: 

 

17 Biomass Scenario Model Workshop Results. (October 25, 2006). Office of the Biomass Program, Office 

of Energy Efficiency and Renewable Energy, U.S. Department of Energy. 

http://30x30workshop.biomass.govtools.us/documents/061106ScenarioModelWorkshopReport.pdf 

21
• Feedstock Production and Logistics 

o Incorporate high-level regionalized feedstock production data to facilitate 

analysis of regional differences 

o Incorporate additional feedstocks data (starch product, herbaceous energy 

crop, herbaceous residue, woody energy crop, forestry residue, urban 

wood waste) 

o Improve representation of land allocation logic, allowing for multiple uses 

of agricultural land (i.e., competition between annual crops and other 

agricultural uses) 

o Enhance representation of economics associated with feedstock logistics 

• Biomass Conversion 

o Expand model to include additional conversion technologies (starch; starch 

+ fiber; starch + biochemical; biochemical; thermochemical; combination) 

o Enhance representation of investor behavior (venture capitalists; big vs. 

small company) 

• Biofuels Distribution and End Use 

o Incorporate high-level regional desegregation regarding concentration of 

automobiles across the U.S. to facilitate analysis of regional differences 

o Develop structure to capture evolution of system to connect ethanol 

production to fuel use (including logic of greenfield vs. retrofit options for 

pipelines, dispensing stations, etc.) 

o Incorporate multiple distribution modalities (truck, rail, barge, pipeline) and 

allow for greenfield vs. retrofit of pipelines 

• Petroleum Industry Representation 

o Incorporate simple global petroleum supply/demand dynamics, including 

key oil stocks, refinery capacity, and global petroleum demand scenarios. 

In the longer term, additional aspects of the dynamics surrounding the transition from 

petroleum- to biomass-based fuels could be investigated through the following model 

enhancements/expansions: 

• Future energy scenarios that reflect global competition for oil more aggressively 

than DOE-EIA projections 

• Improved characterization of the investment required to advance technologies 

from RD&D to the point of viable commercialization 

• Ability to look at impacts of failed RD&D and ethanol production facilities 

(current model assumes 100% success) 

• Additional biofuels capabilities beyond ethanol 

• Competition from other alternative fuels (e.g., coal-to-liquids) 

22
• Addition of forestry land allocation logic 

• Environmental implications/aspects costs and benefits: water, GHG, sustainability 

• International/global fuel market impacts 

• Optimum timing of government policies and incentives. 

23
24
Appendix A: An Overview of Symbology Used by the 

STELLA Modeling Tool 

The system dynamics framework and the STELLA software tool were chosen for the 

BSM development effort because of their ability to address systems problems. The 

software, developed by ISEE Systems, Inc. (www.iseesystems.com) provides a simple, 

yet powerful visual language for representing and simulating dynamic systems and 

processes, thus making the system dynamics framework accessible as a desktop tool for 

analysts and policy makers. 

STELLA uses the symbolic language of system dynamics to represent dynamic systems 

and processes. This language is composed of a small set of primitives. These building 

blocks for the language are summarized in Table A-1. 

Table A-1. Primitives of the system dynamics language as implemented in the STELLA 

software 

Symbol Name Purpose Mathematical 

Analog 

Language/ 

Grammar 

Analog 

Stock Accumulate “stuff” 

(like a bathtub 

accumulates water) 

State variable 

time integration 

Noun 

Flow Fill or deplete 

accumulations over 

time 

Time derivative Verb 

or

Conv erter

Converters Input/output 

relationship; decision 

rule; model constant 

Auxiliary 

equation 

Adjectives and 

adverbs 

Connector, 

wire 

Connect inputs to 

outputs. Show 

dependency 

relationships 

f( ) Conjunction 

Together, these language primitives make it possible to create models of rich dynamic 

complexity. Often these models are based on a simple accounting structure that captures 

key physical relationships in the system of interest. A simple example of such physical 

relationships is shown in Figure A-1. In this structure, stocks and flows are used to 

account for the distribution of crops in switchgrass and in other uses. Flows capture the 

movement from other uses to switchgrass as well as the reverting of switchgrass back to 

other uses. Converters and connectors are used to capture the logic behind the movement 

into switchgrass (farmers move land to switchgrass based on its economic attractiveness 

relative to other uses), as well as the logic controlling the movement of land from 

switchgrass back to other uses (reversion is proportional to the useful economic lifetime 

switchgrass). 

