EA~ United States ._.,.,~ Environmental Protection 

"' Agency 

Nonroad Engine Population Growth 

Estimates in MOVES2014b 
NOTICE 

This technical report does not necessarily represent final EPA decisions or 

positions. It is intended to present technical analysis of issues using data 

that are currently available. The purpose in the release of such reports is to 

facilitate the exchange of technical information and to inform the public of 

technical developments.

ft EA~United States ..... ~ Environmental Protection ,., Agency 

Nonroad Engine Population Growth 

Estimates in MOVES2014b 

Assessment and Standards Division 

Offce of Transportation and Air Quality 

U.S. Environmental Protection Agency 

EPA-420-R-18-010 

July 2018 
Table of Contents 

Table of Contents............................................................................................................................ 1 

1 Introduction............................................................................................................................. 2 

2 Background............................................................................................................................. 2 

3 Methodology........................................................................................................................... 3 

3.1 Surrogate Data for Projecting Future Nonroad Engine Populations................................ 4 

3.2 Surrogate Data for Constructing Historical Nonroad Engine Populations ...................... 5 

4 Results..................................................................................................................................... 8 

5 Peer Review of Draft Report................................................................................................. 15 

5.1 Overview of the Peer-Review ........................................................................................ 15 

5.2 General Comments from Robert F. Sawyer................................................................... 17 

5.3 General Comments from Phil Lewis.............................................................................. 17 

6 References............................................................................................................................. 19 

1 
 

 

 

 

 

 

 

1 Introduction 

Computing accurate nonroad air pollution emissions inventories depends on estimations of 

emission factors and engine activity levels. Changes in nonroad emission factors are driven 

primarily by regulations and will not be discussed here (detailed discussions of nonroad emission 

factors used in MOVES2014b can be found in Technical Reports NR-009d1 and NR-010f2). 

Changes in nonroad engine activity levels over years are the result of complex interactions 

between human population growth, changes in national and local economic factors, and changes 

in the markets for nonroad engines and products they produce. Because trends in nonroad engine 

activity levels are rarely directly measured, MOVES instead starts with base year engine 

populations and estimates growth in the populations of nonroad engines while applying constant 

annual activity values for every engine type (see Technical Report NR-005d3 for a technical 

discussion of annual activity values). This report focuses on the methodology for estimating 

growth in nonroad engine populations in MOVES2014b. 

2 Background 

Previous versions of EPA’s nonroad emissions inventory model – including NONROAD2008, 

which was added to the MOVES model in 2014 – based engine population growth projections on 

a time series analysis of historical (1989 through 1996) nonroad engine populations taken from 

the Power Systems Research (PSR) Parts Link database4,5. This database contains detailed 

information about each engine family sold in the United States and was used to segregate 

nonroad engines by market sector and fuel type. Total engine populations, segregated by fuel 

type, were calculated for each year from 1989 through 1996 for the following broad market 

sectors: Construction, Agriculture, Industrial, Lawn & Garden, Commercial, Logging, Railroad 

Support, Recreational, Recreational Marine, and Airport Service. Future engine populations were 

projected by extrapolating from a simple linear regression of historical populations. 

Some adjustments were made to the PSR-based approach, including: 

• Engine population growth in the Oil Field sector was based on the Department of 

Commerce’s Bureau of Economic Analysis (BEA) estimates of gross state product from 

domestic oil production. This data source was preferred over the PSR database for this 

sector because the 1989-to-1996 decline in oil field equipment reflected in the PSR 

database would have resulted in all oil field equipment disappearing by 2006. 

• Recreational equipment population growth rates derived from the PSR database were 

applied to the Recreational Marine equipment category, but pre-1996 back-casted 

populations of personal watercraft were modified to force a zero population in 1970, as it 

was assumed that personal watercraft were not available in significant numbers prior to 

1970. 

• Within the Recreational equipment sector, population growth rates for all-terrain vehicles 

and off-highway motorcycles were revised based on historical sales information and sales 

growth projections supplied by the Motorcycle Industry Council. Additionally, growth 

projections of snowmobiles were based on information provided by the International 

Snowmobile Manufacturers Association. 

2 
 

 

 

 

 

 

 

 

 

 

3 

In addition to forming the basis of nonroad engine population growth rates in MOVES2014, 

PSR’s engine population database also supplied the model’s default base year national 

populations, which were used as a starting point to estimate future and past year engine 

populations. Population base years varied by engine type and are either 1996, 1998, 1999, or 

2000 (see Technical Report NR-006e6 for a technical discussion of the derivation of base year 

equipment population estimates). Geographic allocation factors derived from surrogate 

information sources (e.g., business activity data, human population data, geographic data) were 

used to allocate national engine populations to the county level (see Technical Report NR-014d7 

for a detailed discussion of geographic allocation factors). 

MOVES2014b will continue to use the base-year engine populations and geographic allocation 

factors used in NONROAD2008,MOVES2014, and MOVES2014a. However, to address 

concerns that long-term national growth rates derived from seven years of engine population data 

limits the model’s ability to accurately portray nonroad engine/emissions growth at the regional 

or state levels, EPA has developed a set of annual, state-level growth indices for projecting 

nonroad engine populations from the population base years. 

