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Contract No. DE-AC36-08GO28308

An Overview of the Biomass 

Scenario Model

Steve Peterson, Corey Peck, and Dana Stright

Lexidyne LLC

Emily Newes, Danny Inman, Laura 

Vimmerstedt, David Hsu, and Brian Bush

National Renewable Energy Laboratory

Presented at the 31st International Conference of the System 

Dynamics Society

Cambridge, Massachusetts

July 21–25, 2013

Conference Paper

NREL/CP-6A20-60172

February 2015
NOTICE

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Introduction

Biofuels are promoted in the United States through legislation, as one part of an overall 

strategy to lessen dependence on imported energy as well as to reduce the emissions of 

greenhouse gases (Office of the Biomass Program and Energy Efficiency and Renewable Energy, 

2008). For example, the Energy Independence and Security Act of 2007 (EISA) mandates 36 

billion gallons of renewable liquid transportation fuel in the U.S. marketplace by the year 2022 

(U.S. Government, 2007). Meeting the volumetric targets has prompted an unprecedented

increase in funding for biofuels research, much of it focused on producing ethanol and other

fuel types from cellulosic feedstocks1 as well as additional biomass sources (such as oil seeds 

and algae feedstock). In order to help propel the biofuels industry, the U.S. government has 

enacted a variety of incentive programs (including subsidies, fixed capital investment grants, loan 

guarantees, vehicle choice credits, and corporate average fuel economy standards) -- the shortand long-term ramifications of which are not well understood. Efforts to better understand the 

impacts of incentive strategies can help policy makers to develop a policy suite which will foster 

industry development while reducing the financial risk associated with government support of 

the nascent biofuels industry. 

Purpose and Overview

This paper describes the Biomass Scenario Model (BSM), a system dynamics model developed 

under the support of the U.S. Department of Energy (DOE). The model is the result of a multiyear project at the National Renewable Energy Laboratory (NREL). It is a tool designed to 

better understand biofuels policy as it impacts the development of the supply chain for biofuels 

in the United States. In its current form, the model represents multiple pathways leading to the 

 1 These feedstocks, such as agricultural and forestry residues, perennial grasses, woody crops, and municipal solid 

wastes, are advantageous because they do not necessarily compete directly with food, feed, and fiber production.
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production of fuel ethanol as well as advanced biofuels such as biomass-based gasoline, diesel, 

jet fuel, and butanol). 

The BSM uses a system dynamics modeling approach (Bush et al., 2008), developed using the 

STELLA software platform (isee systems, 2010) to model the entire biomass-to-biofuels supply 

chain. In order to gain a clear view into the evolution of the supply chain for biofuels, BSM 

focuses on the interplay between marketplace structures, various input scenarios, and 

government policy sets, as shown in Figure 1.

Figure 1. BSM strategy and approach

In this paper we begin with a description of the BSM architecture. Then, we provide a more 

detailed view of the sectors and modules which comprise the BSM. Third, we outline the 

“back-end” system that we have developed in order to support analysis efforts with the BSM. 

Fourth, we describe a set of scenarios used as the basis for policy exploration with the model. 

Finally, we provide a summary of current and potential uses for the model. A set of appendices 

detail important structures relating to pricing, investment, and vehicle vintaging.
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An Overview of the BSM Architecture

BSM has been designed in a top-down, modular fashion which allows material (feedstocks) to 

flow down the supply chain and be converted into various types of biofuels, with feedback 

mechanisms among and between the various modules. In developing the model, we have taken 

care to create a structure that is transparent, modular, and extensible, enabling standalone 

analysis of individual model components as well as testing of different module combinations. As 

shown below in Figure 2, the model is framed as a set of interconnected sectors and modules. 

Figure 2. Overview of BSM structure

Feedstock supply and logistics

The feedstock supply and logistics sector captures the dynamics of cellulosic, oil crop, and 

starch feedstock supply from agricultural lands within the context of the operation of the 

United States agricultural system. It incorporates harvesting and transportation logistics 

associated with cellulosic feedstock, as well as feedstock supply and logistics associated with 

forest, urban, and agricultural residues. 
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Feedstock production from agricultural land occurs against the backdrop of other uses of the 

agricultural land base. These include commodity crop production (corn, wheat, soybean, small 

grains, cotton), hay, pasture, and Conservation Reserve Program (CRP) land. The agricultural 

production system disaggregated regionally into 10 production regions taken from the United 

States Department of Agriculture (USDA), as shown in Figure 3:

Figure 3. USDA farm production regions

Conversion

The conversion sector is composed of six different conversion modules, each corresponding to

a different set of pathways for production of biofuels.
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• Starch to Ethanol: This module represents the conversion capacity acquisition and 

utilization dynamics associated with the existing starch (corn) ethanol industry. This 

industry is considered to be mature; hence, the module provides a simple 

representation of the financial logic that controls acquisition and utilization of 

commercial scale corn ethanol facilities. This module is disaggregated by USDA 

production regions.

• Cellulose to Ethanol: This module captures the development of the cellulose-to-ethanol 

conversion industry. Biochemical and thermochemical conversion options are 

considered on a USDA-regionalized basis. The module represents pilot, demonstration, 

pioneer-commercial and full-commercial scale facilities. It includes learning curve 

dynamics, investment decision logic, and utilization logic for both pioneer and full 

commercial scale facilities. 

• Cellulose to Butanol: This module captures the development of the cellulose-to-butanol 

conversion industry. In BSM, butanol serves as an industrial solvent and as a substitute 

for ethanol in the oxygenate market. A single, regionally-disaggregated cellulose-tobutanol conversion option is captured in the model. The module represents pilot, 

demonstration, pioneer-commercial and full-commercial scale facilities. It includes 

learning curve dynamics, investment decision logic, and utilization logic for both pioneer 

and full commercial scale facilities. 

• Cellulose to “Refinery Ready:” This module captures the industry development of 

cellulose-to-refinery-ready “infrastructure compatible” conversion processes. The 

model structure can accommodate the conversion options:

o Fast Pyrolysis
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o Fischer-Tropsch

o Methanol to Gasoline

o Catalytic Pyrolysis

o Fermentation

o Aqueous Phase Reforming

As with other cellulosic modules, this module is disaggregated by USDA regions. It 

provides a representation of pilot, demonstration, pioneer-commercial and fullcommercial scale facilities. It includes learning curve dynamics, investment decision logic, 

and utilization logic for both pioneer and full commercial scale facilities. Multiple 

products or product substrates can be produced, including gasoline, diesel, and jet fuel. 

The “drop-in point” for various products is determined as a scenario variable.

• Oil crops: The Oil crop module captures development of conversion capacity for soyto-refinery and “other” oilseed to refinery processes. Oil crop conversion facilities are 

represented as U.S. aggregates (rather than disaggregated by USDA production region). 

The module represents pilot, demonstration, pioneer-commercial and full-commercial 

scale facilities. It includes learning curve dynamics, investment decision logic, and 

utilization logic for both pioneer and full commercial scale facilities.

• Algae: The Algae model represents open pond, photobioreactor, and heterotrophic 

conversion options. It is not geographically disaggregated. Algae feedstock production is 

presumed to be vertically integrated in the algae to refinery-ready system. The module 

represents pilot, demonstration, pioneer-commercial and full-commercial scale facilities. 
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It includes learning curve dynamics, investment decision logic, and utilization logic for 

both pioneer and full commercial scale facilities.

In addition to the six conversion modules, the conversion sector includes a simple module that 

knits together the “attractiveness” of the various investments in conversion options, allocating 

limited facility construction capacity among these options based on their perceived relative 

economic value.

Petroleum industry

The petroleum industry sector comprises scenario inputs around crude oil prices, providing

logic that translates these prices into price inputs for the various refinery ready conversion 

modules as well as the pricing/inventory module of the downstream ethanol/butanol sector. 

Additionally, the petroleum industry model provides accounting logic that captures 

displacement of crude by biofuel-derived infrastructure compatible fuels.

Downstream ethanol and butanol

The downstream ethanol and butanol sector is composed of a set of four modules. These 

modules capture activities “downstream” of conversion, for ethanol and butanol. 

• Pricing/Inventory: This module captures pricing and inventory dynamics for both 

ethanol and bio-based-butanol. Ethanol flows into two distinct but coupled markets: the 

“low-blend” oxygenate market and the “high-blend” market associated with flexible fuel 

vehicles. Bio-butanol is assumed to serve as a substitute for ethanol in the oxygenate 

market, and also can supplant butanol produced by other processes in the industrial 

market.
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• Distribution logistics: This module provides a simple representation of the regional 

build-out of the distribution network for fuel ethanol.

• Dispensing stations: The dispensing station module addresses the regional acquisition of 

tankage and equipment capable of dispensing high ethanol blends into flex-fuel capable 

vehicles. Build-out of E85-capable stations is driven by economic considerations, and is 

constrained by regional availability of ethanol from the distribution network.

• Fuel use: The mix of low-ethanol-blend vs. high-ethanol-blend consumption is 

determined by the relative economics of the two products as constrained by the 

regional availability of ethanol for high-blend consumption through dispensing stations.

Vehicles

The vehicle scenario module functions primarily as an accounting structure, which is used in 

BSM to keep track of the cumulative effect of multiple scenarios around volume, vehicle mix, 

vehicle efficiency, and vehicle miles traveled for the car and light duty truck sectors. Its 

structure captures acquisition, aging, and retirement of vehicles, as well as the translation of 

vehicles into potential demand for fuel.

Ethanol Import

The ethanol import module provides a simple representation of the evolution of non-domestic 

ethanol production capacity. It generates imports of ethanol into the United States based on a 

price differential as perceived from outside the U.S. This structure enables the model to 

capture historical patterns of growth and decline in imports of fuel ethanol. It is structured to 

facilitate exploration of multiple scenarios around production cost.
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A More Detailed view of BSM2

Feedstock supply and logistics

The feedstock supply and logistics sector is responsible for generating cellulosic, starch, and oil 

crop feedstocks for the conversion sector in BSM. The U.S. agricultural system forms the 

context for the production of a significant portion of these feedstocks. Accordingly, in 

developing the feedstock supply and logistics sector we have taken care to respect both the 

physical (land use) and economic aspects of U.S. agriculture. The sector is divided into two 

modules: Feedstock Supply and Feedstock Logistics. 

