United States Department of Agriculture

Forest 

Service

Pacific Northwest 

Research Station

General Technical Report

PNW-GTR-906 May

2015

Top-Down and Bottom-Up Approaches 

to Greenhouse Gas Inventory Methods

A Comparison Between National- and Forest-Scale 

Reporting Methods

David Nicholls, Frank Barnes, Felicia Acrea, Chinling Chen, Lara Y. Buluç, 

and Michele M. Parker 
USDA is an equal opportunity provider and employer. To file a complaint of discrimination, write: USDA, Office of the Assistant Secretary for Civil Rights, Office of Adjudication, 1400 Independence Ave., SW, Washington, DC 20250-9410 or call (866) 

632-9992 (Toll-free Customer Service), (800) 877-8339 (Local or Federal relay), 

(866) 377-8642 (Relay voice users).

Authors

David Nicholls is a forest products technologist, Alaska Wood Utilization and Development Center, 204 Siginaka Way, Sitka, AK 99835. Frank Barnes is an engineering 

technician, Tongass National Forest, 204 Siginaka Way, Sitka, AK 99835. Felicia 

Acrea is a program specialist, Forest Service, Geospatial Service and Technology 

Center, 2222 West 2300 South Salt Lake City, UT 84119. Chinling Chen is a natural 

resources specialist, Modoc National Forest—East Zone, 225 West 8th Street, Alturas, 

CA 96101. Lara Y. Buluç is a climate change coordinator, Forest Service, Sustainable 

Operations, 1323 Club Drive; Vallejo, CA 94592. Michele M. Parker is an environmental engineer, Tongass National Forest, P.O. Box 309, Petersburg, AK 99833.

Cover photographs: Michele Parker
Abstract

Nicholls, David; Barnes, Frank; Acrea, Felicia; Chen, Chinling; Buluç, Lara Y.; 

Parker, Michele M. 2015. Top-down and bottom-up approaches to greenhouse 

gas inventory methods—a comparison between national- and forest-scale reporting 

methods. Gen. Tech. Rep. PNW-GTR-906. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 30 p.

Federal agencies are mandated to measure, manage, and reduce greenhouse gas 

(GHG) emissions. The General Services Administration (GSA) Carbon Footprint 

Tool (CFT) is an online tool built to utilize measured GHG inventories to help 

Forest Service units streamline reporting and make informed decisions about operational efficiency. In fiscal year 2013, the Forest Service Sustainable Operations 

GHG Tracking Team completed GHG inventories of three Forest Service units to 

compare top-down (national) and bottom-up (local) inventory reporting approaches. 

In this report, the Track to Zero Team (formerly the GHG Tracking Team) and 

Sustainability Science Team summarize the top-down and bottom-up approaches 

to GHG inventories collected and data input into the GSA CFT for the three pilot 

units: the Northern Research Station Institute for Applied Ecosystem Studies, 

(Rhinelander, Wisconsin, location), the Stevensville Ranger District (Bitterroot 

National Forest, Stevensville, Montana), and the Tongass National Forest (Alaska). 

Because both top-down and bottom-up reporting methods and the scale of the 

three pilot units differ significantly enough to preclude quantitative analysis, this 

report will use qualitative analysis to compare (1) sources and methods of obtaining information, (2) ease of data access to GHG inventories, (3) level of accuracy 

of data within the inventories, (4) confidence in data accuracy, and (5) level of 

data aggregation. By conducting these pilots and comparing the top-down results 

from the national GHG inventory with the bottom-up or local results, we will 

identify methods to improve the accuracy of local GHG inventorying and tracking, 

strengthen the connection between local and national GHG inventories, and promote information sharing.

Keywords: Carbon footprint, top-down analysis, bottom-up analysis, sustainable operations, greenhouse gas inventory, CO2

 emissions.
ii

Contents

1 Introduction

3 What Is a Top-Down Approach?

3 What Is a Bottom-Up Approach?

3 Top-Down Versus Bottom-Up Approach

4 Literature Review

5 Greenhouse Gas Inventory Background

5 The Pilot Units

8 Research Objectives and Scope

9 Methods

10 Top-Down Approach

11 National Data Sources Used

11 Bottom-Up Approach

12 Local Data Sources Used

12 Results

13 Analysis Area 1: Data Source

20 Analysis Area 2: Data Aggregation Level

20 Analysis Area 3: Ease of Access

22 Analysis Area 4: Data Accuracy

23 Discussion

24 Conclusions

26 Acknowledgments

26 Metric Equivalents

27 References
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Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

Introduction

Greenhouse gases (GHGs) are produced by a number of common global activities, 

such as heating and cooling, use of fossil-fuel-powered vehicles, business travel 

and employee commuting, and others (fig. 1). Executive Order (EO) 13514, Federal 

Leadership in Environmental, Energy, and Economic Performance, requires all 

federal agencies to set goals for the reduction of GHG emissions. As specified 

in EO 13514, all goals should be met by fiscal year (FY) 2020. Each year annual 

progress must be measured, and reported in accordance with of annual progress 

toward GHG reduction goals attainable by FY 2020. Estimated U.S. Department of 

Agriculture (USDA) Forest Service emissions are close to 360,000 metric tons of 

carbon dioxide (CO2

) per year when considering heat, power, and motor vehicle use 

(Trapani, n.d.) (table 1), and these emissions decreased between 2010 and 2012 (fig. 

2) (Parker and Polansky 2013).

The linkage between operations at the USDA Forest Service national level 

and those at the forest level is important. This report explores the potential for the 

Tongass National Forest, encompassing close to 17 million ac in southeast Alaska, 

to serve as an example in GHG inventories, as well as methodologies for assessing 

and reducing GHGs. The objective of this research is to qualitatively evaluate GHG 

inventories using both a top-down (national) and a bottom-up (local) approach.

Figure 1—Common sources of federal greenhouse gas emissions. Source: “Sustainability Across 

Boundaries: The Greater Yellowstone Area Climate Action Plan,” June 2011.
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Figure 2—Total USDA Forest Service greenhouse gas emissions by fiscal year 

(metric tons carbon dioxide equivalent). Source: Parker and Polansky 2013.

