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Technical Report

NREL/TP-6A20-71990

November 2018

The Industry Energy Tool (IET): 

Documentation

Colin McMillan and Vinayak Narwade

National Renewable Energy Laboratory 
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Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

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Technical Report

NREL/TP-6A20-71990

November 2018

The Industry Energy Tool (IET): 

Documentation

Colin McMillan and Vinayak Narwade

National Renewable Energy Laboratory 

Suggested Citation

McMillan, Colin and Vinayak Narwade. 2018. The Industry Energy Tool (IET): 

Documentation. Golden, CO: National Renewable Energy Laboratory. 

NREL/TP-6A20-71990. https://www.nrel.gov/docs/fy19osti/71990.pdf.
NOTICE

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Office of Strategic 

Programs. The views expressed herein do not necessarily represent the views of the DOE or the U.S. Government.

This report is available at no cost from the National Renewable 

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

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov.

Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, 

NREL 46526.

NREL prints on paper that contains recycled content.
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Acknowledgments

The authors thank multiple reviewers for their helpful comments and technical review of this 

report, including Sam Baldwin, Steve Capanna, Paul Donohoo-Vallett, Kara Podkaminer, Ookie 

Ma, Paul Spitsen, and Brian Walker of the U.S. Department of Energy (DOE); Bob Adler and 

Tom Lorenz of the U.S. Energy Information Administration; Bill Morrow of the Lawrence 

Berkeley National Laboratory; and Elaine Hale, Trieu Mai, Robert Margolis, and Gian Porro of 

the National Renewable Energy Laboratory. We also thank Mike Meshek for editorial support. 

Any errors or omissions are the sole responsibility of the authors.
ii

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List of Acronyms

AEO Annual Energy Outlook

CBP County Business Patterns

EIA U.S. Energy Information Administration

eGRID Emissions and Generation Resource Integrated Database

EPA U.S. Environmental Protection Agency

FIPS Federal Information Processing Standards

GHG greenhouse gas

GHGRP Greenhouse Gas Reporting Program

IET Industry Energy Tool

IO input-output

IPF iterative proportional fitting

LPG-NGL Liquid petroleum gas-natural gas liquids

MECS Manufacturing Energy Consumption Survey

MMTCO2e million metric tons carbon dioxide equivalent

NAICS North American Industrial Classification System

NEMS National Energy Modeling System

NREL National Renewable Energy Laboratory

TBtu trillion British thermal units

TPC technology possibility curve

UEC unit energy consumption

USDA U.S. Department of Agriculture
iii

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Executive Summary

Studies of industrial energy use typically mention the sector’s heterogeneity and complexity as 

modeling barriers. One of the more significant impediments to analyzing and building models of 

U.S. industrial energy use is the lack of current disaggregated data. The Industry Energy Tool 

(IET) and its foundational data address some of these barriers

The IET has been developed as an open-source, transparent, and flexible tool for exploring 

industrial sector energy and emissions scenarios. The IET takes a simplified approach to 

developing scenario projections of energy and emissions by allowing users to modify prepopulated inputs for achieved energy efficiency, rate of equipment stock turnover, and future 

penetration of renewable electricity. In addition to its transparency and flexibility, the strengths 

of the IET lie in its geographically and operationally disaggregated data set; estimates of coal use 

shown in Figure ES-1 provide an example of this geographic disaggregation. The IET’s 

foundational data are built on energy data first calculated at the facility and end-use levels for 

many of the largest energy-using industries. These data are then aggregated to the county and 

end-use level with other publicly available data for use in scenarios estimation.

Figure ES-1. Estimated industrial coal use in 2014

White shading represents an estimated value of zero.

In this report, we detail two contributions to the current state of industry energy analysis: an 

industry energy data foundation that estimates detailed county-level industrial energy use in 2014 

and the IET itself. The data foundation is built on facility-level calculations of energy use for the 
iv

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largest energy-using industries (vis-a-vis the largest greenhouse gas-emitting industries) and 

provides estimates at the four- to six-digit North American Industrial Classification System 

(NAICS) code level. We have further improved the operational characterization of industrial 

energy use by identifying temperature requirements for boiler and process heating energy use 

by industry.

The IET is a streamlined, transparent, open-source calculator tool for developing forwardlooking scenarios of U.S. industrial energy use at the county, state, and national level. The tool 

examines industry energy use and emissions to 2050 using four components: stock turnover, 

energy efficiency, fuel switching, and emissions.

1 Projections are based on user-defined inputs 

and not economic optimization or other similar approaches.

In this report, we also demonstrate the functionality of the IET by presenting baseline cases (as 

shown in Figure ES-2) along with case studies that explore the tool’s representation of energy 

efficiency and fuel switching. These case studies are meant to demonstrate bounds of the tool 

and do not represent realistic projections of efficiency or fuel switching. Results of these case 

studies are presented at the national, county, and end-use levels. By benchmarking these results

with the National Energy Modeling System, we find that the IET’s current baseline assumptions 

calculate larger reductions in energy use and emissions than projections from the Annual Energy 

Outlook (AEO) 2017; the combination of the IET’s current stock turnover and energy efficiency

assumptions is the likely source of these differences. 

Figure ES-2. IET projected absolute and relative energy use by fuel type for baseline assumptions

 

1 Greenhouse gas emissions are used as an input (i.e., 2014 energy use calculated from reported greenhouse 

gas emissions) and are an output of the IET.
v

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Based on the open-source design of both the tool and its data foundation, the IET is intended to 

be revised and improved over time by a community of users. We have identified several priority 

areas for improvement, including revisiting the baseline efficiency and stock turnover 

assumptions, creating a graphical user interface, and automating figure generation for scenario 

results, based on results of our initial set of case studies and reviewer comments.

