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

Dynamic Modeling of Learning 

in Emerging Energy Industries: 

The Example of Advanced 

Biofuels in the United States

Preprint

Laura J. Vimmerstedt and Brian W. Bush

National Renewable Energy Laboratory

Steven O. Peterson

Lexidyne, LLC

Presented at the 33rd International Conference of the System 

Dynamics Society

Cambridge, Massachusetts

July 19–23, 2015

Conference Paper

NREL/CP-6A20-60984

September 2015
NOTICE

The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 

(Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US 

Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of 

this contribution, or allow others to do so, for US Government purposes.

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 SciTech Connect http:/www.osti.gov/scitech

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

OSTI http://www.osti.gov

Phone: 865.576.8401

Fax: 865.576.5728

Email: reports@osti.gov

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

U.S. Department of Commerce

National Technical Information Service

5301 Shawnee Road

Alexandria, VA 22312

NTIS http://www.ntis.gov

Phone: 800.553.6847 or 703.605.6000

Fax: 703.605.6900

Email: orders@ntis.gov

Cover Photos by Dennis Schroeder: (left to right) NREL 26173, NREL 18302, NREL 19758, NREL 29642, NREL 19795.

NREL prints on paper that contains recycled content.
1

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Dynamic Modeling of Learning in Emerging Energy Industries:

The Example of Advanced Biofuels in the United States

Laura J. Vimmerstedt 1, a, Brian W. Bush 1, and Steven O. Peterson 2

1 National Renewable Energy Laboratory

National Renewable Energy Laboratory RSF 300

15013 Denver West Parkway, Golden, CO 80401-3305 a Corresponding author, 303-384-7346

laura.vimmerstedt@nrel.gov

2 Lexidyne, LLC

19 S. Tejon Street, Suite 212

Colorado Springs, CO 80903

603-359-7188

steve.peterson@lexidyne.com

Acknowledgments

This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08-

GO28308 with the National Renewable Energy Laboratory, 15013 Denver West Parkway, 

Golden, CO 80401-3305.

Abstract

This paper (and its supplemental model) presents novel approaches to modeling interactions and 

related policies among investment, production, and learning in an emerging competitive 

industry. New biomass-to-biofuels pathways are being developed and commercialized to support 

goals for U.S. advanced biofuel use, such as those in the Energy Independence and Security Act 

of 2007. We explore the impact of learning rates and techno-economics in a learning model 

excerpted from the Biomass Scenario Model (BSM), developed by the U.S. Department of Energy 

and the National Renewable Energy Laboratory to explore the impact of biofuel policy on the 

evolution of the biofuels industry. The BSM integrates investment, production, and learning 

among competing biofuel conversion options that are at different stages of industrial 

development. We explain the novel methods used to simulate the impact of differing assumptions 

about mature industry techno-economics and about learning rates while accounting for the 

different maturity levels of various conversion pathways. A sensitivity study shows that the 

parameters studied (fixed capital investment, process yield, progress ratios, and pre-commercial 

investment) exhibit highly interactive effects, and the system, as modeled, tends toward market 

dominance of a single pathway due to competition and learning dynamics.

Keywords

Biomass, Biofuel, Learning, Learning Curve, Experience Curve, Policy, Renewable Fuels 

Standard, System Dynamics
2

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Introduction

Modeling the emergence of an industry presents challenges in assessing the dynamic interactions 

among investment decisions, cost reductions during development (e.g., due to industrial learning

and economies of scale), and levels of utilization and associated production. The Biomass 

Scenario Model (BSM) uses novel approaches to address several of these challenges. It was 

developed by the U.S. Department of Energy (DOE), partnering with the National Renewable 

Energy Laboratory (NREL), in an effort to reduce ambiguity about potential futures and to 

improve understanding of the role of policy in industry development (Bush, 2011; Bush et al., 

2008; Newes et al., 2011; Lin et al., 2013; Peterson et al., 2013; Inman et al., 2014). Developed 

in isee system’s STELLA®1, the model uses a system of coupled ordinary differential equations

and integrates these forward in time. This links variables that represent physical, technical, 

economic, and behavioral features of the biofuels supply chain through interdependent rates of 

change and feedbacks. The BSM has been extensively validated; in particular, the learning 

curves which are used here are based on historical data on learning in ethanol and related 

industries (McCurdy et al., n.d.). The BSM is an analysis platform that is designed to explore the 

impact of biofuel policy on the evolution of the biofuels supply chain. It includes modules 

representing the U.S. agricultural system; pathways that convert starch, cellulosic, oil crop, and 

algal feedstocks into liquid fuels; and downstream distribution and dispensing of fuels.

Today’s biofuels industry includes mature commercial enterprises, such as starch-to-ethanol, 

sugarcane-to-ethanol, and biodiesel. Other emerging technology pathways convert cellulosic and 

bio-oil feedstocks to ethanol, butanol, or other hydrocarbons; they could develop substantially in 

the future but are not yet mature at industrial scales. These biomass-to-biofuel conversion 

technology pathways have potential to reduce carbon emissions, to enhance U.S. energy security, 

and to increase the degree to which non-food biomass resources supplement or displace cornbased fuel ethanol production. The Energy Independence and Security Act of 2007 (EISA)

mandates 36 billion gallons of renewable transportation fuel in the U.S. marketplace by the year 

2022 (Energy Independence and Security Act of 2007), and includes targets for growth of 

advanced biofuels (cellulosic biofuels, biomass-based diesel, and other biofuels that meet 

threshold greenhouse gas emissions reductions of 50%). In response to this mandate, successive 

U.S. administrations have provided incentives for advanced biofuels development.

In developing the BSM, we sought to provide a simple representation of the mechanisms, 

constraints, and trade-offs associated with supply chain evolution based on the physical features 

and relationships of the biomass-to-biofuels industrial system. We drew from a broad set of 

information sources in order to incorporate data about today’s industry, assess the current status 

of less mature pathways, estimate the economic and technical performance of prospective 

pathways, characterize relationships and constraints, and represent the effects of choices by key 

decision makers. A central feature of the BSM is a dynamic framework that integrates

investment, production, and industrial learning among competing biofuel conversion options.