Feedstock supply

Feedstock supply refers to the production of different feedstocks required as substrate for 

conversion, as summarized in Table 1.

 2 The following sections provide a “deep dive” into specifics related to each sector/module in the BSM. Readers 

with a more aggregate interest may prefer to move ahead to the “Interconnections between sectors” section 

(page 48).
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Feedstock Source Use Notes

Corn Crop Land Ethanol

Soy Crop Land “Refinery Ready” fuels

Other Oil seed

Crop Land (small 

grains)

“Refinery Ready” fuels

Model does not explicitly represent land 

allocation to other oil seed (e.g., 

rapeseed)

Crop Residue Crop Land

Ethanol | Butanol |

“Refinery Ready” fuels

Model allows residue collection from 

corn, wheat, other grains, cotton 

Herbaceous 

Cellulosic energy 

crop

Crop land

Pasture land

Ethanol | Butanol

“Refinery Ready” fuels

Woody cellulosic 

energy crop

Crop land

Pasture land

Ethanol | Butanol

“Refinery Ready” fuels

Pasture Pasture land

Ethanol | Butanol

“Refinery Ready” fuels

Urban residue Urban areas

Ethanol | Butanol

“Refinery Ready” fuels

Represented as simple price-response 

supply curve

Forest residue Forest lands

Ethanol | Butanol

“Refinery Ready” fuels

Represented as simple price-response 

supply curve

Table 1. Summary of feedstocks produced by Feedstock Supply Module

As indicated in Table 1, urban and forest residue feedstocks are generated using simple pricesupply relationships. All other feedstocks are produced by the agricultural land base. Figure 4

identifies the different land categories represented within the feedstock supply module.
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Figure 4. Land categories represented within Feedstock Supply Module

Within each of the ten USDA regions represented in the model, land is divided among three 

high-level categories: cropland used for crops, Conservation Reserve Program (CRP) land, and 

cropland used for pasture. These land bases are typically treated as static quantities over the 

course of a simulation run. However, as indicated in Figure 4, BSM structure supports scenarios 

that will cause land to move from CRP or pasture into cropland used for crops. Within each 

land base, land is allocated among different uses based on expected relative per-acre grower 

payment accruing to producers from the various products. Land allocation is region-specific, 

reflecting the production economics of different crops in different regions. Allocation of land to 

cellulosic crops is more restrictive: only those producers who have adopted the practice of 

producing cellulosic products (either residue or perennials) consider cellulosic grower 

payments in their decision making. “New practice” producers can grow over time based on the 
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potential profitability of cellulosics, but constrained by the requirements of the existing and 

prospective conversion facilities, as shown in Figure 5.

Figure 5. The agricultural land allocation algorithm

Not shown in Figure 5, but essential to the dynamics of BSM, is the logic surrounding pricing for 

the various commodity crops, cellulosic products, and hay. This logic is central to a feedback 

mechanism that uses land allocation to equilibrate production and consumption across all 

product categories in the model. A more detailed treatment of the pricing structure used in 

BSM is provided in Appendix A. Figure 6 shows in simple terms the feedbacks around price in 

the feedstock supply module.
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Figure 6. Price feedbacks in feedstock supply module.

Feedstock Logistics

The feedstock logistics module provides a simple accounting structure that captures the 

following costs:

• Harvesting and collection

• Transport from “farm gate” to “plant gate”

• Storage, queuing, handling, and pre-processing between farm gate and plant gate

These costs are used to translate the per-ton price of cellulosic feedstock at the plant gate into 

a per-ton grower payment at the farm gate. In developing the feedstock logistics module, we 

have drawn from analyses of the Biomass Logistics Model (BLM) developed at the Idaho 

National Laboratory (INL). In its current form, the feedstock logistics module supports cost 

accounting for both pioneer and advanced storage, pre-processing, and queuing/handling 

processes.
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The feedstock logistics module underscores the high degree of interplay among different cost 

components. For example, truck transport is viewed as a primary mechanism for moving 

feedstock from farm gate to plant gate. Depending upon the feedstock involved, the mass 

transported on the truck varies, with residue resulting significantly lighter loads than woody 

cellulosic crops. Other things equal, this implies a higher logistics cost per ton for residues than 

for woody cellulosic crops.

Additionally, the logistics module emphasizes the importance of the travel distance from farm 

to conversion facility. The model estimates farm-plant distances regionally, by considering the 

following components:

• The total number of cellulosic plants requiring agriculturally-produced feedstock

• The total volume of agricultural land allocated to producing cellulosic feedstock

• The aggregate average yield of those producing acres

• An estimate of the fraction of land within the “plant-shed” that is available for cellulosic 

harvesting

• Geometric factors that relate the resultant plant-shed area to average travel distance 

from farm to plant.

Conversion Sector

The conversion sector is responsible for transforming feedstock into liquid fuels, including 

ethanol, butanol, and refinery-ready fuels (gasoline, diesel, jet fuel) suitable for insertion into the 

existing fuel infrastructure as refinery feedstocks, blendstocks, or finished products. In the BSM, 

the conversion module comprises a significant fraction of the overall model structure. It 

consists of seven modules. Six of these modules look at the dynamics of industry development 
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for sets of conversion pathway. These dynamics include operations at different scale factors, 

learning along multiple dimensions, logic surrounding the attractiveness of investment in new 

facilities, and utilization of existing facilities. A seventh module compares investment 

attractiveness across all conversion options, allocating scarce investment capacity among these 

options based on their net present value.

As indicated in Table 2, there is significant overlap among the different industry development 

modules. In particular, most modules share the following characteristics:

• Multiple conversion options, represented using an arrayed variable structure

• Regional disaggregation, following the feedstock supply module’s use of ten USDA 

production regions

• Incorporation of pre-commercial pilot and demo-scale operations

• Representation of pioneer-commercial-scale operations

• Representation of full-scale operations

• Learning curve dynamics
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Starch to 

Ethanol

Single pathway Yes Corn Ethanol No No Yes

No (assume 

mature 

industry)

Cellulose 

to Ethanol

Biochemical

Thermochemical

Yes

Cellulosic 

Feedstock

Ethanol Yes Yes Yes Yes

Cellulose 

to Butanol

Single Pathway Yes

Cellulosic 

Feedstock

Butanol Yes Yes Yes Yes

Cellulose 

to 

Refinery 

Fast Pyrolysis

Fischer-Tropsch

Methanol to 

Gasoline

Catalytic Pyrolysis

Fermentation

APR

Yes

Cellulosic 

Feedstock

Gasoline

Diesel

Jet fuel

(3 drop-in 

points)

Yes Yes Yes Yes

Oil Crop 

to 

Refinery

Soy

Other

No Oil crop

Diesel

Jet fuel (3 dropin points)Yes Yes Yes Yes

Algae to 

Refinery

Pond

Photobioreactor

Heterotrophic

No

Algae—

treated as 

part of 

conversio

n process

Diesel

Jet fuel (3 dropin points)Yes Yes Yes

Yes (feedstock 

supply 

considered

endogenous to 

module and 

subject to 

learning curve)

Table 2. "Dimensionality" of Conversion Sector
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Within the sector, there are a few departures from the generic structure. For example, in the 

oil crop and algae modules, regional production of feedstock is of secondary importance. As a 

result we have chosen not to disaggregate these modules by region. The starch to ethanol 

industry, to take another example, is assumed to have reached maturity. Hence, there is no 

need to represent the dynamics of pilot, demo, or pioneer scale operations, nor is there a 

requirement to represent learning curve dynamics. In the algal module, feedstock production is 

considered as endogenous to the algae system rather than produced by the feedstock supply 

sector. Algal feedstock production costs are subject to learning curves in the algae module.

Within the typical conversion module, there are multiple processes that govern the 

development of the conversion options under consideration and their production of fuel. These

processes, as shown in Figure 7, are centered on:

• Pilot and Demo Scale Operations

• Pioneer-commercial scale operations

• Full-commercial scale operations

• Expected economic value of the “next” investment

• Allocation of scarce capital in the investment decision

• Learning along multiple dimensions

• Industry aggregate average utilization rates for existing facilities
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Figure 7. Key interactions within the typical conversion module

Pilot- and Demonstration-Scale Operations

Pilot and demonstration-scale operations are represented with a simple stock/flow structure in 

the model, as shown in Figure 8.

Figure 8. Pre-commercial structure and illustrative output
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There are several important features of the pilot and demonstration scale structures in the 

model. First, the structure for each pre-commercial scale operation is arrayed, based on the 

number of conversion options at play within the module. Technology on/off switches, set by the 

user, enable the activation or deactivation of each conversion option. Second, both pilot and 

demo operations are specified as exogenous scenario inputs. These scenario inputs enable an 

arbitrary pattern of initiation to be specified by the end user as a scenario. Third, the model 

explicitly represents the dwell time between initiation and completion of development using 

conveyors. Fourth, the time for which operations are active—used in the model to generate 

learning—is limited in duration. Finally, note that cumulative completed operations are tracked 

by the structure. Figure 8 provides illustrative output that translates an arbitrary initiation 

scenario into development, “on line,” and completed operations.

Pioneer-commercial-scale operations

The model accommodates two commercial scale operations: Pioneer-and full-commercial scale 

operations. In the model, Pioneer-commercial-scale facilities are often the first commercial 

scale plants to come on line. These facilities have a smaller (about 1/3) capacity than fullcommercial-scale facilities. They do not take full advantage of economies of scale. Hence, in 

typical simulations of BSM subsidies are required to stimulate investment in pioneer plants.

(Note that the starch to ethanol module excludes pioneer facilities from analysis.)

As shown in Figure 9, two stock/flow chains are used to account for pioneer scale plants. 