Future improvements to the IET will build on the tool’s existing strengths as a streamlined, 

transparent, and flexible tool for industry energy analysis. Additionally, the IET’s foundational 

energy use data enables scenario projections at the county and specific industry level. This 

improved level of data resolution combined with the IET’s other features offers new 

opportunities for analysts and policymakers to understand energy use in the U.S. 

industrial sector. 
vi

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Table of Contents

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

2 Foundational Data ................................................................................................................................ 3

2.1 Manufacturing ............................................................................................................................... 8

2.1.1 Facility-Level Combustion Energy and Net Electricity Calculations............................ 10

2.1.2 Regional-Average Energy Intensity Approach .............................................................. 10

2.1.3 Combining Calculated Manufacturing Energy Use ....................................................... 12

2.2 Agriculture .................................................................................................................................. 12

2.3 Construction ................................................................................................................................ 13

2.4 Mining......................................................................................................................................... 15

2.5 End-Use and Temperature Disaggregation ................................................................................. 16

2.6 County-Level Summary and Comparisons with Publicly Available Industry Energy Data ....... 19

2.6.1 Summary of County-Level Energy Estimates................................................................ 19

2.6.2 Comparisons with Published Industry Energy Data....................................................... 23

3 IET Components ................................................................................................................................. 27

3.1 Stock Turnover............................................................................................................................ 27

3.2 Energy Efficiency........................................................................................................................ 28

3.3 Fuel Switching............................................................................................................................. 29

3.4 Emissions .................................................................................................................................... 29

4 Case Studies ....................................................................................................................................... 31

4.1 Fuel Switching............................................................................................................................. 34

4.2 Energy Efficiency........................................................................................................................ 36

5 Data Improvements and Tool Expansion......................................................................................... 39

6 Conclusions ........................................................................................................................................ 40

References................................................................................................................................................. 41

Appendix A. Dynamic Hybrid Input-Output Module .............................................................................. 46

A.1 Hybrid IO Model......................................................................................................................... 46

A.2 RAS Algorithm ........................................................................................................................... 47

Appendix B. Comparison of IET Foundational Data and EIA State Energy Data System (SEDS).... 50

Appendix C. Energy Efficiency Assumptions........................................................................................ 53
vii

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List of Figures

Figure ES-1. Estimated industrial coal use in 2014 .....................................................................................iii

Figure ES-2. IET projected absolute and relative energy use by fuel type for baseline assumptions ......... iv

Figure 1. Overview of county-level manufacturing energy use estimation .................................................. 9

Figure 2. Overview of county-level agricultural energy calculation .......................................................... 13

Figure 3. Overview of county-level construction energy calculation......................................................... 14

Figure 4. Overview of county-level mining energy calculation ................................................................. 16

Figure 5. Associated temperature range for 2014 county-level conventional boiler use and process

heating energy use.................................................................................................................. 18

Figure 6. Estimated industrial coal use in 2014 (IET) ................................................................................ 19

Figure 7. Estimated industrial natural gas use in 2014 (IET) ..................................................................... 20

Figure 8. Estimated net electricity use in 2014 (IET)................................................................................. 21

Figure 9. Estimated total energy use for iron and steel mills and ferroalloy manufacturing 

(NAICS 331110) in 2014 (IET)............................................................................................. 22

Figure 10. Estimated industrial energy use per industry employment (IET).............................................. 23

Figure 11. National industry historicala total greenhouse gas emissions and projections by scenario ....... 32

Figure 12. Comparison of (a) absolute and (b) relative national industry fuel energy projections 

from AEO 2017 and IET Baseline Stock scenario................................................................. 33

Figure 13. IET national projected coal, natural gas, and electricity use in baseline and fuel switching 

scenarios................................................................................................................................. 34

Figure 14. IET electricity generation emission factor by census region based on AEO 2017

projections.............................................................................................................................. 35

Figure 15. IET fuel switching scenario: Relative emissions savings by county in 2050............................ 36

Figure 16. IET baseline and energy efficiency scenario projected emissions by end use .......................... 37

Figure 17. IET energy efficiency scenario emissions savings in 2050 relative to baseline scenario.......... 38

Figure 18. Relative difference between state-aggregated IET foundational data and SEDS, by fuel......... 50

List of Tables

Table 1. Sources of IET Foundational Data and Historical U.S. Industrial Energy Data............................. 5

Table 2. Summary of 2014 MECS Coverage of Manufacturing Energy Use as Fuel .................................. 7

Table 3. Electricity End Uses for Minnesota Dairy Industries in 2015 ...................................................... 17

Table 4. Mapping of Mining Operational End Use to MECS End Use...................................................... 17

Table 5. Comparison of 2014 Industrial Energy Data Estimated by EIA and the IET Data Foundation ... 24

Table 6. Difference between Industry Energy Use in 2014 Reported by EIA State Energy Data 

System (SEDS) (EIA 2017g) and State-Level Aggregation of Data Foundation .................. 25

Table 7. Summary of Smallest and Largest Relative Differences between Data Foundation and

MECS 2014 (%)..................................................................................................................... 26

Table 8. Summary of Energy Efficiency Potentials from Industry Bandwidth Studies and TPCs............. 29

Table 9. Comparison of State-Level Aggregated IET Foundational Data and SEDS ................................ 51

Table C-1. Bandwidth Efficiency Data Assumptions by NAICS Code (Assumed Reduction in 2050 

Energy Use Relative to 2014)................................................................................................ 53
1

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1 Introduction

The U.S. industrial sector is the largest energy end-use sector, and it accounts for about one-third 

of the country’s total energy use, which has experienced only small changes in the past 15 years 

(EIA 2018a). Of the industrial subsectors—agriculture, construction, manufacturing, and 

mining—manufacturing is the most significant, accounting for over 80% of the sector total 

(EIA 2018b). 

The goal of the Industry Energy Tool (IET) is to function as a streamlined, transparent, opensource calculator tool for U.S. industrial energy use for energy analysts and policymakers. Based 

on the open-source design of both the tool and its data foundation, the IET is intended to 

be revised and improved over time by users. This report introduces the IET to a potential user 

community. 

The IET is composed of five basic components, one of which is the foundational energy data 

used by the tool and four of which are associated with operating the tool: stock turnover, energy 

efficiency, fuel switching, and emissions. A sixth component—a hybrid input-output (IO) 

model—has been developed for estimating the energy impacts of material efficiency,2 but it is 

not currently integrated with the tool. We discuss this component in Appendix A. We first briefly 

summarize the foundational data and IET tool components below and then provide detailed 

descriptions later in the report.

1. Foundational Data: a county-level data set for 2014 derived from several sources of 

publicly available data provides energy use by industry North American Industrial 

Classification System (NAICS)

3 code, fuel type (i.e., coal, coke and breeze, natural gas, 

liquid petroleum gases and natural gas liquids [LPG NGL], diesel, residual fuel oil, 

other), end use (e.g., conventional boiler use, cogeneration, process heating, machine 

drive), and binned temperature requirement (where appropriate). The foundational data

do not include feedstock energy (i.e., nonfuel) use such as hydrocarbon gas liquids used 

in producing ethylene or petroleum used in producing asphalt. 

2. IET Components

a. Stock Turnover: Annual Energy Outlook (AEO) 2017 (EIA 2017a) value of 

shipments projections are used to project energy use to 2050 and as a proxy for 

industrial capital stock. New stock is added each year to replace existing stock 

that is retired and to meet any increase in value of shipments. Existing stock is 

assumed to retire linearly with a default lifetime of 20 years; new stock (post2014 stock) is assumed to retire linearly with a default lifetime of 30 years. 