Table 1—Total estimated U.S. Department of Agriculture 

Forest Service carbon footprint from heat, power, and 

motor vehicles

Source Amount

Fiscal year 

of reporting

Metric tons carbon 

dioxide per year)

Electricity 202,000 2003

Natural gas 22,000 2003

Propane 36,000 2003

Fuel oil 14,000 2003

Coal 2,500 2003

Motor vehicles 86,500 2005 and 2006 

(average of)

Total 360,000

Source: Trapani, n.d.
3

Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

What Is a Top-Down Approach?

A top-down approach to GHG inventories is one that originates from “the top,” and 

is often directed based on decisionmaking from “the top.” Within the USDA Forest 

Service, top-down data are aggregated and analyzed, entered, and calculated at the 

headquarters level (Washington office). The Forest Service agencywide emissions 

inventory is then reported to the USDA, where it is aggregated with other USDA 

agencies and reported to the White House’s Council on Environmental Quality. 

Under the top-down approach, there is little interaction between managers at the 

headquarter level and those in the field. Although this system may result in efficiency gains, it can also lead to errors in which aggregated data do not accurately 

reflect local conditions. 

What Is a Bottom-Up Approach?

A bottom-up approach describes data collected and processed at the local level, 

often the same as an individual Forest Service facility. For this report, the bottomup approach accounts only for emissions resulting from business operations and 

employee activities at the “pilot” site location. Greenhouse gas emissions accounting at a given location does not extend to the activities of vendors, visitors, or other 

partners. Data-gathering efforts for the GHG inventories are consistently a challenge because data requirements do not always reflect the data historically tracked 

for a given location. This illustrates one of the disadvantages of bottom-up GHG 

accounting: Data collection and accounting procedures can differ from site to site, 

from person to person, and over time. Thus, managers using bottom-up results to 

assess GHG emissions must assess the overall quality of data whether evaluating 

short timeframes or longer term trends.

The smallest local site (such as a district office or laboratory) using the bottomup approach may collect emissions data at a finer resolution than the national level. 

This might lead one to believe that the bottom-up approach would be more accurate. However, top-down reporting accounts for all emissions-producing activities 

throughout the entire agency and is aggregated from all Forest Service sites, even if 

the local site had conducted a full GHG inventory. 

Top-Down Versus Bottom-Up Approach

Top-down and bottom-up modelling approaches can sometimes lead to different 

results, given that they were “conceived and designed through different disciplines 

and for different purposes” (Wilson and Swisher 1993). Whether top-down or 

bottom-up techniques are being used, a few questions often remain central to the 

analysis: What quantity of GHG emissions are being emitted? How much energy 
4

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can be saved, and at what cost? (Hoogwijk et al., n.d.). In either approach, a GHG 

inventory can help an organization identify its “environmental footprint in this 

case, a measure of the demands of the Forest Service on the natural system in terms 

of consumption of renewable and nonrenewable resources” (Parker 2008). 

By quantifying environmental footprints, units can better prioritize efforts to 

become more efficient, resulting in actions that can save money and decrease GHG 

emissions. However, conducting meaningful GHG inventories can take several 

dedicated staff (i.e., “project champions”) to complete, which must be balanced 

against other uses of staff time. A national, top-down approach uses less on-theground resources for reporting, so may be less costly to perform. Conversely, the 

bottom-up approach requires more local resources and, hence, may be more costly 

to perform. The bottom-up approach may bring to light certain strategies or incentives to reduce GHG emissions by having control of information, including input 

data, charts, and reports that can be immediately conveyed to local managers. 

Ultimately, if standardized measurements are used, improved bottom-up reporting will diffuse meaningful information to national managers. This will result in a 

more accurate report of GHG emissions, and the mainstream use of GHG inventories and tools will help point to areas where agency environmental footprints can 

most easily be reduced.

Literature Review

Much research has been completed on both top-down and bottom-up analyses. 

The scale of these studies is broad and can produce meaningful results on scales 

as small as a university faculty (Aroonsrimorakot et al. 2013). Other studies have 

been conducted on a given ecosystem (Carpenter et al. 2012) or a national forest 

scale (Parker 2008). Footprinting studies are often conducted at the national scale, 

for example Denmark (Jacobsen 1998), Nepal (Khadka and Vacik 2012), Ireland 

(Kenny and Gray 2009), and the United States (Lutsey and Sperling 2008, Tuladhar 

et al. 2009). This scale can be expanded to include multiple nations, such as the 

European Union (Radu et al. 2013). Similar methods can be used to evaluate 

GHGs even at the global scale (Creutzig et al. 2012, Hoogwijk et al. 2010, van 

Vuuren et al. 2009). 

In some cases, details from a bottom-up approach for a given energy system 

can be combined with a top-down analysis (often done for an overall economy) 

(Bohringer and Rutherford 2008). This approach is often referred to as a hybrid 

model and can include a “general equilibrium” approach in which both elements are 

included (Frei et al. 2003). Hybrid models have been used successfully to evaluate 

technology choices, for example industrial steam generation in Canada (Rivers and 

Jaccard 2005). 

By quantifying 

environmental 

footprints, units can 

better prioritize efforts 

to become more 

efficient, resulting in 

actions that can save 

money and decrease 

GHG emissions.
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Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

Greenhouse Gas Inventory Background 

Forest Service involvement— 

The USDA Forest Service is a large land management agency within the federal 

government, managing approximately 193 million ac of public land. It is also the 

largest forestry research organization in the world, providing technical assistance to 

state, private, and international forestry agencies. Forest scientists have characterized healthy forests as net “carbon sinks,” and U.S. forests play a significant role—

when considering all forest growth and removals—by absorbing up to 20 percent of 

U.S. carbon emissions annually (USDA FS 2014). On a much smaller scale, Forest 

Service operations are a net contributor to GHG emissions, potentially equivalent to 

the annual electricity usage of 45,000 U.S. households (Trapani, n.d.). 

In 2007, the Forest Service was one of the first federal agencies to join the 

Environmental Protection Agency (EPA) Climate Leaders Program. Members 

of Climate Leaders commit to completing an inventory of their GHG emissions, 

setting reduction goals, and reporting annually to the EPA (Parker 2008). Since FY 

2010, the Forest Service has reported its national-level GHG inventories using a 

top-down approach. The Forest Service’s GHG emissions level has decreased from 

470,000 MTCO2e (metric tons of carbon dioxide equivalent) in FY 2010 to 360,000 

MTCO2e in FY 2012 (fig. 2) (Parker and Polansky 2013). 