Depending on the module in question, these chains are arrayed by conversion option and/or by 

region (see Table 2 for details). The top chain represents the number of plants in design and 

construction, in startup, and in use. The bottom chain is a co-flow structure that is used to 

account for the process yield (gallons of output per ton of feedstock input). These two 
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concepts—facilities and process yield—jointly determine the output capacity for pioneer 

facilities in the aggregate. Output capacity is a reflection of the total ability of pioneer-scale 

facilities to produce fuel via a particular conversion.

The co-flow structure here is essential for the accurate accounting of facilities and their 

associated process yields. Whenever a new facility enters the system through the initiating flow, 

the model samples the current state of the industry process yield for the associated conversion 

option. This process yield then moves along with the facility through the development process, 

eventually being used as an input for the average process yield of on-line facilities. This 

structure enables the model to dynamically track the cumulative impact of growth in process 

yields for “new” plants, as the industry moves from “blue sky” to “Nth plant maturity.”

Figure 9. Accounting for pioneer facilities, process yield, and output capacity

Note that structure has been provided to account for retirement of plants. In the current 

version of the model, we assume a retirement fraction of zero, which implies that no plants are 

taken permanently off line over the course of a simulation. The logic that controls utilization 
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factors, discussed below, accounts for the dynamics of short-term plant idling in response to 

market forces.

Full-commercial-scale operations

Figure 10 shows the structure that accounts for full-commercial-scale operations in the model. 

As with pioneer plants, commercial plants use two stock/flow chains to represent the design 

and construction, the start-up, and the on-line phases of the facility life cycle. As with pioneer 

plants, the structure for commercial operations is arrayed by conversion option and/or by 

region within each module.

A comparison of Figure 9 and Figure 10 will reveal two notable differences between the

pioneer and commercial accounting structures. First, note that in contrast to pioneer facilities, 

commercial facilities in the start-up phase are assumed to contribute to the overall output 

capacity. A utilization rate (less than 1) is assumed for plants during the period that they are in 

startup.

Second, note that a new flow has made its way into the process yield chain for commercial

scale facilities. This flow enables the model to capture the effect of process yield improvements 

to be incorporated into the existing capital stock. The user of the model can specify the specific 

rate at which a yield gap—measured as the discrepancy between the state of the industry 

process yield for a particular conversion option and the existing industry average process yield 

for that conversion option—is eliminated.
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Figure 10. Accounting for commercial operations, process yield, and output 

capacity

Expected Economic Value of the “Next” Investment

In order to represent economic valuation of potential investments, it was essential to develop a 

simple, mechanism for determining the viability of investment in the “next” plant, at either 

pioneer or commercial scale, for the various conversion options within the regions under 

consideration. A dynamic economic mechanism to evaluate industry growth options was 

achieved through a structure culminating in a net present value calculation. At any point in 

simulated time, this structure captures important streams of costs and revenues associated with 

a prospective project investment. By discounting these streams to the present, it captures the 

dynamics of an evolving industry using a simple metric that enables comparison of prospective 
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investments across multiple conversion options, regions, and scales. In turn, this metric enables 

the model to allocate scarce capital toward its highest valued uses.

A parallel algorithm is used for Net Present Value (NPV) calculations within each conversion 

module. As appropriate to each module, the algorithm reflects conversion options, regional 

considerations, and scale. Wherever possible, the algorithm operates at the highest possible 

degree of aggregation by rolling up sub-categories into high-level summaries. For example, for 

purposes of the NPV calculation, factor inputs and expected per-gallon revenues are held 

constant over the plant lifetime. Figure 11 provides a simple influence diagram showing the logic 

flow leading to the NPV calculation.

Figure 11. Logic of NPV calculation

In developing the NPV logic, we have adopted some important simplifications. In addition to 

simplifications around revenue streams, we assume straight-line depreciation of the plant in 

question. Additionally, we divide the overall project life cycle into distinct phases, as shown in 
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Figure 12. In the model, NPV calculations are made for each phase of the project life cycle, and 

then rolled up to create an overall NPV for the plant.

Figure 12. Phases of project life cycle

Allocation of scarce capital

Within the BSM conversion sector, multiple opportunities present themselves to potential 

investors at any point in time. At the extreme, thirteen conversion options can be active, 

competing across ten regions and often at both pioneer and commercial scale. Within the 

model, each conversion module uses NPV as a basis for determining the attractiveness of the 

various investment options under consideration using a logit function. (See Appendix B for 

more details.) The resultant attractiveness metrics are then compared within the Relative 

Attractiveness (RAT) module, which also includes a default “other” investment category. The 

relative attractiveness for each alternative is then applied to a scenario-driven maximum 

construction capacity, which generates a platform and scale-specific yearly start rate. This 

“desired” start flux is communicated back into the conversion modules, where it is allocated 

regionally if required and “batchified” so as to send a discrete signal to begin plant development, 

as shown in Figure 13.
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Figure 13. Translating NPV into project start signal

Learning Along Multiple Dimensions

For most conversion options under consideration in BSM, the initial performance along multiple 

dimensions would fall far short of expected mature industry (or “Nth plant”) performance, as 

reported in other design studies. Industry evolution is in no small measure the story of 

performance improvement that results from learning by doing. Merrow’s research on cost 

growth in capital-intensive industries (Merrow, 1983), for example, underscores the importance 

role of experience at prior scale in reducing the risk of capital cost growth at commercial scale. 

Henderson’s work with the Boston Consulting Group in the 1970s (Hax & Majluf, 1982)

demonstrates the role of accelerating industrial learning as a cost-reduction strategy at 

commercial scales. 

Given the important connections between learning and industry evolution, we needed to 

develop a simple, and consistent mechanism to translate the accumulation of experience into a 
26

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set of performance parameters to represent the current “state of the industry” for each 

conversion option. Our approach, which we call “cascading learning curves” draws upon simple 

learning curve principles in order to address learning for multiple conversion options at 

multiple development stages, addressing multiple performance attributes. There are three

fundamental tasks, illustrated in Figure 14, involved in the cascading learning curve approach:

• Develop separate cascading curves for each conversion option. By providing separate 

structure for each conversion option, we have created the possibility to separately 

characterize different initial conditions, mature industry conditions, and learning rates on 

a conversion option-specific basis. 

• Capture learning for each conversion option at three distinct development stages. In 

BSM, we look at learning for pilot scale operations, for demonstration scale operations, 

and for commercial scale operations (including pilot and full commercial scale). A staged 

approach to learning enables us to capture prior scale effects (important for capital cost 

growth). It also enables us to explore the implications of stage-specific progress rates as 

well as the analysis of timing and placement of policy initiatives.

• Use learning to create indices of maturity. These indices of maturity, in turn, drive 

essential technology attributes that are used within BSM. Key attributes of performance 

for each conversion option are:

o Process yield

o Likelihood of “technical failure”

o Feedstock throughput capacity—the degree to which facilities are able to 

perform at nameplate capacity
27

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o Capital cost growth—the premium in capital cost, beyond Nth plant estimate, 

which would be observed if development of a facility was begun today.

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

investors would require for investment in the facility.

o Access to debt financing—the portion of the expected facility capital cost that 

would be financed via borrowing (vs. equity investment)

Figure 14. Industry learning curve structure

There are some differences between the representation of the learning curve structure in BSM 

and other, perhaps more common, formulations of learning curves. In the classic formulation of 

a learning curve, for example, a power law is used to relate cumulative experience to a single 

attribute such as cost. The asymptote of cost is often implicitly set to zero. By contrast, in BSM, 

cumulative learning at each stage of development is reflected along a 0-1 scale in pilot, demo, or 
28

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commercial maturity. As experience accrues, the model calculates explicitly the rate at which 

experience is doubling. This rate of doubling is applied to a maturity gap (simply the difference 

between current maturity and full maturity) to generate learning. Maturity, in turn, drives 

movement along a vector of attributes.

A second set of differences involves the development stages over which learning is applied. 

While a typical learning curve analysis might consider cost reductions for relatively stable

developed industries, in BSM we consider multiple attributes over multiple development stages. 

Figure 15 shows how the learning curves cascade over these development stages.

Figure 15. Cascading learning curves

At any point in simulated time, the current industry technology attributes reflect the 

performance and cost characteristics associated with an investment in a pioneer or fullcommercial scale facility for a given conversion option. At each stage, multipliers that are 

passed on to the next stage are calculated as a weighted average, with the maturity level used 

as the weighting factor. 
29

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Dynamically, this structure enables BSM to jump from one performance trajectory to another 

based on the behavior of pilot, demo and commercial operations. Figure 16 illustrates. In the 

figure, simple exogenously-defined scenarios for pilot, demo, and commercial scale operations 

drive learning at each stage. Cost and yield parameters follow three distinct pathways as the 

industry evolves.

Figure 16. Illustrative learning curve dynamics

Learning curve dynamics, of course, do not occur in isolation from the overall dynamics of the 

industry. For a given conversion option, learning curves are at the heart of feedbacks that 

surround the investment process, and which can underwrite industry “take-off” (as shown in 

Figure 17). Each of these mechanisms is a positive feedback loop, reinforcing development of 

the conversion option in question.
30

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Figure 17. Key feedbacks emerging from learning curve structure

Utilization of existing facilities

Multiple processes are at work in the conversion sector to generate the production of biofuels. 

A final set of processes concerns the utilization of existing facilities. A fundamental premise of 

basic economics is that “sunk costs” don’t matter. In BSM, conversion facilities are assumed to 

follow this premise; the capacity utilization rate for each conversion option (within each region, 

as appropriate) at either pioneer or full commercial scale is developed as a response to the 

“cost-price ratio” for its products. As the price received for its product (including any 

subsidies) grows relative to the per-gallon cost of producing that product (after factoring net 

per-gallon co-product revenues into the mix), utilization increases to its maximum. On the 

other hand, as the price-cost ratio declines below unity, utilization rates decline, as shown in 

Figure 18.
31

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Figure 18. Determining utilization from cost-price ratios

Utilization is a central element of the feedback structure within BSM, as it controls both the 

production of products and consumption of feedstock.

