NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC.

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Effects of Deployment 

Investment on the Growth of the 

Biofuels Industry

Laura J. Vimmerstedt and Brian W. Bush

Technical Report

NREL/TP-6A20-60802

December 2013
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC.

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Effects of Deployment 

Investment on the Growth of 

the Biofuels Industry

Laura J. Vimmerstedt and Brian W. Bush

Prepared under Task No. BZ14.1002

Technical Report

NREL/TP-6A20-60802

December 2013
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62 

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste. 
iii

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Acknowledgments

The authors would like to acknowledge the Biomass Scenario Model developers and analysts. 

Robert Wallace (Booz Allen Hamilton, formerly with the National Renewable Energy 

Laboratory) and John Sheehan (The University of Minnesota, formerly with the National 

Renewable Energy Laboratory) were instrumental in initial model development. From the U.S. 

Department of Energy, Zia Haq, Kristen Johnson, and Alicia Lindauer have supported analyses 

of the biofuels system and Brian Duff (formerly with the U.S. Department of Energy), Jim 

Spaeth, and Travis Tempel have supported analyses of demonstration and deployment of new 

biomass to biofuels conversion technology pathways. We thank Jim Spaeth, Travis Tempel, and 

Glenn Doyle of the U.S. Department of Energy and Thomas Jenkin of the National Renewable 

Energy Laboratory for their helpful reviews. In addition, other Biomass Scenario Model team 

members provided helpful insights, discussions, and reviews, including Josh Gesick, Amy 

Schwab, and Mike Talmadge of the National Renewable Energy Laboratory.
iv

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Executive Summary

In support of the national goals for biofuel use in the United States, numerous technologies have 

been developed that convert biomass to biofuels. Some of these biomass to biofuel conversion 

technology pathways are operating at commercial scales, while others are in earlier stages of 

development. The advancement of a new pathway toward commercialization involves various 

types of progress, including yield improvements, process engineering, and financial 

performance. Actions of private investors and public programs can accelerate the demonstration 

and deployment of new conversion technology pathways. These investors (both private and

public) will pursue a range of pilot, demonstration, and pioneer scale biorefinery investments; 

the most cost-effective set of investments for advancing the maturity of any given biomass to 

biofuel conversion technology pathway is unknown. In some cases, whether or not the pathway 

itself will ultimately be technically and financially successful is also unknown. This report 

presents results from the Biomass Scenario Model—a system dynamics model of the biomass to 

biofuels system—that estimate effects of investments in biorefineries at different maturity levels 

and operational scales. The report discusses challenges in estimating effects of such investments

and explores the interaction between this deployment investment and a volumetric production 

incentive. Model results show that investments in demonstration and deployment have a

substantial growth impact on the development of the biofuels industry. Results also show that 

other conditions, such as accompanying incentives, have major impacts on the effectiveness of 

such investments. This report does not advocate for or against investments, incentives, or 

policies, but analyzes simulations of their effects.
v

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table of Contents

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

2 Modeling Commercialization of Biofuels in the Biomass Scenario Model..................................... 3

3 Results and Discussion ..................................................................................................................... 11

4 Conclusions and Possible Next Steps ............................................................................................. 18

References................................................................................................................................................. 19

Appendix.................................................................................................................................................... 21
vi

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

List of Figures

Figure 1. Major modules in the Biomass Scenario Model represent major sectors of the 

biofuels industry.........................................................................................................................4

Figure 2. The Biomass Scenario Model considers multiple conversion pathways..........................5

Figure 3. The Biomass Scenario Model includes a reinforcing feedback around industrial 

development, financial performance, and industrial production and capacity ..........................6

Figure 4. Computations of the financial characteristics of prospective new biorefineries in the 

Biomass Scenario Model are used to estimate net present value...............................................7

Figure 5. Number of pioneer and commercial biofuels production facilities with baseline and 

additional deployment investment and incentives, showing starch to ethanol facilities .........11

Figure 6. Biofuels production with baseline and additional deployment investment and 

incentives, showing starch to ethanol production....................................................................12

Figure 7. Number of pioneer and commercial advanced biofuels production facilities with 

baseline and additional deployment investment and incentives..............................................13

Figure 8. Advanced biofuels production with baseline and additional deployment investment 

and incentives...........................................................................................................................14

Figure 9. Effect of deployment investment and incentives on maturity of targeted pathways......15

Figure 10. Effect of progress ratio assumption on maturity improvement....................................16

Figure A-1. Scheduled advanced biorefinery construction with baseline and additional 

deployment investment, by pathway, year, and scale..............................................................23

List of Tables

Table 1. Four Combinations of Incentive Conditions and Deployment Investment Conditions 

Used in the Simulations.............................................................................................................8

Table 2. Two Incentive Scenarios Used ..........................................................................................9

Table 3. Scenarios for Scheduled Advanced Biorefinery Construction with Baseline and with 

Additional Deployment Investment.........................................................................................10

Table A-1. Scheduled Advanced Biorefinery Construction with Baseline or Additional 

Deployment Investment, by Pathway, Year, and Scale ...........................................................21
1

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

1 Introduction

The Energy Independence and Security Act of 2007 (EISA) established a national goal of