25
Acres in Switchgrass Acres in Other Crops

planting in switchgrass

shif ting to other crops

switchgrass lif etime

relativ e attractiv eness 

of switchgrass

Figure A-1. Simple Structure Accounting for Allocation of Land Between Switchgrass and 

Other Uses 

26
Appendix B: Structural Description of the Biomass Scenario 

Model (BSM) 

Introduction 

From a conceptual perspective, the BSM is organized around five modules, each of 

which corresponds to a stage in the biomass-to-biofuels supply chain. This supply chain 

is shown below, in Figure B-1: 

Feedstock 

Production 

Feedstock 

Logistics 

Biofuels 

Production 

Biofuels 

Distribution 

Biofuels 

End Use 

Figure B-1. The Biomass-to-Biofuels Supply Chain 

Because the model is a prototype, not all modules suggested by the supply chain are 

represented in deep detail within the BSM. In particular, biofuels distribution from point 

of production to point of use is not represented within the model. For the remaining 

modules, a comprehensive description is provided from the perspective of: 

• Module purpose and approach 

• Key module outputs and inputs 

• The structure, logic, and decision rules embedded into the module 

• Policy levers and scenario variables that impact the module. 

Feedstock Production Module 

The feedstock production module serves to provide a basis for the production of 

cellulosic feedstocks within BSM, respecting the constraints imposed by the agricultural 

land base in a manner consistent with economics. In its approach, the module is very 

simple. Specifically: 

• The module considers a single dedicated energy crop (switchgrass). This energy 

crop competes against an amalgam of “all other” crops. 

• Existing analyses (POLYSYS) are used to generate supply curves of cellulosic 

agriculture residues and forest residues. 

• To manage detail complexity, the agriculture system is modeled as a single 

aggregated region. 

• Two land types are considered: prime and marginal agricultural land. Marginal 

land corresponds roughly with Conservation Reserve Program (CRP) land, while 

prime land corresponds with USDA’s “Cropland Used for Crops” classification. 

27
Key Performance Indicators, Outputs, and Inputs 

Table B-1 summarizes key performance indicators, module outputs (to other modules), 

and module inputs (from other modules and from policy levers/scenarios). Because the 

Feedstock Production module is at the beginning of the supply chain, most of its 

interaction is with the Biofuels Production module. 

Table B-1. KPIs, Outputs, and Inputs Associated with Feedstock Production Module 

Key Performance Indicators: Feedstock Production Module 

Indicator Notes

Total Feedstock Production Over time, the total amount of biomass (including energy crops, 

agricultural residues, and forest residues) available for conversion. 

Total Switchgrass Production Total amount of switchgrass produced by agriculture. This can be 

considered a proxy for production of perennial cellulosic energy 

crops. 

Total Agricultural Residue 

Production 

Supply curve for agriculture residue developed via POLYSYS 

analysis. 

Total Forest Residue Production Supply curve for forest residue developed via POLYSYS analysis. 

Agricultural Yield Yield for switchgrass, both in research lab and in “real world.” 

Feedstock Price Single feedstock price assumed to hold for residues and for energy 

crops (switchgrass). Price jointly determined by supply (from 

Feedstock Production module) and cellulosic requirements (from 

Biofuels Production module). Price includes feedstock subsidy. 

Land Allocation Between switchgrass and all other uses, for prime and marginal land. 

Outputs to Other Modules from Feedstock Production Module 

Output Used In How Used Notes 

Feedstock Price Biofuels 

Production 

Input to plant addition logic Feedstock price + transport cost 

– subsidies drive calculation of 

minimum cost of producing 

ethanol. 

Total Feedstock 

Production 

Biofuels 

Production 

Input to plant capacity 

utilization logic 

Under situations of supply 

shock, feedstock supply can 

constrain utilization of 

conversion capacity, hence 

production of EtOH. 

Inputs to Feedstock Production Module

Input From How Used Notes 

Feedstock Demand Biofuels 

Production 

Used, along with feedstock 

production, to determine 

feedstock price 

In this (8/07) version of BSM, 

there is no accumulation of 

biomass inventory; hence no 

inventory pressure on price. 