In this paper, we focus on a subset of the BSM by addressing learning and associated investment 

in conversion pathways that are in early stages of development and whose mature technoeconomic performance is unknown—not the mature, established conversion pathways that 

dominate current production of biofuels in the United States. We show how the model represents 

 1 http://www.iseesystems.com
3

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

learning concepts and highlight how our approach differs from the approaches commonly used in 

other energy models. We then explain how the BSM couples together investment, production, 

and learning processes in the context of competing biomass-to-biofuel conversion technology 

pathways. A sensitivity analysis of the simplified model is used to derive insights into the role of 

pre-commercial investment, learning rates, mature-technology capital costs, and maturetechnology yields in the competition among pathways for investment and in overall stimulation 

of industry development.

Applying learning curve concepts in the BSM

Cost reduction through learning-by-doing is a prominent feature of many analyses of the energy 

sector (Junginger et al., 2010). Learning curves (also known as experience curves) are based on 

the empirical observation that in many industries’ unit cost declines along with increases in 

production experience. This finding was documented in the 1930s for labor input for airframe 

manufacture (Wright, 1936). Beginning in the mid-1960s, analyses used the experience curve 

concept to explain the cost behavior and the resultant market share dynamics in competitive 

industries (Hax and Majluf, 1982; Kouvaritakis et al., 2000). In the energy sector, empirical 

observation has also supported a learning curve approach. For example, endogenous learning is a 

component in studies focusing on ethanol production in Brazil (Van den Wall Bake et al., 2009)

and the United States (Chen and Khanna, 2012; Chum et al., 2013) and in studies focusing on 

electricity generation, including nuclear, coal, hydropower, wind, and solar photovoltaics

(McDonald and Schrattenholzer, 2001).

The system dynamics literature describes the use of learning curves in models of strategy 

dynamics, technological development, industry growth, and investment decision-making. For 

example, Naim and Towill (1994) present various learning curve formulations and their 

application in system dynamics models. Fiddaman’s work on integrated climate-economy system 

dynamics models (1997) explicitly accounts for learning-by-doing. Morrison (2005) uses an

extension of learning curve theory to show the potential emergence of positive feedbacks in 

productivity in the context of throughput constraints. Sterman et al. (2007) describe a system 

dynamics model with learning curves that is used to explore “get big fast” strategies under 

conditions of bounded rationality for actors in the system. Learning curves are also a prominent 

feature of a system dynamics model of the photovoltaic industry (Jeon and Shin, 2014). This 

model uses system dynamics and Monte Carlo analysis to examine how the value of PV 

technology varies over time in response to uncertainty and volatility in 11 key input drivers.

A single-factor learning curve is commonly used for analyses in the energy sector. Less 

commonly used are multiple-factor learning curves that incorporate, for example, research and 

development investment (Junginger et al., 2010). In the single-factor formulation, each doubling 

of experience yields a constant percentage decrease in unit costs. The relationship is often

expressed as a power law:
4

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Y = aXb (1)

Y current unit cost

X cumulative production

aunit cost of initial unit

b slope of the function when plotted on a log-log scale

PP = 2−b (2)

L = 1 − PP (3)

PP is the progress ratio, the relative cost after each doubling of cumulative 

production

L is the learning rate, the relative cost reduction after each doubling of 

cumulative production

A single-factor learning curve yields results such as those shown in Figure 1.

Figure 1. The chart illustrates a single-factor learning curve relationship under three different progress ratios. With 

each doubling of cumulative production, the cost index is reduced by a constant percentage based on the progress 

ratio.

The learning curve approaches in the literature generally observe a historical progress ratio, 

which may then be extrapolated to the future. For a single factor learning curve for unit cost, this 

would result in an implicit ultimate cost of near zero. In the BSM, we use a learning curve 

approach to enable us to interpolate between the characteristics of current and future technology

for each technology pathway under consideration. In doing so, we have adapted the single-factor 

learning curve in several ways. First, instead of using an implicit asymptote of zero for unit cost, 

in the BSM we define an explicit asymptote of a “mature technology.” As discussed in Newes et 

al. (2011), we introduce a variable called maturity, M:

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12

Cost Index (uintless)

Cumulative Production (widgets)

Progress Ratio = 0.9

Progress Ratio = 0.8

Progress Ratio = 0.7
5

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

( )



















 



 − −

=





 



 −

0

ln 2

1

0

*

1 1

M

E

L M

M

PR

otherwise

for E ≥ L*

(4)

L* = max{L, E0 } (5)

M0 is initial maturity

L is the minimum experience for learning

L* is the effective minimum experience for learning

E is the cumulative experience

E0 is the initial cumulative experience

PR is the progress ratio

From Newes et al. (2011).

Second, we adapt the single-factor learning curve approach that typically is connected to a single 

technical attribute (unit cost). Instead of a single measure, we define a vector of technical 

attributes that are assumed to depend on industry maturity. This vector includes the following 

attributes, each of which has a mature value input and a current state-of-industry value that is 

calculated. As the industry matures, each value is assumed to approach the mature value or n

th

plant characteristics.

• Process yield

• Feedstock throughput capacity—the degree to which facilities are able to perform at 

nameplate capacity

• Capital cost—the premium in capital cost, beyond the n

th plant estimate, which would be 

observed if development of a facility was begun today

• Investor risk premium—the additional premium, beyond normal hurdle rate, that 

investors would require for investment in the facility

• Access to debt financing—the portion of the expected facility capital cost that would be 

financed via borrowing (as contrasted with equity investment).