36 billion gallons/year of renewable liquid transportation fuel in the United States by 2022 [1]. A 

variety of biomass resources can be converted to biofuels [2][3][4][5][6][7][8][9], including 

conversion of corn starch, sugar cane, or other biomass to ethanol; biological oils to biodiesel;

and cellulose or other biomass to hydrocarbons. Some biomass to biofuel conversion technology 

pathways are financially viable at commercial scales, while others face more uncertainty because 

they are in earlier stages of development. For example, ethanol from corn starch is a large-scale, 

fully commercial industry with 13.8 billion gallons annual production capacity [10] and biodiesel 

production capacity is 2.1 billion gallons per year [11]. Other conversion pathways started 

commercial production in 2013 [12]. KiOR’s Columbus, Mississippi, pioneer facility with an 

11-million-gallon-per-year capacity started production in March 2013 using a biomass to 

hydrocarbon conversion pathway [12], and INEOS Bio’s Vero Beach, Florida, 8-million-gallonper-year capacity pioneer facility started production using a cellulose to ethanol pathway in July 

2013 [12][13].

Commercialization of new biomass to biofuels conversion technology pathways may require

improvements that include yield improvements, process engineering improvements, and 

financial performance developments that together drive down costs and reduce risks [14].

Actions of both private investors and public programs contribute to the demonstration and 

deployment of new pathways. For example, the U.S. Department of Energy, Bioenergy 

Technologies Office, invests in research, development, demonstration, and deployment activities 

that aim to advance the commercialization of biofuels [15] among other goals. The set of 

investments (both private and public) that will most cost-effectively advance the maturity of a 

new pathway is not known and is inherently uncertain. Deployment investments may take place 

at a variety of operational scales and levels of maturity ranging from investment in pilot, to 

demonstration, to pioneer scale biorefineries. This report estimates effects of investment in 

selected sets of biorefineries and discusses challenges in making such estimates. The estimates 

are based on results from the Biomass Scenario Model (BSM)—a system dynamics model of the 

biomass to biofuels system that can be used to understand system behavior and policy effects but 

is not intended for making precise predictions. Based on the results presented in this report, 

investments in demonstration and deployment of biomass to biofuels conversion technologies 

have a positive effect on the development of the biofuels industry, and supportive policies, 

among other conditions, have major impacts on the effectiveness of such investments. This 

report does not advocate for or against investments, incentives, or policies. 

Steps toward deployment are made by proving various aspects of performance at different scales. 

Smaller-scale, less-costly, shorter-duration activities are completed first, on the theory that 

successful completion of these smaller-scale activities improves the chances of success of largerscale facilities that follow, reducing financial risk. For purposes of this report, we discuss four 

biorefinery scales that are distinguished by throughput capacity and maturity of operations: pilot, 

demonstration, pioneer, and commercial. At the pilot scale (typically greater than one dry tonne

of feedstock processed per day and less than one-fiftieth of commercial scale), process yield 

improvement is a major emphasis. This may involve optimizing inputs, catalysts, microorganisms, temperatures, pressures, residence times, and other process engineering parameters. 

Successful pilot operations identify problems to be addressed before scale-up and provide 
2

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

essential data for demonstration scale design. At the demonstration scale (typically on the order 

of 50 tons of feedstock processed per day, or one-tenth to one-fiftieth of full commercial scale), 

proving total system operation is a key challenge, especially maintaining process yield at this 

larger scale while proving the efficacy of process and material handling systems. Successful 

demonstration provides critical industrial-scale design information for the pioneer scale. At the 

pioneer scale (typically hundreds of tons of feedstock processed per day), maintaining 

performance at a larger scale is again a focus, and the successful pioneer scale biorefinery will 

result in proof of all aspects of commercial scale system operations, reducing risk and enabling 

future biorefineries to secure financing on better terms.1 Pioneer scale is not always defined 

separately from commercial scale in the literature or in general usage, and is sometimes called 

first-of-a-kind. Distinguishing a pioneer scale from commercial scale is useful here because it 

highlights the higher costs and risks that are still present for biorefineries at early stages of 

commercialization. While pioneer facilities are too risky to receive regular project financing, 

successful pioneer operations could enable future full-scale commercial facilities to receive 

project financing at more favorable interest rates. Pioneer is also distinguished from commercial 

scale in modeling so that higher costs and risks of these early commercial plants are included 

in calculations.

This report addresses the question, “What might be the effect of concerted investment in a set of

biorefineries on advancing biomass to biofuels conversion technology pathways toward 

commercialization?” Answering this question could help either public or private investment 

portfolios by informing their design and potentially improving their cost-effectiveness.

Section 2 of the report describes how the BSM simulates the commercialization process, 

especially the effects of demonstration and deployment investment at different scales. Section 3 

presents results under two investment and two incentive conditions: baseline or additional 

demonstration and deployment investment conditions, with or without a production incentive. 

This section also discusses limitations of the results. Section 4 summarizes conclusions and 

possible next steps to further understand the role of investment in biorefineries (at all scales of 

operation) in advancing commercialization of conversion pathways.

