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Contract No. DE-AC36-08GO28308

Geography of Existing and 

Potential Alternative Fuel 

Markets in the United States

Caley Johnson and Dylan Hettinger

Technical Report

NREL/TP-5400-60891

November 2014
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

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

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

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

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303-275-3000 • www.nrel.gov

Geography of Existing and 

Potential Alternative Fuel 

Markets in the United States

Caley Johnson and Dylan Hettinger

Prepared under Task No. VTP2.0020

Technical Report

NREL/TP-5400-60891

November 2014
NOTICE

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

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

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

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

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

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

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

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Acknowledgments

Funding for this report was provided by the U.S. Department of Energy’s (DOE’s) Clean Cities 

program, housed within DOE’s Vehicle Technologies Office. The Clean Cities program also 

provided the channels through which the authors and many of the cited market experts gained 

insight into regional alternative fuel markets. 

The authors wish to thank several fuels market experts for the time and insight they shared 

during the development of this report. Electric vehicle market experts include Justin Ong and 

Leia Guccione of the Rocky Mountain Institute and Mike Simpson of the National Renewable 

Energy Laboratory (NREL). Biodiesel market experts include Steve Howell and Alan Weber of 

MARC-IV, Owen Wagner of LMC International, and Teresa Alleman of NREL. Ethanol market 

experts include Mike O’Brian and Chris Bliley of Growth Energy and Kristi Moriarty and Ryan 

Daley of NREL. Propane market experts include Michael Taylor of the Propane Education and 

Research Council and John Gonzales of NREL. CNG market experts include Scott Baker of 

Cummins Westport Inc. and George Mitchell and John Gonzales of NREL.

All judgments in the final weighting of market factors, resulting maps, and analytical 

interpretations are the responsibility of the authors and not of the experts acknowledged above.
iv

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List of Acronyms

AFDC Alternative Fuels Data Center

AFV alternative fuel vehicle

B20 20% biodiesel in 80% petroleum diesel

CNG compressed natural gas

DOE U.S. Department of Energy

E85 blends containing 51% to 83% ethanol, depending 

on geography and season

FFV flexible fuel vehicle

HEV hybrid electric vehicle

MV market value

NREL National Renewable Energy Laboratory

OPIS Oil Price Information Service

PEV plug-in electric vehicle

PV photovoltaic
v

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Executive Summary

When deploying alternative fuels, it is paramount to match the right fuel with the right location, 

in accordance with local market conditions. We used six market indicators to evaluate the 

existing and potential regional market strength for each of the five most commonly deployed 

alternative fuels: electricity (used by plug-in electric vehicles), biodiesel (blends of B20 and 

higher), E85 ethanol, compressed natural gas (CNG), and propane. Each market indicator was 

mapped, combined with the others, and evaluated and adjusted by industry experts. This process 

revealed the weight the market indicators should be given, with the proximity of fueling stations 

being the most important indicator, followed by alternative fuel vehicle density, gasoline prices, 

state incentives, nearby resources, and finally, environmental benefit.

Though markets vary among states, no state received “weak” potential for all five fuels, 

indicating that all states have an opportunity to use at least one alternative fuel. California, 

Illinois, Indiana, Pennsylvania, and Washington appear to have the best potential markets for 

alternative fuels in general, with each sporting strong markets for four of the fuels. Wyoming 

showed the least potential, with weak markets for all alternative fuels except for CNG, for which 

it has a patchy market. Of all the fuels, CNG is promising in the greatest number of states—

largely because freight traffic provides potential demand for many far-reaching corridor markets 

and because the sources of CNG are so widespread geographically.
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Table of Contents

List of Acronyms........................................................................................................................................ iv

Executive Summary.................................................................................................................................... v

List of Figures ............................................................................................................................................ vi

List of Tables .............................................................................................................................................. vi

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

Methodology................................................................................................................................................ 1

General Market Indicators...................................................................................................................... 2

Generalized Prioritization and Weighting System ................................................................................. 7

Results and Discussion.............................................................................................................................. 9

Electricity ............................................................................................................................................. 10

Biodiesel............................................................................................................................................... 12

Ethanol.................................................................................................................................................. 13

Compressed Natural Gas...................................................................................................................... 15

Propane................................................................................................................................................. 17

State-Based Categorizations................................................................................................................. 18

Conclusion................................................................................................................................................. 21

References................................................................................................................................................. 22

Appendix A. State-Based Market Categorization Data.......................................................................... 25

List of Figures

Figure 1. Gasoline price percentiles by ZIP code. Data source: OPIS 2010. ........................................ 4

Figure 2. Average retail fuel prices for gasoline, diesel, and alternative fuels. ................................... 8

Figure 3. Map of the most active vehicle electricity markets ............................................................... 10

Figure 4. Map of the most active vehicle biodiesel markets ................................................................ 12

Figure 5. Map of the most active vehicle ethanol markets ................................................................... 13

Figure 6. Map of the most active vehicle CNG markets ........................................................................ 15

Figure 7. Map of the most active vehicle propane markets.................................................................. 17

Figure 8. Number of states with alternative fuel markets, by fuel and market strength ................... 21

List of Tables

Table 1. Screening Criteria for Market Potential, Weighting, and Data Availability............................. 7

Table 2. Weighting of Screening Criteria for Various Fuels ................................................................. 10

Table 3. Market Categorization by State and Fuel ................................................................................. 19

Table A-1 State Electric Vehicle Market Categorization Data............................................................... 25

Table A-2. State Biodiesel Market Categorization Data ........................................................................ 27

Table A-3. State Ethanol Market Categorization Data ........................................................................... 28

Table A-4. State CNG Market Categorization Data ................................................................................ 30

Table A-5. State Propane Market Categorization Data .......................................................................... 32
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Introduction

It has become a common mantra in the alternative fuel deployment world that there will be no 

single “silver bullet” to reduce consumption of conventional fuels. Rather, a combination of all 

viable fuels and vehicle technologies is necessary to improve U.S. energy security and reduce 

environmental impacts. But in any given location, some fuels may be better than others in terms 

of economic viability, ease of deployment, and environmental benefits. This report maps 

indicators of existing and potential fuel supply and demand to provide insight into the regional 

applicability of various alternative transportation fuels. Because no alternative fuel markets are 

close to saturation, significant market activity in a particular geographic area also indicates

strong potential for future growth. The process by which the fuel markets are mapped reveals the 

relative prioritization of market indicators, enabling policymakers, entrepreneurs, investors, 

fleets, and analysts in a particular geographic area to pursue the most appropriate or promising 

fuel. This prioritization scheme also highlights the market components most in need of 

improvement for those hoping to prime a specific market in a given area. Finally, the report gives 

a fuel-based summary of the state markets for use by national-level policymakers, investors, and 

entrepreneurs.

This type of analysis has been commonly deployed for the incipient hydrogen market (Agnolucci 

and McDowall 2013), but little such work has been performed for fuels with greater market 

penetration. Analysis of these fuel markets has the potential of higher fidelity and usefulness, 

because there is more data to inform the analysis.

Fuel markets considered by this study include electricity (for use by plug-in electric vehicles

[PEVs]), biodiesel (blends of B20 and higher), propane, compressed natural gas (CNG), and E85 

(blends containing 51% to 83% ethanol, depending on geography and season). Our analysis does 

not include hydrogen, as our focus is on markets that are more developed. Nor does it include 

liquefied natural gas, which is strategically deployed by large fleets and not considered a 

promising fuel for non-fleet vehicles.

Methodology

Our project employed the following general steps in order to identify market potential for 

alternative fuels in various geographic areas of the United States:

1. Gather market indicators—data that have been shown (or are assumed by market experts) 

to have a positive association with existing or potential supply or demand of alternative 

fuels.