Accordingly, we define a multiplier m that is arrayed by technical attribute i, where n denotes input

n

th plant values. This multiplier is a function of maturity, as described next. Generally, the value of 

the technical attributes A in any simulated year is calculated as:
6

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Ai = mi Ai,n (6)

A is a vector of technical attributes (various units)

m is a multiplier (unitless)

i is the index of technical attributes

n is the ultimate nth plant attributes

< mi 0 (7)

Figure 2 summarizes these interactions in a causal loop diagram.

Figure 2. A causal loop diagram summarizing the interactions of maturity, the vector of technical attributes, 

investment attractiveness, conversion facilities, and production.

Third, instead of a single learning curve metric, we have applied learning curves in a cascading 

fashion to multiple industry development scales (pilot, demonstration, pioneer, and commercial).

Pilot and demonstration facilities are constructed without the intention of producing fuel for 

commercial sale. For most technologies in the BSM, commercial scale is over 2,000 short tons 

per day throughput capacity. Pioneer scale refers to the early commercial developments that are 

assumed to be approximately one-third the scale of commercial facilities and to have higher 

capital costs. As a result, at any point in simulated time, the performance and cost characteristics 

of a pioneer or commercial-scale facility are estimated and used to inform the model’s 

investment allocation. To accomplish this, maturity is calculated at each developmental scale, 

and the multiplier m in the equation above is calculated based on the impact of each development 

scale:
7

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

[ (1 )] (1 ) mi = CiMC + DiM D + Pi − M D × − MC (8)

0 < M ≤1 (9)

MC is commercial maturity and MD is demonstration maturity

C is the vector of mature commercial multipliers

D is the vector of mature demonstration multipliers

P is the vector of mature pilot multipliers

Mature multipliers at commercial, demonstration, and pilot scale (C, D, and P) define the maximum 

performance on each technical attribute that can be achieved through experience at that scale.

With these three modifications to the single-factor learning curve, we have developed a novel 

modeling approach to modeling learning that accounts for performance in multiple technical 

attributes that approach non-zero ultimate values through development at multiple scales. This 

approach is best suited to situations wherein the modeler can readily assume knowledge of the 

ultimate technical attributes of the technology and the mature technology characteristics, and 

would like to explore scenarios for the rate at which current technology approaches this mature 

performance. We are not aware of other literature describing this application of learning curves. In 

situations where mature technology characteristics can be estimated, this approach replaces the 

implicit assumption in much of the literature of ultimate near-zero cost (if unit cost is the attribute). 

For the BSM, we operationally define “mature technology” by drawing from engineering design 

studies that estimate capital investment, process yield, and fixed and variable operating costs for 

the “n

th plant” (where n is a large number) of a given pathway. Because the BSM relies on design 

studies to provide estimates for n

th plant techno-economics, its results are subject to uncertainties in 

yield and cost estimates as well as scale effects of assumed commercial plant sizes.

Figure 3 shows an illustrative example of the correspondence between production (or 

experience), maturity, and values of technical attributes over the simulated time. We use 

different units of experience at different scales, as shown in panel a. As shown in panel b, the 

maturity multipliers include experience at each scale, increasing as cumulative production grows 

over time and asymptotic to one. Panel c illustrates how current (or state-of-industry) values for 

two of the technical attributes change as they approach mature values as cumulative production 

increases over time.

Because the BSM is a forward-looking simulation model of technologies that are not yet 

commercialized, there is no empirical data from which to estimate many of the progress ratios 

that we use. Instead, it uses assumed values that are informed by statistically estimated values for 

progress ratios from the literature about related technologies, such as starch ethanol production 

and, more generally, the chemical process industry. In their review of retrospective studies of 

learning in the energy sector, McDonald and Schrattenholzer (2001) identify a high degree of 

variability in estimates for learning rates—both within a given technology and across differing

energy technologies. They suggest that modelers should consider the uncertainty in the value of 

learning rates (McDonald and Schrattenholzer, 2001). For the BSM, this points to the importance 

of including sensitivity analysis of learning rates in this paper. Model results are not predictions,

but should be viewed as plausible scenarios subject to uncertainties in model structure and data 

inputs.
8

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 3. (a) The first panel illustrates experience profiles at pilot and demo scales, as well as commercial scale

(measured in industry output). Pre-commercial curves are based on accumulation of operating time at pilot and 

demo scales. Cumulative industry output grows as a quadratic, reflecting constant linear growth in annual industry 

output beginning in year 14. (b) The second panel shows change in maturity index with increasing cumulative pilot 

experience, demonstration experience, or commercial production over time. The corresponding experience or 

production curves are shown in panel a. Pilot experience precedes demonstration experience, which precedes

commercial production. Maturity is correlated with doublings of experience at each scale. The maturity index is an 

abstract, unitless multiplier that is multiplied by metrics of mature technological performance to estimate the state of 

the industry. (c) The third panel illustrates a cascading effect of maturation on selected technical attributes. 

Technical attributes improve with maturity, based on a distinct calculation for each measure. The technical attributes 

shown are a subset of those used for commercial biorefineries. At a given year in simulated time, investment technoeconomics will reflect the cumulative effect of pilot, demo, and commercial maturity levels. Technical attributes 

progress differently at different industry-development scales.
9

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Coupling learning, production, and investment in the BSM

Production and investment in the BSM depend upon performance in each technical attribute, as 

calculated in the learning process. In the BSM, techno-economic information drawn from design 

reports and engineering studies is used to select the input assumptions for the initial and mature 

industry (or n

th plant) technical attributes vector (listed above), the financial and performance

characteristics for different biomass-to-biofuel conversion technology pathways. Over simulated 

time, the model’s learning curve logic determines how the state of each pathway moves from 

today’s characteristics toward n

th plant maturity. Together with other inputs, notably feedstock 

cost and product price, the state of each pathway defines streams of expected costs and revenues 

associated with a prospective project investment based on costs and revenues in the simulated 

year, without investor foresight. In the BSM, we discount these expected costs and revenues

streams to the current simulated year to calculate the net present value (NPV) of the prospective 

investment, as follows:

[ ] j j j Cj Tj NPV = R − I + + (10)

NPV is the net present value of investment in a biorefinery of technology i

R is the NPV of expected revenue less expected operating costs for technology j

Tj is the initial equity investment for technology j

C is the present value of the debt for technology j

T is the net present value of the taxes less tax credits for technology j

The net present value calculation uses the required rate of return, which changes with maturity, 

and is calculated according to:

∑= + =

n

t

t

t

d n d

R

R

0

, (1 ) (11)

Rd,n is the expected revenue less expected operating cost, discounted by the 

required rate of return d over n time periods

Rt is the net revenue in time period t

d is the required rate of return

t is the current time period

n is the final time period

Then, we use a logistic function to allocate investment among different conversion pathway 

options or non-biofuels options based on the relative financial attractiveness of each. This

approach is often used to model consumer choice situations (Train, 2009); in the BSM, we use 

logistic formulations to allocate limited resources, such as plant construction capacity, among 

competing uses.

Sj = e(k+cNNNj)

∑ e(k+cNNNj)

j

(12)

S is the share construction capacity allocated to pathway j

k is a constant

c is a constant scaling factor
10

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

As suggested in Figure 4, reinforcing feedbacks (also called “positive” feedbacks) emerge from 

the coupling of investment, production, learning, and the maturation of a set of performance 

attributes.

Figure 4. The chart is an overview of key concepts, mechanisms, and processes in the BSM. Reinforcing feedbacks 

around investment, production, and learning drive biomass-to-biofuel conversion technology pathway and industry 

evolution.

Sensitivity study design

The BSM is a dynamically complex model that is rich in feedback and nonlinearities (Peterson et 

al., 2013). To investigate the impact of learning, investment, and techno-economic inputs on 

biofuel industry development, we extracted from the BSM a simplified model, focusing on

biomass-to-biofuel conversion processes that occur at biorefineries. While the BSM considers 

ten U.S. regions, this simplified model considers only one region and only three conversion 

technology pathways (labeled “A,” “B,” and “C”), as opposed to more than ten conversion 

technology pathways in the larger model. Costs of feedstocks entering the biorefinery and prices 

of products that the biorefinery produces are defined as scenario inputs. The model is configured 

such that the three hypothetical pathways under consideration differ in their n

th plant technoeconomic characteristics, their initial levels of maturity, and their pre-commercial investment 

profiles. These baseline conditions serve as a springboard for a sensitivity analysis in which we 

vary select techno-economic attributes, learning rates, and pre-commercial investment scenarios

(Table 1). The sensitivity ranges are relatively narrow (McDonald and Schrattenholzer, 2001), 

but sufficient to cause substantial variation in the key result, technology market share.
11

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The techno-economic attributes were selected to mimic real trade-offs among capital, feedstock, 

and other operating costs, without specifically representing existing technologies. For example, 

there are actual technologies with higher first-costs and lower operating costs, higher feedstock 

costs and higher co-product sales, lower operating costs and lower yield, and lower maturity and 

lower expected future performance. In the baseline condition:

• Pathway A: This conversion technology pathway represents an extremely promising 

pathway that is very early in its development. 

• Pathway B is quite promising as well (although its expected yield at maturity is lower 

than A) and is closer to commercialization than A. 

• Pathway C is very close to commercialization but has a very high capital cost and 

significantly lower yields. On the other hand, pathway C appears to have a favorable 

variable cost structure relative to the other two options.

The sensitivity variations perturb the attractiveness of the pathways along one or more 

dimensions. Initial technical maturity (MP, D, or C) and progress ratio (PR) are the learning 

attributes in the sensitivity variation. Initial technical maturity is the initial value of the maturity 

index that was described in the learning section. It has a value between 0 and 1, where 1 

represents full n

th plant maturity and associated performance. The selected progress ratio value of 

PR = 0.75, and the sensitivity value of PR = 0.79 are within the range of historical progress ratios 

for starch ethanol and chemical processes. Pre-commercial investment projects at pilot and 

demonstration scales are projects not intended for commercial production that contribute to the 

initial learning of each pathway, as described above. Simulations are conducted against a policy 

backdrop designed to stimulate initial take-off of all pathways. Policies consist of loan 

guarantees and capital cost grants for pioneer-scale facilities that expire in 2022. An initial 

product subsidy applies to each gallon of production, up to the first 1 billion gallons of industry 

output; this is reduced to a nominal amount for the duration of the simulation. It is important to 

note that this is a hypothetical policy set. The policies are derived from a scenario library 

developed elsewhere in the BSM project. For details on these scenarios, see Inman et al. (2014).
12

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 1. Conditions for baseline simulation.

Pathway A

Baseline

Pathway B

Baseline

Pathway C

Baseline

Sensitivity 

Variation

Mature 

Industry 

Technoeconomics

Feedstock Input 

(dry short ton/day)

2,000 2,000 2,000

Fixed Capital 

Investment ($)

300,000,000 300,000,000 400,000,000 1.15 ×

baseline

Fixed Operating 

Cost ($/yr)

15,000,000 15,000,000 15,000,000

Other (nonfeedstock) 

Variable 

Operating Cost 

($/yr)

50,000,000 40,000,000 5,000,000

Co-Product Sales 

Revenue ($/yr)

5,000,000 16,000,000 10,000,000

Process Yield 

(gal/dry short ton)

100 90 66 0.85 ×

baseline

Initial 

Technical 

Maturity (unitless)

Pilot 0.1 0.5 0.85 0.85 ×

baseline

(pathway 

“B” only)

Demonstration 0 0.5 0.75

Commercial 0 0 0

Progress Ratio 

(1/doubling)

Pilot 0.75 0.75 0.75 1.05 ×

Demonstration 0.75 0.75 0.75 baseline

Commercial 0.75 0.75 0.75

PreCommercial 

Investment

(projects/yr)

Pilot pre-2012: 2

2012: 4

2013: 4

2014: 4

pre-2012: 2

2012: 4

2013: 4

2014: 4

pre-2012: 2

other yrs: 0

Demo pre-2012: 0

2014: 4

2015: 4

2016: 4

pre-2012: 0

2014: 4

2015: 4

2016: 4

pre-2012: 0

other yrs: 0

For the sensitivity study, baseline conditions are modified as shown in the final column. Each combination of 

conditions was simulated, resulting in two conditions each for three parameters (fixed capital investment, process 

yield, and progress ratio) for each of three pathways and two conditions of a fourth parameter (initial precommercial maturity) for pathway B only.