Conversion Sector in Summary

Each conversion module within the conversion sector is built up from multiple simpler 

structures that represent pre-commercial demonstration and pilot scale operations, pioneer 

and full commercial scale operations, the expected economic value of investment, learning and 

utilization. These structures are connected within each module in order to generate products 

(diesel, jet fuel, gasoline, butanol, ethanol). They are connected across modules via the logic 

within the relative attractiveness module that allocates scarce investment capital. The 

conversion sector is connected upstream in the supply chain to the agricultural system through 

feedstock supply dynamics, and to both the oil industry (algae, oil crop and cellulose to refinery-
32

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ready modules) and the downstream ethanol sector. These downstream sectors determine 

price (and in the case of ethanol, demand) signals which are sent to the conversion sector

modules. 

Downstream EtOH sector

The downstream ethanol sector comprises a set of interconnected modules that take fuel 

ethanol from conversion facilities to end users, both in low-blend (E10 or E15) and high blend 

(nominally, E85) form. Additionally, the downstream sector contains logic that controls the use 

of bio-butanol as a substitute for ethanol in the low-blend market. The model assumes that 

physical characteristics of ethanol require separate infrastructure for distribution and dispensing 

than for petroleum-based fuels. A significant portion of the downstream sector, therefore, is 

focused on distribution and dispensing station dynamics.

Figure 19 provides a picture of the content of the downstream sector. As suggested by the 

diagram, downstream dynamics focus on the build-out of distribution infrastructure, the 

development of dispensing infrastructure, and decision making around fuel usage.

Figure 19. An overview of downstream dynamics
33

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To support these dynamics, multiple modules comprise the downstream sector of BSM3, 

including:

• Distribution logistics

• Dispensing station

• Fuel use

• Pricing and inventory

Distribution Logistics Module

A fundamental challenge associated with ethanol as a transportation fuel is its apparent 

incompatibility with existing infrastructure. The distribution logistics module provides a simple 

representation of the build-out of ethanol-friendly distribution infrastructure. Rather than 

speculating on the build-out of specific distribution modalities for ethanol (such as rail, barge, or 

dedicated pipeline), the logistics module focuses on capturing the implications of build-out on 

the rest of the downstream system. The structure focuses on the acquisition of ethanol 

infrastructure for terminals within each region. The module is silent on the specific details of 

infrastructure, instead focusing on the drivers, time delays, and feedback loops associated with 

regional build-out, as shown in Figure 20.

 3 Detailed analysis of downstream ethanol dynamics can be found in (Vimmerstedt, Bush, & Peterson, 2012).
34

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Figure 20. Distribution logistics

A two-stage supply-push approach (first within a region, and then across regions) is embedded 

within the module. Within a region, the model first seeks to balance ethanol production 

capacity against terminal capacity to distribute that ethanol. As production capacity within a 

region grows, there is pressure within the region for terminals to acquire ethanol-compatible 

distribution infrastructure. Second, as build-out occurs within each region, any excess regional 

production capacity creates pressure for acquisition of infrastructure in other regions, in 

proportion to the terminal density within each region. 

The result of this two-stage supply-push algorithm is an initial build-out of distribution 

infrastructure in ethanol producing regions, followed by a slower build-out in non-producing 

regions. Infrastructure coverage within any region constrains regional investment in ethanol 

dispensing tankage and equipment, thus setting a limit on the uptake of ethanol in high-blend 

form.
35

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Dispensing Station Module

The dispensing station module focuses the decision making associated with the acquisition and 

use of high-blend tankage and equipment by retail dispensing stations. The module considers 

roughly 120,000 stations, distributed both regionally and by ownership among oil-owned, 

branded independents, unbranded independents, and hypermart. The fundamental decision for 

each station is the acquisition of tankage and dispensing equipment required to dispense highethanol blends into flex-fuel vehicles. The module assumes that ten percent of stations have 

repurposable mid-grade tanks. The capital cost of repurposing is assumed to be significantly 

lower than investment in new tankage and equipment for hi-blends ($20k vs. $60k).

The basic logic within the dispensing station module combines the physics of high blend 

availability with the economics of the investment decision. Stations will not consider investment 

unless distribution infrastructure is sufficient within the region. They will not invest unless the 

investment makes economic sense, as reflected in a net present value calculation that captures 

the discounted stream of expected costs and benefits from the investment. 

Thus, two fundamental structures are at play within the dispensing station module. The first is 

an accounting structure that considers the movement of stations as they adopt high-blend 

tankage and equipment (Figure 21). The second provides a detailed view into the net present 

value (NPV) calculation that undergirds the decision to invest in high blend tankage and 

equipment (Figure 22).
36

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Figure 21. Dispensing station accounting structure. NPV calculation captures 

estimated costs and revenues of prospective investment

As shown in Figure 21, stations exist in one of three states with respect to investment in high 

blend tankage and equipment. Depending on the dynamics of regional distribution infrastructure 

availability, a portion of those stations not considering investment put the investment decision 

on the table each year. Based on the economic viability of the investment (as reflected in the 

NPV of the decision), the consideration of investment culminates in a decision to invest or to 

stop considering the decision. This investment process is disaggregated by region (so as to 

account for differential degrees of distribution infrastructure within each region), by ownership 

(to enable different potential affinities for high-blend ethanol sales among different ownership 

types, and to account for different business details for different ownership types), and by 

repurpose vs. new investment (to account for different capital costs associated with 

repurposing vs. new investment in tankage and equipment).
37

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Figure 22. Logic behind NPV calculation for stations

As shown in Figure 22, the NPV calculation considers major categories of revenue and expense 

associated with station investment. In addition to the capital cost of the investment, the NPV

calculation considers marginal cost and revenue streams associated with changes in the mix of 

high-blend vs. “straight” gasoline sales, changes to station traffic (to account for first-mover 

advantage) and other revenues from operations. 

Just as the distribution logistics module provides a context that constrains the acquisition of 

tankage and equipment for stations, the dispensing station module provides a context for fuel 

use. Accessibility of high blend stations within a region will constrain the potential for flex-fuel 

vehicles to access high blend fuels. Regional dispensing station coverage thus sets a physical limit 

on ethanol uptake in the system.
38

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Fuel Use Module

The fuel use module captures both the effects of regional high-blend fuel availability and the 

effects of relative gasoline/high-blend pricing on the decision making for flex fuel vehicle (FFV)

owners, with respect to the use of high-ethanol fuel blends. The module contains two major 

interconnected components, as shown in Figure 23. The first component accounts for the 

affinity of FFV owners toward high-blend fuels. The second uses a logit function (see Appendix 

B) to allocate fuel use between for FFV owners who are “occasional” and “regular” users of 

high-blend fuels.

Figure 23. FFV accounting structure

As shown in Figure 23, FFV users (expressed as a % of regional FFV vehicles) are divided into 

three distinct categories: non high-blend users, occasional high-blend users, and regular highblend users. Non high-blend users do not use high blend because a) they do not have access to 

stations that dispense high blend; b) they do not know they have an FFV; or c) they do not 

desire to use high blend, for non-economic reasons. Based on regional dispensing station 

coverage and a fraction of non-users who are assumed to be amenable to using high blends, FFV 
39

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owners leak over time from the non-user to occasional user category. Under conditions of 

price parity between high blend and regular gasoline, occasional users are assumed to fill 20% of 

their fuel requirements using high blend. Regular users, on the other hand, are assumed to fill 

80% of their fuel requirements using high blend under conditions of price parity. Movement 

between occasional and regular users is driven by a long-term retail price differential between 

the two products.

The distribution of high blend users provides a physical basis for ethanol usage among FFVs. 

Logit functions are used to translate relative high-blend/gasoline retail prices into instantaneous 

usage shares for both occasional and regular high blend users. The distribution of occasional 

and regular users is then applied to these usage shares. The resultant user-weighted usage 

shares are multiplied against potential high blend fuel consumption in order to generate actual 

high blend consumption within each region, as shown in Figure 24.

Figure 24. Logic behind high blend consumption
40

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Pricing and Inventory Module

The final module within the downstream sector accounts for ethanol pricing and inventory 

dynamics. Pricing and inventory for butanol, which in the model forms a substitute for ethanol 

in the low-blend market, are also captured here. 

Ethanol inventory is aggregated across the entire supply chain within each region, allowing for 

cross-regional movement of ethanol based upon regional surpluses or shortfalls within each 

region (as shown in Figure 25).

There are several important features to this pricing and inventory structure. First, three 

sources of regional ethanol production are incorporated: the starch to ethanol module, the 

cellulose to ethanol module, and the import module. Second, regional import/export structure 

that facilitates cross-regional movement of ethanol is incorporated. Third, a single driver of 

ethanol consumption is used, reflecting total ethanol demand from both low-blend (i.e. E10) and 

high-blend (i.e. E85) uses. Finally, multiple feedback elements that drive cross-regional 

movement of ethanol are incorporated. 
41

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Figure 25. Downstream ethanol inventory dynamics

This cross-regional movement algorithm is relatively straightforward, and is described below:

• Calculate desired inventory adjustment in each region required to bring inventories to 

desired levels (blue connections in Figure 25)

• Calculate the regional production/consumption gap as the difference between regional 

production and consumption (green connections in Figure 25)

• Sum the inventory adjustment and production/consumption gap to arrive at overall 

desired movement in ethanol by region
42

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• Roll up total desired imports and exports across all regions

• Limit total inter-regional movement to minimum of total desired imports, exports

• Allocate exports, imports in proportion to relative desired imports, exports.

Pricing for ethanol is considered at multiple downstream points along the supply chain. Figure 

26 provides an overview of the approach. Ethanol price is calculated at point of production, at 

point of distribution, and at the pump. Supply/demand imbalances in the downstream supply 

chain drive changes in price at point of production (see Appendix A). Transport and storage 

costs, which vary based on distribution infrastructure within a region, are applied to the point 

of production price in order to generate an ethanol point of distribution price. The price for 

high-blend ethanol at the pump is determined as a weighted average of point of distribution 

price and gasoline prices, based on a regression analysis of the two. Not shown in Figure 26, 

but relevant to policy analysis, are multiple points along the supply chain where initiatives can 

work to reduce costs and/or change price as perceived by producers, distributors, retailers, or 

end users of ethanol or high blend.