Ag R&D Spend Scenario Drives yield improvements for 

switchgrass 

Maturation Time for 

Switchgrass 

Scenario Can set time between planting 

and maturity 

Default set to two years 

Residue Supply Scenario Allows user to switch “on” or 

“off” residue availability for 

conversion 

Constant Price Scenario Allows user to set constant 

price for feedstock for 

duration of simulation 

28
Structure, Logic, and Decision Rules for the Feedstock Production Module 

The Feedstock Production module functions as a framework for generating cellulosic 

feedstocks, both from dedicated crops (switchgrass) and from residues. Figure B-2 

presents a highly simplified representation of the framework and the key feedback 

relationships that drive behavior within the Feedstock Production module. 

Land Established in Switchgrasss

switchgrass production

Land in Alternativ e Uses Maturation Delay

conv erting to 

Switchgrass

becoming 

mature

 Switchgrass

Rotating f rom Switchgrass 

to Other Uses

switchgrass y ield

ag & f orest 

residue 

production

total 

 f eedstock

 production

Feedstock Price

f eedstock price changes

f eedstock demand

f eedstock supply demand balance

relativ e v alue of 

v alue of land in land in switchgrass

alternativ e uses

Maturation 

Delay Time

Figure B-2. Simplified view land allocation and feedstock production 

As the diagram suggests, there are two fundamental components to the supply of 

cellulosics. The first component is residues from agriculture and forestry. These residues 

29
are provided as a function of feedstock price. As price increases, quantity supplied 

follows suit. The supply curves for both agricultural residues and forest residues were 

developed using POLYSYS.18 

The second component of cellulosic supply is driven by the quantity of land in 

switchgrass, represented by the maturation delay and the land established in switchgrass. 

The switchgrass yield per acre is applied to the acres in switchgrass to generate 

switchgrass production. Note that land that is in the maturation delay is assumed to have 

a lower yield than mature land. 

An important feature of feedstock supply is the structure that constrains overall 

production to the total amount of land in the system. As shown in the diagram, land 

moves “other uses” through the maturation delay into fully productive switchgrass and 

then back again to other uses. Land cannot be in switchgrass indefinitely. In the model it 

is assumed that land spends 10 years on average in switchgrass before rotating back into 

other uses. Because both land bases represented in the module—prime and marginal—are 

conserved, the module ensures that there are no free lunches from a land availability 

perspective. 

The movement of land is driven by a simple pricing mechanism. In the model, land is 

assumed to go to its highest-valued use. Here’s how the pricing/land allocation system 

works: 

• Price drives the quantity supplied for residues. 

• Price also provides a basis for farmer calculation of net revenue per acre for 

switchgrass, for both prime and marginal land. 

• Farmers compare revenue per acre for switchgrass against expected revenue per 

acre for land in other uses. As relative value of land in switchgrass rises, land 

tends to move toward use in switchgrass. 

• As land moves toward switchgrass, the value of land in alternative uses tends to 

increase. This in turn slows the movement of land away from other uses toward 

switchgrass. 

• Specific sensitivities are assumed to differ for prime and marginal land. Marginal 

land in other uses is assumed to move into switchgrass at lower feedstock price. 

Its relative value cost is assumed to rise less quickly as it is moved into 

switchgrass. 

• Price changes are driven by imbalances between demand for feedstock (from the 

Biofuels Production module) and the supply of feedstock (both switchgrass and 

residues). 

 

18 De La Torre Ugarte, Daniel G. and Daryll E. Ray (2000) Biomass and Bioenergy Applications of the 

POLYSYS Modeling Framework, Biomass and Bioenergy, 4 (3):118. 

http://apacweb.ag.utk.edu/polysys.html 

30
When woven together, these relationships result in dynamics that work to equilibrate 

supply and demand of feedstocks as the industry evolves over time. 

A second portion of the feedstock supply module captures the relationships between 

R&D spending and yield improvements. Figure B-3 provides a high-level view into these 

relationships. 

Av erage Yield f or New 

Plantings in 'Real World'

y ield growth in real world

y ield growth dwell time 

research station to real world

Research Station

 Yield per Acre

y ield growth at research station

inv estment in y ield growth

Figure B-3. Simplified structure for tracking yield growth in switchgrass 

This structure is a simple representation of the agricultural research process, as well as 

the process by which improvements in crop yields make their way from a research setting 

into the real world. Using corn as an analog, the model relates yearly investment in yield 

growth to the growth in yield at the level of agricultural research stations. Yield growth is 

assumed to be exponential at first, and then linear after yield has reached a (user-defined) 

threshold level. After a “dwell” time (again, set by the user), yields change for new 

plantings of switchgrass in the real world. These yields are then tracked for land as it 

works its way through the cycling between alternative uses and switchgrass. 