Results and insights

Figure 5 shows simulation results under baseline conditions. With this set of baseline policy 

conditions and input values for feedstock cost and product price, the model generates a phased

evolution in which pathway C is first to mature. Over time, growth in pathway C stabilizes, 

while pathway B, and then pathway A, grow to significant volumes (Figure 5).
13

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 5. The figure shows baseline results by conversion technology pathway. (a) The first panel shows baseline 

production. For the purposes of this study, pathway characteristics were selected to show industry growth for all 

three pathways under the background policy conditions that were modeled. (b) The second panel shows baseline 

results for commercial-scale NPV by conversion technology pathway. Modeled decisions to invest in commercialscale facilities are based on NPV for a new commercial facility constructed in each year. NPV is calculated based on 

capital costs, operating expenses, revenue from product sales, and effects of incentives, all of which change over 

time. Positive NPV in Figure 8 corresponds with early industry growth in Figure 7. There are 2 discontinuities in 

each NPV trace. The first is the result of expiration of FCI loan guarantees. The second is the result of expiration of 

fuel product startup subsidies that applies to the first 1 billion gallons of industry output. (c) The third panel shows 

baseline results for commercial maturity by conversion technology pathway. The chart shows that pathway C is 

initially more mature and obtains early investment that advances its maturity. Pathways A and B are able to compete 

sufficiently such that they also mature.

0

2

4

6

8

10

2011 2016 2021 2026 2031

Output (billion gal/yr)

year

(a) Baseline Model Output: Production by Pathway

Pathway A

Pathway B

Pathway C

0

0.2

0.4

0.6

0.8

1

2011 2016 2021 2026

Maturity (unitless)

year

(c) Baseline Model Output: Commercial Maturity

Pathway A

Pathway B

Pathway C

-250

-150

-50

50

150

250

2011 2016 2021 2026 2031

NPV(000,000 $)

year

(b) Baseline Model Output: Commercial-Scale NPV

Pathway A

Pathway B

Pathway C
14

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The results from this sensitivity study (see Figure 6) illustrate competition among biomass-tobiofuel conversion technology pathways, interaction among the sensitivity parameters studied, 

and a tendency for a single pathway to gain most of the technology market share. Each 

horizontal, colored line represents a different amount of biofuel production in the year 2030 from 

one of the conversion technology pathways. The numerical x-axis labels indicate simulation 

condition values that are held constant within that column while other sensitivity parameters are 

varied. In any given column, there are many different year 2030 production levels for each 

pathway. These represent results of different simulations for sensitivity parameters other than the 

one for which the column is labeled.

Figure 6. The chart is a summary of sensitivity study results. Interpretation of this chart is discussed in the text. In 

each column the baseline case is at 1.00 on the x axis. Each horizontal bar in the chart represents the biofuel 

production for one of the three pathways (indicated by the color or the bar) in the year 2030 under one set of 

conditions in the sensitivity study. Each of the 10 columns labeled with a sensitivity variable name at the bottom 

separates the simulation results for a low value of that input variable from those for a high value of that variable, 

such that all of the simulations in the study are shown once in each of the 10 columns. The slanted lines indicate 

how the mean output of each pathway varies as a function of the sensitivity variable for that column.

Competition

Competition among the pathways shapes results of the sensitivity study. In general, sensitivity 

cases that reduce the performance of a given conversion technology pathway affect fuel 

production from that pathway the most, and the other pathways tend to benefit. Conversely, 

improved performance primarily increases production from the improved pathway, with 

secondary effects decreasing production from the other pathways. The examples below give 

trends in one direction only, but each of these could be expressed either as an increase or a 

decrease.
15

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

• See Figure 6, column 1, “Initial Maturity B.” Sub-column labeled “0.85” has lower initial 

maturity than sub-column labeled “1.00.” Production in “0.85” is lower on average than 

production in “1.00” as indicated by the slope and endpoints of the orange line. Production 

from conversion pathway B drops dramatically if its initial maturity is lowered, and 

production from conversion pathways A and C increases modestly on average.

• See Figure 6, columns 2–4, “Progress Ratio A, B, or C.” Sub-columns labeled “1.04” 

have higher progress ratios than sub-columns labeled “1.00” for pathway A, B, or C, 

respectively. An increase in progress ratio for pathway A, B, or C causes production for 

that conversion pathway to plummet, as indicated by the slopes and endpoints of the blue 

line in the column “Progress Ratio A,” by the orange line in the column “Progress Ratio 

B,” and by the green line in the column “Progress Ratio C.” The other pathways increase 

production slightly in each case, as shown by the other two line colors. Pathway A is 

more sensitive to changes in its progress ratio than the other pathways, primarily because 

of its initial immaturity, as indicated by the steeper slope of the blue line in the column 

“Progress Ratio A” relative to the slopes of the orange line in pathway B and green line 

in pathway C.