Emission units— 

The conventionally accepted approach to reporting GHG emissions is to convert 

non-CO2 GHGs, such as methane and nitrous oxide, to a CO2 equivalent. This approach allows all GHG emissions to be compared on an “apples-to-apples” basis. 

To convert emissions of non-CO2 gases into their CO2 equivalent, the emissions 

are multiplied by the gas’s Global Warming Potential (GWP) factor. The GWP is a 

relative measure of how much heat a GHG traps in the atmosphere relative to the 

amount of heat trapped by a similar mass of CO2. Emission results are shown in 

MTCO2e. 

The Pilot Units

Tongass National Forest— 

The Tongass National Forest is the largest forest in the National Forest System, with 

over 17 million ac of land area. It contains the world’s largest temperate rain forest 

and hundreds of miles of shoreline, islands, and wetlands. The headquarters of the 

Tongass National Forest are in Ketchikan, Alaska, with other supervisory offices 

in Sitka and Petersburg, Alaska. The forest includes 10 ranger districts (fig. 3) and 

Forest scientists 

have characterized 

healthy forests as net 

“carbon sinks,” and 

U.S. forests play a 

significant role—when 

considering all forest 

growth and removals—

by absorbing up to 20 

percent of U.S. carbon 

emissions annually.
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Figure 3—Southeast Alaska, showing ranger districts on the Tongass National Forest.

is a good example of multiple, complex facilities spread out over a large land area. 

During FY 2008, Forest Service motor vehicle use on the Tongass National Forest 

accounted for 655,895 mi driven, requiring more than 47,000 gal of fuel (table 2).

Not only is the Tongass spread across nearly 700 mi, but many locations are 

accessible only by boat or plane, resulting in potentially greater GHG emissions 

than on forests in the continental United States that have well-defined road systems. 

And, given the remoteness of southeast Alaska, most goods are shipped in rather 

than being produced locally, which also has implications for the region’s GHG 

emissions.

Not only is the 

Tongass spread across 

nearly 700 mi, but 

many locations are 

accessible only by 

boat or plane, resulting 

in potentially greater 

GHG emissions than 

on forests in the 

continental United 

States that have welldefined road systems.
7

Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

Table 2—Tongass National Forest miles driven and fuel use during fiscal 

year 2008 (gasoline)

Ranger district

Heavyduty 

trucks

Lightduty 

trucks

Passenger 

cars

Miles 

driven Gallons

Average 

miles per 

gallon

- - - - - - - Number - - - - - - -

Craig 5 9 83,411 5,513 15.1

Hoonah 6 2 19,353 1,402 13.8

Juneau 10 9 3 79,086 5,520 14.3

Ketchikan 21 9 11 90,431 6,194 14.6

Petersburg 21 9 11 90,431 6,194 14.6

Sitka 6 4 14,981 1,066 14.1

Thorne Bay 28 9 217,664 17,407 12.5

Wrangell 11 5 46,035 3,259 14.1

Yakatat 7 1 22,810 1,614 14.1

Total 115 59 4 655,895 47,032 13.9

Source: Parker 2008.

The Tongass National Forest has been a leader in GHG inventories for several 

years. Following the Climate Leaders Program’s established protocols, the Tongass 

National Forest conducted its first bottom-up GHG inventory in FY 2007. Additional GHG inventories have been conducted annually since then (except for FY 

2009).

Stevensville Ranger District—

The 1.6-million-ac Bitterroot National Forest, in west-central Montana and eastcentral Idaho, is part of the Northern Rocky Mountains. The district encompasses 

about 250,000 ac of public land, including about 100,000 ac within the scenic and 

majestic Selway-Bitterroot Wilderness Area. About 2,600 mi of Forest Service 

roads are open to motorized vehicles on the Bitterroot National Forest. The 

Stevensville Ranger District Office is located in the historic town of Stevensville, 

Montana. 

There is no public transportation in the Bitterroot Valley, so employees 

commute to work an average of 17 mi daily. Some of the field-going employees are 

“zoned,” working across more than one ranger district. Because they may travel 

across the entire forest as part of their work duties, employees may require large 

fleets of motorized vehicles. Stevensville Ranger District inventory sites included a 

residential building, fire warehouse, office and old warehouse, hazardous material 

building, Charles Waters Host and Handicap Site, Larry Creek Campground, and 
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the Kootenai Creek Trailhead. The Stevensville Ranger District had already been 

collecting utility data but was new to reporting and calculating GHG emissions.

Northern Research Station— 

The Northern Research Station (NRS) laboratory Institute for Applied Ecosystem 

Studies, located in Rhinelander, Wisconsin, represents the smallest physical area of 

the three pilot units. This NRS facility reported energy data for one building covering about 38,000 ft2

, and three fleet vehicles. The institute’s main goal in participating with the GHG inventory was to have a clear sense of energy use and costs both 

before and after a planned changeover from old, inefficient boilers to new, more efficient condensing boilers in its main building. The boiler replacement was completed in mid-2013, during the time that energy data were being regularly inventoried. 

The pilot inventory and local weather records have proved that the institute 

used fewer British thermal units this winter (winter of 2013 to 2014) versus the 

same period last winter, even though this was a much colder winter overall. The 

cost of heating the building has increased owing to higher fuel costs this year. 

If the boilers had not been replaced in summer 2013, heating costs would have 

likely been higher owing to higher fuel prices coupled with the use of less efficient 

equipment. Like the Stevensville Ranger District, the Rhinelander NRS facility had 

already been collecting utility data but was new to reporting and calculating GHG 

emissions.

Research Objectives and Scope

This report compares the top-down and bottom-up approaches to GHG data collection and reporting. By identifying gaps and barriers, and by bringing them to 

light, we expect to improve the accuracy of local GHG inventorying and tracking, 

find ways to streamline the process at the unit level, and present options for future 

implementation of GHG inventory data collection. 

We hope this publication will generate discussions between the Forest Service 

and practitioners who have been engaged in grassroots GHG inventories and are 

willing to offer their expertise to improve future reporting efforts. We also hope 

that unit managers will recognize the “new normal” condition of knowing their 

unit’s GHG emissions sources, and will seize opportunities to reduce GHG emissions. Local green team leaders, energy managers, unit climate change and sustainable operation coordinators, volunteers, and unit staff will play a crucial role in 

continuing local GHG data collection and input and open discussions. 