 1 Although the BSM uses a definition of pioneer and commercial scale based on size, commercial scale could be 

defined as any biorefinery that can make a profit. Biorefineries that produce high-value products could be 

commercial at smaller scales than the BSM commercial sizes. 
3

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

2 Modeling Commercialization of Biofuels in the 

Biomass Scenario Model

The U.S. Department of Energy-Bioenergy Technologies Office and the National Renewable 

Energy Laboratory (NREL) developed the BSM to explore the development of a U.S. biofuels 

industry [16][17][18][19]. Using a system dynamics modeling approach, the BSM is built on the 

STELLA software platform [20]. The model represents the dynamic interactions of the major 

sectors of the biofuels industry—feedstock production and logistics, feedstock conversion, and 

downstream elements (inventory, dispensing, distribution, fuel use, and vehicle fleet). The BSM 

represents contextual aspects of the developing biofuels industry, including investment in new 

biomass to biofuel conversion technologies, competition from petroleum fuels, vehicle demand 

for biofuels, and various government policies, using all of these to simulate the development of 

the industry. The purpose of the BSM is to generate and explore plausible scenarios for the 

evolution of a biofuels industry in the United States, and as a high-level system model it is not 

designed for precise, quantitative forecasting. Instead, it is best used to (1) analyze and evaluate

alternate policies; (2) generate scenarios; (3) identify high-impact levers and bottlenecks to 

system evolution; and (4) seed focused discussion among policymakers, analysts, and 

stakeholders. In this report, the BSM will be used to explore how public or private investment at 

pilot, demonstration, and pioneer biorefineries might affect biofuels industry development under 

different incentive conditions.

2.1 Biomass Scenario Model Overview

The major sectors of the biofuel industry and the associated BSM modules are shown in 

Figure 1. Previous publications [16][17][18][19] offer a more detailed discussion of the BSM, 

including its geographic stratification, module logic and structure, and data sources. The part of 

the model most relevant to this report is the conversion module of the BSM, which simulates the 

conversion of biomass to biofuels, including the demonstration and deployment of 

new pathways. 

Figure 2 shows the feedstocks, fuels or blendstocks, and biomass to biofuel conversion pathways 

in the BSM.
4

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 1. Major modules in the Biomass Scenario Model represent major sectors of the 

biofuels industry
5 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 2. The Biomass Scenario Model considers multiple conversion pathways; line formats 

show that there are multiple possibilities for integrating biomass-derived products

Industrial learning [14] is central to the modeling of technology deployment. The BSM simulates 

the benefits of learning through experience,2 separate from economies of scale. Learning through 

experience primarily addresses improvements to cost and performance metrics; while 

improvements in safety or environmental compliance may also occur through experience, 

adherence to safety and environmental standards is assumed at all scales, even in their immature 

states, because construction of biorefineries would rely on the established capability of the 

chemical industry to meet such standards. Learning through experience is also distinct from 

improvements that are made through research and development. In the BSM, research and 

development could improve expected performance of the mature commercial biorefinery and 

improve the initial state of a conversion technology pathway. It could also reduce the risk that 

commercial biorefineries using the targeted conversion pathway would fail to perform at 

expected levels; this risk is not explicit in the BSM. 

 

2 Learning through experience is also called learning-by-doing or experiential learning. 

Gasoline

Diesel

Jet

Lignocellulosic

Biomass

Energy crops 

(herbaceous and 

woody)

Residues 

(herbaceous, 

woody, urban)

Catalytic synthesis 

Fischer -Tropsch synthesis

Hydro-processing

Catalytic Upgrading

Fermentation 

Bio-Oils

Syn Gas

Sugars

Fermentation

Fermentation

Hydrodeoxygenation Oils

Natural Oils

(Oilseeds 

and Algae)

Corn

Butanol

Ethanol

Ethanol and 

Mixed Alcohols

Diesel and Jet

Methanol Synthesis,

Methanol-to-Gasoline Gasoline

Gasification

Extraction

Pyrolysis

Pretreatment 

& Hydrolysis

Hydrolysis

Processing at biorefinery

Optional processing

Biorefinery

Processing

Biomass 

Feedstocks

Petrochemical 

Refining

Blending at 

Refinery

Finished

Fuels

“Drop In” points for infrastructure- compatible fuels:

Sugars

Ethanol
6 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Learning drives the major dynamic feature of the BSM conversion module through reinforcing 

feedback as shown in Figure 3. The model estimates the growth of the industry on the basis of 

four scales of operation3

 (pilot, demonstration, pioneer, and commercial) for five performance 

metrics (yield of conversion process, input capacity for feedstocks, capital cost, risk premium 

that investors require to compensate for additional risk, and eligibility for debt financing). In the 

BSM, commercial facilities are assumed to be both larger and less expensive then pioneer 

facilities because of the additional learning that occurs at the pioneer scale.

Figure 3. The Biomass Scenario Model includes a reinforcing feedback around industrial 

development, financial performance, and industrial production and capacity

In this study, assumptions about the rate of experiential learning impact results. The 

effectiveness of pilot, demonstration, pioneer, and commercial scale activities in advancing the 

maturity level of a pathway is an uncertain but highly influential assumption. This relationship 

determines the effect of deployment investment in the simulations, as described further below. 

The attractiveness of an investment in a biorefinery is a key metric of the commercial maturity of 

a pathway and a critical driver of further deployment. The model approximates investors’ 

considerations through calculation of the expected net present value of an investment in a new 

pioneer or full-scale commercial biorefinery. A simplified schematic of these calculations is 

 

3 In the BSM, a single throughput is assumed for commercial scale biorefineries within a given conversion 

technology pathway. For example, commercial biorefineries in cellulose to ethanol and cellulose to hydrocarbons 

pathways are assumed to have a 2,000 Mg/day feedstock throughput. Pioneer biorefineries are assumed to have 30% 

of commercial throughput.