• See Figure 6, columns 5–7, “Yield A, B, or C.” Sub-columns labeled “0.85” have a lower 

yield than sub-columns labeled “1.00.” A drop in yield results in a dramatic loss in 

production capacity for the affected conversion pathway, shown by the slopes and 

endpoints of the blue line in the “Yield A” column, the orange line in the “Yield B” 

column, and the green line in the “Yield C” column. The drop in yield in a given 

conversion pathway generally increases the capacity of the other pathways. This is shown 

in the figure because the slopes of the two lines for the unaffected pathways are negative, 

but the slope for the affected pathway is positive. For example, in the “Yield A” column, 

production for pathway B (orange) and pathway C (green) is higher in sub-column “0.85” 

than in sub-column “1.00.” The increase may or may not be sufficient to make up the 

production capacity loss. For example, a drop in yield in pathway A or C could cause an 

increase in pathway B that is sufficient to make up the overall production loss because 

production from pathway B generally can increase more rapidly and attain higher levels 

than from pathways A and C. 

• See Figure 6, columns 8–10, “FCI A, B, or C.” Sub-columns labeled “1.15” have a higher 

capital cost than sub-columns labeled “1.00.” An increase in the capital cost of pathway 

A causes much lower production capacity from pathway A, as shown by the negative 

slope of the blue line in the “FCI A” column. Pathways B and C only increase their 

production capacity a little in response, with the orange and green lines having a small 

positive slope. Conversely, the production capacity of conversion pathway A benefits 

greatly from increases in the capital cost of the other pathways, especially for increases in 

FCI C, as shown by the relatively large positive slope of the blue lines (for pathway A) in 

column “FCI C.” Pathways B and C show drastic loss in production at higher capital 

costs, with large negative slopes for the orange (B) and green (C) lines in columns “FCI 

B” and “FCI C,” respectively. Pathway B, and especially pathway C, are less influenced 

by increased capital costs in the other conversion pathways, as shown, for example, by 

the relatively low positive slope of the green lines (for pathway C) in the “FCI A” and 

“FCI B” columns.
16

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Note that this simple model does not include competition among pathways for feedstock or for 

end users. Studies with the BSM have indicated that these can be significant factors under some 

scenario conditions.

Interaction

The input parameters exhibit interacting effects. Every combination of input parameters (fixed 

capital investment, process yield, progress ratios, and pre-commercial investment) shows 

interaction in its effects on simulation results. These interactions are not simple linear 

relationships, but rather, nonlinear dependencies rooted in the feedback mechanisms inherent in 

the dynamics of coupled learning and investment. Small differences in progress ratio, cost, and 

yield shape the evolution of the system through the feedback structure of the model in a complex

and nonlinear manner. 

Reducing a pathway’s assumed performance does not necessarily lead to increased production 

capacity for competing pathways, but in certain situations, an increase may be dramatic. For 

example, Figure 6 shows a small subset of the sensitivity studies in which dramatic changes may 

be observed. These situations are likely conditions that are favorable for industry growth, such 

that the growth opportunity will be met by the most competitive pathway, and lower 

performance of one pathway will open an opportunity for another.

Figure 6. The chart is a subset of sensitivity analysis results showing examples of significant change with change in 

pathway performance. 

Market dominance

In our sensitivity analysis, the case where all three pathways exhibit high output is the base case,

which was selected with that goal. There are only a handful of cases where two exhibit high 

output. In the cases where two pathways exhibit high output, each pathway must remain 

sufficiently competitive with the other to attract investment during key periods of market 

development. This tendency toward market dominance is a key feature of the behavior of this 

model, given the reinforcing feedback as encoded in the logistic, techno-economic, and learning 
17

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

parameters used in this study. In Figure 7, points at the vertices of the triangle are dominated by 

a single pathway. The figure 7 shows that many of these single-pathway simulations are also 

those with higher biofuels production output (shown by larger, darker blue circles). A few of the 

high-output cases have high production from 2 pathways (points along one of the sides of the 

triangle). High output cases do not have pathway C as the largest-producing pathway, but include 

cases with dominance from A, from B, or combined production with either A or B having the 

largest production. Figure 8 zooms in on a small subset where more than one pathway develops.

Figure 7. This figure shows total biofuels production in 2030 and the distribution of biofuels production among 

pathways. The points that fall at the vertices show dominance by a single pathway; points that fall along a side have 

production from the two pathways corresponding to the two adjoining vertices; and points in the middle share 

production among all three pathways.
18

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 8. The chart is a subset of sensitivity analysis results showing examples of multiple pathways increasing 

production.

In this discussion, market dominance refers to dominance of a technology, not dominance of a 

single firm. This conceptualization of market dominance is consistent with a competitive market. 

This is application of the “competitive exclusion principle” (Meadows, 2008) and an example of 

the “success to the successful” system archetype as described in Senge (1990). Figure 9 shows

this as a causal loop diagram, illustrating in greater detail the reinforcing feedback shown in 

Figure 2. Pathways can dominate through combinations of superior mature techno-economics, 

higher learning rates, and greater initial maturity. This is one of the fundamental mechanisms by 

which a pathway that initially attracts slightly more investment could develop more rapidly and 

ultimately dominate the market. The pathway that initially attracts more investment develops 

more biorefineries, produces more fuel, and advances down the learning curve through these 

experiences. This increase in maturity, translated into more favorable cost and performance, 

makes it even more attractive to future investment. Annual investment in construction of new 

biorefineries is assumed to be constrained. Pathways compete directly for this investment, so the

reinforcing feedback can cause favorable pathways to attract most of the investment and 

dominate the market. Even without complete market dominance, successful pathways have a 

tendency to suppress less favorable ones, so it is rare that all pathways succeed equally well. This 

can only happen in the unlikely situation where technological parameters are tuned to maintain 

sufficient competitiveness among all pathways.

The implications of the tendency toward market dominance of one or two pathways for actual 

industry development are challenging to assess. In this simple model, the tendency for market 
19

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

dominance may occur in part because of the small number of pathways considered, and perhaps 

also because the model does not sufficiently represent details of different market segments, 

which might provide competitive advantage to multiple pathways, each in its respective niche.