2. Convert all indicators to a percentile value (attributed to 10X10 km squares) to ensure 

equal baselines when weighting and comparing indicators. 

3. Apply a generalized prioritization scheme and weighting of those parameters, and 

combine to calculate a single market-strength value for each fuel in a specified 

geographic area.

4. Poll market experts to ensure that the calculated scores match the known, general pattern 

of existing markets and deployment patterns of each fuel.
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5. Adjust parameter weights so that the most active market areas better match the 

expectations of industry experts. 

6. Study locations in which the order of prioritization of a given fuel was changed from the 

generalized prioritization scheme, as such cases have the highest potential to reveal 

market insights.

7. For each fuel, categorize states (in categories ranging from weak to strongest) based on 

each state’s overall market potential and the geographical distribution pattern of market 

potential within the state. 

General Market Indicators

Each fuel has market indicators that are either proven through previous analysis or assumed to be 

reasonable by industry experts. Proven indicators have a positive association with the potential or 

existing supply or demand of alternative fuels. Assumed indicators have been included to help 

test the actual influence of known market indicators.

The six market indicators used in this analysis are described below.

1. Existing Fueling Stations

The availability of fueling infrastructure is a primary indicator of a strong market for a given 

fuel. If fueling stations are operating in a given area, we can safely assume there are vehicles that 

can use the fuel and a population that is willing to use it. We assigned a five-mile circle of 

influence to each station (public and private) listed in the Alternative Fuels Data Center (AFDC) 

stations database (AFDC 2013b). This limit reflects the relative unwillingness of drivers to travel 

out of their way to refuel. Daley et al. (forthcoming) found that only 14% of surveyed federal 

fleet drivers were willing to drive five or more miles to refuel with an alternative fuel when they 

had readier access to a conventional fuel compatible with their vehicles. Notably, stations selling 

only low-level blends of ethanol (lower than E51) or biodiesel (lower than B20) are not tracked 

by the AFDC. Consequently, this study does not include such stations in the analysis.

2. Vehicle Density

The density of light-duty alternative fuel vehicles (AFVs) using a given fuel is another strong 

indicator of existing and/or potential demand. We calculated vehicle density for each fuel by 

dividing the number of vehicles in each U.S. ZIP code by the area of that ZIP code. Density is 

more telling than the absolute number of vehicles, because ZIP codes vary greatly by geographic 

size—a given ZIP code may have a large total number of vehicles simply because it is physically 

large. Vehicle density better describes the number of AFVs that could conveniently refuel at a 

given location rather than the total number that could possibly refuel. 

We used diesel vehicles as a proxy for biodiesel vehicles because vehicle registrations do not 

differentiate between vehicles that can use B20 and vehicles that cannot. Furthermore, a majority 

of diesel vehicle models can use B20 (National Biodiesel Board 2012).

We included an additional factor in the vehicle density calculations for three of the five fuel 

types. Biodiesel market analysis also accounts for freight ton-miles (and weighs it higher than 

diesel light-duty vehicles) because the majority of freight is transported by diesel (I.C.F. 

Consulting 2005). Number of freight ton-miles is, therefore, a key indicator of diesel demand. 
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This demand for diesel translates to potential B20 demand, because, as previously noted, a 

majority of heavy-duty diesel engine models are able to use B20 (National Biodiesel Board 

2012). The CNG market analysis also considers freight ton-miles, because CNG is being used in 

an increasing number of long-haul applications (Baker 2013).

In the electric vehicle market analysis, we used density data from the more mature hybrid electric 

vehicle (HEV) market to supplement the plug-in electric vehicle (PEV) density data, as HEV 

ownership has proven to be a prime indicator of potential PEV purchasers (Tal et al. 2013). 

Given the incipiency of the PEV market at the time the data were gathered, we assigned a higher 

weighting to HEV density than to PEV density.

Vehicle counts used in this report are light-duty vehicle registrations aggregated from local 

motor vehicle departments by R.L. Polk & Co. CNG and propane vehicles are underreported in 

this dataset, as they are often the products of after-market conversions or classified as dualfueled vehicles. These nuances are not reflected in the vehicle identification number, which the 

data collection process relies on. We adjusted the study strategy for this shortcoming, as 

discussed in the results section.

3. Gasoline and Diesel Prices

When other factors are equal, higher gasoline and diesel prices improve the economic case for 

using alternative fuels (Johnson and Melendez 2007; Johnson 2010). Therefore, we took into 

consideration gasoline or diesel prices (depending on which competes with the alternative fuel in 

question) when showing which regions of the nation are favorable for alternative fuel 

deployment. Figure 1 maps the average gasoline price (in percentiles to facilitate comparison)

throughout the United States, as recorded by the Oil Price Information Service (OPIS 2010). We 

used 2010 average fuel prices because it was a year of relative price stability and few events that 

would have regional influence on prices. The diesel map exhibits similar trends. In general, these 

fuel prices are lowest along the Gulf Coast and increase in the North and West. Hawaii has the 

most expensive gasoline, followed by Alaska, California, Washington, Nevada, Oregon, New 

York, Idaho, and Connecticut. Gasoline price is highly dependent on state boundaries because 

the state fuel tax is one of the most regionally variable components of the price.

4. State Incentives

State incentives range from tax credits for installing fueling infrastructure to allowing AFVs to 

drive in high occupancy vehicle lanes. By definition, these incentives encourage the use of 

targeted fuels and technologies. We gave them less weight than we gave to resource proximity 

because of the wide range of type, structure, and monetary value of incentives. State incentives 

are tracked by the Laws and Incentives section of the AFDC (AFDC 2013a). The keepers of the 

AFDC are currently assessing the efficacy of various categories of laws and incentives, but there 

work is not done yet so all laws and incentives are given an equal weighting.
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Figure 1. Gasoline price percentiles by ZIP code. Data source: OPIS 2010.

5. Resource Proximity

Proximity to biodiesel and ethanol refineries is used as an indicator of supply. In general, we 

assumed that locations closer to refineries have lower fuel transportation costs and, therefore, 

lower fuel prices. This is particularly important for fuels such as ethanol and biodiesel, which are 

transported by truck and rail rather than pipelines (Wu 2008). Furthermore, communities near 

biofuel refineries have been shown to be supportive of biofuel consumption because they expect 

job creation, new markets for farm products, and greater energy security (Selfa 2010). We 

assigned a radius of influence of 100 miles to reflect the general limit at which ethanol can be 

trucked cost-effectively (Johnson and Melendez 2007). We did not account for rail lines in the 

distance calculations, because, while they can reduce transportation costs, they do not offer the 

community benefits that biorefineries do.

Natural gas processing plants, as reported and mapped by the U.S. Energy Information 

Administration (2013), serve as the “resources” for CNG. Both natural gas processing plants and 

petroleum refineries serve as the resources for propane, because propane is produced in 

approximately equal parts in both facilities (National Propane Gas Association 2013). These 

resources are assumed to have local influence in a manner similar to biorefineries and are, 
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therefore, also mapped with a circle of influence. However, breaks at radii of 50, 100, and 150 

miles are used to reflect a diminishing influence as distance from the plant increases.

Given that access to electricity is nearly ubiquitous in the continental Unites States, resource 

proximity does not vary regionally in any meaningful way for the purposes of this study. 

However, proximity to photovoltaic (PV) panels does serve as a good indicator of demand for 

electricity for use by PEVs, because ownership of PEVs is highly correlated with PV panel 

ownership, as demonstrated in a survey of PEV-owning California households by Tal et al.

(2013). The study found that 42% of PEV owners surveyed had PV panels, a significantly higher 

ownership rate than the statewide average of less than 1%.