Methodology 

EPA considered four primary methods for projecting nonroad engine population growth trends 

(Table 3.1)8

: 

Table 3.1 Methods for projecting nonroad engine activity and population growth trends 

Projection Methodology Description 

Equipment activity projections 

Tend to focus on a narrow or specialized equipment sectors (e.g., the Federal 

Aviation Administration’s Terminal Area Forecasts tool for projecting 

commercial aviation operations could be used to project airport service 

equipment activity) 

Census population projections 

Considered reliable because population demographics are well understood. 

Often used in nonpoint source emission inventory projections where activity 

is closely correlated with human population size and could be used in certain 

nonroad equipment sectors (e.g., recreational vehicles, residential lawn and 

garden). 

Economic projections 

Sometimes used as surrogates to approximate changes in emissionsgenerating activity. Examples of economic data include employment 

statistics, gross domestic product, and volumes of product output. 

Energy use (fuel consumption) 

projections 

Fuel consumption data and fuel consumption projections from specific 

economic sectors such as construction, agriculture, and mining can be 

adopted as surrogates for projecting future nonroad engine 

populations/emissions trends in those sectors. This methodology is 

appropriate for projecting nonroad engine activity and emissions because 

fuel consumed by nonroad engines is proportional to their use levels. 

Because the model assigns constant hours-per-year activity rates to each piece of nonroad 

equipment, changes in emissions-generating activity levels are instead approximated by 

estimating changes in nonroad engine populations. Projections such as those described in Table 

3.1 can be adopted as surrogates for projecting future nonroad engine populations. 

3 
 

 

 

 

 

 

 

 

 

 

EPA identified sets of projections to serve as surrogates for constructing annual growth indices 

from 2014 to 2040 for each nonroad equipment sector (see Section 3.1). The projections are 

independent of fuel type. The growth indices function as annual multipliers that are applied to 

base year nonroad engine populations in order to estimate the engine population for a given year. 

For example, a growth index of 2.0 for a particular year indicates that the engine population in 

that year is double that of the base year population. The model then linearly extrapolates, based 

on projected engine population estimates for 2039 and 2040, to project populations further into 

the future. 

EPA also identified historical datasets to serve as surrogates for constructing annual, equipment 

sector-specific growth indices from the population base years (1996, 1998, 1999, or 2000) to 

2014 (see Section 3.2). The selected historical datasets closely resemble the 2014-2040 

projections with which they’re mapped (e.g., fuel sales data are matched with fuel consumption 

projections; human population data are matched with human population projections). 

EPA’s methodology for applying annual, sector-specific growth indices to estimate nonroad 

engine populations beyond the population base years is summarized in Table 3.2. 

Table 3.2 Methodology for applying nonroad engine population growth indices in MOVES2014b 

Calendar Years Method 

Population base year (1996, 1998, 1999, 2000) 

through 2014 

Apply annual historical growth indices to base year 

populations 

2014 through 2040 Apply annual projection growth indices to estimated 

populations for 2014 

2040 through 2060 Linearly extrapolate from the 2039 and 2040 population 

estimates 

3.1 Surrogate Data for Projecting Future Nonroad Engine Populations 

The equipment sector-specific sets of projections in Table 3.3 are largely derived from publicly 

available sources. While state-level projections are preferred, this level of spatial detail is not 

available for all equipment sectors. 

State-level projections of gross domestic product (GDP) from Moody’s Analytics9 are used as 

growth surrogates for the Industrial and Commercial equipment sectors. Given that many of the 

equipment types (e.g., generator sets, forklifts, pumps) in the Industrial and Commercial 

equipment sectors are spread throughout the economy and not confined to a specific economic 

sector, a high-level economic indicator like GDP is assumed to be the best option for projecting 

growth in these equipment sectors. 

The Lawn and Garden (residential and commercial) and Recreational Vehicles equipment sectors 

use state-level human population projections10 as surrogates for engine population growth, as 

activity in these sectors is assumed to be closely tied to population size. State-level projections of 

commercial aviation operations from the Federal Aviation Administration (FAA)’s Terminal 

Area Forecasts11 are used as growth surrogates for the Airport Service equipment sector. 

4 
 

 

 

 

 

 

 

 

 

National projections of revenue ton miles and recreational marine fuel consumption from the 

Energy Information Administration (EIA)’s Annual Energy Outlook (AEO) 2016 are used for the 

Rail Maintenance12 and Recreational Marine13 sectors, respectively. 

Finally, census region (Table 3.4) projections of energy consumption from the EIA’s AEO2016 

are applied to the Construction14, Agriculture15, Logging16, Oil Field17, and Underground 

Mining18,19 equipment sectors. EIA furnishes unpublished energy consumption projections for 

specific economic sectors; these sectors are mapped to corresponding nonroad equipment 

sectors, as noted in Table 3.3. 