Industry Development

Multiple Technologies/Multiple Regions

Multiple Scales 

 Pilot 

 Demo 

 Pioneer 

 Commercial

Learning Curve Dynamics

Fuel Production

Pioneer Scale Financials

Multiple Technologies/Multiple Regions

Pro Forma Financials

Net Present Value of “Next” Plant 

 

Commercial Scale Financials

Multiple Technologies/Multiple Regions

Pro Forma Financials

Net Present Value of “Next” Plant 

 

Industry Production and Capacity 

Multiple Technologies/Multiple Regions

Pioneer and Commercial Scale

Allocation of Plant Construction Capacity 

Initiation of Construction of Discrete 

Plants 

Process Yield

Input Capacity

Capital Cost Growth

Investor Risk Premium

Debt Financing Access

Net Present Value of “Next” Plant

Capacity Additions

Maturity in 

terms of...

Reinforcing feedback (+)
7

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

shown in Figure 4. As shown in Figure 3, the estimated net present value of a new biorefinery

increases with industry maturity, improving the financial attractiveness of investing.

Figure 4. Computations of the financial characteristics of prospective new biorefineries in the 

Biomass Scenario Model are used to estimate net present value; the figure shows stages, types, 

and interrelationships among the financial computations

Policy conditions can affect the attractiveness of investment, and incentive policies are important 

to biofuels industry development. The BSM can represent a wide variety of policies that provide

incentives during conversion pathway start up or throughout its development, including point-ofproduction incentives; construction incentives (fixed capital investment, government loan 

guarantee); feedstock incentives; and downstream incentives (downstream point of use, 

distribution and storage, dispensing station fixed capital investment or capital expenditures, 

dispensing station repurposing, high-blend point of use). A library of incentive scenarios is 

available for BSM simulations, as described in Inman et al. [18]. For this study, two incentive

conditions were considered: a case with a production incentive and a case without a 

production incentive. 

The model represents biorefinery construction based on two different methods. First, biorefinery 

construction starts can be generated from within the model: if investment conditions are 

favorable and demand for biofuels is sufficient, the algorithm will initiate construction of a 

biorefinery. However, resources to build additional production capacity are constrained. The 

allocation choice distributes these resources among biorefinery construction projects by 

Revenue from 

Product, Power, 

and Coproduct 

Sales

Revenue Net of 

Operating Cost

Operating 

Cost Feedstock 

Expense

Non-Feedstock 

Operating Expense

Capital Investment 

After Any Subsidies

Initial Equity 

Investment

Equity 

Fraction

Depreciation 

Expense

Taxable 

Income

Tax

Tax 

Rate

Present Value of 

Loan Payment Annual Loan 

Payment

Interest 

Expense

Term of 

Loan

Interest 

Rate

Net Present 

Value (NPV) of 

Investment

Depreciation 

Period Loan 

Principal
8

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

considering their characteristics: conversion pathway, pioneer or full commercial scales, and 

geographic region. Biorefineries also must compete for these construction resources with similar 

investments outside of the biofuels industry. Not all financially attractive biorefineries are 

assumed to be built because other investments might be more attractive (other biorefinery types 

or investments outside of biorefining) or there might not be available capacity for construction of 

large industrial facilities.

Second, biorefinery starts can be scheduled: Inputs to the model can specify the year, pathway, 

location, and size of new biorefineries. In this study, this method is used to insert into the model

two different schedules for planned biorefinery construction starts: one based on an assumed 

baseline industry and public investment and another based on an assumed additional 

deployment investment.

2.2 Study Design

A set of BSM simulations were developed to explore the effects on industry growth of 

investment in biorefineries under a variety of conditions. This report focuses on deployment 

investment and production incentives, a key subset of a much larger set of simulations that 

involved the consideration of other policies, variations in ethanol demand, assumptions about 

industrial learning, uncertainties in techno-economic assessments, and other factors. The 

scenarios examined here were based on two dimensions or thematic categories that consisted of 

groups of related input parameters. These two dimensions were incentives and cellulose to 

hydrocarbon deployment investment. These dimensions are described in Sections 2.2.1 and 

2.2.2, and Table 1 shows how two incentive conditions and two deployment investment 

conditions were combined in the four distinct cases for this study.

Table 1. Four Combinations of Incentive Conditions and Deployment Investment Conditions Used 

in the Simulations

Baseline Deployment Investment

With Production Incentive

Additional Deployment Investment

With Production Incentive

Baseline Deployment Investment

Without Production Incentive

Additional Deployment Investment

Without Production Incentive

2.2.1 Incentives

This study selects two incentive scenarios from the BSM scenario library. Here, these are labeled

With Production Incentive and Without Production Incentive; in Inman et al., they are called the 

Point-of-Production-Focused Incentives Scenario and the Minimal Incentives Scenario. Features 

of these scenarios are shown in Table 2.

The production incentive was a point-of-production payment of $0.45/gallon (by volume, not 

energy content) for production of cellulose to hydrocarbons, cellulosic ethanol, and butanol. This 

amount of incentive is within the range of historical renewable identification number Renewable 
9

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Identification Number (RIN) market values, and this point-of-production payment scenario 

condition could be considered a very rough approximation of RIN market effects. 

Table 2. Two Incentive Scenarios Used

Scenario Name Incentives Rationale

With Production 

Incentive

Point-of-production payment of $0.45/gal 

for cellulose to hydrocarbons, cellulosic 

ethanol, and butanol for all simulation 

years.