6. Environmental Benefit

Environmental benefit is one of the most commonly cited reasons for using alternative fuels. For 

some alternative fuels, the degree of benefit is dependent on geographic location. This regional 

variability is most pronounced for electricity, where the degree of benefit ultimately depends on 

the fuels used to generate the electricity in a given region. In this report, we used carbon intensity 

of electricity generation as an indicator of the environmental benefit of PEVs. We derived carbon 

intensity by interpolating carbon dioxide emissions factors at electricity generating facilities for 

each location (Ventyx 2008).

The environmental benefits of the other alternative fuels we examine here are relatively 

consistent across geographic locations and, therefore, were not considered in this study.

Indicators Not Included

The market strength of each alternative fuel is determined by numerous factors with varying 

degrees of influence. This report focuses on a limited number of indicators that we considered to 

be the most telling and for which there are robust data available. It is worth noting some 

indicators we did not consider and our reasoning for excluding them.

Household income: While household income is a reasonable indicator of the ability to purchase 

AFVs, we opted to use the more direct indicator of AFV registrations. In the incipient PEV 

market, two other indicators (HEV ownership and PV ownership) serve as more direct indicators 

of willingness to pay a premium than does income alone (Tal et al. 2013). Furthermore, gas 

prices directly impact payback periods for AFVs and are, therefore, considered more important 

than income.

Population density: We do not directly take this factor into account because it is represented by 

both vehicle density and existing infrastructure, which requires a minimum population density to 

support.

Commuting distance: This factor has two distinct and opposing effects on a commuter’s 

likelihood of purchasing an AFV. A shorter commute can increase the appeal of AFVs by 

reducing the impact of range anxiety, whereby commuters feel uncomfortable driving an AFV 

distances close to its maximum range. However, longer commute distances make AFVs a better 

investment with a shorter payback period (O’Keefe et al. 2011). These two effects mitigate each 

other, and commuting distance, therefore, is not taken into account.
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Average regional temperature: We received recommendations to consider using this factor as 

an indicator because PEVs operate more efficiently in warm temperatures than in cold ones. 

However, repeated exposure to high ambient temperatures reduces battery life (Smith et al. 

2012). Currently, no data yet exist to definitively characterize the impacts of local climatic 

factors on PEV deployment, so we omitted this factor from consideration in the electricity 

market.

Temperatures do not have significant effects on any of the other alternative fuels examined in 

this study. Flex-fuel vehicles (FFVs) do experience drivability impacts in cold weather when 

using fuel with a very high percentage of ethanol; however, fuel suppliers reduce the percentage 

of ethanol in E85 during cold months to prevent such problems. 

PEV hot spots: PEV hot spots are locations in which public and private PEV-promotional 

programs are focusing their efforts. They have been treated as indicators in past market

assessments (Johnson et al. 2011), but enough time has passed since their initiation that the 

market effects of these programs should be captured by indicators we have already considered 

here, including charging station availability, PEV densities, and pro-PEV incentives. Any 

location that is a PEV hot spot that does not exhibit strong station availability or PEV densities

could potentially be considered a poor market because previous efforts have not been successful

there. Alternatively, it is possible that the program has not been executed effectively and thus has 

not achieved its intended effects in the PEV market. 

Education campaigns: As with PEV hot spots, educational campaigns for propane have been 

proposed as indicators for increased demand (Taylor 2013). However, it is quite difficult to 

accurately specify geographic boundaries for such campaigns. Furthermore, there is significant 

variability in scale and scope among campaigns, thus precluding us from assigning a single 

weight to characterize their effects in the market.

Heavy-duty vehicle density: No datasets exist that fairly represent a broad spectrum of heavyduty AFVs and adequately tether them to a given location. Some fuel-specific industry 

organizations track and publish data on various subsets of the heavy-duty vehicle population, but 

other subsets are completely unrepresented. Attempts to use heavy-duty vehicle registration data 

are hampered by the fact that most registrations are tied to corporate headquarters rather than to 

garage or delivery locations.

Proportion of households with more than one vehicle: This factor has served as an indicator 

of areas in which the population is likely to purchase limited-range AFVs (such as CNG vehicles 

and PEVs) in some studies (see, e.g., Melendez and Milbrandt 2007). This assumption is logical 

because a multi-car household can still make necessary long-distance trips by utilizing its

conventional vehicle. However, we consider AFV registrations to be a more direct indicator of 

AFV-purchasing populations than the prevalence of second vehicles.

Voting preferences: This factor has also been used as an indicator of alternative fuel markets in 

past studies (see, e.g., Melendez and Milbrandt 2007), under the assumption that populations that 

vote pro-environment and pro-energy independence are also likely to purchase AFVs. Our study 

uses two indicators that are more directly tied to propensity to purchase a PEV. First is the 
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number of enacted laws and incentives in an area, which reflects the electorate’s political support 

for such policies. The second indicator is the number of AFVs that have actually been purchased.

Generalized Prioritization and Weighting System

Each market indicator has its own magnitude of impact on the alternative fuel market. Therefore, 

each indicator must be weighted accordingly. We derived a weight for each indicator through a 

series of reviews for each fuel (the details for this process are provided in the results and 

discussion section below). However, a generalized prioritization was necessary to develop a 

consistent starting point. Table 1 lists the criteria and order of the general prioritization scheme 

from left to right. It also notes whether data is available to map.

Table 1. Screening Criteria for Market Potential, Weighting, and Data Availability Fuel Existing Fueling Stations Freight or HEVs Vehicle Density Gasoline or Diesel Prices State Incentives Resource Proximity Environmental Benefit

Criterion

Weight Heavy Light

Electricity X X X X X X X

Biodiesel X X X X X X Consistent

Ethanol X — X X X X Consistent

Natural Gas X X X X X X Consistent

Propane X — X X X X Consistent

X denotes that applicable data is available to map, and it is suitable to inform decisions.

— denotes that no applicable data is available.

“Consistent” indicates that the data do not depend on location.

Existing infrastructure is weighted the most heavily because we assume it to be indicative of a 

variety of factors conducive to retailing a fuel within a given region. For most fuels, this is a 

prerequisite to having any alternative fuel market at all. This data is available, complete, and 

relevant to all fuels.

Freight and HEVs is one of two indicators representing potential demand for AFVs and, 

therefore, demand for alternative fuel. For the biodiesel market analysis, freight is weighted more 

heavily than diesel vehicle density because it represents a more concentrated source of demand 

along major highways. Furthermore, this indicator allows us to account for the demand of heavyduty vehicles. HEV density is weighted more heavily than PEV density because the PEV market 

was in its infancy when the dataset was developed. There is also strong evidence showing that 

early adopters of HEVs become the early adopters of PEVs (Tal et al. 2013).
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Vehicle density is prioritized over the remaining indicators because it is not only a driver of 

healthy alternative fuel markets, but also a result of healthy markets; the remaining indicators are 

only considered causes of healthy markets. There are, however, caveats to the relative 

applicability for each fuel type, which we detail in the results section.

Gasoline or diesel prices are weighted more heavily than state incentives because they are more 

influential on alternative fuel fleet and station business plans than state incentives are (Johnson 

and Melendez 2007; Johnson 2010). These prices are also weighted more heavily than resource

proximity because the primary driver by which a nearby resource improves the alternative fuel 

market is through price reduction, but an equal rise in gasoline prices can achieve the same 

relative price advantage. Gasoline and diesel prices are readily available and relevant to all 

alternative fuel markets. However, they are most relevant for CNG and electricity markets 

because, as Figure 2 shows, the prices of these two fuels correlate least with the price of 

gasoline. Therefore, in the face of high gasoline prices, these fuels afford greater fuel savings.

Figure 2. Average retail fuel prices for gasoline, diesel, and alternative fuels.