Table 3.3 Surrogate data for projecting future (2014-2040) growth of nonroad engine populations20 

Equipment Sector Surrogate Data 

Source Surrogate Data for Future Projections 

Industrial Moody’s Analytics GDP from warehousing sector 

Commercial Moody’s Analytics Economy-wide GDP 

Lawn and Garden 

(residential and commercial) U.S. Census Bureau Human population 

Recreational U.S. Census Bureau Human population 

Airport Service FAA TAF Model Number of commercial aviation operations 

Rail Maintenance EIA AEO Revenue ton miles 

Recreational Marine EIA AEO Fuel consumption (recreational marine) 

Construction EIA AEO Energy consumption (construction sector) 

Agriculture EIA AEO Energy consumption (agriculture sector) 

Logging EIA AEO Energy consumption (other agriculture sector) 

Oil Field EIA AEO Energy consumption (oil and gas mining sector) 

Underground Mining EIA AEO Energy consumption (sum of the coal sector and 

metallic & non-metallic mining sector) 

Table 3.4 Census regions of the United States21 

Census Region States 

West Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, 

Oregon, Utah, Washington, Wyoming 

Midwest Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North 

Dakota, Ohio, South Dakota, Wisconsin 

South 

Alabama, Arkansas, District of Columbia, Floridaa, Georgia, Kentucky, Louisiana, 

Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, 

Virginia, West Virginia 

Northeast Connecticut, Delaware, Maine, Massachusetts, New Hampshire, New Jersey, New York, 

Pennsylvania, Rhode Island, Vermont 

Note: 

a Growth indices developed for Florida are used to estimate growth in nonroad engine populations in Puerto Rico 

and the U.S. Virgin Islands. 

3.2 Surrogate Data for Constructing Historical Nonroad Engine Populations 

A second set of annual growth indices is required in order to project nonroad engine populations 

from the population base years (1996, 1998, 1999, or 2000) to 2014. EPA identified publicly 

5 
 

 

 

 

 

 

available datasets (Table 3.5) to serve as surrogates to estimate historical engine populations, 

seeking to match the projection methodologies used for constructing historical populations with 

those used to project growth in engine populations beyond 2014. With the exception of the 

national-scale revenue ton miles dataset (Oak Ridge National Laboratory (ORNL)’s 

Transportation Energy Data Book22) used as growth surrogates for Rail Maintenance equipment, 

state-level data are used as historical growth surrogates for all equipment sectors. 

Table 3.5 Surrogate data for projecting nonroad engine populations from the base years to 2014 

Equipment Sector Surrogate Data Source Surrogate Data for Historical 

Projections 

Industrial Bureau of Economic Analysis GDP from multiple economic sectorsa 

Commercial Bureau of Economic Analysis GDP from multiple economic sectorsa 

Lawn and Garden 

(residential) U.S. Census Bureau Human population 

Lawn and Garden 

(commercial) U.S. Census Bureau Number of landscaping services establishments 

Recreational U.S. Census Bureau Human population 

Airport Service FAA Terminal Area Forecasts Number of commercial aviation operations 

Rail Maintenance ORLN Transportation Energy Data Book Revenue ton miles 

Recreational Marine National Marine Manufacturers Association Boat registrations 

Construction EIA Fuel Oil and Kerosene Sales Fuel delivered to off-highway 

(construction) consumers 

Agriculture EIA Fuel Oil and Kerosene Sales Fuel delivered to farm consumers 

Logging EIA Fuel Oil and Kerosene Sales Fuel delivered to off-highway (nonconstruction) consumers 

Oil Field EIA Fuel Oil and Kerosene Sales Fuel delivered to oil company consumers 

Underground Mining EIA Fuel Oil and Kerosene Sales Fuel delivered to industrial consumers 

Note: 

a See Table 3.6 for a list of economic sectors used in the analysis. 

For the Industrial and Commercial equipment sectors, GDP by state from several industries 

(Table 3.6) thought to utilize Industrial and Commercial equipment were obtained from the U.S. 

Department of Commerce Bureau of Economic Analysis23. GDP by state from the selected 

industries were summed to create the time series of state-level GDP which serve as surrogates 

for projecting Industrial and Commercial engine populations from the base years to 2014. 

6 
 

 

 

 

 

 

 

 

 

 

Table 3.6 North American Industry Classification System (NAICS) 24 economic sectors from which 

state-level GDP serve as surrogates for projecting Industrial and Commercial engine populations 

NAICS 

Sector NAICS Sector Description Industrial Equipment 

Commercial 

Equipment 

11 Agriculture, Forestry, Fishing, and Hunting X X 

21 Mining, Quarrying, and Oil and Gas Extraction X X 

22 Utilities X X 

23 Construction X X 

31-33 Manufacturing X X 

42 Wholesale Trade X X 

44-45 Retail Trade X 

48-49 Transportation and Warehousing X X 

51 Information X 

53 Real Estate and Rental and Leasing X 

56 Administrative and Support & 

Waste Management and Remediation Services X X 

To maintain consistency with the surrogate data selected to project future Lawn and Garden 

(residential) and Recreational Vehicles equipment populations, state-level annual human 

population data sourced from the U.S. Census Bureau25 are used as historical growth surrogates 

for these equipment categories. Similarly, the number of commercial aviation operations in each 

state from FAA’s Terminal Area Forecasts model are used as growth surrogates for Airport 

Service equipment. The number of landscaping establishments for each state, as reported in the 

U.S. Census Bureau County Business Patterns Database (NAICS code 561730)26, serve as 

historical growth surrogates for Commercial Lawn & Garden equipment. 

State recreational boat registrations for the period 2004-2013, compiled by the National Marine 

Manufacturers Association27, are used as historical growth surrogates for the Recreational 

Marine equipment category. A simple linear regression of 2004-2013 registrations is used to 

construct historical growth indices for the missing periods of 1996-2004 and 2013-2014. 