This report does not attempt to compare GHG emissions results quantitatively 

among the national and pilot units; however, it is hoped that it will shed light on 
9

Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

where the national GHG emission numbers are aligned (as well as where they are 

not). Some of the national GHG inventory numbers are estimated based on the 

number of full-time equivalents employed by the Forest Service and not on measured emissions. Also, there are different levels of completeness for some emissions 

categories because of issues with data availability. The same is true with the pilot 

units.

The comparisons conducted in this research will highlight the different 

approaches for characterizing GHG emissions and are expected to help local units 

collect, enter, and display their GHG emissions data, while learning ways to reduce 

them. A comparison of unit data in a quantitative manner is not made on account of 

inherent differences in the geographic size of the units, the number of facilities, and 

the number of employees at each location. 

Methods 

The methods we used to compare bottom-up and top-down approaches included 

soliciting experts in GHG emissions methods as well as interviewing Forest Service 

staff members. To accomplish this, we contacted agency staff involved in the GHG 

Tracking Team and program managers at the national level to obtain first-hand 

information. We relied heavily on experts outside of the Forest Service, including 

feedback from partners at the U.S. Department of Energy’s National Renewable 

Energy Laboratory (NREL). 

One of the first steps in conducting a GHG inventory is determining which tool 

to use. The Forest Service and NREL evaluated three tools and selected the General 

Services Administration (GSA) Carbon Footprint Tool (CFT) as being the most 

appropriate. Establishing data collection processes simplifies subsequent GHG data 

collection and reporting. A data reporting structure was then established within the 

GSA CFT, so all pilot units could be considered on the same basis. The initial focus 

for the pilot units was to get raw data into the GSA CFT. As part of this program, 

all the technical calculations required for GHG reporting were done automatically once the data were entered. It was not necessary for support staff at the pilot 

locations to be energy experts or engineers, just conscientious about reporting and 

recording data accurately.

The pilot units differed significantly in geographic location, site size, number 

of employees, seasonal staff and housing, number and age of buildings or facilities, 

miles of roads, and management priorities and activities. Although variability of 

location and size exists among the different units, the process these units followed 

to input GHG inventory data into the GSA CFT was essentially the same. 

The comparisons 

conducted in 

this research will 

highlight the different 

approaches for 

characterizing GHG 

emissions and are 

expected to help local 

units collect, enter, 

and display their GHG 

emissions data, while 

learning ways to reduce 

them.
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Top-Down Approach

At the national level, the GHG inventory tracking team used the Microsoft Excel®

workbook developed by the U.S. Department of Energy’s Federal Energy Management Program (FEMP) (FEMP 2011). This workbook is used by all federal agencies 

to conduct their GHG emissions inventories annually. The FEMP tool uses internationally accepted formulas and equations that aggregate the populated data fields 

to estimate GHG emissions in multiple categories and scopes. Thus, there was a 

certain degree of automation to this approach.

The top-down example used for comparison is the national-level FY 2011 report 

obtained from the Washington office Sustainable Operations group. Here, emissions are broken down into 11 categories, with “mobile emissions” and “purchased 

electricity” accounting for more than 75 percent of the total (fig. 4). When comparing a national data collection effort to a local or site-specific data collection effort, 

some discrepancies are expected. This report identifies these discrepancies, as well 

as areas where the two methodologies are most appropriate.

Figure 4—U.S. Department of Agriculture Forest Service total emissions during fiscal year 2010, by 

category. MTCO2e = metric tons of carbon dioxide equivalent. Source: USDA FS 2011.
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Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

National Data Sources Used

The following data sources and coding protocols from the national top-down 

approach are summarized:

National Finance Center (NFC)— 

This center serves all of USDA in managing administrative, financial, personnel, 

security, and workforce data (https://www.nfc.usda.gov/).

GSA FAST— 

General Services Administration Federal Automotive Statistical Tool (FAST) is 

used by all federal agencies to report their on-highway vehicle fuel consumption. 

Data are analyzed and reported by the Forest Service national fleet manager

(http://drivethru.fas.gsa.gov/drivethru/drivethru/).

GSA TravelTrax®—

TravelTrax(sm) is used by all federal agencies to report air and business travel information. Data are automatically uploaded from each agency’s Travel Management 

Information System (https://gsa.traveltrax.com).

FAMWeb— 

The Fire and Aviation Management Web applications (FAMWeb 2013) site brings 

together a variety of applications, tools, and services related to interagency fire and 

aviation management managed by the National Wildfire Coordinating Group and 

participating agencies. The website provides detailed information, data access, and 

application entry points for system users, interagency partners, providers, and the 

public.

Budget Object Classification Codes— 

Budget Object Classification Codes are used by USDA agencies and departments 

serviced by the office of the Chief Financial Officer. These codes are used when obligations are first incurred to record financial transactions according to the nature of 

services provided or received (https://cod.nfc.usda.gov/documents/docs/boc.pdf). 

Bottom-Up Approach

The online GSA CFT was used to report the forest-level (bottom-up) data. This 

includes data input for stationary combustion (heating oil, propane, natural gas), 

mobile combustion (vehicle, aircraft, and watercraft fuel use), purchased electricity 

(kilowatt hours), solid waste disposal, employee commuting, and business travel 

data. These categories were used by the GSA CFT to calculate the GHG emissions 

from the facilities and activities of the three pilot units.
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Local Data Sources Used 

For the Tongass National Forest, some of the data was available online from scanned 

invoices that were recorded and paid at NFC. The data were given a higher confidence rating owing to their presumed accuracy. Local payment data had to be 

collected by reviewing the paper files and contacting utility providers in all of the 

Tongass National Forest districts. This resulted in the data being given a lower 

confidence rating. These data were then entered into an Excel spreadsheet to make 

them readily accessible to the engineers responsible for energy management and in 

a format compatible with the GSA CFT. For the other two pilot locations, the data 

were collected locally.

All three pilots used a custom Employee/Business Commuter Survey rather 

than the national- or GSA-supplied one for more relevant results in the bottom-up 

approach. The custom survey was developed to align with GSA input fields to elicit 

more accurate results. The Stevensville Ranger District pilot unit was new to the 

GHG inventory process. Because they had no experience with reporting GHG emissions, they were provided the template spreadsheet used on the Tongass National 

Forest, and they entered their data into that spreadsheet. The NRS laboratory in 

Rhinelander used their own spreadsheet design. Although local data collecting is 

a multistep process that can involve numerous people and introduce opportunities 

for error, it also has the ability to capture local operating conditions very well when 

properly conducted.