Feedstock Logistics Module. The feedstock logistics module accounts for the cost 

associated with moving biomass feedstock from point of production (i.e., “farm-gate”) to 

point of conversion (i.e., “plant-gate”). Rather than model alternative 

infrastructure/transport systems, truck transport was assumed for the purpose of 

calculating cost of transport. 

31
Key Performance Indicators, Outputs, and Inputs 

Table B-2 summarizes key performance indicators, module outputs (to other modules), 

and module inputs (from other modules and from policy levers/scenarios). As suggested 

by Table B-2, this stage in the supply chain is represented simply. 

Table B-2. KPIs, Outputs, and Inputs Associated with Feedstock Logistics Module 

Key Performance Indicators: Feedstock Logistics Module 

Indicator Notes

Transport charge Calculated for each plant size (demonstration, pioneer, full scale) 

Outputs to Other Modules from Feedstock Logistics Module 

Output Used in How Used Notes 

Transport charge Biofuels Production Combined with farm gate 

cost per ton to drive 

overall feedstock cost per 

ton 

Calculated for each plant 

size (demonstration, 

pioneer, full scale) 

Inputs to Feedstock Logistics Module

Input From How Used Notes 

Average plant input 

capacity in tons per 

day 

Biofuels Production Used to calculate average 

distance to plant 

Calculated for each plant 

size (demonstration, 

pioneer, full scale) 

Average days per year 

online 

Biofuels Production Used to calculate average 

distance to plant 

Average switchgrass 

yield 

Feedstock 

Production 

Used to calculate average 

distance to plant 

Fraction of land 

surrounding plant in 

switchgrass 

Assumption/Scenario Used to calculate average 

distance to plant 

Winding factor Assumption/Scenario Used to calculate transport 

charge 

Accounts for road windage 

from field to plant 

Fixed transport 

charge/ton 

Scenario Used to calculate transport 

charge 

Total cost is sum of fixed 

and variable costs 

Cost per ton-mile Scenario Used to calculate transport 

charge 

Total cost is sum of fixed 

and variable costs 

Structure, Logic, and Decision Rules for the Feedstock Logistics Module 

The essential logic for the feedstock transport cost calculation is shown in Figure B-4. 

32
plant input capacity 

tons per day

Transport Charge Day s per Year Online

Fraction of Surrounding 

Land in Energy Crop

Miles to Plant

acres per sq mi

$ per ton mile

YPA Ov erall

Winding Factor

Fixed Transport $ per ton

Figure B-4. Inputs and Outputs Associated with Feedstock Logistics Module 

As Figure B-4 suggests, transport charge has a fixed and a variable component. Variable 

costs are determined by applying a winding factor to the product of cost per ton-mile and 

the radial distance from farm to plant. Average distance to plant is calculated by 

assuming that feedstock production is distributed in a space centered on the plant. Plant 

feedstock requirements, agricultural yield, and an assumption about the amount of land 

surround the plant that is dedicated to energy crops, serve as input to a calculation of the 

average distance required to transport feedstock from farm to pant. 

This structure is very simple and consistent with the prototype nature of BSM. In 

subsequent versions of the BSM, the transport module will see significant enrichments 

and enhancements. 

Biofuels Production Module 

As the name suggests, the Biofuels Production module is concerned with the conversion 

of feedstocks into ethanol for fuel. There are three focal points to the Biofuels Production 

module logic: 

• Representing the logic associated with the decision to add new plants to the 

system 

• Tracking key attributes (number and yield) of demonstration-scale, pioneer-scale 

and full-scale plants as they move through stages of design/construction, startup, 

and fully on-line 

• Providing a framework for the capture of plant yield dynamics—at the precommercial and post-commercial levels. 

33
Key Performance Indicators, Outputs, and Inputs 

Table B-3 summarizes key performance indicators, module outputs (to other modules), 

and module inputs (from other modules and from policy levers/scenarios). 