Figure 26. Simplified ethanol pricing structure
43

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The pricing and inventory for butanol follows similar logic to that of ethanol, with some notable 

exceptions:

• A single, national inventory is considered.

• In addition to its use in the low-blend oxygenate market, butanol can be consumed for 

industrial uses.

• Pricing for butanol is captured at point of production only. There is neither a point of 

distribution nor a point of use price for butanol.

The dynamics of butanol use and pricing center on the substitution of bio-butanol (produced 

within the BSM cellulose to butanol module) for butanol produced by other means, and on the 

substitution of butanol for ethanol in low-blend uses. These substitution dynamics are 

determined by relative price considerations. To capture these two dynamics, logit formulations

are employed that translate relative prices into market shares. For industrial uses, the price of 

bio-butanol competes against an assumed alternative price of $4.00/gal (this value can be varied 

as a scenario). For completion against ethanol, the endogenously-generated bio-butanol price is 

compared against the price of ethanol. 

Vehicle Module

The primary purpose of the vehicle module in BSM is to provide inputs that represent potential 

demand streams for ethanol and for gasoline, from “regular” vehicles and from FFVs. In order 

to provide these inputs to the rest of the model, we have developed a simplified accounting 

structure for vehicles of multiple types. Focusing on light duty vehicles, this vintaging chain 

captures the cumulative impact of multiple scenarios around volume of new vehicles each year, 

new vehicle mix, new vehicle efficiency, vehicle miles traveled (VMT), and vehicle mortality. 
44

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The model aggregates vehicles nationally. Regional population distributions are used to 

apportion fuel consumption among the 10 USDA regions used by the model. In its operation, 

the module applies age-specific survivorship estimates to vehicles as they vintage through the 

chain. The model focuses on two distinct vehicle types (automobiles and light trucks) and 10 

engine types (gasoline, diesel, plug hybrid, hydrogen, CNG, FFV, gas HEV, gas PHEV, bi-fuel, 

other) within the light duty fleet. For each of these 20 combinations, a scenario for new vehicle 

sales over time is accompanied by a scenario for new vehicle efficiency. The model dynamics 

tracks the implications of these new vehicle scenarios for overall vehicle efficiency and resultant 

fuel demand, as shown in Figure 27.

Each stage in the stock-flow chain represents a cohort of vehicles. Mortality flows remove 

vehicles from the system; vehicles that survive to the end of the cohort’s time horizon are 

moved to the next cohort in the sequence. Cohorts 1-4 are each four years in duration. 

Cohort 5 contains vehicles that are 16 or more years of age. (See Appendix C for details on 

the BSM approach to aggregating vehicles into four-year-sized lumps).

The parallel pathway, shown in Figure 27, accounts for the efficiency of vehicles in each cohort. 

Cohort-specific values for vehicles, efficiency, and vehicle miles traveled are used to calculate 

cohort-specific potential fuel usage, which is then summed over all cohorts to calculate overall 

potential fuel use.
45

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Figure 27. Tracking Vehicles and Efficiency (2 Cohorts)

The vehicle module is designed to facilitate exploration of the cumulative impact resulting from 

changes in volume, mix, mortality, VMT, and efficiency. Structure in the model captures the 

effects of changes in fuel prices, consumer attitudes, and the like. While we have not provided 

an explicit representation of consumer choice mechanisms in the vehicle module, we have 

created the potential to develop internally consistent scenario sets in which vehicle inputs 

maintain a logical consistency with petroleum price scenarios. On the vehicle influx side levers 

within the vehicle module enable use of (or departure from) Annual Energy Outlook (AEO),

which is updated annually in the BSM, projections for inflow volume, inflow mix, and efficiency. 

Similarly it is possible to use or depart from National Energy Modeling System (NEMS)

mortality rates, and to use or modify AEO VMT scenarios.
46

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Oil Industry Sector

The oil industry sector in BSM is relatively simple, containing a single module that houses:

• A set of scenarios used to determine crude oil prices

• Refinery product prices for diesel, jet fuel, and gasoline

• Algebraic relationships that translate crude oil prices, refinery product prices, and an 

assumed refinery “drop in point” for each fungible fuel pathway into price inputs for the 

different conversion modules and for the downstream pricing and inventory module.

• Accounting structure that captures petroleum displaced by diesel, jet fuel, and gasoline 

produced by the different fungible fuel pathways.

Import Module

The import module is a simple structure focused on the import of fuel ethanol from outside 

U.S. borders based on relative price considerations. This structure compares the ethanol point 

of production price generated within the downstream pricing and inventory module against a 

threshold (including tariffs) that reflects the cost of bringing fuel ethanol into the U.S. As the 

price within the U.S. exceeds the threshold, an increasing fraction of offshore production 

capacity is utilized. This simple structure enables analysis of scenarios around tariff policies, cost 

reduction, and capacity growth for offshore ethanol production facilities.
47

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Interconnections among sectors

Figure 2 provided a high level overview of the sectors that comprise BSM, and the previous 

discussion has given a detailed view into the modules that are found within each sector. 

Another perspective on the system is given by the nature of the interconnections among the 

different sectors. As shown in Table 3, the connections between sectors are relatively few in 

number, typically consisting of price signals and supply/demand quantities.

From/To

Feedstock 

Supply & 

Logistics

Conversion Import Oil Industry Downstream

Feedstock 

Supply & 

Logistics

• Feedstock 

consumption

• Feedstock 

price (plant

gate)

Conversion • Feedstock 

demand

• Cost to price 

ratios

• Output 

capacity

• Infrastructurecompatible

fuel 

production by 

pathway

• Ethanol

Production

• Butanol 

Production

Import • Ethanol import

Oil Industry • Gasoline 

point of 

distribution

price

• Modulespecific price 

input

• Gasoline point of 

distribution price

Downstream • Ethanol point 

of production 

price

• Butanol point 

of production 

Price input

• Ethanol price 

input

Vehicles • Potential loblend 

consumption 

from FFV

• Potential lo blend 

consumption

from non-FFV

• Potential hi-blend 

consumption

• Potential gasoline 

consumption

Table 3. Inter-sector connections
48

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Data Inputs

Multiple data inputs are required to run BSM, including agricultural cost and yield parameters, 

performance and learning parameters for the various conversion modules, logit coefficients, 

petroleum prices, and adoption rates for new farm practices and for dispensing station owners. 

Given the forward-looking nature of BSM, it is not surprising that the availability and quality of 

input data is highly variable. In many instances, assumptions or informed opinion were used to 

populate the parameter space, as shown in Table 4.

Input data area Source(s) Comments

Feedstock 

Supply & 

Logistics

Crop production costs ORNL/POLYSYS Assumed constant over simulation time frame

Ongoing interaction with ORNL analysts

Energy price/crop 

production price coupling

Pacey study (McNulty, 

2010) Price coupling factors derived from Pacey report

Yields ORNL/POLYSYS yield growth treated as assumption/scenario

Calibration data for 

production, prices

USDA baseline (United 

States Department of 

Agriculture)

Calibration done ~annually based on annual 

updates to baseline and updates to input data 

from ORNL

Logit parameters Assumption Assumptions modified as needed as part of 

calibration process

Harvest, transportation, 

Q&H, preprocessing 

logistics

INL/Biomass Logistics 

Model (BLM)

Structure and input data updated periodically to 

reflect ongoing interaction with INL analysts

Conversion

Performance and cost 

data for different 

conversion options

NREL design reports

PNL design reports

Analysis papers

Expert opinion

Internal secondary 

analysis/interpolation

NREL staff are assembling and vetting these data. 

For some conversion options, formal analysis 

reports do not exist. We are in process of 

vetting available data, developing assumptions, 

and facilitating an expert review

Learning curve 

parameters

Assumption informed by

Beck study (RW Beck, 

2010)

Sensitivity analysis planned

Logit parameters Assumption Plan sensitivity and robustness analysis around 

current logit parameters

Construction capacity Assumption

NPV of “other” option Assumption

Oil Industry

Oil price

EIA (United States Energy 

Information Agency), 

arbitrary scenarios

Data taken from “official” scenarios. Oil price 

shocks, other scenarios available to the system

Fuel mix Assumption Treated as assumption but informed by design 

reports

Drop in points Assumption
49

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Distribution Terminals by 

region EIA Developed from EIA data in 2008

Initial mix of terminals 

with/without 

infrastructure

Assumption

Infrastructure acquisition 

rate Assumption

Number, distribution of 

dispensing stations by 

ownership, dispensing 

station economics

NREL (Johnson & 

Melendez, 2007 draft)

NACS (National 

Association of 

Convenience Stores, 

2007)

NACS provides a rich perspective on “other” 

sales associated with dispensing stations in the 

spreadsheets that accompany the text of their 

annual report

Initial repurposable 

stations Assumption

Station adoption rates as 

f(NPV) Assumption

Logit parameters for fuel 

use

Assumption

Vehicle influx, miles 

traveled, miles per gallon EIA/NEMS

Ethanol price at point of 

distribution Assumed Assumed values for storage and transport applied to endogenous point of production price

High blend point of use 

price NREL/Lexidyne regression Regression of available data provides weighting factors for point of use price

Import

Capacity, price threshold 

for import, learning curve 

parameter

Assumed Values used to calibrate against observed data 

for fuel ethanol imports

Table 4. Summary of data inputs to BSM

Analysis Infrastructure

As has been outlined above, the BSM is a robust model that has undergone rigorous testing, 

validation and refining by the BSM team, and was built to explore multiple facets of the biofuels 

supply chain and its numerous drivers, bottlenecks and system interactions. The model was 

designed to be a comprehensive, agile tool that would allow analysts to perform quickturnaround analyses in response to evolving policy, scenario, and research questions. In order 

to quickly perform multiple runs of the model with different scenario inputs and to always be 

able to review runs that were made historically, it was important to set up a framework for 

storing all inputs and outputs of the model for all runs made for important analyses.
50

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We use modern software-engineering methodologies to maintain model quality and enable 

flexibility and responsiveness in response to analysis requirements that evolve as new bioenergy 

issues gain interest from stakeholders. An open-source configuration management and version 

control system, named Subversion, is used to track changes in the BSM model, documentation, 

and other project-related files. Documentation and metadata for variables are embedded 

directly in the STELLA model. Input data are stored, raw data sources are archived, and 

provenance/pedigree metadata is tracked within a relational database: furthermore, input data 

sets are processed within that database. Multidimensional data analysis, statistics, and

visualization tools are linked to the database in an architecture that allows for the automated 

“refresh” of visualizations and analyses when new scenarios are run. This database-centric 

approach makes it easy to develop and package “scenario libraries” for stakeholder use, as 

shown in Figure 28.
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Figure 28. Computing infrastructure for the BSM.