Electricity and CNG markets are the least tied to gasoline and diesel prices. Note that propane prices appear inflated 

due to retailers that sell for higher-value usage such as campers.

State incentives are weighted more heavily than resource proximity for two reasons: Incentives 

can change the economics of using an alternative fuel to overcome additional transportation costs 

of long-distance imports. Secondly, state incentives signify a population’s acceptance of and 

support for a fuel.

Resource proximity is viewed as more influential than environmental benefit because, despite 

consumer intentions and stated preferences, economics typically trump environmental concerns 
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in transportation choices (Hensher 2010). These economics are heavily impacted by 

transportation costs, so the proximity of a resource creates downward pressure on fuel prices.

Environmental benefit is rated as the weakest indicator we consider. The environmental benefit 

afforded by most fuels does not vary from one region to the next, which renders this factor 

irrelevant for those fuels, for the purposes of this study. The exception is electricity, whose 

climate change impacts vary widely among regions. This is discussed in greater depth in the 

Results section.

Results and Discussion

In this study, we mapped the factors listed above for each fuel and combined them using 

weightings determined in collaboration with market experts. We calculated final “score” values 

by determining the relevant parameter value at each location across the United States (using a 

10-km grid and excluding Alaska, where the data were insufficient). For each metric, the values 

at each location were then converted to percentiles, ignoring unknown and 0 values, in order to 

create unit-free values that are readily combined. These percentiles were combined using the 

final parameter weightings shown in Table 2.

In this section, we provide a composite map for each fuel and discuss general patterns and 

primary drivers of these patterns. The composite maps display market intensities from 0 to 1, 

reflecting the cumulative weighted percentiles. They have different thresholds for various colors, 

as shown in the legends, reflecting the fact that market strength is comparable within fuels but 

less directly so between fuels. Portions of the map assessments are broken down by state, in 

which cases the analysis is aided by the state mean market strength, as shown in Appendix A. 

We go on to highlight unexpectedly strong and weak areas for each fuel type. This process is 

intended to guide policymakers, investors, and entrepreneurs in evaluating alternative fuel 

markets within a given region and help assess market health in smaller areas (not identifiable on 

the map) and in future times as market conditions change. 

Finally, this section addresses how the factor weightings for each fuel deviate from the general 

weighting system listed above. By discussing which factors are the most important indicators of 

market health, this section will also enable policymakers to better target their efforts and funds as 

they seek to prime markets for a given fuel. 
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Table 2. Weighting of Screening Criteria for Various Fuels

FuelExisting InfrastructureFreight or HEVsVehicle DensityGasoline/Diesel PricesState IncentivesResourceProximityEnvironmental Benefit

Electricity 19% 20% 16% 14% 10% 9% 12%

Biodiesel 24% 20% 16% 15% 13% 12% —

Ethanol 30% — 21% 10% 16% 23% —

Natural Gas 34% 12% 11% 20% 15% 8% —

Propane 32% — 16% 19% 17% 16% —

Average 28% — 18% 16% 14% 14% —

Electricity

Figure 3. Map of the most active vehicle electricity markets
11

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Our analysis indicates that the electricity markets for PEVs are strongest in Hawaii, along the 

West Coast, and in the Northeast, followed by isolated urban areas throughout the nation and 

linked urban areas in the Great Lakes region, Florida, North Carolina, Arizona, and Nevada.

Geographic areas with unexpected market strength include the western half of Arizona and 

southern portion of Nevada. This is driven by the high prevalence of PV installations, lowcarbon-intensity electricity, and high number of PEV incentives in this area. Western Texas is 

another surprise, with market potential largely due to the state’s high proportion of low-carbon 

wind power (which makes PEVs environmentally attractive) and higher gasoline prices (which 

make them economically attractive). North Carolina’s PEV market potential is strong largely 

because of a high number of PEV incentives and a high PEV density. Eastern California and 

Washington are attractive despite their relatively low population density, largely because of high

PEV density. Numerous statewide incentives also help, as does eastern Washington’s clean 

electricity. Experts feel that the low population density of northern Michigan makes it an 

unlikely location for good PEV market, yet the clean electricity, high HEV density, and strong 

state incentives point to stronger-than-expected market potential.

Changes in factor prioritization included a reduction in the weighting of existing infrastructure. 

We deemed this factor less influential than originally proposed because 75% to 80% of charging 

events occur at home via residential charging equipment (EV Project 2013). We increased the 

weight assigned to environmental benefit because PEVs are appealing to early adopters 

motivated more by environmental concerns and energy independence benefits than by economics 

(Ungar and Fell 2010).
12

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Biodiesel 

Figure 4. Map of the most active vehicle biodiesel markets

Markets for biodiesel appear primed throughout the Midwest, centered on the Chicago area. This 

is where the majority of biodiesel production occurs, providing both reduced shipping costs and 

a pro-biodiesel population. Urban areas and linking highways from Atlanta to Boston also show 

strong market potential because of high freight traffic and diesel registrations throughout the 

greater region. Memphis, Houston, Dallas, Denver, and Phoenix appear as islands of promising 

market potential due to high diesel prices and well-connected corridors of high freight tonnage. 

Wyoming, Utah, and Idaho have the weakest biodiesel markets despite the activity in 

Yellowstone National Park and Salt Lake City and the existence of interstate highways 

connecting Idaho Falls to Boise.

Geographic areas with unexpected market strength include North Dakota, the Oklahoma 

panhandle, and southwestern Oregon, where biodiesel refineries and a high number of state 

incentives push market ratings into high categories. Despite low population densities, these areas 

should be of interest to strategists.

North Carolina has many pro-biodiesel incentives and a disproportionate number of B20 

refueling stations: 18% of the nation’s total, with only 3% of the U.S. population (AFDC 2013b). 
13

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The stations are well dispersed throughout the state. Pennsylvania shows great promise due to 

high gasoline prices, freight tonnage, diesel vehicle registrations, and a number of biodiesel 

refineries. Hawaii is surprisingly strong as well: it has very high gasoline prices, many state 

incentives, a high density of diesel registrations, and seven B20 stations. However, the lack of 

biodiesel supply is currently overriding these indicators of high demand. Appalachia has 

surprisingly poor biodiesel market potential, considering it is surrounded by relatively strong 

biodiesel markets. 

We did not observe any need for changes in factor prioritization for biodiesel.

Ethanol

Figure 5. Map of the most active vehicle ethanol markets

Ethanol markets are most robust in the Midwest. Outside the Midwest, the urban areas 

surrounding the San Francisco Bay (including Sacramento), Los Angeles, Denver, New York

City, Rochester-Buffalo, Houston, Miami, and Dallas have large markets (listed in order of the 

size of the geographic area that received scores in the highest quintile). The weakest ethanol 

markets are found in the region comprising Wyoming, Utah, Idaho, Nevada, and Montana. These 

states have very few pro-ethanol incentives, low FFV density, few fueling stations, and only one 

ethanol refinery. 
14

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Experts were surprised that markets in Kansas and Missouri were not as strong as some of the 

areas to their northeast. This is likely because Missouri has some of the cheapest gasoline prices 

in the country, few ethanol incentives, and no local sources in the southwest portion of the state. 

The ethanol market in southern Kansas is hampered by inexpensive gasoline, a shortage of E85 

stations, and no local fuel sources in the southeast portion of the state. Louisiana appeared better 

than expected, largely because of a refinery within the state, a fair number of ethanol incentives, 

and high FFV density. The addition of a relatively small number of E85 stations would result in 

strong scores for Louisiana’s ethanol market.

Following the initial analysis, we made several changes to the prioritization of ethanol market 

indicators. Gasoline price is less important for spurring adoption of FFVs than for other AFVs. 