Finally, EPA selected data from EIA’s Fuel Oil and Kerosene Sales (FOKS) report28 to serve as 

surrogates for projecting engine populations from the base years to 2014 for the Construction, 

Agriculture, Logging, Oil Field and Underground Mining equipment sectors. The Adjusted 

Distillate Fuel Oil and Kerosene Sales by End Use survey reports different grades of diesel and 

distillate fuels for various end use sectors in major economic sectors. EPA mapped FOKS end 

use sectors to nonroad equipment sectors and compiled state-level sales data for the reported fuel 

grades provided in Table 3.7. 

7 
 

 

 

 

 

 

 

 

 

 

 

 

4 

Table 3.7 Fractions of reported fuel grades used to construct state-level historical trends, 

by FOKS end use sector and nonroad equipment sector 

Distillate Fuel Grade (Fraction) 

FOKS End Use 

Sector 

Nonroad 

Equipment 

Sector 

Distillate 

Fuel Oil 

No. 2 Distillate – 

High Sulfur 

Diesel 

No. 2 Distillate – 

Low Sulfur 

Diesel 

No. 1 

Distillate 

Off-Highway: 

Construction Construction 1.0 

Off-Highway: 

Non-Construction Logging 1.0 

Industrial Underground 

Mining 1.0 1.0 0.4 

Oil Company Oil Field 1.0 

Farm Agriculture 1.0 

Due to the volatile behavior of the raw sales series, we elected not to use a value for a single year 

as the basis from which to forecast future fuels sales. Consequently, we performed aggregation 

of the raw series, using a technique common in econometric analysis. 

As a first step in constructing historical sales trends for each state, the 5-point weighted-centered 

moving average (WCMA5) of the raw sales trend was calculated, with fuel sales expressed as 

thousands of gallons (1,000 gal). Five time points (years) were included, i.e. t-2, t-1, t, t+1 and 

t+2, with each weighted as 2, 3, 5, 3 and 2, respectively. To obtain the average, the weighted 

sales sum was divided by 15, or the total sum of the weights. 

In the final year of the series (2014), at which point five time points are not available, the 

average was calculated as a 3-point trailing moving average that included points t-2, t-1 and t 

(weighted, respectively, as 2, 3 and 5), and with a reduced sum of weights of 10. 

After calculating the WCMA5, the average sales were indexed to a population base year of 2000. 

However, in some cases, values in specific years were modified to avoid nonsensical or 

unreasonable results in some states in some sectors. Specifically, if the WCMA5 in any year and 

state in any of the target sectors was between zero and 1,000 gallons, the values were reset to 

1,000 gallons. 

Results 

Annual, state-level growth indices for each equipment sector are applied to every equipment type 

within the sector, from their respective population base year to 2040. The resulting 

nrgrowthindex database contains over 55,000 entries, so in the interest of brevity, a sample of 

results are presented here. Figures 4.1- 4.13 show time series of annual growth indices 

corresponding to one state from each U.S. Census Region (Table 3.4). Although the model 

linearly extrapolates engine populations from 2039 and 2040 for years beyond 2040, for 

illustrative purposes, Figures 4.1- 4.13 include linearly extrapolated growth indices. Example 

states vary by sector to better illustrate the sector growth. The national average index across all 

states is also plotted. 

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Figure 4.1 shows the results from the Industrial equipment sector (1998 base year). Surrogate 

data for historical indices is GDP from multiple economic sectors; surrogate data for future 

projections is GDP from the warehousing sector (NAICS sector 48-49). Growth indices for 

Illinois (Midwest Census Region), Massachusetts (Northeast Census Region), Texas (South 

Census Region), and Arizona (West Census Region) are shown. 

Figure 4.1 Annual growth indices from 1998 base year to 2060 for Industrial equipment. The vertical 

line at 2014 indicates the transition between historical and projection indices. 

Results from the Commercial equipment sector (1998 base year) for Illinois, Massachusetts, 

Arizona, and Texas are shown in Figure 4.2. Surrogate data for historical indices is state-level 

GDP from multiple economic sectors; surrogate data for future projections is the economy-wide 

state GDP. 

Figure 4.2 Annual growth indices from 1998 base year to 2060 for Commercial equipment. The 

vertical line at 2014 indicates the transition between historical and projection indices. 

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Example growth indices corresponding to the Lawn and Garden equipment sector are shown in 

Figure 4.3 (residential lawn and garden equipment) and Figure 4.4 (commercial lawn and garden 

equipment). Both plots assume a 1998 population base year and results from Illinois, 

Massachusetts, California, and Texas are highlighted. Human population projections are used as 

surrogates to project future populations of residential and commercial lawn and garden 

equipment. Historical growth indices for residential lawn and garden equipment populations are 

based on human population data; the number of landscaping services establishments is the basis 

for the historical indices used for commercial lawn and garden equipment. 

Figure 4.3 Annual growth indices from 1998 base year to 2060 for Residential Lawn and Garden 

equipment. The vertical line at 2014 indicates the transition between historical and projection indices. 

Figure 4.4 Annual growth indices from 1998 base year to 2060 for Commercial Lawn and Garden 

equipment. The vertical line at 2014 indicates the transition between historical and projection indices. 