The aforementioned computing infrastructure supports a high-throughput analysis process that 

is outlined in Figure 29. In particular, it enables a “design-of-experiments” approach for 

simulation studies that involve complex combinations of policy scenarios, sensitivity analysis, 

and uncertainty quantification. The automation of simulation studies involves retrieving input 

parameters from the database, running STELLA models run in “batch mode”, and then storing 

output into the database. To further enhance the approach, the BSM source files can be copied 

and run on multiple machines at once to quickly make thousands of runs simultaneously. The 

required output variables are specified in the database, and the values for these variables are 

taken from STELLA for the specified runs and transferred to the database. In this way, we have 
52

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a central system where any team member can re-create any past scenario -- either by viewing 

the previous runs or finding the correct model on the model repository. The outputs can then 

be imported into any graphics software to visualize the simulation results, analyze trends and 

develop insights. Using these techniques, we have been able to analyze and compare thousands 

of BSM runs, completing analyses that have been included in over 15 internal analysis reports, 8 

external publications, and 10 forthcoming publications.

Figure 29. High level overview of the BSM analysis process.
53

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Analysis, Scenario Development, and Insights4

Specific policy-relevant scenarios or past scenarios can be used to drive the BSM simulations, 

though the BSM is not limited to scenario analysis. Under a specified scenario, the BSM can be 

used to track the hypothetical development of the biofuels industry given the deployment of 

new technologies within various elements of the supply chain and the reaction of the 

investment community to those technologies and given the competing oil market, vehicle 

demand for biofuels, and various government policies over an extended timeframe. Note, 

however, that high-level models such as the BSM are not typically used to generate precise 

estimates but rather to:

• compare the efficacy of alternate research, development, and deployment strategies, as 

well as energy policy options

• generate highly cost-effective scenarios

• identify high-impact levers and bottlenecks

• focus discussion among policymakers, analysts, and stakeholders

When BSM output includes unexpected system behaviors, modeling assumptions—particularly 

the behavioral aspects of decision making and the adequacy of the representation of feedback—

need careful reexamination to distinguish potential insights from model limitations. The model 

itself often indicates what assumptions need the most scrutiny; hence, it helps define the 

research and learning agenda.

 4 Analysis efforts using the BSM have been ongoing since early in 2010, initially using an earlier version of the model 

that focused on ethanol from cellulose and starch crops. Beginning in the fall of 2011, the BSM team has been 

designing experiments, creating scenarios, and conducting analyses using the current version of the model which 

includes both ethanol and infrastructure-compatible fuels.
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Although the BSM inputs can be altered to include any combination of policies, initial analysis 

efforts included establishing a “reference policy case” to which subsequent scenarios could be 

compared. The BSM reference policy case includes moderate incentives for ethanol production 

and a 50 cent per gallon gasoline tax (which could be interpreted as a “carbon” or GHG tax in 

dollars per ton of carbon dioxide). Policies are phased out in a staged manner, with the policies 

involving grants for capital equipment or loan guarantees ending earlier and the policies 

involving volumetric subsidies phasing out anywhere from 2020 to 2050. Each of the policies 

included in the reference case is based on historical precedence or future plausibility.5

As BSM functionality increased and research questions from stakeholders became more 

sophisticated, we created an expanded list of scenarios to be easily incorporated, tested, and 

analyzed. These scenarios are not intended to be prescriptive or comprehensive, but instead 

represent an extended backdrop of cases against which policies can be tested and possible 

industry evolution can be explored. The scenario library approach has proven to be quite agile 

and useful from an analysis perspective, and the BSM team expects this functionality to be 

augmented and expanded as part of the project’s ongoing development efforts.

Scenario Name Scenario objective/constraints Strategy employed

1: Minimal Policy Starch until 2012 Apply minimal subsidies and policies

2: Ethanol Only Ethanol pathways only Provide support for ethanol only; analogous to BSM

reference case

3: Equal Access All pathways in order to produce 

36 billion gallons/year by 2031

Allow all fuel types equal access to generous scenario 

subsidies

4: Output-focused To maximize growth restricted to 

$10 billion per year

Target most promising technology and withhold 

subsidy access from other pathways

5: Pathway Diversity To maximize pathways restricted 

to $10 billion per year

Design subsidy timeline to enable take-off of multiple 

fuel pathways by staggering start and end dates based 

on pathway progress and potential

Table 5. Scenarios in current BSM scenario library.

 5 Inman, D., L. Vimmerstedt, and B. Bush. 2014. Biomass Scenario Model scenario library: definitions, construction, 

and description. Technical Report NREL/TP-6A20-60386, April 2014.
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Scenario 1: Minimum policy

The minimum policy scenario (Table 5, Figure 30) includes only a $0.45 price subsidy at the 

point of production for starch ethanol that expires in 2012; it does not have any additional 

subsidies directed towards renewable fuel production. Without government intervention in the 

form of renewable fuel subsidies and given the oil price assumptions used in the model, neither 

the cellulosic ethanol nor fungible fuel industries gain industrial momentum, and thus fail to 

“take off” to any significant extent. Starch ethanol is able to satisfy the market for oxygenate in 

gasoline. The declining demand for oxygenate over the BSM time period is attributable to the 

overall decline in gasoline demand as more fuel-efficient vehicles enter the market in response 

increased Corporate Average Fuel Economy (CAFE) standards. 

Scenario 2: Ethanol only

The ethanol only policy, (Table 5, Figure 30) applies all subsidies to the renewable ethanol 

industry exclusively for the duration of the simulation (Figure 30). With all cellulosic feedstock 

stock available to the ethanol industry, the cellulosic ethanol industry is able to reach nearly 9 

billion gallons in annual production (bgy). Annual spending peaks at $6 billion (aside from the 

initial starch subsidy.)

Scenario 3: Equal access

The renewable fuel standard (RFS2) mandates that 36 billion gallons of renewable liquid 

transportation fuels will be in the market place by the year 2022; the annual RFS2 volumes are 

allowed to be adjusted by the Environmental Protection Agency based on installed capacity and 

the amount of fuel demanded (U.S. EPA 2011). The equal access scenario is designed to mimic 

pathway agnostic policies such as the RFS. In this scenario, subsidies are set to levels that spur 

renewable fuel output to RFS2 levels (i.e., 36 bgy). The results of this scenario need to be 
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viewed in the context of the initial settings and assumptions regarding the industrial maturity of 

the fungible fuel technologies examined. 

Scenario 4: Output-focused

Scenario 4 focuses subsidies on one pathway, i.e., fast pyrolysis. Fixed capital investment 

subsidies and loan guarantees were limited to fast pyrolysis and not available to other fuel 

pathways (Figure 30). Spending was limited to $10 billion per year, and after the expiration of 

the starch ethanol price subsidy at the end of 2012, total subsidies reached a peak of only $5.3 

billion in 2023. As the output for fast pyrolysis grows in later years, the fast pyrolysis subsidies 

grow as well because of the price subsidy on each gallon of fuel. The exposure to loan 

guarantees is not counted in the total subsidy figure. Fungible fuels – almost exclusively fast 

pyrolysis – contributed 34.1 billion gallons to the total 51.4 billion gallons produced in 2030. 

Scenario 5: Pathway diversity

In the “pathway diversity” scenario, we explored the possibility of promoting pathway 

production diversity by launching four different technologies to produce volumes of significant 

output (over 1 billion gallons) with a total annual budget of $10 billion. After selecting the four 

most competitive technologies through preliminary analysis (Fischer-Tropsch, fast pyrolysis, 

fermentation, and methanol-to-gasoline), different subsidy amounts, start times, and durations 

for each technology were applied in order to achieve output levels spread most evenly across 

the technologies. The staggered start times and durations increase the attractiveness of 

technologies with promising mature commercial plant techno-economic parameters but starting 

with limited industry maturity and experience. By the time pathways are ready to take off 

(around 2023), technologies are on more even footing, allowing for greater pathway diversity 

than the other scenarios. Industry output for fungible fuels reaches the 3 billion gallons 
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volumetric limit just before 2023, at which time heavier “startup” subsidy values switch to 

lower “background” subsidy values (as indicated in Figure 30). Annual production reaches 34.9 

billion gallons of renewable fuels per year in 2030 and reaches a peak of $8.9 billion of spending 

in year 2023. Of the total production in 2030, fast pyrolysis, Fischer-Tropsch, fermentation, and 

methanol-to-gasoline produce 5.7 billion, 5.3 billion, 1.3 billion, and 5.5 billion gallons, 

respectively.

Scenario Insights

BSM simulations based on these scenario libraries have provided a wide range of insights.

Potential policies designed to accelerate the development and sustainability of the biofuels 

industry can be easily tested across these embedded scenarios under a wide range of 

assumptions regarding the magnitude, duration, and sequencing of various policy interventions. 

Subsequent sensitivity studies on important model parameters are used to quantify the 

responsiveness of various key BSM output metrics to policy initiatives, alone or in combination, 

in these different scenario cases. This rigorous testing has built confidence in the robustness of 

the BSM, as well as informed key insights into the nature of the evolution of the biomass-tobiofuels supply chain.