The majority of FFVs on U.S. roads are owned by individual consumers, who tend to prioritize 

performance and design over lifecycle cost much more than fleets (Consumer Reports 2013;

National Association of Fleet Administrators 2012). Furthermore, retailers seem to peg the price 

of E85 to that of gasoline (as shown in Figure 2), meaning high gasoline costs do not necessarily 

translate to substantial fuel savings when using E85. Therefore, the current pricing strategy of 

E85 is not providing as much incentive to use E85 as other fuels that have their price based on 

the feedstock and production cost of the fuel. Nearby resources were weighted more heavily than 

all other factors, excepting existing E85 fueling infrastructure. More so for ethanol than for other 

fuels (and better documented than for biodiesel), there are strong social, economic, and cultural 

ties to the feedstock and fuel production plants. Ethanol plants are located near corn-based 

economies, where farmers feel the positive effects that corn-based ethanol production has on 

corn prices and where a great deal of farm work requires light-duty trucks, which are 

disproportionately FFVs (Clean Cities 2013).
15

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Compressed Natural Gas

Figure 6. Map of the most active vehicle CNG markets

Connecticut, New York, Rhode Island, California, Pennsylvania, Utah, Massachusetts, West 

Virginia, Ohio, and Washington have the strongest markets for CNG vehicles. Many of these 

states are bolstered by the natural gas processing plants over the Marcellus shale play. 

Oklahoma’s CNG market is stronger than it appears on the map: The state has large numbers of 

CNG vehicles and fueling stations, but these factors tend to be concentrated in small geographic

areas. As a result, the state as a whole is pulled into a lower score category by low gasoline 

prices. This is reflected in a large difference between the state average market value and the state 

median market value (see Appendix A). Oklahoma’s strong spots are some of the most highly 

rated in the nation. 

A swath of relatively poor CNG markets stretch from Florida to Idaho, including the states of

Alabama, Georgia, South Carolina, Tennessee, Missouri, Iowa, Nebraska, Minnesota, South 

Dakota, Wyoming, and Montana. Notable exceptions within these states are Atlanta, 

Birmingham, Kansas City, and the area of North Dakota over the Bakken shale play (where

multiple natural gas processing plants exist). These cities are an exception due to CNG stations 

and vehicle densities, despite low gasoline prices and few state incentives.
16

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It surprised market experts to see that sparsely populated areas in eastern Washington, 

southeastern California, southwestern Colorado, and southeast Utah rated high on the CNG 

market map. The markets in these areas were bolstered by numerous natural gas processing 

plants despite having a low density of CNG vehicles. Weighting the CNG station and CNG 

vehicle density portions of this map more heavily resulted in lower scores for these regions but 

distorted other portions of the map; so these adjustments were not made. Policymakers, 

investors, and entrepreneurs in these sparsely populated regions should be cautious when 

interpreting this map. However, they should consider the potential for emerging markets in 

municipalities and along heavily traveled transportation corridors within these regions, given 

their high gasoline prices, state incentives, and proximity to natural gas processing plants.

Changes in factor prioritization included a heavier weighting for CNG fueling stations than we 

assigned for stations offering any other fuel. This allowed us to better reflect population density 

(which is generally a prerequisite for fueling stations) in the CNG market map in response to 

multiple experts pointing out errant areas in the fifth quintile that were in areas of too low 

population to truly have a strong market. We chose to increase the weight for fueling stations 

rather than for vehicle density because the CNG vehicle density data only included dedicated 

CNG vehicles, and not dual-fueled vehicles. Therefore, an increased weighting of vehicle 

densities would have exaggerated market activity in locations with a high proportion of 

dedicated vehicles while under-representing market activity in locations with a high proportion 

of dual-fuel vehicles. As such, we reduced the weight assigned to vehicle densities. We reduced 

the weight assigned to freight because CNG use by long-haul trucks is still an incipient market, 

albeit a quickly developing one (Baker 2013). We reduced the weighting of resource proximity 

because natural gas is less expensively transported than other alternative fuels, generally over a 

vast and efficient pipeline network (Stilwell 2013).
17

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Propane

Figure 7. Map of the most active vehicle propane markets

Our analysis revealed the propane market to be quite fragmented, with a high frequency of strong 

markets in Indiana, Connecticut, California, Illinois, Washington, Pennsylvania, Texas, West 

Virginia, Ohio, and Mississippi. The only areas in which locations of high activity conglomerate 

into a large, consistent market are in Indiana, Connecticut, California, and Texas. The markets in 

South Carolina, Georgia, Iowa, Nebraska, South Dakota, and Wyoming are particularly weak.

Lack of reliable vehicle density data makes our propane market analysis the least reliable of all 

the fuels we consider in this study. Heavy-duty propane vehicles substantially bolster demand for 

the fuel, but they are not captured in vehicle registration data, so we were unable to include them 

in the map. In particular, propane market experts noted that Georgia and South Carolina have 

strong propane markets, as propane-powered Blue Bird and Thomas Built buses are 

manufactured in this area. However, this factor is not captured in our map because there is no 

geospatial database of heavy-duty propane vehicles. In another example, Omaha, Nebraska, is 

home to a large fleet of propane-powered school buses, but this is not reflected in the map either. 
18

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We reduced the weighting of vehicle density to match the weighting of resource proximity as the 

lowest-weighted parameters in order to account for the incomplete vehicle registration dataset 

(only dedicated OEM-manufactured light-duty vehicles are captured). 

State-Based Categorizations

The majority of incentives and laws facilitating the deployment of alternative fuels in the United 

States are enacted at the state level (AFDC 2013a). As such, state policymakers require reliable 

quantitative and qualitative information about potential and existing market strength for these 

fuels in their respective states, which is exactly what we aim to provide in this report. In Table 3 

we describe the market potential in each state for each alternative fuel analyzed in this report,

using categories ranging from Strongest to Weak. To arrive at these categorizations, we 

compared each state’s mean and median market strength scores and the size of the area in that 

state that received scores in the two highest score quintiles (orange and red zones in the 

preceding maps), as explained in greater detail below and shown in Appendix A. In addition to 

these quantitative geographic information system boundaries, the authors’ interpretations of the 

maps above also influenced the ratings. 

The market-strength categories are as follows:

• Strongest Market Potential: The majority of the state has high levels of potential supply

of and demand for the alternative fuel, indicated by high mean and median market 

strength scores.

• Healthy Market Potential: The state has a mean market strength score below that of states 

with Strongest market potential and above that of most states with Patchy market 

potential. The mean and median market strength scores for states with healthy markets 

are usually relatively close together, indicating a fairly uniform market across the state. 

For comparison, in states with Patchy market potential, the mean is usually larger than 

the median.

• Patchy Market Potential: The state has isolated patches and corridors of strong market

potential. The patches are usually in urban areas with high densities of vehicles and 

fueling stations. The corridors are along major highways with high volumes of freight 

traffic. Only biodiesel and CNG have such markets along corridors. Patchy markets tend 

to have mean market strength scores between those of Healthy and Weak markets. What 

differentiates them from Weak markets is a large area (in real terms, not as a percent of 

the state’s area) receiving scores within the two highest quintiles for a given fuel and a 

mean market score that is much higher than the median score. This latter factor is an 

indicator that the market potential for a particular fuel is not uniform throughout the state, 

and that certain areas within the state show strong potential, while the majority of the 

state is relatively weak.

• Weak Market Potential: The state has little or no potential supply of or demand for a 

given alternative fuel, indicated by the state’s low mean and median market strength 

scores.