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Equipment population growth indices for the Recreational equipment sector (1998 base year) for 

Minnesota, Massachusetts, Wyoming, and Texas are shown in Figure 4.5. Human population 

data and projections serve as surrogates for both historical and projected equipment populations. 

Figure 4.5 Annual growth indices from 1998 base year to 2060 for Recreational equipment. The 

vertical line at 2014 indicates the transition between historical and projection indices. 

State-level equipment population growth indices for the Airport Service equipment sector (1998 

base year) for Illinois, New York, Arizona, and Texas are shown in Figure 4.6. Both the 

historical and future projections are based on the number of commercial aviation operations 

reported by FAA’s TAF model. 

Figure 4.6 Annual growth indices from 1998 base year to 2060 for Airport Service equipment. The 

vertical line at 2014 indicates the transition between historical and projection indices. 

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The national-scale growth indices for the Railway Maintenance equipment sector (1998 and 

2000 base years) are shown in Figure 4.7. National revenue ton miles are the basis for both 

historical and future projections. 

Figure 4.7 Annual growth indices from 1998 and 2000 base years to 2060 for Railway Maintenance 

equipment. The vertical line at 2014 indicates the transition between historical and projection indices. 

Figure 4.8 shows the growth indices for the Recreational Marine equipment sector (1998 base 

year) for Minnesota, New York, Washington, and Florida. The pre-2014 historical indices are 

based on state-level boat registration data; human population projections are used to project 

future populations of Recreational Marine equipment. 

Figure 4.8 Annual growth indices from 1998 base year to 2060 for Recreational Marine equipment. 

The vertical line at 2014 indicates the transition between historical and projection indices. 

Equipment population growth indices for the Construction equipment sector (2000 base year) for 

Illinois, New York, Arizona, and Texas are shown in Figure 4.9. Historical indices are based on 

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sector-specific Fuel Oil and Kerosene Sales data, while the future growth indices are based on 

projected fuel consumption in the construction sector. 

Figure 4.9 Annual growth indices from 2000 base year to 2060 for Construction equipment. The 

vertical line at 2014 indicates the transition between historical and projection indices. 

Similar to the Construction equipment sector, the Agricultural equipment sector uses sectorspecific fuel sales data and energy consumption projections are surrogates for historical and 

future growth indices. Results from Nebraska, Vermont, California, and North Carolina (2000 

base year) are highlighted in Figure 4.10. 

Figure 4.10 Annual growth indices from 2000 base year to 2060 for Agricultural equipment. The 

vertical line at 2014 indicates the transition between historical and projection indices. 

Figure 4.11 shows the growth indices for the Logging equipment sector (2000 base year) for 

Minnesota, Vermont, Oregon, and Georgia. Historical indices are based on sales of fuel 

13 
 

 

 

 

 

 

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·······--········ ............ . ················· ·········· ········ 

------------------- 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 

......... SD ~ PA ---4- AK - - - TX - National Average 

delivered to off-highway consumers, while the future projections are based on projected energy 

consumption in the “other agriculture” sector. 

Figure 4.11 Annual growth indices from 2000 base year to 2060 for Logging equipment. The vertical 

line at 2014 indicates the transition between historical and projection indices. 

Equipment population growth indices for the Oil Field equipment sector (2000 base year) for 

South Dakota, Pennsylvania, Alaska, and Texas are shown in Figure 4.12. Projections of energy 

consumption in the oil and gas mining sector serve as surrogates for future growth indices, and 

fuel sales to oil company consumers are used to construct historical growth indices. 

Figure 4.12 Annual growth indices from 2000 base year to 2060 for Oil Field equipment. The vertical 

line at 2014 indicates the transition between historical and projection indices. 

Figure 4.13 shows the growth indices for the Underground Mining equipment sector (2000 base 

year) for Illinois, Pennsylvania, Wyoming, and West Virginia. Historical indices are based on 

14 
 

 

 

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......... IL ~ PA ---+- WY - - - WV - National Average 

5 

sales of fuel delivered to industrial consumers, while the future projections are based on 

projected energy consumption in the coal sector and metallic & non-metallic mining sector. 

Figure 4.13 Annual growth indices from 2000 base year to 2060 for Underground Mining equipment. 

The vertical line at 2014 indicates the transition between historical and projection indices 

Peer Review of Draft Report 

This section contains comments on the draft report of Nonroad Engine Population Growth 

Estimates in MOVES2014b from two peer reviewers and EPA’s responses to those comments. 

The reviewers were selected by a third-party contractor, ICF International, facilitating a peer 

review of MOVES technical reports. The submitted peer review comments are publicly available 

on the EPA Science Inventory database29 . 

5.1 Overview of the Peer-Review 

The two experts who reviewed the draft report of Nonroad Engine Population Growth Estimates 

in MOVES2014b were: 

Robert F. Sawyer, Ph.D. Phil Lewis, Ph.D. 

Partner Associate Professor 

Sawyer Associates Department of Construction Science 

College of Architecture 

Texas A&M University 

The peer-reviewers were given the following charge: 

We are submitting this material for you to review selected methods and underlying assumptions, 

their consistency with the current science as you understand it, and the clarity and completeness 

15 
 

of the presentation. For this review, no independent data analysis is required. Rather, we ask 

that you assess whether the information provided is representative of the state of current 

understanding, and whether incorporating the information into EPA’s MOVES model will result 

in appropriate predictions and conclusions. 