 

2 See McMillan (2018) for a proposed analysis framework based on a dynamic hybrid input-output model. 3 NAICS is a hierarchical system for categorizing the general type of economic activity of businesses. For more 

information, see https://www.census.gov/eos/www/naics.
2

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b. Energy Efficiency: the unit energy consumption (UEC or energy used per dollar 

value of shipments) of new and existing stock improves at constant annual rates 

based on the technical possibility curves (TPCs) used in AEO 2014 (EIA 2014). 

Additional efficiency improvement is implemented through a parameterization of 

U.S. Department of Energy (DOE) “bandwidth” studies.

4

c. Fuel Switching: Fuel use in new stock is substituted for an alternate fuel at a 

constant annual rate defined by the user. The current assumption is switching

occurs at 1:1 ratio and does not account for differences in energy efficiency.5

Additionally, this feature is currently limited to electrification. 

d. Emissions: GHG emissions are calculated for projected energy use with U.S. 

Environmental Protection Agency (EPA) emission factors. Regional electricity 

emission factors are based on annual AEO 2017 Reference Case grid mix 

projections, with the option for user-defined changes to renewable electricity 

penetration.

 

4 See “Energy Analysis, Data and Reports,” DOE, https://www.energy.gov/eere/amo/energy-analysis-data-andreports. 5 This current simplification does not capture the potentially greater site energy efficiency of many electric 

technologies relative to combustion technologies or any potential interactions with assumed IET energy efficiency 

potential. 
3

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2 Foundational Data

Studies of industrial energy use typically mention the sector’s heterogeneity and complexity 

(Worrell, Ramesohl, and Boyd 2004; Fleiter, Worrell, and Eichhammer 2011; Saygin et al. 2012; 

Fais, Sabio, and Strachan 2016). One of the most significant impediments to analyzing and 

building models of U.S. industrial energy use is the lack of current disaggregated data that reflect 

the sector’s heterogeneity and complexity. Very little about the state of industrial energy data 

has fundamentally changed in the 30 years since the Energy Modeling Forum (EMF) 8 

recommended “serious consideration be given” to six areas of energy-related data collection 

(EMF 1987).

6 These recommendations are worth repeating here:

1) Create an establishment-level database on energy consumption and expenditures for the 

manufacturing sector. The Manufacturing Energy Consumption Survey (MECS) should 

be expanded to allow data to be published at the 4-digit Standard Industrial Classification

[SIC, replaced by NAICS] level by state and Standard Metropolitan Statistical Area. 

2) The MECS should be extended to include data on costs, operating characteristics, and the 

utilization of other energy-related technologies.

3) A study should be undertaken on the appropriate industrial energy-related data to be 

collected by the public sector, with the remaining data being collected by the private 

sector.

4) Private groups that collect microlevel data on manufacturing production, energy 

consumption, etc. should be encouraged to provide public-use sets of their information 

for specified research projects.

5) Public-use files of government micro databases should be developed.

6) EIA and other agencies (e.g., the National Research Council, the National Academy of 

Engineering) should develop a source book on costs, operating characteristics, and 

standards to evaluate new energy-related technologies, and develop or expand existing 

surveys of energy consumption and technology data for the nonmanufacturing portion of 

the industrial sector. 

To our knowledge, little progress has been made toward these six recommendations. This is 

particularly true for those that call for greater disaggregation of energy and operational data or 

for the non-manufacturing industries—agriculture, mining, and construction—whose energy data 

sources were described by EMF 8 as “very sketchy” (EMF 1987, iii). For example, published 

MECS data files provide results at the census region level and not at the state or Metropolitan 

Statistical Area; no public-use files of industry energy use are available from the U.S. Census 

Bureau (U.S. Census Bureau 2018); and non-manufacturing industries are not included in U.S. 

Energy Information Administration (EIA) energy end-use surveys (EIA 2018c). However, since 

 

6 One significant, additional category of data improvement not highlighted by EMF 8 is time of energy use, 

including industry operating schedules.
4

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EMF 8 was convened, MECS has introduced survey questions related to fuel switching, 

cogeneration, and energy-saving technologies (EIA 2009).

In the spirit of the data recommendations made by EMF 8, we have made a significant step in 

improving the state of industrial energy data by developing a county-level energy data set whose 

basis is facility-level energy data derived from greenhouse gas (GHG) emissions reported to the 

EPA.

7 We consider the IET foundational data to be a new and continuously-improving bottomup source of industrial energy data.

Table 1 summarizes the publicly available sources of historical data for and related to U.S. 

industrial energy use. The table also indicates whether these data sources are used to construct 

the IET’s foundational data. Although these data sources identify how much energy was used by 

an industry in each location, only MECS provides information on how the energy was used (i.e., 

energy end uses). MECS identifies 13 end-use categories; we are not aware of sources of end-use 

data for agriculture, construction, and mining that are similarly consistent, detailed, and 

complete. 

 

7 For description of the methodology used to construct this data set, see McMillan et al. 2016. For the Python 

code implementation of this methodology, see “Industrial Heat Demand Analysis,” Jupyter notebook at 

https://github.com/NREL/Industrial-Heat-Demand-Analysis. 
5

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Table 1. Sources of IET Foundational Data and Historical U.S. Industrial Energy Data

Data Source Agency Units Frequency Extent of Industry 

Disaggregation

Extent of Geographic 

Disaggregation

Used by 

the IET

Monthly Energy 

Reviewa

EIA Energy Monthly Total industry National No

State Energy 

Data System 

(SEDS)b

EIA Energy Annual Total industry State No

MECSc EIA Energy Quadrennial Manufacturing; up 

to six-digit NAICS 

code and by 

end use

Census Region Yes

Form EIA-923d EIA Energy (electricity 

only)

Annual Up to six-digit 

NAICS

Facility Yes

Census of 

Agriculturee

U.S. 