• NA: The state has insufficient data available to determine market potential.
19

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Table 3. Market Categorization by State and Fuel

State Electricity Biodiesel Ethanol CNG Propane

Alabama Patchy Patchy Weak Patchy Patchy

Alaska NA NA NA NA NA

Arizona Healthy Patchy Weak Healthy Patchy

Arkansas Weak Patchy Weak Healthy Patchy

California Strongest Strongest Healthy Strongest Strongest

Colorado Patchy Patchy Patchy Strongest Healthy

Connecticut Strongest Strongest Weak Strongest Strongest

Delaware Strongest Healthy Weak Weak Healthy

Florida Strongest Healthy Healthy Patchy Weak

Georgia Patchy Healthy Patchy Patchy Weak

Hawaii Strongest Healthy Weak Healthy Patchy

Idaho Weak Weak Weak Patchy Weak

Illinois Healthy Strongest Strongest Strongest Strongest

Indiana Healthy Strongest Strongest Strongest Strongest

Iowa Patchy Strongest Strongest Weak Weak

Kansas Weak Patchy Strongest Healthy Patchy

Kentucky Weak Healthy Healthy Patchy Patchy

Louisiana Patchy Patchy Weak Healthy Patchy

Maine Healthy Weak Weak Weak Weak

Maryland Strongest Healthy Patchy Weak Patchy

Massachusetts Strongest Strongest Patchy Strongest Patchy

Michigan Strongest Patchy Healthy Patchy Patchy

Minnesota Patchy Patchy Strongest Weak Patchy

Mississippi Weak Patchy Weak Healthy Healthy

Missouri Weak Patchy Healthy Weak Weak

Montana Patchy Weak Weak Weak Patchy

Nebraska Weak Weak Strongest Patchy Weak

Nevada Healthy Weak Weak Healthy Patchy

New Hampshire Strongest Patchy Weak Weak Weak

New Jersey Strongest Healthy Weak Strongest Patchy

New Mexico Patchy Patchy Weak Patchy Patchy

New York Strongest Healthy Strongest Strongest Patchy

North Carolina Strongest Strongest Healthy Patchy Patchy

North Dakota Weak Healthy Strongest Patchy Weak

Ohio Healthy Strongest Strongest Strongest Healthy

Oklahoma Patchy Patchy Patchy Healthy Patchy

Oregon Strongest Patchy Patchy Healthy Patchy

Pennsylvania Strongest Strongest Healthy Strongest Strongest

Rhode Island Strongest Strongest Weak Strongest Healthy

South Carolina Patchy Healthy Patchy Weak Weak

South Dakota Weak Patchy Strongest Weak Weak

Tennessee Patchy Strongest Strongest Patchy Patchy
20

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State Electricity Biodiesel Ethanol CNG Propane

Texas Patchy Patchy Patchy Healthy Strongest

Utah Patchy Weak Weak Strongest Healthy

Vermont Strongest Healthy Weak Strongest Patchy

Virginia Healthy Strongest Patchy Healthy Patchy

Washington Strongest Strongest Healthy Strongest Strongest

West Virginia Healthy Patchy Weak Strongest Strongest

Wisconsin Healthy Healthy Strongest Healthy Healthy

Wyoming Weak Weak Weak Patchy Weak

As Table 3 shows, some locations have promising markets for numerous fuels, while others are 

promising for fewer fuels. No state, though, has a Weak rating for all five fuels, indicating that 

all of them have opportunities to pursue successful deployment of at least one alternative fuel in 

a portion of the state.

California, Illinois, Indiana, Pennsylvania, and Washington appear to have the greatest market 

potential for alternative fuels in general. Each was rated Strongest for four fuels and Healthy for 

one fuel. Ohio follows closely, with three Strongest markets and two Healthy markets. 

Connecticut also shows much promise, with Strongest ratings for four fuels and a Weak rating 

for the remaining one. Next come New York and Rhode Island, each with three Strongest

markets and one Healthy market. 

On the opposite end of the spectrum, Wyoming and Idaho exhibit Weak market potential for all 

alternative fuels except CNG, for which they have only Patchy market potential. Alabama and 

New Mexico do not have any Strongest or Healthy markets, but they each have Patchy markets 

for four fuels. Maine has four Weak markets, but it has a Healthy market for PEVs. 

Policymakers, investors, and entrepreneurs may also gain important insights by examining the 

proportion of states that have strong market potential for a given fuel. In Figure 8, we show the 

breakdown of market strength among all states for each alternative fuel. This is not meant to 

represent the relative potential for each fuel in the United States as a whole, because the state 

markets do not take size (in square miles, vehicles, or potential fuel sales) into account. For 

example, a physically large state with Patchy market potential for electricity could feasibly 

support vigorous deployment of PEVs and charging infrastructure in certain dense urban areas 

linked by charging corridors. Figure 8 shows CNG to be promising in the largest number of 

states, largely because freight traffic serves as a potential source of demand for many farreaching corridor markets and because the sources of CNG are so widespread.
21

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Figure 8. Number of states with alternative fuel markets, by fuel and market strength

Conclusion

Taken together, the results of this study may serve as a roadmap of sorts in the strategic 

deployment of alternative fuels in support of environmental, economic, and energy security 

goals. By identifying locations that have the greatest potential for success, we offer the 

opportunity for more targeted approaches to policymaking, public and private investments, 

business development, and mitigation of environmental impacts. By summarizing the market 

potential for a given fuel at the state level, we have flagged which fuels could offer the greatest 

market penetration for a given input of resources. 

The analyses we conducted in this study included all relevant, reliable data sets available at the 

time. Unfortunately, data pertaining to the operational locations and fuel types of heavy-duty 

vehicles were sorely lacking and represent a lacuna in our current understanding of alternative 

fuel markets. Heavy-duty vehicles are a large source of fuel demand, and insights into this 

segment of the transportation market stand to further inform policymaking and investment 

decisions. Notably, numerous organizations and initiatives in the public and private sectors are 

now undertaking efforts to better characterize this market segment. The DOE’s Clean Cities 

program works with large private-sector fleets through the National Clean Fleets Partnership to 

support and measure the efforts of member fleets to reduce petroleum use, including through the 

deployment of alternative fuels. The Propane Education and Research Council and DOE’s 

Federal Fleets program are beginning to document garage locations for heavy-duty vehicles that 

use alternative fuels. Future studies should work to obtain data from these and similar initiatives 

to determine the heavy-duty segment’s contributions to market potential.
22

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Appendix A. State-Based Market Categorization Data

This appendix provides the data we used to categorize potential market strength for each fuel in 

each state except Alaska. Each of the tables below uses the following metrics:

1. Mean market value (MV) is the state average of the values assigned to each 10-km 

square, as mapped in Figures 3 through 7. We derived each of the component values 

through the percentile and weighting system described in the Methodology section of this 

report. This is the primary value used to categorize states into strongest, healthy, and 

weak markets, with additional metrics used to determine which states had patchy 

markets.

2. Median MV is the state median of the values assigned to each 10-km square, as mapped 

in Figures 3 through 7.

3. The difference between the Mean MV and the Median MV is shown as “Mean–Median.” 

This is useful when differentiating between patchy and healthy markets. States with a 

large difference between mean and median market values are less uniform and therefore 

more likely to have patchy market potential.

4. The final metric in each row is the total area (in square kilometers) within a state that 

received scores in either of the top two quintiles, indicated in red or orange on the maps 

of Figures 3 through 7. This metric is useful when discerning patchy from weak markets. 

We reasoned that, for a market to be categorized as “patchy,” the isolated areas receiving 

high scores had to meet a certain minimum size threshold. In other words, if a patch falls 

below the minimum size, it does not represent sufficient market activity or potential to be 

very meaningful in the contexts of state policy or private-sector investment.

5. Notes include explanations for our categorizations of states for which little reliable data 

exists. The analyses we applied to these states relied more heavily upon our subjective 

judgment.