Table B-3. KPIs, Outputs, and Inputs Associated with Biofuels Production Module 

Key Performance Indicators: Biofuels Production Module 

Indicator Notes

Total EtOH production Includes corn and cellulosic 

Corn EtOH production 

Cellulosic EtOH 

production 

Number of on-line plants Broken down by scales (demonstration, pioneer, and full scale) 

Minimum ethanol selling 

price 

Given current state of system (including economic “drivers” such as % 

equity financing, required rate of return, taxes, subsidies, co-product credits, 

etc.) determines the minimum price at which ethanol can be sold 

Cumulative capital 

spending on plants 

Includes both private and government capital 

Cumulative government 

EtOH subsidy 

Cumulative government 

feedstock subsidy 

Cumulative government 

R&D spend 

Outputs to Other Modules from Biofuels Production Module 

Output Used In How Used Notes 

EtOH supply Biofuels 

End Use 

Used to calculate “filling” of 

E10, E85 markets 

Feedstock demand Feedstock 

Production 

Used in conjunction with 

feedstock supply to determine 

price movement 

Average plant input 

capacity in tons per day 

Feedstock 

Logistics 

Used to calculate average 

distance to plant 

Calculated for each plant size 

(demonstration, pioneer, full 

scale) 

Average days per year 

online 

Feedstock 

Logistics 

Used to calculate average 

distance to plant 

Inputs to Biofuels Production Module

Input From How Used Notes 

Potential EtOH demand Biofuels 

End Use 

Input to plant addition logic As EtOH market reaches 

saturation, growth in industry 

slows. Potential demand broken 

into E10 and E85 components 

Gasoline price Biofuels 

End Use 

Input to plant addition logic Attractiveness of new plant 

additions is constrained by 

relative price of EtOH and 

gasoline (BTU equivalency 

basis) 

Transportation cost Feedstock 

Logistics 

Input to plant addition logic 

Project subsidy Scenario Set annual spend, max % of 

project subsidized, and max 

Can independently set levers for 

Demonstration, pioneer, and 

34
subsidy per project by project 

scale 

full scale plants 

Government precommercial R&D 

spending 

Scenario Set spending at fundamental 

research and at pilot plant 

level 

Decreases risk at “next” level of 

scale. Moves system more 

quickly to “mature capacity” 

yields 

Ethanol subsidy Scenario Makes all ethanol more 

attractive to producers 

Cellulose-specific 

subsidy 

Scenario Makes cellulosic ethanol more 

attractive to producers 

Active only when there is no 

general ethanol subsidy 

Feedstock subsidy Scenario Lowers cost of delivered 

feedstock (relative to price 

received by feedstock 

producers) 

Feedstock captive/free 

switches 

Scenario Make (unsubsidized) delivered 

cost of feedstock zero 

Limited to first 2 million 

tons/yr of industry input 

capacity 

Financial parameters Scenario Set requirements for equity 

return, debt interest rate, tax 

rate, % plant financing by 

plant scale 

Investor parameters Scenario Set investor type (aggressive, 

moderate, conservative), 

enable venture capitalists 

Corn ethanol industry 

parameters 

Scenario Set corn grain price, capital 

cost of corn ethanol plants, 

max corn ethanol plants 

Rudimentary corn ethanol 

industry generates supply of 

corn ethanol; not tightly 

integrated with rest of biofuels 

production module 

Structure, Logic, and Decision Rules for the Biofuels Production Module 

There are three fundamental components to the Biofuels Production module. One 

component represents the decision rules associated with the addition of new plants to the 

system. The second component tracks key attributes of plants (number, size, and 

conversion yield) as they move through design and construction, to startup, to fully online status. The third component considers pre-commercial and post-commercial R&D 

activities as they relate to yield. Consider each in turn. 

Decision rules associated with plant addition: Figure B-5 presents a highly simplified 

representation of the decision logic used to drive the addition of plants to the industry. 

35
attractiv eness of EtOH 

plant inv estment

prev ailing gasoline 

wholesale price Capital Cost per Gallon 

Feedstock Cost per Gallon 

inv estor ty pe

Overall Industry Size

min EtOH selling price

required rate of return

btu equiv alency f actor

experience at prior scale

cost growth f rom experience

taxes 

etc

capital subsidy

cost of capital and cost growth f actor capital cost taxes per gallon

cost growth f rom R&D

capital cost of nth 

plant by scale plant output capacity Cumulative R&D effort by scale gals per yr

bottom line EtOH cost

Figure B-5. Simplified View of Logic Determining Attractiveness of EtOH Plant Investment 

As the diagram suggests, the fundamental nature of the decision logic involves 

determination of the attractiveness of an EtOH plant investment. This is done by 

comparing the minimum selling price for ethanol, after accounting for volumetric BTU 

differences as well as various subsidies and credits, against the cost of the highest-valued 

alternative (corn ethanol when RFS is being “filled”, wholesale gasoline otherwise). 