Department of 

Agriculture

(USDA)

Expenditures Quinquennial Agriculture; up 

to four-digit 

NAICS code

County Yes

Agriculture 

Surveyf

USDA Expenditures Annual Total agriculture State Yes

Economic 

Censusg

Census 

Bureau

Expenditures, 

some energy units 

for mining

Quinquennial Construction, 

manufacturing, and 

mining; up to sixdigit NAICS

State Yes

Annual Survey of 

Manufacturersh

Census 

Bureau

Expenditures; 

energy units 

for generated 

electricity

Annual Manufacturing; up 

to six-digit NAICS

National for sixdigit NAICs; state 

for two-digit NAICS

No
6

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Data Source Agency Units Frequency Extent of Industry 

Disaggregation

Extent of Geographic 

Disaggregation

Used by 

the IET

CBPi Census 

Bureau

Establishment 

counts

Annual Up to six-digit 

NAICS

County Yes

GHGRPj EPA Emissions, energy 

in some cases

Annual Up to six-digit 

NAICS

Facility Yes

a “Total Energy: Monthly Energy Review,” EIA, https://www.eia.gov/totalenergy/data/monthly/index.php

b “U.S. States Profile: State Profiles and Energy Estimates: About SEDS,” EIA, https://www.eia.gov/state/seds/

c “Manufacturing Energy Consumption Survey (MECS),” EIA, https://www.eia.gov/consumption/manufacturing/index.php

d “Electricity: Form EIA-923 Detailed Data with Previous Form Data (EIA-906/920),” EIA, https://www.eia.gov/electricity/data/eia923/

e “USDA Census of Agriculture,” USDA, https://agcensus.usda.gov/

f “USDA National Agricultural Statistics Service: Quick Stats,” USDA, https://quickstats.nass.usda.gov/

g “Economic Census,” U.S. Census Bureau, https://www.census.gov/programs-surveys/economic-census.html

h “Annual Survey of Manufactures (ASM),” U.S. Census Bureau, https://www.census.gov/programs-surveys/asm.html

i “County Business Patterns (CBP),” U.S. Census Bureau, https://www.census.gov/programs-surveys/cbp.html

j “Greenhouse Gas Customized Search,” EPA, https://www.epa.gov/enviro/greenhouse-gas-customized-search
7

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Today—as was the case when EMF 8 convened—the most well-developed industrial energy data 

exist for manufacturing industries, which are responsible for about 80% of U.S. industry energy 

use. MECS is the main source of manufacturing energy data, which EIA publishes every four 

years.

8 The survey is conducted by EIA, which contracts the U.S. Census Bureau to collect data 

from about 15,000 manufacturing establishments (EIA 2002). Microdata are available, but the 

U.S. Census Bureau limits researcher access and use (U.S. Bureau of the Census 2009). MECS 

also withholds data and limits its reporting resolution to avoid identifying specific companies. 

To get a sense of the constraints for characterizing manufacturing energy use, we break down 

MECS coverage into dimensions of operational characteristics (represented by NAICS code, fuel 

type disaggregation, and end-use disaggregation) and geography, as summarized in Table 2. Of 

the 14,903 trillion British thermal units (TBtu) of fuel energy reported by U.S. manufacturing 

establishments in 2014, about 75% is represented by six-digit NAICS codes, fuel type, and 

census region. We note that the portion of total energy use for each census region that is 

disaggregated by NAICS code and fuel type varies, with a range from 63% in the Northeast to 

82% in the South. About 41% of manufacturing energy use is covered at the six-digit NAICS 

code and fuel type at the national scale when disaggregated by end-use category; MECS 

currently does not report energy by end-use category and by NAICS code at the census region, 

which would be useful for revealing regional differences in how energy is used within industries.

Table 2. Summary of 2014 MECS Coverage of Manufacturing Energy Use as Fuel

Operational/Geographic

Coverage

National Census Region

six-digit NAICS 76% 75%a

six-digit NAICS, fuel type 75% 75%a

six-digit NAICS, fuel type, end use 41% 0%

a The coverage of regional total energy use varies within each census region, from 63% in the Northeast,

66% in the Midwest, 72% in the West, to 82% in the South.

The IET's foundational data, as well as the calculations behind their derivation, are publicly 

available.9 We invite energy analysts, computer scientists, and other researchers and practitioners 

to push the state of industrial energy data forward and contribute improvements to our initial 

methods. The following sections detail the assumptions and calculations used to construct

the IET’s foundational data and we compare the results to existing EIA sources of industrial 

energy data to benchmark its representation of industrial energy use.

 

8 MECS was conducted every three years from 1985 through 1994. Since then, it has been conducted every 

four years. 

9 Data are available from the NREL Data Catalog (https://doi.org/10.7799/1481899). The calculations 

used in their construction are available at https://github.com/NREL/Industry-Energy-Tool.
8

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2.1 Manufacturing 

The method used by the IET to calculate county-level energy use for manufacturing industries

is based on (1) county counts of manufacturing establishments by industry and employment size

from Census County Business Patterns (CBP) data (U.S. Census Bureau 2016) and (2) either 

facility-level energy data or regional-average energy data. Facility-level estimates of energy 

use are calculated for large energy-users using EPA Greenhouse Gas Reporting Program 

(GHGRP) emissions data (EPA 2017) and electricity use reported by industry generators above 

1 MW from Form EIA-923 (EIA 2017b).

10 Regional-average energy intensity values (energy per 

establishment) are estimated by NAICS code, fuel type, and employment size class using data 

derived from 2010 MECS (EIA 2013)11 and iterative proportional fitting, described in Section 

2.1.2 below. 

In general, the method first identifies the count of GHGRP- and Form EIA-923-reporting

facilities by county and then subtracts their number from the CBP establishment counts. Energy 

data for the GHGRP reporters are estimated using reported emissions data and Form EIA-923 

electricity data. Energy use for the remaining establishments is calculated based on NAICS 

code, fuel type, and employment size class using the derived regional-average energy intensity 

values.12 These aspects of the approach are summarized in Figure 1 and described in detail 

below.

 

10 Not all GHGRP reporters have electricity generation or cogeneration units with more 1 MW of capacity. 

Net electricity use for the GHGRP-reporting facilities that do not report with Form EIA-923 is estimated using 

regional-average net electricity intensity. 11 2014 MECS data were not published when the IET’s foundational data were developed. Updating the 

calculations to the most recent MECS has been identified for future work.