Table A-1 State Electric Vehicle Market Categorization Data

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

HI Strongest 0.627 0.616 0.011 16,700

CT Strongest 0.621 0.635 -0.014 12,600

MA Strongest 0.537 0.529 0.008 20,600

CA Strongest 0.522 0.485 0.037 407,900

NJ Strongest 0.508 0.518 -0.010 18,600

RI Strongest 0.487 0.487 0.000 2,700

MD Strongest 0.479 0.464 0.015 23,000

VT Strongest 0.458 0.447 0.011 24,600

WA Strongest 0.442 0.413 0.029 174,000

DE Strongest 0.440 0.404 0.036 4,600
26

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

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

NY Strongest 0.434 0.398 0.036 115,700

FL Strongest 0.425 0.385 0.040 120,800

NC Strongest 0.400 0.377 0.023 106,000

NH Strongest 0.389 0.375 0.014 20,600

PA Strongest 0.387 0.360 0.027 83,000

OR Strongest 0.386 0.354 0.032 211,400

MI Strongest 0.383 0.363 0.020 115,400

IL Healthy 0.374 0.346 0.028 94,300

AZ Healthy 0.364 0.347 0.017 175,100

WI Healthy 0.358 0.343 0.015 92,800

IN Healthy 0.355 0.345 0.010 58,100

ME Healthy 0.347 0.331 0.016 47,800

VA Healthy 0.343 0.319 0.024 53,900

NV Healthy 0.338 0.321 0.017 148,200

WV Healthy 0.326 0.313 0.013 28,700

OH Healthy 0.320 0.298 0.022 43,400

GA Patchy 0.305 0.282 0.023 43,100

Break point between healthy 

and patchy set at 1/3 area of 

the state being in top two 

quintiles.