Insight 1: Momentum in the fungible fuels industry causes hierarchical competition for 

feedstocks, thus reducing ethanol market share.

The existence of three renewable fuels industries creates an interesting hierarchical 

competition: the fungible fuels industry competes with the cellulosic ethanol industry for 

feedstocks, while the starch ethanol industry competes with the cellulosic ethanol industry for 

market share (both low- and high-blends). In the minimal policy scenario, the unsupported 
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biofuels industry produces only 12 billion gallons of starch ethanol output in the year 2030; the 

cellulosic ethanol industry produces only about 60 million gallons of ethanol in the year 2030. 

Starch ethanol is well established and continues to provide oxygenate for gasoline and highblend ethanol gasoline (E85), accounting for most, if not all, of the market for ethanol. Without 

government intervention, the starch industry does not face competition from the cellulosic 

ethanol industry and is able to meet all the ethanol demand.

When subsidies are applied exclusively to ethanol (with an emphasis on cellulosic ethanol), the 

hierarchy between starch and cellulosic ethanol is salient. To prevent significant industrial 

bottlenecks and encourage market penetration, downstream infrastructure subsidies are 

critical. When cellulosic ethanol subsidies are high, the industry takes market share away from 

the starch ethanol industry, but the latter is able to recover in the long-term because of its 

maturity. Although this case subsidizes cellulosic ethanol to a greater extent than other 

pathways, ultimately growth is restricted by limited market for ethanol (described above) as 

starch ethanol and cellulosic ethanol compete for the same market. In the competition for 

market share, the minimal policy and ethanol only scenarios confirm that cellulosic ethanol is 

able to compete with starch only with sufficient subsidies in the developing years.

Providing subsidies for all pathways in the RFS2 scenario, the fungible fuels industry is able to 

outbid cellulosic ethanol, driving up feedstock costs, which disadvantages the cellulosic ethanol 

industry compared to the starch ethanol industry. Additionally, unlike ethanol, the fungible fuels 

are infrastructure compatible and are modeled with unlimited demand and no interference 

(bottlenecks) from lack of downstream infrastructure. The net effect of these factors is a 59% 

reduction in cellulosic output by the end of the simulation (i.e., 2030) relative to its peak 

output, and a rapidly growing fungible fuels industry. 
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Insight 2: RFS2 volumes are achievable in 2030 with startup subsidies.

Under the Ethanol only scenario (Table 5), a total of 35.9 billion gallon per year (bgy) of 

renewable fuels is produced in the year 2030. Fungible fuels contribute over half of this amount 

(18.7 billion gallons), while the starch- and cellulosic-based ethanol industries cumulative 

comprise 17.2 bgy. The RFS2 timeline is shown to be impractical because the high-blend ethanol 

market applies pressure on the system (i.e., the high-blend EtOH market is not large enough), 

and the fungible fuels industry is not mature enough to produce 36 billion gallons in 2022. 

Reaching this level of production requires investment in the form of start-up subsidies, 

particularly fixed capital investment subsidies and loan guarantees for commercial-scale facilities. 

Total annual subsidies for the industry peak at $34.2 billion in the year 2024. The start-up fixed 

capital investment and loan guarantee subsidies (especially commercial-scale) are more effective 

at quickly building the industry than other subsidies but are far more costly than the others.
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Figure 30. Subsidy summary for scenarios 1 through 5. "Startup" value refers to values left of the annotated limit 

line(s) for the technology, background values refer to values right of each respective line; thickness of the duration 

bar indicates relative magnitudes of subsidies. Start and end years can be mapped to the year axis below.

Point of production [$/gallon]

Fixed Capital Investment (FCI) 

for Pioneer [%]

2015202020252030201520202025203020152020202520302015202020252030

Starch Ethanol 0.45 0.45 0.45 0.45

Pathway

StartupBackground

Cellulosic 

Ethanol

All EthanolFast 

Pyrolysis

FischerTropsch Methanol to Gasoline Fermentation

StartupBackground

Scenario 1

Minimal Policy

Scenario 2

Ethanol Only

Scenario 3

RFS2

Scenario 4

Output-focused

Scenario 5

Diverse pathways

StartupBackgroundStartupBackgroundStartupBackground

.15

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0

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2015202020252030

1 billion 

cellulosic 

gallon startup 

limit 

1 billion 

cellulosic 

gallon startup 

limit 

1 billion 

cellulosic 

gallon startup 

limit 

1 billion 

cellulosic 

gallon startup 

limit 

1 billion fungible 

fuel gallon 

startup limit for 

all other 

subsidies 

3 billion 

fungible fuel 

gallon startup 

limit 

0.3 billion fungible 

fuel gallon startup 

limit for FCI 

Commercial 

1 billion fungible 

fuel gallon 

startup limit

FCI for Commercial [%]

Loan for Commercial [%]

Downstream Distribution and storage [$/gallon]

Downstream Point of use [$/gallon]

Fungible Fuels

3.75

1.0

0

1.0

1.0

3.75

1.0

0

1.0

1.0

3.75

1.0

0

1.0

1.0

Loan for Pioneer [%]
61

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Insight 3: Production levels can exceed RFS2 levels if subsidies promote the most 

economically attractive pathway

Even though the volumetric output of Scenario 4 was higher than Scenario 3 (RFS2), the 

spending in Scenario 4 was less than that of Scenario 3 (RFS2). In Scenario 3, most years had 

annual spending on subsidies exceeding $10 billion, with the highest year at $34 billion. While 

Scenario 3 applied the same startup Fixed Capital Investment (FCI) subsidies to all fungible fuel 

pathways, Scenario 4 saves the most favorable startup FCI subsidies for fast pyrolysis. Because 

these FCI subsidies end up being directed toward fast pyrolysis, the subsidies are more efficient 

in promoting take off of that pathway than if subsidies are spread to different pathways or if 

subsidies are directed to a pathway that is not as economically attractive. As a result, even 

though a $10 billion annual subsidy was allowed, subsidies in Scenario 4 never surpassed $6 

billion in any year after 2012. 

Fast pyrolysis receives fixed capital investment subsidies for pioneer and commercial plants in 

2012-14. After that time, cellulosic ethanol becomes a more attractive investment because it 

starts with more learning at the pilot and demo levels and builds on that head start. Only when 

this initial wave of fast pyrolysis plants is built and generates its own learning do fast pyrolysis 

plants again become attractive investments. Additional fast pyrolysis commercial plants are built 

after 2017 without a subsidy. The initial subsidies in 2012-14 are enough to set in motion the 

learning necessary to make commercial plants an attractive investment without additional FCI 

subsidies. The threshold volume of 0.3 billion gallons for the FCI subsidy for commercial plants 

is reached in 2017. As a result, after 2014, fast pyrolysis is supported by only the price subsidy 

and by loan guarantees.
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Although additional money was available to spend in each year, greater spending on subsidies 

did not result in substantially more output. Spending more money on subsidies toward fast 

pyrolysis ends up subsidizing plants that would have come online without subsidies and/or 

result in more output but at a rate of spending above $10 billion per year. Additional subsidies 

available to pathways other than fast pyrolysis have little effect. By restricting subsidies to fast 

pyrolysis, it becomes more mature and locks out other pathways. 

Though it appears most economically efficient, relying on this single pathway presents nontrivial 

technology risks. Relying on a pathway with unfavorable long-term economics could result in 

less volumetric output (Biomass Scenario Model Analysis Report: August 2011). In the BSM, the 

most economically attractive pathway is obvious based on the available input data. In reality, the 

consequences of choosing a less than ideal pathway may not be evident until several years after 

a policy decision.

Insight 4: Technologies with favorable long-term economic cost structures can succeed if 

supported by targeted subsidies.

BSM simulations have shown that technological “lock-in” is likely to occur. Fischer-Tropsch has 

the highest initial level of maturity among the fungible fuels; its initial settings for pilot-scale and 

demo-scale maturity are higher than or equal to all other pathways (with the exception of the 

starch and cellulosic ethanol pathways). However, the mature commercial plant economics of 

fast pyrolysis are better than that of Fischer-Tropsch, based on the available process designs. In 

Scenario 4, the maturity of fast pyrolysis has to increase in order to make the investment look 

more attractive and to prevent technology lock out from Fischer-Tropsch.
63 

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In Scenario 5, subsidy policies are crafted to avoid lock out by any one pathway. To overcome 

lock out, subsidies target learning through pioneer plants and do not include commercial plants. 

By staggering policy start times and varying durations of subsidies according to maturity, the 

other technologies have a chance to build experience. Limiting more mature or economically 

attractive technology subsidies to turn on after the volumetric threshold is reached allows the 

other technologies to also develop. Rather than pouring extra subsidies into a relatively mature 

technology, this approach provides the minimum subsidies needed for a more mature 

technology (such as Fischer-Tropsch) to develop on a commercial scale, while providing the 

others with the extra support they need to accelerate their experience levels. This approach 

allows the successful take-off of four technologies while also approaching RFS2 production 

levels in 2030. 

However, although subsidies may help overcome initial maturity differences, they are not 

necessarily sufficient in overcoming differences in long-term economic cost structures. 

Technology, such as fermentation (which is as commercially mature as the other pathways by 

the end of the simulation), may need support beyond the subsidies exercised in this analysis 

order to reach greater production levels.

Current and Potential Use of BSM

The BSM has been used by the U.S. Department of Energy's Bioenergy Technologies Office 

(BETO) as well as others the U.S. Environmental Protection Agency (EPA)for gaining 

understanding around the biomass-to-biofuels supply chain, and the insights detailed herein only 

begin to address the impact the project has had in building understanding around several key 

industry dynamics. The BSM has also been utilized in collaboration with other parties, such as 

the EPA. Although to date the model has been mainly used by the DOE and other 
64 

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governmental agencies, it has the potential to be highly useful to many different stakeholders 

and across a wide range of analysis areas within the biofuels industry, as shown in Table 6. 