12 The employment size classes of GHGRP-reporting facilities are not provided in GHGRP data. We assume 

the largest employment size class for GHGRP-reporting facilities when correcting CBP establishment counts 

by county. However, we take a conservative approach and do not adjust CBP establishment counts when 

deriving regional-average energy intensity values. This may lead to overestimating energy intensity and 

the resulting county energy values. Future work could examine the sensitivity.
9

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Figure 1. Overview of county-level manufacturing energy use estimation

Regional-average manufacturing 

energy intensity (energy 

per establishment)

2010 CBP 

establishment counts

2010 MECS

County-level manufacturing 

energy use (TBtu)

Iterative proportional fitting 

(IPF)

Combustion energy and net electricity 

use for GHGRP non-reporters

2014 Form EIA923 facility counts

GHGRP- and Form EIA-923-

adjusted 2014 CBP establishment 

counts

2014 GHGRP 

facility counts 

2014 CBP establishment counts

2014 GHGRPreported emissions

GHG emission factors

Combustion energy ese 

for GHGRP reporters

Form EIA-923 

electricity data

Derived regional energy use by fuel 

type, NAICS code, and employment size 

Facility-Level Approach (GHGRP Reporters)

Average Intensity Approach (GHGRP Non-Reporters)

Combustion energy and net 

electricity use for GHGRP

reporters
10

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2.1.1 Facility-Level Combustion Energy and Net Electricity Calculations

Facilities with annual GHG emissions that exceed 25,000 metric tons carbon dioxide-equivalent 

(MMTCO2e) are required to report emissions under the GHGRP (Mandatory Greenhouse Gas 

Reporting 2009). GHG emissions reported under Subpart C General Stationary Combustion 

Sources and Subpart D Electricity Generation13 are used with average GHG emission factors 

by fuel type (EPA 2018)14 to back-calculate facility combustion energy use for relevant 

manufacturing, mining, and agriculture facilities.15

A crosswalk table of electricity data is created based on facility name and location for facilities 

reporting to the GHGRP and Form EIA-923. Note that Form EIA-923 provides electricity use 

data only for industrial facilities with onsite generation—cogenerating or non-cogenerating—

exceeding 1 MW. Net electricity use for these facilities is calculated as the sum of incoming 

electricity and generation from noncombustible renewable sources, less the sum of retail sales, 

sales for resale (i.e., wholesale sales), tolling agreements, and all other outgoing electricity. 

The net electricity use for GHGRP-reporting facilities that are not listed on Form EIA-923 

is calculated using regional-average net electricity intensities, which are described in the 

next section. 

2.1.2 Regional-Average Energy Intensity Approach

Regional-average energy intensities per manufacturing establishment are used to calculate the 

combustion energy use of establishments that do not report under the GHGRP. Likewise, 

regional-average net electricity use intensities are calculated to estimate electricity use of 

establishments that do not report to EIA Form-923. This section describes the process of first 

deriving average energy values by census region, fuel type, NAICS code, and employment size 

class using IPF and 2010 MECS data. CBP establishment counts by NAICS code and 

employment size class are then used to calculated energy intensity values. Finally, these perestablishment energy intensities are multiplied by adjusted CBP establishment counts to estimate 

the energy use of facilities that do not report under the GHGRP or Form EIA-923. This final step 

involves portioning the number of CBP-reported manufacturing establishments into GHGRPreporting and GHGRP non-reporting facilities within a given county to avoid double-counting 

energy use.16

 

13 Utilities and other non-industrial reporters are excluded from the energy calculations based on Subpart D 

emissions.

14 Using average emission factors and not fuel-specific carbon content data does introduce uncertainty into our 

facility-level energy calculations. We have not yet quantified this uncertainty. 15 Note that energy calculations based on GHGRP data do not include electricity use. For details, refer to the method 

description in McMillan et al. (2016). This method does undercount energy use by cement plants that use continuous 

emissions monitoring systems (CEMS). Typically, the calculation method would calculate energy use by facilities 

with CEMS, but as of September 2017 the GHGRP reporting tool continues to record emissions under the incorrect 

subpart (email communication with Sydnie Lieb, EPA, December 7, 2016).

16 In 2014, 307 manufacturing facilities reported to the GHGRP under Subpart C General Stationary Combustion 

Sources that are not matched to a CBP county and NAICS code. We assumed that the GHGRP-reported NAICS 

codes for these facilities are correct and that they are not captured in the CBP data. This assumption may result in 

double-counting of energy use in counties where the GHGRP-reported NAICS codes are incorrect. 
11

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

We apply iterative proportional fitting (IPF)17 to 2010 MECS data (Table 3.2 and Table 3.3) 

to derive energy use by census region, fuel type,

18 NAICS code,19 and employment size class. 

Facility-average combustion energy and electricity use intensities are then calculated by dividing 

the IPF energy results by 2010 CBP establishment counts. Note again this method does not 

adjust MECS energy data for combustion energy use calculated for GHGRP reporters.

Regional-average energy intensity values are applied to adjusted 2014 CBP establishment counts 

by NAICS code and employment size class. To avoid double-counting energy use estimated with 

the facility-level approach, the CBP establishment counts are partitioned into groups based on 

whether establishments report under the GHGRP or EIA Form-923. This partitioning involves 

matching GHGRP facilities that reported in 2014 to 2014 CBP establishment counts based on 

their reported county and state Federal Information Processing Standards (FIPS) codes and 

primary and secondary NAICS code. A GHGRP-reporting facility that is matched to a county 

and NAICS code is assumed to be represented by the largest employment size class reported by 

the CBP. The adjusted 2014 CBP establishment counts are calculated by subtracting the number 

of GHGRP-reporting facilities by county and NAICS code by descending employment size class.

A separate partitioning approach is used to calculate manufacturing electricity use. The 

procedure follows the general approach described above except that it uses county-level 

establishment counts of the subset of GHGRP-reporting facilities that also report electricity 

data with Form EIA-923.

County-level combustion energy use for non-GHGRP reporters is then calculated by multiplying 

the combustion fuel intensity by the adjusted 2014 CBP establishment count. County-level 

electricity use for facilities that are not GHGRP reporters and do not report electricity data 

with Form EIA-923 is calculated by multiplying the facility-average net electricity20 intensity 

and 2014 CBP establishment counts that have been adjusted for GHGRP and Form EIA-923-

reporters.

 

17 IPF is an algorithm for updating a table of values based on specific constraints; in the two-dimensional case, 

these constraints are row and column sums. We use IPF to estimate MECS data not reported publicly based on 

a combination of two public data tables.

18 Combustion energy use is calculated according to MECS fuel type: net electricity, natural gas, residual fuel oil, 

diesel, liquid petroleum gas-natural gas liquids (LPG-NGL), coal, coke and breeze, and “other.” 19 MECS does not report energy use for all six-digit manufacturing NAICS codes, but all CBP establishment counts 

are reported at the six-digit level. CBP data are aggregated to match the level of detail provided by MECS. As a 

result, energy intensities may be calculated at the three-digit NAICS code level and applied to establishment counts 

at the six-digit NAICS code level. 20 EIA defines net electricity as the sum of purchases, transfers in, and generation from noncombustible renewable 

resources, minus quantities sold and transferred out. Electricity inputs from onsite cogeneration and generation from 

combustible fuels are not included because their energy value is already captured by the fuel use data. Conversely, 

EIA defines net demand for electricity as the sum of purchases, transfers in, and total onsite electricity generation, 

minus sales and transfers offsite. Net demand for electricity is the total amount of electricity used by establishments 

(EIA 2017c).
12

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

2.1.3 Combining Calculated Manufacturing Energy Use 

Combustion energy use calculated using the facility-level approach is aggregated by MECS fuel 

type, and state and county FIPS and combined with combustion energy and net electricity use 

calculated using the regional-average approach by state and county FIPS codes and six-digit 

NAICS code. These results are then combined with net electricity use obtained from Form EIA923 by state and county FIPS codes and six-digit NAICS code.