Figure 9. The chart shows key factors in the competition between pathways for investment. Attractiveness of 

investment in a given pathway is determined by mature industry techno-economics, as modified by current state of 

maturity. Progress ratio governs the speed by which production translates into increases in maturity. Lock-in 

dynamics can result from differences in (1) initial maturity, (2) progress ratios, or (3) n

th plant techno-economics 

such as capital cost or yield.

The converse of market dominance—pathway failure—is also common. If a pathway does not 

reach some minimal level of learning-based feedback, it is unable to attract sufficient investment 

and therefore fails to develop. There are thresholds for progress ratios, and to a lesser extent, for 

techno-economics and initial maturity, in their contribution to this reinforcing feedback between 

industry development and learning-based cost reductions.

Production capacity and maturity both contribute to learning, but on different timescales, 

resulting in a sequence of feedback responses. Production capacity, when utilized, directly and 

immediately increases learning from experience, a function of production. This is especially 

influential in the early years of pathway growth. Maturity influences learning; increased maturity 

facilitates investment in new plants, but guarantees neither production capacity construction nor 

production of biofuels due to external conditions and competition between pathways.
20

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Limitations

This analysis has several important limitations. First, the learning model boundaries include only 

the biomass-to-biofuels conversion portion of the supply chain, eliminating interactions with 

feedstock supply and fuel demand. This portion of the supply chain is illustrative of the interplay 

between investment, technology development, and production. Second, the concept of n

th plant 

performance is not grounded in purely physical limitations, so quantitative values for n

th plant 

performance are uncertain. Third, learning rates (or progress ratios) are highly uncertain, and the 

full range of possibilities is not explored here. Fourth, learning rates may not reflect omitted 

variables. Fifth, modeled markets are simplified, eliminating niches that may counteract the 

tendency to market dominance. Sixth, this novel formulation of learning curves is not validated 

elsewhere in the literature. Despite these limitations, we offer this paper and the associated 

model as a potentially valuable approach to understanding emerging industry development.

Conclusions

The simplified learning model illustrates implementation of the novel approach to learning that is 

used in the full BSM model. This approach contrasts with the single factor learning model in (1)

progress toward mature performance instead of an implicit zero asymptote of (2) multiple 

technical attributes instead of only unit cost at (3) multiple development scales. The sensitivity 

study shows that the parameters studied (fixed capital investment, process yield, progress ratios, 

and pre-commercial investment) exhibit highly interactive effects. The system tends to show 

market dominance of a single pathway due to competition and learning dynamics. While these 

results do not predict biofuels industry development, they illustrate the types of behavior that 

could occur for different combinations of techno-economic and learning parameters for biomassto-biofuel conversion technology pathways. This modeling approach could be broadly useful in 

representing the interactions among investment decisions, cost reductions during development, 

and utilization and production in the emergence of other industries. The results could inform 

future analytic work to explore the robustness of the finding of a tendency toward market 

dominance, and the policy implications of this kind of system behavior could be considered.

The coupling of learning and investment, within the context of a dynamic model, can generate 

results that show a variety of potential behaviors of the system. These may inform the 

development of a shared understanding among stakeholders of possible effects of policy, 

external factors, decision making (e.g., by investors, growers, and consumers), infrastructure 

constraints, and learning on the development trajectory of the domestic biofuels industry.

Exploration of learning and investment scenarios for biofuels industry development could help 

inform consideration of biofuels as part of U.S. energy and climate strategy. For example, the 

system behavior showing a tendency toward dominance of single pathways could prompt 

discussion about whether this outcome appears likely or is an artifact of the model formulation, 

how mitigating circumstances (e.g., more detailed representation of different market segments) 

could overcome this tendency, and social costs or benefits of such an outcome. Similarly, the 

wide range of potential learning rates could focus discussion on rates of progress that are or are 

not conducive to further public or private investment. Through its uses as an analysis platform 

and scenario generator, the BSM can inform the pursuit of these biofuels goals. 
21 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix

Table A1. Indices for Equations

Table A2. Selected Equations

Eq. No. or

Symbol

Equation and Variable Name in Model Units Variable Name in Paper

4 ( )



















 



 −− =





 



 −

0

ln 2

1

0

* 11

M

E

L M

M

PR

 

otherwise

≥ LEfor *

0 ≤ M ≤ 1

Pilot Maturity

Demo Maturity

Commercial Maturity

Unitless Pilot maturity

Demonstration maturity

Commercial maturity

L 

Min Pilot Experience for Learning

Min Demo Experience for Learning 

Min Cume Industry Output for Learning

year Minimum experience for learning

0 ≤ PR ≤ 1 Pilot Progress Ratios, Demo Project Ratios, Project Ratios 

Commercial

1/doubling Progress ratio

E 

Cumulative Pilot Experience

Cumulative Demo Experience 

Cumulative Industry Output

year

year

billion gallons

Cumulative experience

6 = AmA ,niii

A State of Industry Process Yield 

FS Throughput C, FS Throughput P

gal/short ton

short ton/day

Process yield

Feedstock throughput capacity 

Index Symbol Definition Variables Using Index

0 Initial value M

n Ultimate value A

i Attributes of technologies A

C, D, P Scale (Commercial, Demo, Pilot) M

j Technology pathways NPV, S
22 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Eq. No. or

Symbol

Equation and Variable Name in Model Units Variable Name in Paper

(vector of 

technical 

attributes)

Expected FCI C, Expected FCI P

Req’d Rate of Return as %

Expected Equity Fraction

$ Capital cost growth 

Investor risk premium

Access to debt financing

< mi 0 State of Industry Multipliers (one for each technical 

attribute A)

unitless multiplier

8 [ )1()]1(iDiCii MPMDMCm D −×−++= MC

C 

D 

P

Mature Commercial Multipliers

Mature Demo Multipliers

Mature Pilot Multipliers

(one for each technical attribute A)

unitless

unitless 

unitless 

Mature commercial multiplier

Mature demonstration multiplier

Mature pilot multiplier

10 [ ] ++−= TCIRNPV jjjjj

NPV

R 

I 

C 

T

NPV Investment C

NPV Revs Net of Op Cost C

Initial Equity Investment C 

PV Loan Payment C

NPV Taxes C

$

$ 

$ 

$ 

$ 

Net present value

Expected revenue – operating costs 

Initial equity investment

Present value of debt 

NPV of taxes – tax credits

12 Sj = e(k+cNNNj)

∑ e(k+cNNNj) j

This equation summarizes several 

calculations in the model.