Results

We found that having complete and accurate data available is essential to the GHG 

reporting process. A Utility Bill Cleanup Project is the recommended first step, and 

units that have completed this phase are usually well on their way to having accurate data available. The goal of the utility bill clean-up is to:

...provide the tools necessary to understand the cost and consumption 

associated with a unit’s energy use, water use, and solid waste disposal 

and to identify places where efficiencies in billing and consumption can be 

made. (USDA 2011).

Some units have previously been tracking utility usage on their own, in which 

case the Utility Bill Cleanup Project may not be necessary. The GSA CFT will 

accept monthly data in most categories, but annual data can also be used. Smaller 

units should enter their data manually site by site while larger, more complex units 
13

Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

can use batch uploads to enter multiple sites. Thus, a finding of this research is that 

bottom-up procedures can vary, depending on the size of the local unit, level of 

support available, and other factors.

The results of our qualitative analysis between pilot locations are summarized 

in tables 3 through 7. Each table addresses one aspect of data completeness and 

integrity. We discuss the following data quality characteristics among the three 

pilot areas: 

• Sources or methods in which the GHG data were obtained

• Level of aggregation of data within the inventories

• Ease of access to GHG inventories, and 

• Confidence in data accuracy of each category 

In tables 3 through 7, an item is not applicable (N/A) if the pilot unit has no 

operative category that would contribute to GHG emissions (e.g., aviation or 

watercraft), and an item is “Not reported” if a unit may have an operative category 

that would contribute to GHG emissions, but for whatever reason the pilot did 

not provide data. An inherent limitation of these results is that not all scopes and 

categories of emissions were reported by each location.

Analysis Area 1: Data Source

Sources of data between the top-down and bottom-up approach are compared, 

with differences shown between categories (table 3). Most of the national-level data 

came from the Washington office. The primary sources are the Washington office 

Engineering “data call”—an annual call to forest energy managers requesting 

information on a subset of emissions categories not captured in the Forest Service 

systems of record (e.g., renewable energy generation and refrigerant use, among 

other categories). Last year, the data call was issued by the Forest Service Sustainable Operations group, in close cooperation with Forest Service engineers. 

A second source of information is the “system of record”—corporate databases 

that track data connected with Forest Service hard targets. These databases are used 

to capture statistics that represent critical Forest Service goals as defined in the 

agency’s Program and Budget Advice. For example, fleet managers reported fuel 

consumption for both the Tongass National Forest and Stevensville Ranger District. 

Only the Tongass National Forest reported both aviation and watercraft data, and 

the Stevensville Ranger District alone reported all-terrain-vehicle and snowmobile 

fuel consumption. In both pilot units, fuel consumption in these vehicles was 

estimated through hands-on, labor-intensive data conversion methods that may not 
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Table 3—Sources of data used for greenhouse gas emissions analyses (continued)

Category National

Tongass 

National Forest

Northern 

Research Station

Stevensville 

Ranger District

Scope 1: Goal subject 

Buildings energy

Budget Object 

Codes

NFC/ECM database 

plus utility company 

records

Tracked internally ACE spreadsheet 

prepared for forest fleet manager

Scope 1: FAST Mobile 

(vehicle fleet) energy

GSA FAST Data sheets from 

forest fleet manager

Not reported Data sheets 

from forest 

fleet manager

Scope 1: Aviation FAMWeb Calculated from 

dispatch flight 

records

N/A N/A

Scope 1: Watercraft Included in 

vehicles and 

equipment (below)

Engine hours— 

dispatch records and 

Budget and Finance 

purchases 

N/A N/A

Scope 1: Vehicles and 

equipment energy

Estimate from 

national fleet

manager

Not reported Not reported All terrain vehicle 

and snowmobile 

estimated

Scope 1: Mixed refrigerants and fugitive 

F-gases

WO engineering 

data call

N/A N/A N/A

Scope 1: Agencyoperated wastewater 

treatment facilities

WO engineering 

data call

N/A N/A N/A

Scope 1: Fugitive 

landfill gases

WO engineering 

data call

N/A N/A N/A 

Scope 2: Electricity NFC NFC/ECM database 

plus utility company 

records

Tracked internally ACE

Scope 2: Steam and 

hot water

WO engineering 

data call

N/A N/A N/A

Scope 2: Chilled water WO engineering 

data call

N/A N/A N/A

Scope 3: Transmission 

and distribution losses

Automatically 

calculated 

Automatically calculated by GSA ToolAutomatically calculated by GSA toolAutomatically calculated by GSA 

tool 

Scope 3: Business travel 

(air and ground)

GSA Travel Trax Employee survey 

(Survey Monkey)

Employee survey 

(Survey Monkey)

Employee survey 

(Survey Monkey)
15

Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

Table 3—Sources of data used for greenhouse gas emissions analyses (continued)

Category National

Tongass 

National Forest

Northern 

Research Station

Stevensville 

Ranger District

Scope 3: Commuter 

travel

Employee survey Employee survey 

(Survey Monkey)

Employee survey 

(Survey Monkey)

Employee survey 

(Survey Monkey)

Scope 3: Contracted 

wastewater treatment

WO engineering 

data call

NFC/ECM, city 

records

Not reported Not reported

Scope 3: Contracted 

waste disposal

U.S. Department 

of Agriculture 

reported

NFC/ECM, city 

records

Not reported Ace provided cost, 

then tonnage was 

estimated from 

cost

Renewable energy WO engineering 

data call

N/A N/A N/A 

N/A = not applicable.

NFC = National Finance Center.

ACE = Administrative Center of Excellence.

ECM = Enterprise content management.

FAST = Federal Automotive Statistical Tool.

GSA = General Services Administration.

WO = Washington office.

Table 4—Greenhouse gas emission analysis scopes, categories, and sources 

used to quantify emissions

Scope Categories Sources

Scope 1 Direct emissions Emissions from fossil fuels burned on site, emissions 

from entity-owned or entity-leased vehicles, and other 

direct sources.

Scope 2 Indirect emissions Emissions resulting from the generation of electricity, 

heating and cooling, or steam-generated off site but 

purchased by the entity and the transmission and distribution losses associated with some purchased utilities 

(e.g., chilled water, steam, and high-temperature hot 

water).