2.2 Agriculture

The overall approach to calculate county-level agriculture energy use is separated into liquid 

fuels (i.e., diesel, LPG, gasoline, and “other”) and electricity. The calculation process is 

summarized in Figure 2. Liquid fuel use is estimated by first calculating the liquid fuel fraction 

of total fuel and lubricants expenses by region from 2014 USDA agricultural survey data (USDA 

National Agricultural Statistics Service 2018). These fractions are then multiplied by the statelevel 2012 total fuel and lubricants expenses by four- to six-digit NAICS code from the Census 

of Agriculture (United States Department of Agriculture 2017) and adjusted based on the ratio of 

2014:2012 fuel expenses by region. 

State-level electricity expenses by four- and six-digit NAICS code are calculated by multiplying 

2014 state-level electricity expenses (USDA Economic Research Service 2018) by the state-level 

fraction of 2012 utility expenditures by four- to six-digit NAICS code from the 2012 Census 

of Agriculture.

Liquid fuel and electricity expenditures are converted into energy units using 2014 EIA stateand regional-level energy prices (EIA 2017d, 2017e).

21 State energy totals are then apportioned 

to the county-level using farm counts by four- to six-digit NAICS code from the 2012 Census 

of Agriculture. 

 

21 These final calculations also involve using energy density unit conversion (e.g., diesel prices are provided in

$/gallon and the energy density of diesel is assumed to be 5.774 million Btu/gallon).
13

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

Figure 2. Overview of county-level agricultural energy calculation

2.3 Construction

The overall calculation approach—summarized in Figure 3—is to first estimate state-level 

construction energy use derived from 2012 Economic Census data and to then allocate to the 

county-level using CBP establishment counts. State-level calculations begin by using IPF to 

derive “Cost of materials, parts, supplies, electricity, and fuels ($1,000)” by three-digit NAICS 

code, employment size class, and state from the 2012 Economic Census. The fuel cost fraction 

of “Cost of materials, parts, supplies, electricity, and fuels ($1,000)”22 is then calculated for each 

 

22 Data for “Cost of gasoline and diesel fuel ($1,000)” is assumed to represent diesel fuel only, based on zero 

motor gasoline use, as reported in AEO 2017 projections for construction sector (EIA 2017f).

2014 diesel, LPG, gasoline, 

and other fuels fraction of 

fuels, including lubricants, 

expense by region (%)

2012 county farm 

counts by state and 

four- to six-digit NAICS 

USDA 2012 Census 

of Agriculture

2012 fuels, including 

lubricants, expense by 

state and four- to six-digit 

NAICS code ($)

2014:2012 fuels expense 

multiplier by region

2014 EIA fuel and 

electricity price ($/Btu)

USDA 2014 

agricultural survey

USDA 2012 

agricultural survey

2014 production 

expense by state, 

electricity ($)

2012 fraction of utilities 

expense by state and 

four- to six-digit 

NAICS code (%)

USDA 2014 

production expenses

2014 electricity expense 

by state and four- to sixdigit NAICS code ($) 2014 diesel, LPG, gasoline, 

and other fuels expense by 

state and four- to six-digit 

NAICS code ($)

2014 county agricultural energy 

use by four- to six-digit 

NAICS code (TBtu)
14

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

fuel type by state and three-digit NAICS. The energy associated with asphalt use is not captured 

in the calculation. “Other fuels and lubricants” are assumed to be LPG.

The fuel cost fraction is multiplied by the results of the IPF to calculate fuel type expenses by 

three-digit NAICS code, employment size class, and state. These fuel type expenses are then 

multiplied by EIA 2012 state- or Petroleum Administration for Defense District-level fuel price 

per million Btu. The compound annual growth rate from 2012 to 2014 of construction GDP by 

state is then used to calculate energy values in 2014. Energy values are then allocated to the 

county-level using CBP establishment counts by three-digit NAICS code and employment size 

class.

Figure 3. Overview of county-level construction energy calculation

2012 Economic 

Census

Cost of materials, parts, supplies, 

electricity, and fuels by state and 

three-digit NAICS code ($1,000)

Cost of materials, parts, supplies, electricity, 

and fuels by state, two-digit NAICS code, 

and employment size class ($1,000)

IPF

Cost of materials, parts, supplies, electricity, 

and fuels by state, three-digit NAICS code, 

and employment size class ($1,000)

2014 CBP establishment counts 

by three-digit NAICS and 

employment size class

Costs by fuel type, state 

and three-digit NAICS code 

($1,000)

Total fuel costs by state and 

three-digit NAICS code

($1,000)

Fuel cost fraction by 

state and three-digit 

NAICS code (%)

2012 EIA fuel costs by 

state or region ($/Btu)

2012–2014 Bureau of Economic 

Analysis construction sector GDP 

growth rate by state (%)

2014 county construction energy use 

by three-digit NAICS code (TBtu)

2012 energy use by fuel type, state, 

three-digit NAICS code, and 

employment size class (TBtu)
15

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

2.4 Mining

As with the manufacturing energy calculations, mining energy use is calculated using a facilitylevel approach and an average intensity approach. Facility-level detail for combustion fuels 

is provided by GHGRP data for large emitters; facility-level detail for net electricity use is 

provided by the EIA in Form EIA-923 for covered establishments. National-average energy 

intensities are calculated by fuel type and six-digit NAICS code from 2012 Economic Census 

data-adjusted CBP establishment counts by county and six-digit NAICS code. The two 

approaches are summarized in Figure 4 (page 16).

The 2012 Economic Census data provide detailed national-level delivered cost data by fuel type 

by six-digit NAICS code; use of certain fuels in physical quantities is also provided for several 

mining industries. Cost data that had been withheld were estimated based on total fuel costs, 

where appropriate. As a result, the fuel cost data by fuel type capture over 95% of the total 

delivered fuel costs for most of the mining industries. Fuel use in energy units is then calculated 

by multiplying fuel cost data by their 2012 national-average price and heat of combustion value. 

Data on electricity purchased for heat and power (in 1,000 kWh) is provided separately by the 

2012 Economic Census but is likewise available at the six-digit NAICS level for the United 

States. Fuel and electricity use in 2014 is calculated by scaling 2012 values using the 2012–2014 

change in physical production data from USGS mineral and commodity data (USGS 2016) by 

six-digit NAICS code.