MN Patchy 0.301 0.293 0.008 73,400

UT Patchy 0.296 0.274 0.022 36,600

TN Patchy 0.294 0.270 0.024 34,900

NM Patchy 0.289 0.281 0.008 77,900

IA Patchy 0.276 0.268 0.008 27,500

TX Patchy 0.270 0.254 0.016 118,900

CO Patchy 0.258 0.245 0.013 29,700

ID Weak 0.250 0.246 0.004 9,900

Too uniform and patch area 

too small to be categorized as 

patchy

OK Patchy 0.250 0.244 0.006 14,900

LA Patchy 0.244 0.230 0.014 12,300

SC Patchy 0.232 0.210 0.022 13,600

AL Patchy 0.230 0.219 0.011 13,900

MT Patchy 0.228 0.239 -0.011 16,200

KY Weak 0.210 0.199 0.011 6,500

NE Weak 0.200 0.196 0.004 2,800

MO Weak 0.193 0.183 0.010 9,700

AR Weak 0.189 0.180 0.009 5,300
27

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

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

MS Weak 0.185 0.175 0.010 5,000

ND Weak 0.173 0.169 0.004 1,000

SD Weak 0.172 0.167 0.005 2,200

KS Weak 0.170 0.159 0.011 4,300

WY Weak 0.124 0.127 -0.003 -

Table A-2. State Biodiesel Market Categorization Data

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

NC Strongest 0.511 0.545 -0.034 115,400

RI Strongest 0.462 0.407 0.055 2,600

WA Strongest 0.445 0.427 0.018 164,200

IN Strongest 0.439 0.432 0.007 91,400

CT Strongest 0.418 0.418 0.000 12,400

MA Strongest 0.406 0.389 0.017 17,900

IL Strongest 0.405 0.397 0.008 133,700

IA Strongest 0.402 0.403 -0.001 139,700

CA Strongest 0.398 0.361 0.037 393,900

OH Strongest 0.393 0.373 0.020 91,300

PA Strongest 0.389 0.388 0.001 99,000

TN Strongest 0.389 0.357 0.032 88,700

VA Strongest 0.386 0.375 0.011 99,200

WI Healthy 0.373 0.380 -0.007 122,400

MD Healthy 0.371 0.347 0.024 21,000

SC Healthy 0.370 0.332 0.038 56,800

DE Healthy 0.368 0.350 0.018 4,400

KY Healthy 0.359 0.354 0.005 84,500

NY Healthy 0.358 0.348 0.010 95,800

GA Healthy 0.355 0.339 0.016 116,300

ND Healthy 0.350 0.329 0.021 137,700

FL Healthy 0.341 0.331 0.010 106,000

NJ Healthy 0.338 0.314 0.024 13,800

HI Healthy 0.328 0.311 0.017 13,100

VT Healthy 0.302 0.295 0.007 16,500
28

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

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

NH Patchy 0.296 0.276 0.020 12,500

Break point between healthy 

and patchy at 50% of state’s 

area of top two quintiles

OR Patchy 0.283 0.257 0.026 92,300

KS Patchy 0.279 0.264 0.015 101,000

MO Patchy 0.276 0.271 0.005 91,400

AL Patchy 0.273 0.260 0.013 62,200

WV Patchy 0.268 0.257 0.011 28,500 Connects to strong PA market

MN Patchy 0.265 0.249 0.016 92,600

TX Patchy 0.262 0.252 0.010 282,400

AZ Patchy 0.258 0.242 0.016 83,600

LA Patchy 0.257 0.247 0.010 48,500

OK Patchy 0.256 0.251 0.005 73,100

AR Patchy 0.254 0.249 0.005 52,700

NM Patchy 0.245 0.236 0.009 75,500

CO Patchy 0.241 0.227 0.014 70,000

ME Weak 0.239 0.207 0.032 21,300 Small area overrides high 

Mean–Median value

SD Patchy 0.233 0.221 0.012 44,900

MI Patchy 0.232 0.211 0.021 41,000 Non-uniform and large area of 

good markets

MS Patchy 0.224 0.222 0.002 33,600 Connects to corridors in AR 

and LA

MT Weak 0.204 0.194 0.010 35,900

NV Weak 0.201 0.192 0.009 20,900

NE Weak 0.190 0.174 0.016 27,700

ID Weak 0.182 0.164 0.018 18,000

UT Weak 0.150 0.141 0.009 7,800

WY Weak 0.118 0.101 0.017 8,600

Table A-3. State Ethanol Market Categorization Data

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

IL Strongest 0.627 0.585 0.042 136,500

IN Strongest 0.605 0.579 0.026 82,500

IA Strongest 0.594 0.606 -0.012 134,200

WI Strongest 0.483 0.463 0.020 76,500
29

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

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

MN Strongest 0.479 0.481 -0.002 117,800

OH Strongest 0.444 0.428 0.016 41,800

NE Strongest 0.381 0.371 0.010 42,600

NY Strongest 0.374 0.372 0.002 22,800

SD Strongest 0.352 0.334 0.018 42,200

TN Strongest 0.352 0.337 0.015 17,100

ND Strongest 0.351 0.312 0.039 32,000

KS Strongest 0.346 0.354 -0.008 20,300

MI Healthy 0.336 0.282 0.054 40,500 Cutoff here due to low median

PA Healthy 0.330 0.323 0.007 12,900

KY Healthy 0.328 0.311 0.017 12,800

CA Healthy 0.325 0.285 0.040 30,600

NC Healthy 0.317 0.292 0.025 14,300

WA Healthy 0.309 0.284 0.025 10,300

MO Healthy 0.307 0.290 0.017 35,100

FL Healthy 0.300 0.275 0.025 14,600

HI Weak 0.296 0.266 0.030 1,100 Too small of area for top two 

quintiles to be patchy

VA Patchy 0.273 0.255 0.018 7,500 Break between healthy and 

patchy at 10% area

CT Weak 0.268 0.263 0.005 100 Too uniform and too small of 

area for patchy

GA Patchy 0.253 0.220 0.033 8,300

MA Patchy 0.252 0.239 0.013 1,100

CO Patchy 0.250 0.212 0.038 16,600

RI Weak 0.250 0.225 0.025 0 No area in top two quintiles

SC Patchy 0.250 0.188 0.062 5,700

MD Patchy 0.242 0.201 0.041 3,100

OR Patchy 0.241 0.222 0.019 4,600

OK Patchy 0.239 0.215 0.024 5,400

LA Weak 0.232 0.231 0.001 1,400 Too uniform to be patchy

VT Weak 0.226 0.224 0.002 0

WV Weak 0.226 0.212 0.014 500

DE Weak 0.223 0.196 0.027 100

NJ Weak 0.210 0.181 0.029 300

MS Weak 0.205 0.166 0.039 100

TX Patchy 0.205 0.183 0.022 15,300 Large Mean–Median and large 

area of top two quintiles

NM Weak 0.202 0.188 0.014 2,700
30

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

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

AL Weak 0.201 0.152 0.049 4,700

ME Weak 0.189 0.171 0.018 0

AZ Weak 0.188 0.180 0.008 4,600

MT Weak 0.170 0.165 0.005 100

AR Weak 0.162 0.141 0.021 3,400

NH Weak 0.162 0.163 -0.001 0

NV Weak 0.157 0.150 0.007 1,800

ID Weak 0.137 0.113 0.024 300

UT Weak 0.118 0.109 0.009 200

WY Weak 0.095 0.093 0.002 100

Table A-4. State CNG Market Categorization Data

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

CT Strongest 0.482 0.414 0.068 12,500

NY Strongest 0.455 0.402 0.053 123,400

RI Strongest 0.452 0.503 -0.051 2,100

CA Strongest 0.446 0.397 0.049 404,200

PA Strongest 0.388 0.363 0.025 98,700

UT Strongest 0.386 0.362 0.024 192,300

MA Strongest 0.369 0.315 0.054 12,300

WV Strongest 0.366 0.372 -0.006 58,900

OH Strongest 0.365 0.346 0.019 81,400

WA Strongest 0.365 0.345 0.020 159,600

IN Strongest 0.361 0.350 0.011 77,000

NJ Strongest 0.361 0.314 0.047 10,200

CO Strongest 0.347 0.338 0.009 193,600

IL Strongest 0.347 0.334 0.013 103,000

VT Strongest 0.346 0.340 0.006 18,600

OK Healthy 0.340 0.294 0.046 83,600 Boundary largely due to low 

median, relative to mean

TX Healthy 0.327 0.316 0.011 408,500

LA Healthy 0.326 0.309 0.017 65,000

VA Healthy 0.324 0.307 0.017 59,400

WI Healthy 0.316 0.300 0.016 72,500
31

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

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

AZ Healthy 0.312 0.302 0.010 151,600

OR Healthy 0.307 0.300 0.007 125,800

NV Healthy 0.305 0.293 0.012 125,200

HI Healthy 0.298 0.282 0.016 3,000

MS Healthy 0.286 0.281 0.005 46,400

AR Healthy 0.283 0.273 0.010 46,000

KS Healthy 0.282 0.279 0.003 74,000 Too uniform to be patchy

NC Patchy 0.280 0.259 0.021 32,900

KY Patchy 0.278 0.276 0.002 35,200 Connects to patches all around 

it

DE Weak 0.276 0.248 0.028 1,100 Area too small to be patchy

NM Patchy 0.276 0.271 0.005 97,200

ND Patchy 0.274 0.277 -0.003 59,400

FL Patchy 0.266 0.254 0.012 34,800

MI Patchy 0.261 0.248 0.013 32,300

GA Patchy 0.255 0.238 0.017 26,900

TN Patchy 0.252 0.239 0.013 20,600

ID Patchy 0.244 0.239 0.005 19,400

AL Patchy 0.243 0.229 0.014 19,600

ME Weak 0.243 0.232 0.011 9,300 Area too small to be patchy

NE Patchy 0.242 0.244 -0.002 31,700

NH Weak 0.239 0.220 0.019 3,300 Mean and median too close to 

be patchy

MD Weak 0.237 0.197 0.040 5,900

WY Patchy 0.237 0.227 0.010 32,500

Patchy due to large area of 

Area of Top Two Quintiles 

(square km)

MT Weak 0.234 0.231 0.003 52,800

MN Weak 0.227 0.222 0.005 16,300

IA Weak 0.223 0.222 0.001 8,800

SD Weak 0.197 0.199 -0.002 5,900

MO Weak 0.181 0.170 0.011 7,900

SC Weak 0.134 0.125 0.009 3,000
32

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

Table A-5. State Propane Market Categorization Data

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

IN Strongest 0.586 0.616 -0.030 89,100

CT Strongest 0.506 0.490 0.016 10,100

CA Strongest 0.497 0.465 0.032 394,900

IL Strongest 0.465 0.408 0.057 125,400

WA Strongest 0.461 0.441 0.020 157,400

PA Strongest 0.452 0.400 0.052 101,900

TX Strongest 0.448 0.394 0.054 473,900

WV Strongest 0.447 0.430 0.017 59,600

OH Healthy 0.435 0.386 0.049 77,700

Break at a large drop in mean, 

median, and % Area of Top 

Two Quintiles (square km)

MS Healthy 0.434 0.494 -0.060 70,800

DE Healthy 0.433 0.371 0.062 3,100

UT Healthy 0.424 0.421 0.003 187,900

RI Healthy 0.418 0.449 -0.031 2,200

CO Healthy 0.415 0.401 0.014 210,600

WI Healthy 0.407 0.384 0.023 90,300

VA Patchy 0.405 0.357 0.048 51,400 Patchy because of large Mean–

Median value

NV Patchy 0.402 0.392 0.010 208,300

NY Patchy 0.401 0.362 0.039 65,100

OK Patchy 0.379 0.335 0.044 46,700

HI Patchy 0.375 0.333 0.042 6,700

LA Patchy 0.374 0.349 0.025 47,400

AZ Patchy 0.370 0.355 0.015 143,300

NM Patchy 0.367 0.355 0.012 144,300

KY Patchy 0.363 0.318 0.045 36,600

NJ Patchy 0.361 0.309 0.052 7,700

TN Patchy 0.358 0.310 0.048 38,000

NC Patchy 0.356 0.324 0.032 44,800

OR Patchy 0.355 0.343 0.012 96,100

VT Patchy 0.353 0.343 0.010 4,200

AR Patchy 0.346 0.314 0.032 34,300

AL Patchy 0.344 0.299 0.045 49,000

KS Patchy 0.337 0.320 0.017 59,600

MN Patchy 0.332 0.323 0.009 52,200

MD Patchy 0.331 0.269 0.062 9,200
33

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

State Category

Mean 

MV

Median 

MV

Mean–

Median

Area of 

Top Two 

Quintiles 

(square 

km) Notes

MT Patchy 0.319 0.314 0.005 83,400

MI Patchy 0.315 0.276 0.039 39,400 Patchy because of large area in 

top two quintiles

MA Patchy 0.314 0.260 0.054 8,000

Patchy because high Mean–

Median value and close to CT 

and NY patches

ND Weak 0.312 0.322 -0.010 35,800

MO Weak 0.289 0.251 0.038 41,400

ME Weak 0.287 0.276 0.011 5,200

ID Weak 0.282 0.274 0.008 12,300

FL Weak 0.265 0.229 0.036 25,700

NH Weak 0.263 0.219 0.044 5,700

WY Weak 0.262 0.251 0.011 13,200

SD Weak 0.249 0.236 0.013 18,900

NE Weak 0.246 0.236 0.010 15,000

IA Weak 0.242 0.214 0.028 16,500

GA Weak 0.241 0.219 0.022 20,000

SC Weak 0.232 0.191 0.041 13,100