We request you provide us comments on substantive content sequentially. These will be listed as 

an appendix to the final published report, along with EPA’s responses. Comments on 

organization, formatting, and other minor issues are welcome, but should be provided 

separately. 

Below are questions to define the scope of the review; we are not expecting individual responses 

to the questions, but would like them to help guide your response. 

General Questions to Consider: 

1. Does the presentation describe the selected data sources sufficiently to allow the reader 

to form a general view of the quantity, quality and representativeness of data used in the 

analysis? Are you able to recommend alternate data sources that might better allow the 

model to estimate national or regional default values? 

2. Is the description of analytic methods and procedures clear and detailed enough to allow 

the reader to develop an adequate understanding of the steps taken and assumptions 

made by EPA while developing the model inputs? Are examples selected for tables and 

figures well-chosen and effective in improving the reader’s understanding of approaches 

and methods? 

3. Are the methods and procedures employed technically appropriate and reasonable, with 

respect to the relevant disciplines, including physics, chemistry, engineering, 

mathematics and statistics? Are you able to suggest or recommend alternate approaches 

that might better achieve the goal of developing accurate and representative model 

inputs? In making recommendations, please distinguish between instances involving 

reasonable disagreement in adoption of methods as opposed to instances where you 

conclude that current methods involve specific technical errors. 

4. Where EPA has concluded that applicable data is meager or unavailable, and 

consequently has made assumptions to frame approaches and arrive at solutions, do you 

agree that the assumptions are appropriate and reasonable? If not, and you are able to 

do so, please suggest alternative assumptions that might lead to more reasonable or 

accurate model inputs. 

5. Are the resulting model inputs appropriate and, to the best of your knowledge and 

experience, reasonably consistent with physical and chemical processes involved in 

mobile source emissions, formation and control? Are the resulting model inputs 

empirically consistent with the body of data and literature with which you are familiar? 

Specific Questions: 

16 
 

 

 

 

 

In addition to the general review, we request specific responses to the following questions: 

1. This report describes a method for aggregating Fuel Oil and Kerosene Sales (FOKS) 

data to generate growth indices for various nonroad equipment sectors. Are there better 

or alternative methods for aggregating time series data such as the FOKS dataset? 

2. Are there data sources EPA selected to serve as surrogates for historical and future 

growth indices appropriate for the nonroad equipment sectors to which they are 

assigned? 

5.2 General Comments from Robert F. Sawyer 

The methodology outlined to establish the baseline population is a reasonable, but 

unfortunate, compromise based on a lack of contemporary data. The database, now 22 years old, 

is valuable in that it contains a reasonably detailed breakdown nonroad engines by sector and 

fuel. It would appear that there is no provision for electricity as a fuel in any of the sectors. 

(Perhaps this will be accounted for in the emission factor part of the model.) In Table 3.1, the 

fourth projection methodology is unclear. 

RESPONSE: MOVES does not currently account for the relatively small fraction of 

nonroad equipment powered by electricity. EPA recognizes that electricity as a fuel for 

nonroad mobile sources is growing in market share, and that this penetration will need to 

be addressed in future versions of MOVES. 

The description for adopting fuel consumption data and projections as surrogates for 

estimating growth in nonroad engine populations has been edited for clarity. 

The surrogate data for estimating nonroad engine population growth methodology 

outlined in Table 3.3 and Table 3.5 is reasonable. The use of fuel sales to develop population is 

an important check on projections based on historical trends, but appears, incorrectly, to assume 

that engine efficiencies are constant over time. Statistical methods for smoothing data are 

reasonable. Projections to 2060 are, of course, highly uncertain but this is not critical since the 

model and base data will certainly continue to be improved periodically. The methodology for 

developing state level nonroad engine populations is reasonable. 

RESPONSE: We take the reviewer’s point, and agree that the projections described here 

are not sophisticated enough to explicitly account for projected trends in fuel efficiency. 

However, developing this sophistication would require investment of significant effort to 

predict an additional uncertain parameter. Given the major uncertainties involved in 

long-term projections, it is not clear that these efforts would substantially improve the 

utility of the model.. 

5.3 General Comments from Phil Lewis 

General Comments: 

17 
 

In my opinion, the reports include an adequate presentation of the methods used to update the 

Nonroad model. As a user of previous versions of Nonroad, I frequently referred to the 

documents and reports to gain better insight to the application of the model. I believe these 

reports provide a sufficient explanation of the assumptions and data used in the updated Nonroad 

model. As a potential user of Nonroad in the future, I am satisfied with the explanations given 

for the assumptions and data sources provided in the reports. Furthermore, I have no 

recommendations for alternate data sources. 

Description of the analytical methods are thorough enough for the reader to gain an 

understanding of the general approaches used to update the Nonroad model; however, it would 

be somewhat difficult to duplicate the methodologies in their entirety. In order to duplicate the 

methodologies, I believe more detail is needed, perhaps in the form of sample calculations. I do 

not believe, however, that duplication is the primary objective of the reports; therefore, I do not 

recommend adding unnecessary details. I also found the examples provided for tables and figures 

to be appropriate for their intended use. 