National-level 2014 energy use data are allocated to the county-level using 2014 CBP 

establishment counts data by six-digit NAICS code. These establishment counts are adjusted 

by subtracting the number of GHGRP-reporting facilities in the mining sector, as the fuel use for 

these facilities is calculated separately from reported emissions. County-level fuel and electricity 

estimates by six-digit NAICS code are then calculated by multiplying the county fraction of total 

mining establishment count by the estimated national-level energy use value. These values are

then combined with the results of the GHGRP-based combustion fuel and EIA Form-923 

electricity calculations for mining industries using the method described in Section 2.1.
16

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

Figure 4. Overview of county-level mining energy calculation

2.5 End-Use and Temperature Disaggregation

The calculated county-level energy use by industry and fuel type described above are further 

disaggregated into end uses based on MECS categories.

23 End-use disaggregation is most 

complete for the manufacturing industries because of the end-use data available from MECS.24

Information on energy end uses for agriculture, construction, and mining was much more 

difficult to find. The data sources we did find in the literature were often many years old. As 

described below, we were unable to disaggregate construction industry end uses because of 

 

23 Conventional boiler use, CHP and/or cogeneration process, process heating, process cooling and refrigeration, 

machine drive, electro-chemical processes, other process use, facility HVAC, facility lighting, other facility support, 

onsite transportation, conventional electricity generation, and other nonprocess use. 24 Although MECS does provide end use data for all manufacturing NAICS codes, many industries are not 

characterized beyond the three-digit NAICS code. We assigned end uses to portions of uncharacterized end use data 

following the approach of Fox, Sutter, and Tester (2011).

2012 Economic Census

2012, 2014 USGS 

Mineral Commodity 

Production

Combustion Energy and Net 

Electricity Use for GHGRP 

Non-Reporters (see Figure 1)

2012, 2014 EIA 

Fuel Production

2012 national delivered cost of 

fuels by type and six-digit 

NAICS code ($)

2012 national quantity of 

electricity purchased for heat and 

power by six-digit NAICS 

code (MWh)

2014 EIA fuel and 

electricity price ($/Btu)

Facility-Level Approach (GHGRP Reporters)

2014:2012 

production ratios

2014 national fuel and 

electricity use by six-digit 

NAICS code (TBtu)

GHGRP- and Form EIA-923-

adjusted 2014 CBP establishment 

counts (see Figure 1)

2014 county mining energy use 

by six-digit NAICS code (TBtu)

Average Intensity Approach (GHGRP Non-Reporters)
17

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

a lack of appropriate end-use data. As a result, the construction industry is excluded from the IET

scenario projections.

We found several sources of end-use data for different agricultural industries, including statespecific detail (The Minnesota Project 2015; E. Brown and Elliott 2005; Ludington and Johnson 

2003). For example, major end uses of electricity in the dairy industry are shown in Table 3

Table 3. Electricity End Uses for Minnesota Dairy Industries in 2015

End Use Portion of Energy Use (%)

Water heating 22.0

Milk cooling 17.6

Ventilation 18.8

Vacuum pumps 14.6

Lighting 12.9

Miscellaneous 14.1

The Minnesota Project (2015)

For the mining sector we used end-use data from the DOE’s mining bandwidth study (DOE

2007). As with the sources of agricultural end-use data, it was necessary to map the reported

mining end uses to our MECS-designated categories. This mapping is shown in Table 4.

Table 4. Mapping of Mining Operational End Use to MECS End Use

Mining End Use MECS End Use Fuel Type (% of total)

Drilling Machine drive Electricity (50), diesel (50)

Blasting Other process use Other

Digging Machine drive Electricity (50), diesel (50)

Ventilation Other process use Electricity (100)

Dewatering Other process use Electricity (100)

Materials handling: electric equipment Machine Drive Electricity (100)

Materials handling: diesel equipment Machine Drive Diesel (100)

Beneficiation Other process use Electricity (50), diesel (50)

Ancillary Other non-process use Electricity (75), diesel (25)

For construction industries, our literature review did find sources that disaggregated energy use 

by the building and non-building sectors, but we were unable to use this information without 

additional detail on the types of fuels used (Stein et al. 1980; Hannon, Stein, Segal, and Serber 

1977; Hannon, Stein, Segal, Serber, et al. 1977). As a result, we did not disaggregate energy 

use in the construction industries.
18

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

Conventional boiler use, CHP and/or generation, and process heating end uses in manufacturing 

industries are further disaggregated by operating temperature range. We first match operating 

temperature data from Brown et al. (1997)—included in the NREL Data Catalog25—to the 

county-level data set by industry and end use. Not all industries appear in the reference data. As 

a result, about 70% and 97% of the 2014 county-level energy use for conventional boiler use and 

process heating, respectively, has associated temperature data. Temperatures are then aggregated 

into five bins: <100°C, 100–249°C, 250–399°C, 400–999°C, and >1,000°C. The portion of 

conventional boiler use and process heating energy use represented by each temperature band is 

summarized in Figure 5. A more detailed disaggregation of process temperature data—including 

industry and narrower temperature bins—is possible using the IET data. Over half of process 

heating energy use is associated with temperatures of 400°C and above. Conversely, nearly all 

conventional boiler energy end use is associated with temperatures below 400°C. 

Figure 5. Associated temperature range for 2014 county-level conventional boiler use 

and process heating energy use

 

25 See https://doi.org/10.7799/1481899.
19

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

2.6 County-Level Summary and Comparisons with Publicly Available 

Industry Energy Data 

The IET’s foundational data are available from the NREL Data Catalog26 and are either 

disaggregated by end use or are not. In this section, we provide a high-level overview of 

estimated county-level energy use by mapping individual fuels, a single industry, and energy 

intensity per industry employment. We also compare our estimates with published sources of 

industrial energy data at the national, state, industry, and fuel type levels.

2.6.1 Summary of County-Level Energy Estimates

We estimate Macon County, Illinois, is the largest industrial coal-using county in the United 

States. Wet corn milling facilities (NAICS 311221) in the county are responsible for nearly 90% 

of its coal use. Alumina refining and primary aluminum production (NAICS 331313) drives coal 

use in the second-largest county, Warrick County, Indiana. Estimated coal use for these counties 

and the rest of the United States is shown in Figure 6.

Figure 6. Estimated industrial coal use in 2014 (IET)

White shading represents an estimated value of zero.