EISA has established RIN markets to 

provide production incentives, and a 

production incentive could continue to 

the end of the simulation period in 2030. 

The $0.45/gal value is within the range of

historic RIN values.

Without Production 

Incentive

Does not include point-of-production 

payment of $0.45/gal

Production incentive could change, so 

the production incentive was removed to 

estimate these effects. 

Note: Two other incentives apply in both point-of-production payment scenarios. A point-of-production 

payment for ethanol, for one simulation year only (2011–2012), mimics the last year of the Volumetric 

Ethanol Excise Tax Credit [26 U.S. Code 6426]. The Biomass Crop Assistance Program [7 U.S. Code 

8111] is represented in the model throughout the simulation period.

2.2.2 Baseline and Additional Deployment Investment

Investment in new biorefineries may prompt advancement of the biomass to biofuels industry 

toward commercial maturity. One possible multi-stage strategy is to invest in a relatively large 

number of pilots, followed by a smaller number of demonstrations, and then by an even smaller 

number of pioneer scale facilities. Such an investment strategy could minimize the amount of 

capital at risk due to uncertainties in the techno-economics of particular processes and pathways. 

In particular, it allows for important and necessary technology advances to be made at lower 

scales and lower cost. This approach allows for the possibility that lack of progress in an earlier 

(pilot or demonstration) stage would cause a pathway not to receive investment in the next larger

scale. Overall, this approach could allow potential commercial pathways to be identified and 

developed at much lower cost because subsequent, larger investments are conditional on the 

successful results of initial, smaller investments. 

For this study, we performed simulations in the BSM with a baseline level of scheduled 

advanced biorefinery development and with an additional deployment investment, as shown in 

Table 3. Additional details on assumptions about pathways and timing of scheduled advanced 

biorefinery development are shown in the appendix. Each scale of biorefinery, and several 

different conversion technologies, is included among the scheduled developments. In addition to 

this scheduled biorefinery development, the BSM will generate biorefinery construction if 

conditions for industry growth are favorable, as described above.

The baseline level of scheduled advanced biorefinery development is based on professional 

judgment and informed by industry data [21][22][23]. The baseline deployment scenario also 

includes scheduled advanced biorefineries that received public incentives from the U.S. 

Department of Energy. In addition to the baseline scheduled biorefinery construction, this study 

tests the effects of additional investment in the set of biorefineries as summarized in Table 3 and 

the appendix. The baseline scheduled advanced biorefinery investment includes both cellulose to 

ethanol and cellulose to hydrocarbon pathways, and includes commercial scale biorefineries. The 

additional scheduled advanced biorefinery investment includes only select cellulose to 
10 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

hydrocarbon pathways, and does not include commercial scale biorefineries, as it is intended to 

represent a higher-risk public or private investment. These baseline and additional deployment 

investment schedules are only two of many possible scenarios for planned construction and are 

used for analysis and discussion but are not forecasts, nor are they representative of any detailed 

market analysis. Further market analysis is planned. 

Table 3. Scenarios for Scheduled Advanced Biorefinery Construction with Baseline and with 

Additional Deployment Investmenta

Scale of 

Biorefinery

Baseline

Advanced 

Biorefineries

Additional

Advanced 

Biorefineries

Baseline + 

Additional 

Advanced 

Biorefineries

Additional 

Advanced 

Biorefinery 

Construction 

Start Date 

Range 

Pilot 11 7 18 2016–2022

Demonstration 18 5 23 2014–2021

Pioneer 7 4 11 2017–2021

Commercial 6 6

Total 42 16 58 a See appendix for details.
11 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

3 Results and Discussion

The BSM was used to perform simulations to explore the effects of demonstration and 

deployment investment under different incentive and investment scenarios. The resulting 

estimated numbers of biorefineries is shown in Figure 5, including starch to ethanol facilities. 

Numbers of plants are normalized to BSM plant sizes and do not correspond to actual 

plant counts. 

Figure 6 presents the corresponding results on the basis of total annual volumetric biofuels 

production rates. The long-term decline in ethanol use is based on the expected declining use of 

gasoline, as light-duty vehicle efficiency continues to improve. 

Figure 5. Number of pioneer and commercial biofuels production facilities with baseline and 

additional deployment investment and incentives, showing starch to ethanol facilities4

 

4 Numbers of plants are normalized to BSM plant sizes and do not correspond to actual plant counts.

 

Baseline Deployment Investment Additional Deployment Investment

2015 2020 2025 2030

Year

2015 2020 2025 2030

Year

Number of Commercial Plants

With Production Incentive Without Production Incentive

0

100

200

300

400

500

0

100

200

300

400

500

Oil Crop to Hydrocarbons

Cellulose to Hydrocarbons

Cellulose to Ethanol

Starch to Ethanol
12 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 6. Biofuels production with baseline and additional deployment investment and incentives, 

showing starch to ethanol production5

 

 

5 Figure displays moving average over 3 years (preceding, current, and following year).

 

Baseline Deployment Investment Additional Deployment Investment

2015 2020 2025 2030

Year

2015 2020 2025 2030

Year

Biofuel Production [billion gal/yr]

With Production Incentive Without Production Incentive

0

5

10

15

0

5

10

15

Oil Crop to Hydrocarbons

Cellulose to Hydrocarbons

Cellulose to Ethanol

Starch to Ethanol
13 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

In Figure 7 and Figure 8, starch facilities and starch production are not shown so that it is easier 

to see the growth in the advanced biomass to biofuels conversion technology pathways. 