Lower relative price for ethanol implies higher attractiveness, and hence a more rapid 

investment. Higher relative price means no incentive to invest. Within the model (but not 

shown in the diagram), an arrayed structure captures the investment decision at each of 

three plant scales: demonstration, pioneer, and full scale. 

Feedstock Price

Transport Charge

f eedstock subsidy

f eedstock cost per ton f eedstock cost per gallon

conversion yield gallons per ton

processing cost per gallon

coproducts credit per gallon

EtOH

Cellulosic EtOH subsidy per gallon

36
Within the model, minimum selling price for ethanol is determined by multiple factors. 

These include: 

• Product subsidies (ethanol, or cellulosic ethanol) 

• Credits for co-product (protein, fuel, power) 

• Plant processing cost per gallon 

• Feedstock cost per gallon. This in turn is determined by: 

o Delivered feedstock cost (feedstock price, net of transportation and 

subsidies) on a per-ton basis 

o Plant conversion yield (converts cost per ton to cost per gallon) 

• The capital cost of the contemplated plant, apportioned on a per-gallon basis. This 

capital cost per gallon is driven by: 

o Estimated (scale-dependent) capital cost of the plant 

o The production or output capacity of the plant 

o The required rate of return for the plant 

o Taxes and other standard financial variables. 

For both the estimated capital cost of the plant and the required rate of return, the state of 

industry evolution is of critical importance. First, the minimum capital cost at each scale 

is driven fundamentally by the cost of the “nth” plant at that scale. To this cost at each 

scale is applied a capital cost growth factor, which itself is determined jointly by 

cumulative R&D effort and experience at prior scales. In effect, if the industry has not 

“done its homework” in R&D and in smaller scale efforts, the capital cost for the 

contemplated plant will be significantly higher. 

Second, the risk premium associated with the emerging industry will decrease as the 

industry matures. In the model each investor type has a risk premium—which translates 

into a higher required rate of return—associated with investing in plants of each scale. 

The specific risk premium is based on overall industry experience at previous scale: the 

lower the level of experience, the higher the risk premium. As the industry grows, this 

risk premium diminishes. 

Not shown in Figure B-5 is the role of venture capitalists in the investment decision. For 

venture capitalists, investment at demonstration scale occurs if the following conditions 

are met: 

• Sufficient research has been conducted at pilot scale 

• The industry size, as measured by its overall production capacity, is small 

• The expected price of gasoline is greater than the anticipated price of btuequivalent ethanol. 

37
Accounting structure to track biofuels production facilities. The stock/flow structure used 

to track the number and conversion yield of biofuels production facilities is shown in 

Figure B-6. 

In Design & Construction Starting Up On Line

initiating design

completing

Construction

completing

startup

attractiv eness ETOH 

plant inv estment

plant addition decision

EtOH production

Figure B-6. Accounting Structure Used to Track Plants and Conversion Yields by Scale 

This accounting structure is very straightforward. An array of plants by scale 

(demonstration, pioneer, full scale) moves through design & construction, startup, and 

on-line phases of development. Design and construction time is assumed to be one year 

for demonstration-scale plants and two years for pioneer and full scale plants. Startup 

time is assumed to be one year for all plants. When a plant is added to the system, the 

prevailing industry conversion yield is “sampled” and thus works its way through the 

chain. Once plants are on line, conversion yields can increase through learning curve 

dynamics, discussed in the next section. 

Pre-and Post-commercial R&D. R&D is critical to the evolution of the cellulosic 

industry. By driving improvements in conversion yields and reducing risk, precommercial R&D sets the stage for decisions to invest in demonstration, pioneer, and full 

scale conversion facilities. Once plants are in place, learning-curve dynamics lead to 

continuous improvement in performance of the industry. Figure B-7 shows a simplified 

representation of the core structure for these R&D efforts. 