Scope 3 Emissions related to 

goods or services 

purchased by the 

agency

Transmission and distribution losses associated with 

purchased electricity, employee travel and commuting, 

contracted solid waste disposal, and contracted wastewater treatment. 
16

general technical report pnw-gtr-906

Table 5—Level of data aggregation

Category National

Tongass 

National Forest

Northern 

Research Station

Stevensville 

Ranger District

Scope 1: Goal subject Buildings 

energy

District Site level Site level Site level

Scope 1: FAST Mobile 

(vehicle fleet) energy

State or GSA region District Not reported District level

Scope 1: Aviation Unit District level N/A N/A

Scope 1: Watercraft Included in estimate for 

non-FAST vehicles and 

equipment

District level N/A N/A

Scope 1: Vehicles and equipment 

energy

Forest Service Not reported Not reported District 

Scope 1: Mixed refrigerants and 

fugitive F-gases

Unit N/A N/A N/A

Scope 1: Agency-operated wastewater treatment facilitiesUnit N/A N/A N/A

Scope 1: Fugitive landfill gases Unit N/A N/A N/A 

Scope 2: Electricity ZIP Code Site level Site level Site level

Scope 2: Steam and hot water Unit N/A N/A N/A 

Scope 2: Chilled water Unit N/A N/A N/A

Scope 3: Transmission and distribution lossesZip Code District level Station level District level 

Scope 3: Business travel (air and 

ground)

Forest Service District level Station level District level

Scope 3: Commuter travel Unit District level Station level District level

Scope 3: Contracted wastewater 

treatment

Unit Site level Not reported N/A 

Scope 3: Contracted waste disposal Forest Service Site level Not reported Site level

Renewable energy Unit N/A N/A N/A 

N/A = not applicable.

FAST = Federal Automated Statistical Tool.

GSA = General Services Administration.
17

Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

Table 6—Ease of data access (continued)

Category National

Tongass 

National Forest 

Northern 

Research 

Station

Stevensville 

Ranger District

Scope 1: Goal subject 

buildings energy

Moderately difficult to get 

access but easy once you 

have it

Moderately difficult for FY 

2011. Moderately easy for 

future years because heating 

oil payments are now made 

through NFC.

N/A Easy—ACE had 

spreadsheet

Scope 1: FAST Mobile 

(vehicle fleet) energy

Moderately difficult Moderate. Dependent on fleet 

manager’s cooperation for 

data.

Not reported Easy—Fleet 

manager provided vehicle 

miles

Scope 1: Aviation Difficult Moderate. Dependent on dispatch data detail availabilityN/A N/A 

Scope 1: Watercraft Not tracked separately Moderate. Dependent on dispatch data detail availabilityN/A N/A

Scope 1: Vehicles and 

equipment energy

Data availability is dependent upon national 

fleet manager completing 

estimate

Not reported Not reported Difficult—Had 

to estimate 

ATV and 

snowmobile 

usage 

Scope 1: Mixed refrigerants and fugitive 

F-gases

Data availability is dependent upon WO engineering data call to field units

N/A N/A N/A 

Scope 1: Agency operated wastewater treatment facilities

Data availability is dependent upon WO engineering data call to field units

N/A N/A N/A 

Scope 1: Fugitive landfill gasesData availability is dependent upon WO engineering data call to field unitsN/A N/A N/A

Scope 2: Electricity Moderately difficult to get 

access but easy once you 

have it

Easy. From NFC and established 

utility company contact for 

billing histories

N/A Easy—ACE 

Scope 2: Steam and hot 

water

Data availability is dependent upon WO engineering data call to field unitsN/A N/A N/A 

Scope 2: Chilled water Data availability is dependent upon WO engineering data call to field unitsN/A N/A N/A
18

general technical report pnw-gtr-906

Table 6—Ease of data access (continued)

Category National

Tongass 

National Forest 

Northern 

Research 

Station

Stevensville 

Ranger District

Scope 3: Transmission 

and distribution losses

Automatically calculated 

by FEMP tool

Automatically calculated by 

GSA GHG Tool

Automatic. 

calculated 

by GSA 

GHG Tool

N/A 

Scope 3: Business travel 

(air and ground)

Moderately difficult to get 

access but easy once you 

have it.

Moderate. Requires local survey Moderate. 

Requires 

local survey

Moderate. 

Requires local 

survey

Scope 3: Commuter 

travel

Difficult. Employee survey Moderate. Requires local survey Moderate. 

Requires 

local survey

Moderate. 

Requires local 

survey

Scope 3: Contracted 

wastewater treatment

Data availability is dependent upon WO engineering data call to field unitsDifficult. Most facilities do 

not have meters, some bill in 

1,000- to 5,000-gal increments.

N/A N/A

Scope 3: Contracted 

waste disposal

Data availability is dependent upon USDA providing estimate. Estimate 

is done at USDA level, 

so an FS estimate is not 

available

Difficult—most utilities provide 

only rough data that requires 

calculations to arrive at tonnage. 

Not reported N/A

Renewable energy Data availability is dependent upon WO engineering data call to field units

N/A N/A N/A

N/A = not applicable.

ACE = Administrative Center of Excellence.

NFC = National Finance Center.

GHG = Greenhouse gas.

WO = Washington office.

GSA = General Services Administration.

FEMP = Federal Energy Management Program.
19

Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

Table 7—Confidence in data accuracy comparison

Category National

Tongass 

National Forest

Northern 

Research 

Station

Stevensville 

Ranger 

District

Scope 1: Goal subject Buildings 

energy

C because many Budget 

Object Codes are coded 

improperly

C for FY 2011, B+ for 

future years

A

Scope 1: FAST Mobile 

(vehicle fleet) energy

B- D for older data, B for 

newer data (estimated 

from mileage reports)

A

Scope 1: Aviation C D for older data, B for 

newer data

N/A N/A

Scope 1: Watercraft N/A D for older data, B for 

newer data

N/A N/A

Scope 1: Vehicles and equipment energyC+ Not reported Not reported D

Scope 1: Mixed refrigerants and 

fugitive F-gases

C N/A N/A N/A

Scope 1: Agency-operated 

wastewater treatment facilities

C N/A N/A N/A

Scope 1: Fugitive landfill gases A because the Forest Service 

no longer uses landfills

N/A N/A N/A

Scope 2: Electricity A- A N/A A

Scope 2: Steam and hot water C because we are not sure if 

we are getting a complete 

set of data

N/A N/A 

Scope 2: Chilled water C because we are not sure if 

we are getting a complete 

set of data

N/A N/A N/A

Scope 3: Transmission and 

distribution losses

A C—GSA CFT method of 

calculation probably not 

relevant to SE Alaska 

A A

Scope 3: Business travel (air 

and ground)

A for air travel, D for 

ground travel

C C N/A 

Scope 3: Commuter travel D in FY 2011 A A

Scope 3: Contracted wastewater 

treatment

C C—No data entered in 

GSA CFT for FY 2011 

(by Prizim) 

N/A Not 

reported 

Scope 3: Contracted waste 

disposal

D C Not reported D

Renewable energy B- N/A N/A N/A

N/A = not applicable.