Figure 7. Number of pioneer and commercial advanced biofuels production facilities with baseline 

and additional deployment investment and incentives6

 

6 Numbers of plants are normalized to BSM plant sizes and do not correspond to actual plant counts.

 

Baseline Deployment Investment Additional Deployment Investment

2015 2020 2025 2030

Year

2015 2020 2025 2030

Year

Number of Commercial Plants

With Production Incentive Without Production Incentive

0

20

40

60

80

100

0

20

40

60

80

100

Oil Crop to Hydrocarbons

Cellulose to Hydrocarbons

Cellulose to Ethanol
14 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 8. Advanced biofuels production with baseline and additional deployment investment 

and incentives 

Figures 5 through 8 show that modeled deployment investment can have a significant effect on 

the development of conversion pathways. Under conditions with the production incentive, the 

additional deployment investment was sufficient to catalyze growth in biorefinery construction 

in the targeted pathways. The additional deployment investment, represented by seeding pilot, 

demonstration, and pioneer scale biorefinery construction starts, improved the maturity of the 

targeted pathways enough that they became sufficiently attractive investments that the model 

began generating plant starts internally. This effect is illustrated in upper right quadrant of the 

figures, where numbers of plants and biofuel production increase sharply. However, the cases 

without the production incentive offered insufficiently favorable conditions for the model to 

generate plant starts in the targeted pathways, even with the additional deployment investment. 

The improvements to pathway maturity are shown in Figure 9, for cases that correspond to the 

preceding figures. These maturity improvements and the industry growth shown in Figures 5–8 

reinforce each other. This is an example of the reinforcing feedback that is shown in Figure 3: 

Biorefineries seeded due to the additional deployment investment produce additional biofuel, and 

that experience increases maturity relative to the baseline. 

 

Baseline Deployment Investment Additional Deployment Investment

2015 2020 2025 2030

Year

2015 2020 2025 2030

Year

Biofuel Production [billion gal/yr]

With Production Incentive Without Production Incentive

0

1

2

3

4

5

6

0

1

2

3

4

5

6

Oil Crop to Hydrocarbons

Cellulose to Hydrocarbons

Cellulose to Ethanol
15 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Fast Pyrolysis and Fischer Tropsch pathways receive less pilot scale investment than others 

because of the extensive piloting that has already been completed. Both of these pathways 

receive pioneer scale investment during the simulation period because they are assumed to have 

experienced sufficient pilot and demonstration scale, and associated maturation, to warrant the 

next scale of investment during this time period. Catalytic upgrading of sugars to hydrocarbons 

and fermentation of sugars to hydrocarbons pathways receive pilot and demonstration scale 

investments during the simulation period. If successful at those stages, a longer-term simulation 

could show subsequent pioneer scale investment in those pathways. Figure 9 also illustrates that

conditions are insufficiently favorable for the fermentation to hydrocarbons pathway to generate

commercial maturity improvement, but this analysis depends on the techno-economic 

assumptions and is not designed to predict the relative commercial prospects of particular

pathways. 

Figure 9. Effect of deployment investment and incentives on maturity of targeted pathways 

Model results are limited and must be interpreted with caution. In addition, a specific limitation 

of these results relates to the uncertainty surrounding the effectiveness of a given deployment 

investment in improving the maturity of the targeted pathway. The size of the effect of the 

deployment investment depends on experiential learning assumptions about effects of different 

Baseline Deployment Investment Additional Deployment Investment

2015 2020 2025 2030

Year

2015 2020 2025 2030

Year

With Production Incentive Without Production Incentive

0%

20%

40%

60%

80%

100%

Pathway Maturity [%]

0%

20%

40%

60%

80%

100%

Pathway Maturity [%]

90%

80%

78%

77%

46%

60%

46%

62%

Fermentation

Catalytic Upgrading

Fast Pyrolysis

Fischer-Tropsch
16 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

types of investment on maturation. These assumptions are calibrated to historical experience, but 

considerable uncertainty remains, and simulation results are highly sensitive to this assumption. 

This limits the application of the results because it is impossible to predict, with any certainty, 

the ultimate effectiveness of any given deployment investment strategy. Figure 10 shows 

sensitivity to assumptions about the progress ratio7 [16]—a measure of the effect of deployment 

investment on maturity. The middle row of Figure 10 (75% progress ratio) is comparable to the 

“With Production Incentive” row of Figure 9, except that the simulation associated with results 

in Figure 10 has different baseline industry conditions. 

Figure 10. Effect of progress ratio assumption on maturity improvement—a 75% progress ratio is 

used in other simulations

 

7 The BSM formulates industrial learning as the rate at which the gap between current maturity and full-scale 

commercial maturity closes. The progress ratio defines how much of the gap remains with each doubling of 

experience, such that more gap remains if the progress ratio is higher, so maturity increases more slowly at higher 

progress ratio values. 

Progress

Ratio Baseline Deployment Investment Additional Deployment Investment

2015 2020 2025 2030

Year

2015 2020 2025 2030

Year

Pathway Maturity [%]

65% PR

75% PR

85% PR

0%

50%

100%

0%

50%

100%

0%

50%

100%

Fermentation

Catalytic Upgrading

Fast Pyrolysis

Fischer Tropsch
17

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Another limitation is the uncertainty about biofuels industry development with baseline

investment. The considered data about industry growth from a variety of sources when 

developing baseline assumptions [21][22][23]. Construction of biorefineries is often slower than 

initial plans propose. Planned scheduled biorefinery construction growth assumptions used for 

this study could be either an under- or an over-estimate of future biofuels industry development. 