Stakeholder / Analysis Area Climate 

Change

Supply 

Curves

Biomass 

Yield

Policy R&D Energy 

Security

Trade Regionspecific

EPA X X

USDA X X X X

DOD X

Oil Companies X X

Biofuels Companies X X

Think Tanks X X X X X X X X

Foreign Governments/

Organizations

X X

Universities X X X X X X X X

Table 6. Potential collaborations for ongoing BSM use

Concluding remarks

The Biomass Scenario Model provides a representation of the supply chain associated with the 

production of biofuels. By integrating feedstock production and logistics, multiple conversion 

options, and market dynamics for butanol, fuel ethanol, and fungible fuels (gasoline, diesel, jet 

fuel), the model serves as a means for exploring the mechanisms by which the biofuels industry 

might develop beyond its current state. By providing an operational structure that reflects both 

the physics and economics of the system, BSM is a tool for building understanding around 

initiatives that seek to stimulate sustainable development of the industry. And by representing 

the system of interactions simply and transparently, the model sheds light on gaps in the data as 

well as areas where understanding of system structure is in need of enrichment. Analyses of 

the BSM—both as standalone modules and in integrated form—have underwritten powerful 

insights about the nature of the biomass-to-biofuels supply chain and of the nature of policy 

initiatives required to stimulate industry take off. Future work will focus on broadening the use 

of the BSM to other audiences, both government and academia as well as continued effort 

directed toward model refinement. 
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Appendix A: Pricing within BSM

In BSM, an endogenous pricing mechanism is an essential component of the structure that 

underwrites industry development. The model incorporates endogenous pricing structures for

• Each of the commodity crops (corn, wheat, cotton, small grains, soy)

• Hay (regional markets)

• Cellulosic feedstocks (regional markets)

• Ethanol

• Butanol

Price mechanisms within BSM can be viewed as central components of an economic control 

system. Each price signal evolves in response to the interplay of the forces of supply and 

demand. As production, consumption, and inventories change over time, price responds to 

imbalances. Prices, in turn, play a critical role in the investment, allocation and utilization 

decisions of producers of agricultural products and of biofuels. They also play a critical role in 

the fuel use decisions for butanol and for high-ethanol-blend fuels.

In developing the pricing structure used in BSM, we were mindful of multiple design 

constraints. First, the pricing mechanism needed to be simple so as to be understandable to a 

broad audience of model users. Second, the structure needed to be sophisticated, in order to 

not generate spurious dynamics. A simplistic pricing formulation can lead to steady-state error 

in controlled quantities or can become trapped in unrealistic states in response to extreme 

condition tests. Finally, the pricing mechanism needed to be flexible enough to support real 

world circumstances such as market initiation and scale-up. 

The basic feedback relationships of the BSM pricing mechanism are shown in Figure 31.
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Figure 31. Stylized view of feedbacks in BSM pricing mechanism

In this simple diagram, price works to balance production and consumption and to balance 

inventory against desired or target levels. Production/consumption imbalances create pressure 

to change price, as do imbalances between inventory and target inventory (which, in turn, 

depends on consumption). In order to accumulate or integrate pressure over time, price must 

be represented as a stock. The representation of price as a stock, in conjunction with pressure 

from inventory, results in oscillatory tendencies in the system; oscillations are dampened by the 

presence of feedback connections around production, consumption, and price. 

Figure 32 shows output from a simplified model of pricing/inventory/producer/consumer 

dynamics, which uses the basic pricing structure found in BSM. The test shows the equilibriumseeking tendencies of the structure, in response to a 10% shift in product demand. 
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Figure 32. Response of pricing system to 10% step-increase in demand

In BSM pricing, the mechanisms that connect production, consumption, and inventory to 

fractional change in price are significantly more detailed. The structural arrangement shown in 

Figure 33 is used to determine dynamic prices of several products throughout BSM, including:

• Ethanol at point of production

• Commodity crops

• Hay

• Cellulosic feedstocks

• Butanol 
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Figure 33. Detail of BSM generic pricing structure

The algorithm associated with this structure uses a bit of sophisticated math, but is relatively 

straightforward. It begins by calculating the price input—either from inventory or from 

production relative to consumption—as a distance from equilibrium in doublings or doublings. 

When the ratio is 1, the input is at its equilibrium value. When it is 2, it is one doubling away 

from equilibrium. When it is 0.5, it is one halving away from equilibrium. To capture this 

distance simply, the model uses logarithm functions as illustrated in Figure 33.

Second, the price input processed through a logistics function to generate a well-behaved 

response curve. Price input and logistics calculations are shown in Figure 34.

Figure 34. Illustrative price input and response curve calculations
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The third step in this algorithm is to scale the response curve by shifting its intercept to (0,0) 

and setting its asymptotes to desired maximum and minimum fractional changes in price. Finally, 

the total fractional change in price is calculated as the sum of fractional changes from inventory 

and production/consumption, and the result is applied to the price to generate a total fractional 

change in price.

In BSM, this generic pricing structure is applied to multiple market situations, with contextspecific details (beyond the scope of this paper) applying to specific fuel markets.

Appendix B: Logit as allocation mechanism within BSM

In BSM, logit functions are a mechanism for allocating resources among multiple competing 

uses. Detailed discussion of the logit function can be found in a variety of texts and articles 

dealing with consumer choice. For example, Train (Train, 2003) provides a thorough 

introduction to the logit, generalized extreme value, and a wide range of other approaches. 

The logit function expresses the likelihood P of choosing alternative i from the set of j

alternatives given an observed utility of x. A simple form of the logit is shown below:

The parameter k reflects unobserved or unexplained utility, while the parameter B is a scaling 

factor. The logit function has several desirable characteristics. Among them:

• It can be interpreted in terms of the utility associated with alternatives within a set of 

choice.
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• The sum of probabilities across all choices is 1

• There is a sigmoid relationship between utility and the resultant probability, which is 

beneficial under extreme conditions

The typical interpretation of the logit formulation, in the context of consumer choice, is the 

probability of choosing a particular alternative. In BSM, this probabilistic interpretation is 

applied to a population of actors (for example, farmers, investors in conversion facilities, 

consumers as they are deciding to fuel their vehicles) in order to generate an aggregate 

allocation of land use, investment, or fuel use.

Logit formulations can be found throughout BSM, as summarized in Table 7.
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Module Usage Dynamic Inputs Notes

Feedstock Supply

Crop land allocation

• Commodity crops

o With/without residues

• Perennial cellulosic crop

• Hay

Pasture land allocation

• As pasture

• As pasture harvested as 

cellulosic feedstock

• Perennial cellulosic energy crop

Per-acre grower 

payment for 

respective uses. 

For crop land, nested logit 

function is used to allocate 

among broad groups (e.g., 

commodity vs perennial 

cellulosic vs hay) and then 

among different 

commodity crops

Conversion and 

Relative 

Attractiveness

Allocation of facility construction 

resources among alternate pioneer 

and commercial scale conversion 

pathways in different regions

NPV of respective 

conversion 

pathways

Nested logit function is 

used to allocate 

construction capacity 

among different 

conversion platforms (e.g., 

fast pyrolysis) and then 

among different regions

Pricing and Inventory 

(Downstream)

Displacement of EtOH by butanol in 

lo-blend mixes

Displacement of non-bio-butanol in 

industrial market

Butanol, EtOH 

prices 

Fuel Use

Allocation of fuel sales between hiblend and gasolinePrice of gasoline

Price of hi blend

Table 7. Uses of logit formulation throughout BSM
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Appendix C: Aggregation of age classes in the Vehicle Module

The current version of the Vehicle module, like the other modules within BSM, reflects design 

tradeoffs between the competing pressures of detail “realism” and usability. It is conceptually 

straightforward to create a model containing great detail around vehicle type, regional 

distribution, and age distribution of vehicles. Unfortunately, the computational overhead 

required to simulate this detail would quickly become unmanageable. In an earlier version of 

BSM which incorporated this detail, we were required to run the vehicle module separately 

from the rest of the model and then import fuel demand scenarios separately.

In the current version of the model, we have reduced computational overhead significantly by 

aggregating age distribution of vehicles. We represent vehicle vintages using 5 distinct cohorts. 

Each cohort represents the 4 years of vehicle life. Within each cohort, each year vehicles are 

scrapped or they get older. Those vehicles that survive to the end of a cohort are transferred 

to the next cohort. A portion of the vehicle aging logic is shown in Figure 35.

Figure 35. Structure of vehicle vintaging

This structure aggregates together vehicles of multiple ages, and it is important to provide a 

reasonable estimate of the distribution of vehicles within each cohort. To do so, we consider 
73

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

the age-specific survival rates within each cohort, using these to derive an approximation of 

distribution of vehicles across the cohort:

Let Sn = survival rate for year n in cohort, 0 <= Sn <=1, S0 = 1

Dn = fraction of cohort population in year n

D1 = S0 / ( S0 + S0

* S1 + S0* S1 * S2 + S0* S1 * S2 * S3)

D2 = S0* S1 / ( S0 + S0* S1 + S0* S1 * S2 + S0* S1 * S2 * S3)

D( = S0* S1 * S2 / ( S0 + S0* S1 + S0* S1 * S2 + S0* S1 * S2 * S3)

D4 = S0

* S1 * S2 * S3 / ( S0 + S0* S1 + S0* S1 * S2 + S0* S1 * S2 * S3)

Age-specific survival rates are then applied to this distribution of vehicles in order to calculate 

distribution-weighted age-specific mortality rates, which are then summed and applied to the 

number of vehicles in the cohort to generate a mortality flow. The survival rate for the last 

year in the cohort is applied to the appropriate distribution, in order to generate movement of 

vehicles to the next cohort.

Figure 36 compares the transient response of a single 4 year cohort of the BSM vintaging 

structure against a simple one-stock structure and against a more disaggregated structure with 

4 1-year cohorts. For both systems, yearly survival rates are set to 50%. In the test, both 

systems are initialized at zero. Inflow to each system is set to 100 initially; the inflow steps 

down to 50 at time 10.
74

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Figure 36. Comparison of BSM and disaggregated vehicle cohorts.
75

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

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