FAST = Federal Automative Statistical Tool.

GSA CGT = General Services Administration Carbon Footprint Tool.
20

general technical report pnw-gtr-906

be truly accurate. The subcategory in which the national and local inventories used 

the same data source is utility/electricity from NFC. Separate employee commuter 

surveys were completed on the national level and at each local unit. When comparing the Tongass National Forest data to the Stevensville Ranger District data, 

it becomes clear that the equipment underlying the data source (i.e., passenger 

vehicles versus snowmobiles versus aircraft) can also influence GHG results.

This first comparison is a good example of the significant differences between 

top-down and bottom-up approaches. National data collected through the Washington office Engineering Data Call provide virtually all information in scope 1 

(mixed refrigerants, wastewater, and landfill gases), scope 2 (steam, hot water, and 

chilled water), and scope 3 (renewable energy) (table 4). None of the local units in 

the pilot contributed any information about these GHG producers. Without the topdown approach, no information about these classes of GHGs would be available. 

Analysis Area 2: Data Aggregation Level

Differences are noted between the national approach (top-down) and the local 

approach (bottom-up) in terms of the level of aggregation of GHG emissions data 

(table 5). At the national level, aggregation varies depending on the system used to 

collect and report data. Pilot unit data are aggregated at the site level. The site-level 

aggregation will not be easy for a national top-down approach owing to the large 

number of individual sites that the Forest Service operates. Site-level data will be 

meaningful for a national data collection effort if all of the sites conduct the inventory consistently. However, that is a major challenge of aggregating at such a fine 

scale.

Analysis Area 3: Ease of Data Access

The ease of access of collecting GHG data is a subjective measure in which data 

managers were asked to use the words “Easy,” “Moderate,” or “Difficult” to best 

describe their experience in each category. However many respondents chose to 

refine their responses over a spectrum of ratings based on their unique experiences 

(table 6). Owing to this subjective nature, we expect the value of qualitative interpretations to be relatively low, and they therefore should be used in combination 

with other reported results.

More data sources are available at the national level than the unit level (table 6), 

providing another example of how the top-down approach has the potential to be 

more successful than the bottom-up approach. For example, if local units do not 
21

Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

collect their own local data, the national database may be used as a proxy for the 

missing data. The data manager at the national level states that most of the data are 

moderately difficult to access, but relatively easy to use once access is obtained. 

It is possible that if one inexperienced data manager were given two different 

units from which to collect and enter data, their assignments of subjective terms 

for the different units might be disparate simply based on the scale (geographic 

size) between units. For instance, the NRS data manager did not have to supply 

answers to 13 out of 17 questions, because NRS was not contributing data in those 

areas. Thus, the perception of the NRS data manager that it was “Easy” to collect 

and enter data could be because there were so few data. Conversely, the Tongass 

National Forest data manager found it “Moderately difficult” or “Difficult” on just 

under half (9 of 17) of the questions, possibly owing to the volume of data alone for 

the size of the Tongass National Forest, and the number of additional data providers 

that the data manager had to contact. 

The Stevensville Ranger District benefitted from its Administrative Center of 

Excellence (ACE), a centralized business organization serving Northern Region 

(Region 1) forests since 2006. The ACE reviews all Region 1 utility accounts (paid 

through NFC) to validate that the meters and accounts are current, correct, and 

belong to the Forest Service. The ACE staff record and track Forest Service utility 

usage monthly by reviewing data directly from online vendor accounts or copies 

of utility bills (Johnson and Bowles, n.d.). Of the questions that the Stevensville 

data manager answered, “ease of access” responses differed greatly, depending on 

whether the district had data already compiled for utility records and vehicle use, or 

whether specific estimates of hours or miles of use had to be calculated by hand. 

Analysis Area 4: Data Accuracy

Comparisons are made in confidence of data accuracy based on FY 2012 results 

(table 7). A subjective grading scale was provided, similar to American educational 

grading standards, in which the letter “A” represents a high level of confidence in 

the accuracy, and the letter “D” represents very little confidence (with “B” and “C” 

corresponding to “Good” and “Satisfactory,” respectively). Rather than a numerical 

or measured grading system (such as a percentage of “correct” answers), this grading scale is designed to be more subjective.

Although this rating system comes from program managers as first-hand 

information input, it is nonetheless subjective in nature owing to responses being 

obtained from four data managers. At first glance, managers are more confident 
22

general technical report pnw-gtr-906

about accuracy at the national level than at the forest level. In the Tongass National 

Forest, the manager gives “C” and “D” ratings for most of the confidence-level 

categories for FY 2011, but an improved outlook for future years. 

This is of interest in terms of the relation between ease of access and level of 

data accuracy. We observed that at the national level, managers initially found it 

difficult to access data systems, but once access was obtained, the level of accuracy was considered moderately high. At the national level, reporting systems are 

assumed to be consistent because effective control systems are in place, whereas 

these control systems may not be present at the local level. 

On the Tongass National Forest, the data manager rated the level of accuracy 

quite low, even though these data were relatively easy to access. Here, the data 

manager was more confident with newer data. For the Stevensville Ranger District, 

half of the categories reviewed (four out of eight) were graded “A,” and the other 

half graded “C” or “D.” 

Data managers at the local level are more directly involved in the data collection, than those at that national level so it should be more apparent to them when the 

data are inaccurate or incomplete. For scope 1 data (table 4), the Tongass National 

Forest manager estimated emissions using units of miles per gallon (car and truck 

fleet) and units of gallons per hour (aircraft), despite the possibility of sacrificing 

some accuracy with these methods. One of the major purposes of conducting an 

inventory is to refine methodologies and improve processes so as to acquire more 

accurate data with higher levels of confidence over time.