38
Av erage Conv ersion 

Figure B-7. Representation of Logic Surrounding Pre- and Post-Commercial R&D 

At the left of the figure is the structure that accounts for pre-commercial R&D. Precommercial R&D spending is allocated to “fundamentals” in the lab, or pilot-scale 

operations. The cumulative investment in these areas drives expected yield toward the 

(theoretical) maximum mature technology yield for a given technology pathways. In the 

model, three technology pathways are contemplated. It’s possible for these pathways to 

exhibit different slopes and asymptotes in their yield curves. At any point in time, the 

new plant yield is a function of what has been invested to date in fundamentals and 

piloting. 

At the right of the figure is a stylized view into the drivers of post-commercial R&D. In 

the model, this form of R&D is characterized as learning-by-doing and represented using 

learning curves. As with pre-commercial, the maximum mature technology yield sets a 

ceiling against which learning curves operate. The gap between this maximum and the 

average industry yield is eliminated in constant proportion to the rate of doubling of 

industry output. 

Biofuels Distribution Module 

No model structure exists around fuel transport from point of production to point of use. 

During development of the BSM, an assumption was made that fuel transportation would 

not limit the development of the biofuels industry. Subsequent (ongoing) development 

efforts include development of simple structure around fuel transport and distribution. 

Biofuels End Use Module 

The Biofuels End Use module drives the consumption of fuel ethanol. The purpose and 

approach of the model is threefold: 

• Provide a physical basis for calculating the potential demand for ethanol as E10 

and E85 

• Provide a framework that accounts for the cumulative impact of initiatives around 

vehicle mix and efficiency 

Cumulativ e Production Experience

EtOH production

gaining experience

Yield f or Existing Plants

learning by doing 

y ield improv ements expected conv ersion 

y ield f or new plants

Cumulativ e $ spent 

on 'Fundamentals'

maximum mature technology y ield

y ield gap

rate of doubling of experience

progress ratio

y early spend on 

R&D f undamentals

Cumulativ e $ 

Spend on Piloting

Pre-commercial 

Post-commercial 

y early spend on pilot

39
• Provide self-contained laboratory for exploring impacts of alternate vehiclefocused policies and scenarios. 

Key Performance Indicators, Outputs, and Inputs 

Table B-4 summarizes key performance indicators, module outputs (to other modules), 

and module inputs (from other modules and from policy levers/scenarios). Taken as a 

whole, these indicators, outputs, and inputs give a sense for how this particular module 

interacts with the overall BSM structure. 

Table B-4. KPIs, Outputs, and Inputs Associated with Biofuels End Use Module 

Key Performance Indicators: Biofuels End Use Module 

Indicator Notes

Gasoline price At wholesale; driven by scenario variable 

Total vehicles Reflects cumulative impact of vehicle influx over time 

Total fuel 

consumption/yr 

In gasoline equivalent 

Flex fuel vehicles Cars and light trucks 

Potential EtOH demand Total demand, including E10 and E85 

Fraction of E10 market 

filled 

E10 demand assumed to “fill” before E85 demand; assume that all EtOH 

produced is sold 

Fraction of E85 market 

filled 

E10 demand assumed to “fill” before E85 demand; assume that all EtOH 

produced is sold 

EtOH supply Generated by Biofuels Production module; assume that all EtOH produced is 

sold until supply meets potential demand 

Outputs to Other Modules from Biofuels End Use Module 

Output Used In How Used Notes 

Potential EtOH demand Biofuels 

Production 

Input to plant addition logic As EtOH market reaches 

saturation, growth in industry 

slows. 

Gasoline price Biofuels 

Production 

Input to plant addition logic Attractiveness of new plant 

additions is constrained by 

relative price of EtOH and 

gasoline (BTU equivalency 

basis). 

Inputs to Biofuels End Use Module

Input From How Used Notes 

EtOH supply Biofuels 

Production 

Used to calculate “filling” of 

E10, E85 markets 

Oil price Scenario Drives logic of plant addition 

Distribution of incoming 

vehicles 

Scenario Distribution is applied to 

yearly influx of new vehicles 

Mix of conventional gas, FFV, 

other for cars, light trucks 

Annual rate of growth in 

vehicles 

Scenario Used to drive growth in 

number of vehicles over time 

Influx of vehicles includes 

replacement of retirement 

Efficiency of incoming 

vehicles 

Scenario Used to track overall average 

efficiency of vehicles in use 

Efficiency tracked for 

conventional gas, FFV, other 

for cars, light trucks 

Miles per vehicle per 

year 

Scenario Used to calculate overall fuel 

demand; used to calculate 

vehicle lifetime 

Miles per vehicle per year 

tracked for conventional gas, 

FFV, other for cars, light trucks 

Avg miles per vehicle 

before vehicle retires 

Scenario Used to calculate vehicle 

lifetime and retirements 

40
Structure, Logic, and Decision Rules for the Biofuels End Use Module 

The Biofuels End Use module functions as a framework for accounting cumulative 

impacts of scenarios around vehicle type, distribution, and efficiency. This framework is 