S

k 

c

share to biofuels

offset for attractiveness weighting 

invest attractiveness weighting

Unitless

unitless 

unitless

Share of construction capacity

Constant 

Constant scaling factor
23

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

References

Bush, B. 2011. Biomass Scenario Model (BSM) Development & Analysis, presented at the 2011 

Office of the Biomass Program Analysis and Sustainability Activities Platform Peer Review, 

April 4, 2011.

Bush, B., Duffy, M., Sandor, D., Peterson, S. 2008. Using system dynamics to model the 

transition to biofuels in the United States. Presented at the Third International Conference on 

System of Systems Engineering, Institute of Electrical and Electronics Engineers, Inc. (IEEE), 

Monterey, California.

Chen, X., Khanna, M. 2012. Explaining the reductions in US corn ethanol processing costs: 

Testing competing hypotheses. Energy Policy 44, 153–159. doi:10.1016/j.enpol.2012.01.032.

Chum, H.L., Warner, E., Seabra, J.E.A., Macedo, I.C. 2013. A comparison of commercial 

ethanol production systems from Brazilian sugarcane and US corn. Biofuels Bioprod. 

Biorefining n/a–n/a. doi:10.1002/bbb.1448.

Energy Independence and Security Act of 2007. 2007. Pub L No. 110-140, 121 Stat 1492.

Fiddaman, T.S. 1997. Feedback Complexity in Integrated Climate-Economy Models. 

Massachusetts Institute of Technology.

Hax, A.C., Majluf, N.S. 1982. Competitive Cost Dynamics: The Experience Curve. Interfaces 

12, 50–61. doi:10.1287/inte.12.5.50.

Inman, D., Vimmerstedt, L., Newes, E., Bush, B., Peterson, S. 2014. Biomass scenario model 

scenario library: definitions, construction, and description (Technical Report No. NREL/TP6A20-60386). National Renewable Energy Laboratory, Golden, Colorado.

Jeon, C., Shin, J. 2014. Long-term renewable energy technology valuation using system 

dynamics and Monte Carlo simulation: Photovoltaic technology case. Energy 66, 447–457. 

doi:10.1016/j.energy.2014.01.050.

Junginger, M., Sark, W., Faaij, A. (Eds.). 2010. Technological Learning in the Energy Sector: 

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

Kouvaritakis, N., Soria, A., Isoard, S. 2000. Modelling energy technology dynamics: 

methodology for adaptive expectations models with learning by doing and learning by searching. 

Int. J. Glob. Energy Issues 14, 104–115.

Lin, Y., Newes, E., Bush, B., Peterson, S., Stright, D. 2013. Biomass Scenario Model 

Documentation: Data and References (Technical Report No. NREL/TP-6A20-57831). National 

Renewable Energy Laboratory, Golden, Colorado.

McCurdy, M., Hegazy, Y., Norland, D., Victor, N., n.d. An Examination of Technology 

Learning Method and its Applications Tailored to Industrial Technologies for Converting 

Lignocellulosic Biomass to Fuels (No. SR-6A20-63543). National Renewable Energy 

Laboratory, Golden, CO.
24

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

McDonald, A., Schrattenholzer, L. 2001. Learning rates for energy technologies. Energy Policy 

29, 255–261. doi:10.1016/S0301-4215(00)00122-1.

Meadows, D. 2008. Thinking in Systems: A Primer. Chelsea Green Publishing.

Morrison, J.B. 2005. Implementation as Learning: An Extension of Learning Curve Theory, in: 

Proceedings of the 23rd International Conference of the System Dynamics Society. Presented at 

the 23rd International Conferences of the System Dynamics Society, Boston, MA.

Naim, M.M., Towill, D.R. 1994. System Dynamics and Learning Curves, in: Proceedings of the 

12th International Conference of the System Dynamics Society. Presented at the 12th 

International Conference of the System Dynamics Society, Stirling, Scotland, pp. 164–173.

Newes, E., Inman, D., Bush, B. 2011. Understanding the Developing Cellulosic Biofuels 

Industry through Dynamic Modeling, in: Dos Santos Bernardes, M.A. (Ed.), Economic Effects of 

Biofuel Production. InTech.

Peterson, S., Newes, E., Inman, D., Vimmerstedt, L., Hsu, D., Peck, C., Stright, D., Bush, B. 

2013. An Overview of the Biomass Scenario Model. Presented at the 31st International 

Conference of the System Dynamics Society, Cambridge, Massachusetts.

Senge, P.M. 1990. The Fifth Discipline: The Art and Practice of the Learning Organization. 

Doubleday/Currency.

Sterman, J.D., Henderson, R., Beinhocker, E.D., Newman, L.I. 2007. Getting Big Too Fast: 

Strategic Dynamics with Increasing Returns and Bounded Rationality. Manag. Sci. 53, 683–696. 

doi:10.1287/mnsc.1060.0673.

Train, K. 2009. Discrete choice methods with simulation, 2nd ed. ed. Cambridge University 

Press, Cambridge ; New York.

Van den Wall Bake, J.D., Junginger, M., Faaij, A., Poot, T., Walter, A. 2009. Explaining the 

experience curve: Cost reductions of Brazilian ethanol from sugarcane. Biomass Bioenergy 33, 

644–658. doi:10.1016/j.biombioe.2008.10.006.

Wright, T. 1936. Factors Affecting the Cost of Airplanes. J. Aeronaut. Sci. 3, 122–128.