In turn, the significance of the additional deployment investment studied here could be under- or 

over-estimated because of this uncertain baseline. A more detailed market analysis, which is 

planned, may serve as a future baseline.
18

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

4 Conclusions and Possible Next Steps

Deployment investments substantially accelerate industrial development under certain 

conditions, as modeled in the BSM. The BSM can detect effects of the different amounts of 

demonstration and deployment investment in pilot, demonstration, and pioneer scale

biorefineries. The size of the effect of this type of deployment investment in simulation results 

depends heavily on other conditions, such as incentives, baseline scheduled biorefinery

investments, and techno-economics. The deployment investment effects that were detected in the 

BSM include accelerated industrial learning, biorefinery construction, and biofuel production in 

the targeted pathways. The results of this study showed sharply increasing biorefinery 

construction and biofuels production when additional deployment investment was combined with 

a production incentive. While simulation results cannot precisely predict real-world events, these 

results suggest that deployment investments can accelerate industrial development if conditions 

are sufficiently favorable.

Next steps could include incorporating further market analysis, refining techno-economic 

estimates, and exploring additional interactions between deployment investment and other 

conditions that constrain or enable the growth of the biofuels industry. More detailed market 

analysis is planned, and when it becomes available, it will be incorporated into industry growth 

scenarios. Techno-economic performance estimates are being developed for an updated set of 

biomass to biofuels conversion technology pathways, and these will be used to update technoeconomic data in the BSM. In addition, analysis of interactions between deployment investment 

and progress due to experiential learning can help quantify the effects of more biorefinery 

construction in one conversion pathway.

Other conditions that are important to biofuels industry growth and can interact with deployment 

investment include overall economic growth, petroleum price, growth of other non-petroleum 

transportation fuels, capacity for construction of biorefineries and other chemical industry 

facilities, ease of industrial learning within and among biomass to biofuels conversion 

technology pathways, ethanol blending policy, and incentive policies that target various parts of 

the biofuels industry with production, point of use, capital, or other incentives. Exploration of 

how these many other conditions interact with deployment investment could reveal bottlenecks 

and synergies that could help target incremental investment where it is most effective. A key 

next step within this exploration would be to understand in greater detail the synergies between 

production incentives and deployment investment in biorefineries and to evaluate the impact of 

investment levels in each category.
19

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

References

[1] U.S. Congress, Energy Independence and Security Act of 2007. BT - Public Law 110-140, 

vol. 121 (2007).

[2] McAloon, A.; Taylor, F.; Yee, W.; Ibsen, K.; Wooley, R. Determining the Cost of 

Producing Ethanol from Corn Starch and Lignocellulosic Feedstocks. NREL/TP-580-

28893. Golden, CO: National Renewable Energy Laboratory, October 2000.

[3] Huber, G.W. “Production of Liquid Alkanes by Aqueous-Phase Processing of BiomassDerived Carbohydrates.” Science (308:5727), June 2005; pp. 1446–1450.

[4] Humbird, D.; Davis, R.; Tao, L.; Kinchin, C.; Hsu, D.; Aden, A.; Schoen, P.; Lukas, J.; 

Olthof, B.; Worley, M.; Sexton, D.; Dudgeon, D. Process Design and Economics for 

Biochemical Conversion of Lignocellulosic Biomass to Ethanol: Dilute-Acid Pretreatment 

and Enzymatic Hydrolysis of Corn Stover. NREL/TP-5100-47764. Golden, CO: National 

Renewable Energy Laboratory, March 2011.

[5] Biddy, M.; Jones, S. Catalytic Upgrading of Sugars to Hydrocarbons Technology Pathway.

PNNL-22319, NREL/TP-5100-58055. Golden, CO: National Renewable Energy 

Laboratory, March 2013.

[6] Jones, S.; Valkenburg, C.; Walton, C.; Elliott, D.; Holladay, J.; Stevens, D.; Kinchin, C.; 

Czernik, S. Production of Gasoline and Diesel from Biomass Via Fast Pyrolysis, 

Hydrotreating and Hydrocracking: A Design Case. PNNL-18284. Richland, WA: Pacific 

Northwest National Laboratory, February 2009.

[7] Dutta, A.; Talmadge, M.; Hensley, J.; Worley, M.; Dudgeon, D.; Barton, D.; Groendijk, P.; 

Ferrari, D.; Stears, B.; Searcy, E.M.; Wright, C.T.; Hess, J.R. Process Design and 

Economics for Conversion of Lignocellulosic Biomass to Ethanol: Thermochemical 

Pathway by Indirect Gasification and Mixed Alcohol Synthesis. NREL/TP-5100-51400. 

Golden, CO: National Renewable Energy Laboratory, May 2011.

[8] Phillips, S.D.; Tarud, J.K.; Biddy, M.J.; Dutta, A. Gasoline from Wood Via Integrated 

Gasification, Synthesis, and Methanol-to-Gasoline Technologies. NREL/TP-5100-47594. 

Golden, CO: National Renewable Energy Laboratory, January 2011.