I believe the methods and procedures are technically and scientifically sound. This is a 

reasonable approach to predict the future of nonroad equipment on a grand scale. I have no 

suggestions or recommendations for improving the approach. 

When trying to predict the future, no one has a High Definition crystal ball; therefore, we are left 

with assumptions. I believe the assumptions chosen to fill in the gaps in data are sufficient 

enough to provide reasonable model outputs. 

Unfortunately, I have limited expertise with the physical and chemical processes associated with 

the formation and speciation of emissions. I believe the model inputs are empirically consistent 

and adequate based on my limited knowledge. 

Specific Comments: 

I believe the FOKS methodology is the most direct approach to estimating growth indices for 

some nonroad equipment sectors. It may be possible to refine the approach in an attempt to gain 

a higher resolution estimate, although I am not sure the added benefits would justify the extra 

effort. Considering the Construction sector as an example, it may be possible to use population 

and GDP data (particularly in specific regions) to estimate the growth in market for construction 

which would result in the need for more nonroad equipment. Presumably, the FOKS 

methodology takes this into account but it is not apparent in the report. I believe for the intended 

use of Nonroad (and ultimately MOVES), the FOKS approach as presented in sufficient. 

RESPONSE: As tailpipe emissions are created during fuel combustion, we considered 

fuel sales and projected consumption to be perhaps the most appropriate surrogate 

available to forecast equipment populations. Nonetheless, for future versions of MOVES, 

EPA is interested in exploring projection methodologies that utilize multiple data sets 

that might provide a more holistic view of nonroad engine activity and population 

growth. 

18 
 

 

 

 

 

 

 

 

 

 

6 

To the best of my knowledge, the surrogates are appropriate to the sectors they were assigned. 

As a general comment, I feel that the approach is rather clever and certainly provides some 

insight in an area where it is difficult to gain any at all. 

Additional Overall Comments: 

I have a minor qualm with the following phrase: 

“Because trends in nonroad engine activity levels are never directly measured, ...” 

In my opinion, this phrase makes me think that the data has never been available nor will it ever 

be available. I agree that heretofore it has been very difficult to acquire real-world activity data 

from nonroad equipment; however, with the advent of telematics and portable activity 

measurement systems (PAMS), collecting large amounts of activity data is becoming more of a 

reality. I realize that future data collection cannot have an impact on this version of Nonroad but 

EPA should consider sponsoring large scale data collection, storage, management, and analysis 

efforts to gain this missing component. Real-world activity data will help fill the gaps and refine 

the assumptions that are inherent in this version of Nonroad. 

RESPONSE: We agree that real-world activity data measured by portable activity and 

telematics measurement systems could serve to enhance the assumptions and refine the 

underlying data in MOVES. Whilst real-world activity data collection efforts in the 

nonroad sector are not yet as widespread as in the onroad sector, and require major and 

sustained research investments, EPA supports these efforts and intends to incorporate 

such datasets into future versions of the model. The opening paragraph has been 

modified to indicate that trends in nonroad engine activity are rarely directed measured. 

References 

1 U.S. Environmental Protection Agency (2018). Exhaust and Crankcase Emission Factors for Nonroad 

Compression-Ignition Engines. EPA-420-R-18-009. Assessment and Standards Division, Office of 

Transportation and Air Quality, U.S. Environmental Protection Agency, Ann Arbor, MI. July, 2018. 

https://www.epa.gov/moves/moves-technical-reports 

2 U.S. Environmental Protection Agency (2010). Exhaust Emission Factors for Nonroad Engine 

Modeling – Spark-Ignition. EPA-420-R-10-019. Assessment and Standards Division, Office of 

Transportation and Air Quality, U.S. Environmental Protection Agency, Ann Arbor, MI. July, 2010. 

https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P10081YF.pdf 

3 U.S. Environmental Protection Agency (2010). Median Life, Annual Activity, and Load Factor Values 

for Nonroad Emissions Modeling. EPA-420-R-10-016. Assessment and Standards Division, Office of 

Transportation and Air Quality, U.S. Environmental Protection Agency, Ann Arbor, MI. July, 2010. 

https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P10081RV.pdf 

4 Power Systems Research (1998). US Parts Link Edition 6.2. St. Paul, MN. 

5 U.S. Environmental Protection Agency (2004). Nonroad Engine Growth Estimates. EPA-420-P-04-008. 

Assessment and Standards Division, Office of Transportation and Air Quality, U.S. Environmental 

19 
 

 

 

 

 

 

 

 

 

Protection Agency, Ann Arbor, MI. April, 2004. 

https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P10001W2.pdf 

6 U.S. Environmental Protection Agency (2010). Nonroad Engine Population Estimates. EPA-420-R-10-

017. Assessment and Standards Division, Office of Transportation and Air Quality, U.S. Environmental 

Protection Agency, Ann Arbor, MI. July, 2010. 

https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P10081T6.pdf 

7 U.S. Environmental Protection Agency (2005). Geographic Allocation of Nonroad Engine Population 

Data to the State and County Level. EPA-420-R-05-021. Assessment and Standards Division, Office of 

Transportation and Air Quality, U.S. Environmental Protection Agency, Ann Arbor, MI. December, 

2005. https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1004LDX.pdf 

8 Wolf, M. and Baker, R. (2016). Summary of Proposed Approaches for Generating Projections of 

Nonroad Population Growth Trends: Technical Memorandum. EPA Contract No. EP-C-12-017, Work 

Assignment No. 4-14, Eastern Research Group (ERG), May 23, 2016. 