then used to fuel demand. Figure B-9 presents a highly simplified representation of the 

accounting framework used in BSM Biofuels End Use module. 

total v ehicles in use

CG FFV Other potential f uel demand

Car Light Truck

v ehicle inf lux logic

Vehicles 'A'

CG FFV Other

Car Light Truck

Vehicles 'B' 

CG FFV Other

Car Light Truck

Vehicles 'C'

CG FFV Other

Car Light Truck

v ehicle inf lux

v intaging A to B v intaging B to C

retiring

v ehicle v intaging 

and retirement logic

v ehicle growth rate

v ehicle distribution

CG FFV Other

Car Light Truck

new v ehicle

 ef f iciency

Oil Prices

av erage miles to retirement

av erage miles 

trav elled per y ear

Note: Accounting structure tracks number and ef f iciency of v ehicles across the f ollowing dimensions:

 + Conv entional Gas Car

 + FFV Car

 + Other Car

 + Conv entional Gas Light Truck

 + FFV Light Truck

 + Other Light Truck

Figure B-8. Simplified Representation of Accounting Framework Used for BSM Biofuels 

End Use 

Central to the accounting framework is the stock and flow structure that captures the 

vintaging of the vehicle population over time. Vehicles are divided into three vintage 

categories labeled “A”, “B”, and “C”. New vehicles accumulate in category “A”. As they 

vintage over time, they move into category “B” and category “C”. Retirements remove 

vehicles from the system. 

The vintaging structure thus represents a third-order material delay. Movement through 

the structure is driven by vehicle vintaging and retirement logic, which in turn is 

developed based upon the scenario variables of average miles traveled per year and 

average miles driven per vehicle until retirement. Vehicle influx is driven by retirements 

(new vehicles replace retirements) as well as by scenario-driven assumptions around 

vehicle growth rates. 

Within the model, this vintaging structure is used to track multiple attributes of the 

vehicle population. The current version of BSM tracks the distribution of vehicles 

41
between cars and light trucks. For each vehicle sub-population, the model captures the 

distribution of vehicles between conventional gasoline, flexible fuel, and other vehicles. 

Additionally, for each of the six sub-groups (vehicle type x fuel type), BSM tracks 

average fuel efficiency. For the influx of new vehicles distributions between car and light 

truck and among fuel type are driven by scenario inputs. Similarly, fuel efficiency of each 

sub-group is driven by a scenario input. 

As shown in Figure B-8 this structure is used to generate fuel demand over time. For the 

Biofuels End Use module, fuel demand comes in three forms: overall fuel demand, 

potential demand for E10, and potential demand for E85. These demand streams (overall 

and potential) are driven by the evolving attributes of vehicles in use, as well as by 

scenario-driven assumptions around miles traveled per year. Importantly, this physical 

reality sets a ceiling on what is physically achievable for the biofuels marketplace. An 

economic ceiling on what is achievable is set by the wholesale price for fuel. This latter 

concept is driven as a scenario input to the BSM. 

42
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1. REPORT DATE (DD-MM-YYYY)

April 2008 

2. REPORT TYPE

Technical Report 

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5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Understanding the Growth of the Cellulosic Ethanol Industry 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/TP-150-42120 

5e. TASK NUMBER

BZ07.1005 

6. AUTHOR(S)

D. Sandor, R. Wallace, and S. Peterson 

5f. WORK UNIT NUMBER

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National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

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13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

This report identifies, outlines, and documents a set of plausible scenarios for producing significant quantities of 

lignocellulosic ethanol in 2017. These scenarios can provide guidance for setting government policy and targeting 

government investment to the areas with greatest potential impact. 

15. SUBJECT TERMS

cellulosic ethanol; lignocellulosic; feedstock; renewable fuel standard; RFS; grower payment; STELLA; BSM 

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