[9] Davis, R.; Biddy, M.J.; Tan, E.; Tao, L.; Jones, S.B. Biological Conversion of Sugars to 

Hydrocarbons Technology Pathway. PNNL-22318. Richland, WA: Pacific Northwest 

National Laboratory, March 2013.

[10] U.S. Energy Information Administration. “U.S. Fuel Ethanol Plant Production Capacity,” 

2013.

[11] U.S. Energy Information Administration. “Monthly Biodiesel Production Report,” 2013.

[12] U.S. Environmental Protection Agency. 2013 Renewable Fuel Standards, 2013; pp. 49794–

49830.

[13] INEOS. “INEOS Bio Produces Cellulosic Ethanol at Commercial Scale,” 2013.

[14] Junginger, M.; Sark, W.; Faaij, A., eds., Technological Learning in the Energy Sector: 

Lessons for Policy, Industry and Science. Cheltenham, UK ; Northampton, MA: Edward 

Elgar, 2010.

[15] U.S. Department of Energy. “Bioenergy Technologies Office Multi-Year Program Plan,” 

May 2013.

[16] Newes, E.; Inman, D.; Bush, B. “Understanding the Developing Cellulosic Biofuels 

Industry through Dynamic Modeling.” Dos Santos Bernardes, M.A., ed. Economic Effects 

of Biofuel Production. InTech, 2011.
20

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

[17] Lin, L.; Newes, E.; Bush, B.; Peterson, S.; Stright, D. “Biomass Scenario Model v2.0 

Documenation: Data and References,” 2013.

[18] Inman, D.; Vimmerstedt, L.; Newes, E.; Bush, B.; Peterson, S. Biomass Scenario Model 

Scenario Library: Definitions, Construction, and Description. NREL/TP-6A20-60386, 

Forthcoming.

[19] Bush, B. “Biomass Scenario Model (BSM) Development & Analysis.” Annapolis, MD: 

2011 Analysis and Sustainability Review, 2011.

[20] isee systems. STELLA: Systems Thinking for Education and Research Software.

[21] Biofuels Digest. Advanced Biofuels & Biobased materials Project Database, 2012.

[22] Bacovsky, D.; Ludwiczek, N.; Ognissanto, M.; Worgetter, M. “Status of Advanced 

Biofuels Demonstration Facilities in 2012: A Report to IEA Bioenergy Task 39.” T39-P1b. 

Washington, DC: International Energy Agency, March 2013.

[23] Advanced Ethanol Council. “Cellulosic Biofuels Industry Progress Report 2012 - 2013,” 

December 2012.
21 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Appendix

This appendix documents the pathway, scale, and timing of facilities that comprise the 

deployment investment. This report uses two scenarios for scheduled biorefinery construction: 

with baseline or with additional deployment investment. Scenarios are shown in two formats: 

Table A-1 and Figure A-1. This is not a forecast. 

Table A-1. Scheduled Advanced Biorefinery Construction with Baseline or Additional Deployment 

Investment, by Pathway, Year, and Scale

 Scale Pathway Construction 

Start Year

Baseline

Deployment 

Investment

Additional

Deployment 

Investment

Cellulose to 

Hydrocarbons Pilot Catalytic Upgrading 2016 1.00

2019 1.00

2022 1.00

Fermentation 2011 1.00 1.00

2019 1.00

 2022 1.00

Fast Pyrolysis 2011 2.00 2.00

2013 2.00 2.00

2014 1.00 1.00

2016 1.00

Fischer Tropsch 2011 3.00 3.00

2012 1.00 1.00

 2014 1.00 1.00

 2019 1.00

Demonstration Catalytic 

Upgrading 2018 1.00

Fermentation 2012 1.00 1.00

 2021 1.00

Fast Pyrolysis 2015 1.00

2018 1.00

Fischer Tropsch 2011 1.00 1.00

2013 2.00 2.00

 2014 1.00

 Methanol to 

Gasoline 2013 1.00 1.00

Pioneer Fast Pyrolysis 2011 1.00 1.00

2012 1.00 1.00

 2021 1.00

2022 1.00

Fischer Tropsch 2017 1.00

2018 1.00
22 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Commercial Catalytic 

Upgrading 2013 1.00 1.00

2014 1.00 1.00

 Fast Pyrolysis 2013 1.00 1.00

2014 1.00 1.00

Cellulose to 

Ethanol Pilot Biochemical 2011 Pilot scale assumed fully 

mature at start of simulation

 Thermochemical 2011 Pilot scale assumed fully 

mature at start of simulation

Demonstration Biochemical 2011 7.00 7.00

2013 1.00 1.00

Thermochemical 2011 3.00 3.00

2012 1.00 1.00

2014 1.00 1.00

Pioneer Biochemical 2013 2.00 2.00

Thermochemical 2012 2.00 2.00

2013 1.00 1.00

 Commercial Biochemical 2013 2.00 2.00
23 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Pilot Demonstration Pioneer Commercial

Baseline Deployment Investment ○ ○ ○ ○

Additional Deployment Investment + + + +

Figure A-1. Scheduled advanced biorefinery construction with baseline and additional deployment investment, by pathway, year, 

and scale

Product Pathway

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Construction Start

Cellulose to

Ethanol

Biochemical

Thermochemical

Cellulose to

Hydrocarbons

Catalytic

Upgrading

Fast Pyrolysis

Fermentation

Fischer Tropsch

Methanol to

Gasoline