The contracted waste disposal category had the lower quality of data owing to 

the scale of estimating required. Unit waste generated is required to be reported 

in short tons. In most cases, the monthly charge incurred was the only number 

available from the utility providers for the bottom-up data. This dollar amount was 

then backed down into an estimate of the number and size of waste containers, 

multiplied by the number of pickups per month for each. Even after estimating 

these numbers, another estimate was made based on the average weight of each size 

container, which varied widely among utility providers and also has no standard 

available for federal agencies to use. Even if there were standard weights available, 

another variable to consider is whether containers were full when picked up. The 

units that reported contracted waste used “100 percent full” in their calculations, so 

the emissions from this category are knowingly overstated.

Because this is a nationwide requirement for GHG data, waste disposal companies can be encouraged to provide waste tonnage information on regular billing 
23

Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

documents or statements. At the top-down level, data availability is dependent upon 

USDA providing an estimate, and because this estimate is done at the USDA level, 

no Forest Service estimate is available.

Discussion

A top-down approach can be used to evaluate GHG emissions within the USDA 

Forest Service as well as contribute to national policies for other agencies. The 

top-down approach helps the agency to establish a baseline estimate of CO2 emissions, identifying opportunities to reduce emissions, and allocating resources in 

priority areas. In some cases, top-down data are dependent on national-level queries 

of field units. In others, these data are calculated directly from existing tools, for 

example, the FEMP tool. In still other cases, the data are provided from an even 

more aggregated source, for example, at the USDA level. Other factors that could 

influence differences include the scale of aggregation of data (i.e., whether the data 

originate from the site, district, or forest level). Clearly, there is no single formula 

for conducting top-down analyses, and the specific procedures used can influence 

overall results.

Although successful in terms of complying with the requirement from the 

USDA and the White House mandate, national reporting systems have the potential 

to differ widely in accuracy. Owing to large discrepancies of different forest units, 

capacity, and regional climate, a national strategy might be more effective by targeting a few early adopters, rather than attempting to characterize local facilities.

A bottom-up approach can be more accurate and closer to quantifying actual 

emissions at a given location. The benefit of a bottom-up approach is that data are 

more immediate: The shortcoming is data from local units are in different forms, 

and may be nonstandardized. Aggregation at the national level from a bottom-up 

approach might not be feasible in all cases. However, it is still important for units 

to compile and analyze GHG data, so that progress can be tracked locally.

The manner in which GHG data are collected raises several questions about 

data quality, including completeness, confidence in accuracy, and timeliness. With 

limited time and resources, perhaps managers can discuss where to focus their 

energy in order to improve data collection. For instance, if scope 1 GHG emissions 

(table 4) are the largest but the level of data confidence is the lowest, then the data 

collection and recording improvements should become an area of focus for the next 

round of GHG emission inventory data improvement. In comparison, if business 

air travel is a small portion of the total GHG emissions, an estimate based on an 

employee survey may be sufficient. 

Although successful 

in terms of complying 

with the requirement 

from the USDA and 

the White House 

mandate, national 

reporting systems have 

the potential to differ 

widely in accuracy.
24

general technical report pnw-gtr-906

The NFC will soon begin using the Ameresco AXIS Invoice Management 

Service for managing utility bills. This service provides a streamlined process 

for compiling utility bill invoices, and analyzing them to find opportunities for 

energy and cost savings. The advanced technology behind the service replaces 

time-intensive manual accounting with fast and accurate data access with multiple 

analysis functions designed to show inefficiencies in current utility use. By significantly improving the availability and accuracy of utility data, the Ameresco AXIS 

system may serve a critical role in helping reduce agency utility costs and personnel requirements while meeting sustainable operations goals for energy reduction 

(Ameresco 2014). 

Conclusions

This report has provided a qualitative comparison of top-down versus bottom-up 

approaches to GHG data collection and reporting within the USDA Forest Service. 

The decision of whether to use top-down or bottom-up methods can have an important influence on results, with numerous pros and cons for each method. Top-down 

methods offer advantages of efficiency and economies of scale, while bottom-up 

methods offer potential gains in accuracy owing to more immediate data collection 

reporting. Other important considerations include the level of aggregation of data, 

the confidence level in data accuracy, the ease of access in data collection, and 

which specific data sources are used. 

We hope that this report will stimulate discussion among all those involved in 

carbon reporting within the USDA Forest Service, including Local Green Team 

leaders, energy managers, unit climate change and sustainable operation coordinators, volunteers, and other interested staff at all levels. The lessons learned and 

observations from managers articulated here should have broader implications for 

other national forests that may soon be embarking on a GHG monitoring program, 

and help them develop efficient and accurate data reporting systems. Likewise, 

these lessons may encourage renewable energy generation as a means of reducing 

GHG emissions, as has been done on the Tongass National Forest with wood energy 

(fig. 5) and solar energy (fig. 6).

Last, we point out that the Forest Service is in a unique position to evaluate 

both its emissions from ongoing operations as well as the carbon sequestered by the 

forests on its 193 million ac under management. Thus, an important area of future 

work could be to compare emissions and sequestration to determine the climate 

change benefits and impacts of the agency as a whole.
25

Top-Down and Bottom-up Approaches to Greenhouse Gas Inventory Methods

Figure 5—Wood energy system at Discovery Center, Ketchikan, Alaska.

Figure 6—Solar panels in use, southeast Alaska.
26

general technical report pnw-gtr-906

Acknowledgments

The authors thank all team members who contributed to this publication: Rachelle 

Meyer, Katie Newcomb, Marina O’Boyle, and Trista Patterson. We also thank those 

who participated in the pilot units: Elizabeth (Betsy) Ballard (Stevensville Ranger 

District), Bill Danfield (Northern Research Station), and Dan Golub (USDA Forest 

Service Washington office).

Metric Equivalents

When you know: Multiply by: To find: 

Feet (ft) 0.305 Meters

Miles (mi) 1.609 Kilometers

Acres (ac) .405 Hectares

Gallons (gal) 3.78 Liters

Tons (ton) .907 Tonnes or megagrams

Kilowatt-hour (kW-hr) 3.6 Megajoules

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