9 Moody’s Analytics (2016). Gross Domestic Product Forecasts. 

10 University of Virginia Weldon Cooper Center for Public Service (2016). Observed and Total 

Population for the U.S. and the States, 2010-2014; May 2016 Update. 

http://demographics.coopercenter.org/national-population-projections/ 

11 Federal Aviation Administration (2015). Terminal Area Forecast (TAF) Model. 

https://www.faa.gov/data_research/aviation/taf/ 

12 U.S. Energy Information Administration (2016). Annual Energy Outlook 2016 – Table 7 

(Transportation Sector Key Indicators and Delivered Energy Consumption). September 15, 2016. 

https://www.eia.gov/outlooks/archive/aeo16/ 

13 U.S. Energy Information Administration (2016). Annual Energy Outlook 2016 – Table 37 

(Transportation Sector Energy Use by Fuel Type Within a Mode). September 15, 2016. 

https://www.eia.gov/outlooks/archive/aeo16/ 

14 U.S. Energy Information Administration (2016). Annual Energy Outlook 2016 – unpublished data 

(INDUSA File – Table 6 [Energy and Macroeconomic Profile for Non-Manufacturing Industry – 

Construction]). Files provided to ERG on July 5, 2016 for EPA Contract No. EP-C-12-017, Work 

Assignment No. 4-14. 

15 U.S. Energy Information Administration (2016). Annual Energy Outlook 2016 – unpublished data 

(INDUSA File – Table 1 [Energy and Macroeconomic Profile for Non-Manufacturing Industry – Crop 

Agriculture]). Files provided to ERG on July 5, 2016 for EPA Contract No. EP-C-12-017, Work 

Assignment No. 4-14. 

16 U.S. Energy Information Administration (2016). Annual Energy Outlook 2016 – unpublished data 

(INDUSA File – Table 2 [Energy and Macroeconomic Profile for Non-Manufacturing Industry – Other 

Agriculture]). Files provided to ERG on July 5, 2016 for EPA Contract No. EP-C-12-017, Work 

Assignment No. 4-14. 

17 U.S. Energy Information Administration (2016). Annual Energy Outlook 2016 – unpublished data 

(INDUSA File – Table 4 [Energy and Macroeconomic Profile for Non-Manufacturing Industry – Oil & 

20 
 

 

 

 

 

Gas Mining]). Files provided to ERG on July 5, 2016 for EPA Contract No. EP-C-12-017, Work 

Assignment No. 4-14. 

18 U.S. Energy Information Administration (2016). Annual Energy Outlook 2016 – unpublished data 

(INDUSA File – Table 5 [Energy and Macroeconomic Profile for Non-Manufacturing Industry – Metallic 

& Non-metallic Mining]). Files provided to ERG on July 5, 2016 for EPA Contract No. EP-C-12-017, 

Work Assignment No. 4-14. 

19 U.S. Energy Information Administration (2016). Annual Energy Outlook 2016 – unpublished data 

(INDUSA File – Table 3 [Energy and Macroeconomic Profile for Non-Manufacturing Industry – Coal 

Mining]). Files provided to ERG on July 5, 2016 for EPA Contract No. EP-C-12-017, Work Assignment 

No. 4-14. 

20 Wolf, M. and Baker, R. (2016). Develop Final Population Growth Rates and Supporting 

Documentation: Technical Memorandum. EPA Contract No. EP-C-12-017, Work Assignment No. 4-14, 

Eastern Research Group (ERG), November 15, 2016. 

21 U.S. Census Bureau (2010). Geographic Terms and Concepts – Census Divisions and Census Regions. 

https://www.census.gov/geo/reference/gtc/gtc_census_divreg.html 

22 Davis, S., Williams, S., and Boundy, R., Oak Ridge National Laboratory Center for Transportation 

Analysis (2016). Transportation Energy Data Book: Edition 35. Oak Ridge, TN, October 31, 2016. 

http://cta.ornl.gov/data/index.shtml 

23 U.S. Department of Commerce Bureau of Economic Analysis (2016). Interactive Tables: Gross 

Domestic Product (GDP). https://www.bea.gov/regional/index.htm 

24 Office of Management and Budget (2017). North American Industry Classification System. 

Washington, D.C. https://www.census.gov/eos/www/naics/2017NAICS/2017_NAICS_Manual.pdf 

25 U.S. Census Bureau (2016). Population and Housing Unit Estimates Datasets. 

https://www.census.gov/programs-surveys/popest/data/data-sets.html 

26 U.S. Census Bureau (2016). County Business Patterns Database. https://www.census.gov/programssurveys/cbp/data/datasets.html 

27 National Marine Manufacturers Association (2016). https://www.nmma.org/ 

28 U.S. Energy Information Administration (2016). Fuel Oil and Kerosene Sales. 

https://www.eia.gov/petroleum/fueloilkerosene/ 

29 U.S. Environmental Protection Agency (2018). Science Inventory. Peer Review: Nonroad (NR) 

Updates to Population Growth, Compression Ignition (CI) Criteria, Toxic Emission Factors and 

Speciation Profiles. https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=339678 

21