 

26 See https://doi.org/10.7799/1481899.
20

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

We estimate that the largest natural gas use occurs in Harris County, Texas, as shown in 

Figure 7. The county, which has a large footprint of petrochemical manufacturing (NAICS 

325110), refining (NAICS 324110), and crude petroleum and natural gas extraction (NAICS 

211111), is responsible for about 3% of total industrial natural gas use in 2014. Other significant 

users of natural gas are North Slope Borough, Alaska, and Kern County, California, both of 

which have significant crude petroleum and natural gas extraction industries.

Figure 7. Estimated industrial natural gas use in 2014 (IET)

White shading represents an estimated value of zero.
21

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

Figure 8 shows that we also calculate that Harris County, Texas, has the largest net electricity 

use. Other counties with significant net electricity use include Los Angeles County, California,

and Cook County, Illinois. Unlike counties with significant coal and natural gas use, the large 

electricity-using counties are characterized more by a large overall manufacturing footprint 

rather than a single large industry.

Figure 8. Estimated net electricity use in 2014 (IET)
22

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

Figure 9 demonstrates the ability of the IET’s foundational data to characterize energy use by 

six-digit NAICS code. Here we show total energy use for iron and steel mills and ferroalloy 

manufacturing (NAICS 331110). Two counties in Indiana—Lake County and Porter County—

are home to 20% of the industry total energy use. In addition to depicting energy use, the figure 

shows the geographic distribution of this industry

Figure 9. Estimated total energy use for iron and steel mills and ferroalloy manufacturing 

(NAICS 331110) in 2014 (IET)
23

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

All previous county figures have shown absolute energy use. We have normalized total county 

energy use by county industry employment27 in Figure 10. North Slope Borough, Alaska stands 

out starkly as the largest industrial energy user on an employment-normalized basis due to its 

large petroleum refining and crude oil and natural gas extraction industries and relatively small 

industry employment.

Figure 10. Estimated industrial energy use per industry employment (IET)

White shading represents an estimated value of zero.

2.6.2 Comparisons with Published Industry Energy Data

The IET foundational data for 2014 represent an attempt to provide new estimates of bottom-up 

industry energy use at an improved level of geographic and operational detail. We have not 

attempted to constrain our calculations to existing energy estimates from EIA or other sources, 

although future versions of the IET foundational data may benefit from a validation procedure 

with SEDS data, for example. In this section of the report, we compare the IET foundational data 

to existing EIA sources of industrial energy data at various levels of aggregation (e.g., national, 

state, census region, NAICS code, and fuel type) to establish how closely the IET matches 

EIA estimates.

 

27 Employment figures are taken from CBP (U.S. Census Bureau 2016) and Agriculture Census (United States 

Department of Agriculture 2017) data. Note that industry employment data are not available for several 

counties.
24

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

Table 5 compares national aggregations of the IET foundational data with analogous EIA 

estimates (EIA 2016, 2017f). Although the IET estimates are within 0.5% and 1% of EIA 

estimates for manufacturing and total industry, large differences exist for the construction sector 

and for residual fuel oil, liquified petroleum gas-natural gas liquids (LPG-NGL). These are areas 

that warrant further examination to determine why such large differences occur. 

Table 5. Comparison of 2014 Industrial Energy Data Estimated by EIA and the IET Data Foundation

National 

Aggregation

EIA 

Estimate 

(TBtu)a

IET Data 

Foundation 

(TBtu)

Difference 

(TBtu)

Difference 

(%)

Sector

Agriculture 1,167 875 -292 -25

Construction 714 1,037 323 45

Mining 2,982 3,085 103 3

Manufacturingb 14,903 14,831 -72 -0.5

Fuel

LPG-NGL 432 200 -232 -54

Diesel 1,361 1,372 11 1

Residual fuel oil 35 147 112 324

Natural gas 7,031 8,803 1,772 25

Coal 871 1,109 238 27

Coke and breeze 562 38 -524 -93

Other 5,921 4,576 -1,345 -23

Net electricity 3,404 3,582 178 5

Total Industry 19,616 19,828 212 1

a AEO 2016 (EIA 2016) is the source of EIA data unless otherwise noted. b MECS 2014 (EIA 2017f)

Table 6 summarizes the relative difference between state-level industrial energy data reported 

by EIA SEDS28 and the state-level aggregation of the data foundation. Note that we have also 

aggregated IET fuel types to match SEDS fuel designations. On average, the IET estimates for 

total energy use are 10% above those of SEDS. Of course, this mean value hides the variation 

that exists by individual state and by fuel type. Across these categories shown in Table 6, 

Louisiana and Hawaii appear twice as minima or maxima, which likely reflects their unique 

energy use; we speculate that this is the result of Louisiana’s large footprint of chemical 

manufacturers and petroleum refineries and Hawaii’s island geography. 

 

28 IET values are compared against SEDS data series names CLOCB for coal, ESICB for electricity, and NGICB for 

natural gas. ARICB, FNICB, FOICB, FSICB, and LUICB were subtracted from PAICB to estimate non-feedstock 

petroleum products use. 
25

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

Table 6. Difference between Industry Energy Use in 2014 Reported by EIA State Energy Data 

System (SEDS) (EIA 2017g) and State-Level Aggregation of Data Foundation

Petroleum 

Products

Natural 

Gas

Coal Net 

Electricity

Total

Mean -30% 41% 237% 9% 10%

Median -45% 8% 35% 7% 9%

Variance 16% 214% 13,296% 16% 9%

Minimum -96% (LA) -44% (LA) -88% (ND) -86% (HI) -52% (ND)

Maximum 67% (MA) 955% (HI) 7,756% (AK) 124% (SD) 137% (VT)

Negative values indicate IET values are less than SEDS values.

The largest relative differences with SEDS are for coal use. SEDS reports zero industrial coal use 

for Connecticut, Delaware, the District of Columbia, New Hampshire, New Jersey, Rhode 

Island, and Vermont. The reported value for Alaska is 0.017 TBtu and its IET value is 1.335 

TBtu; this outlier has an outsized effect on the mean and variance shown in Table 6. The IET 

calculates non-zero industrial coal use for all these states and the District of Columbia based on 

regional-average coal intensities; only New Jersey includes facility-level data. The IET’s reliance 

on regional-average energy intensities likely explains most of the large variation with SEDS 

shown in Table 6. Additionally, basing the regional-average energy intensities on 2010 MECS, 

instead of 2014 MECS, and CBP establishment counts unadjusted for GHGRP reporters may 

also explain the differences with SEDS. 

However, estimates for industrial coal use in New Jersey are also based on facility-reported 

GHGRP emissions from coal combustion. Additional work is needed to verify whether the 

GHGRP is capturing energy use that the methods that inform SEDS may not. In addition to 

differences in estimation approaches, differences in how each data source identifies and 

categorizes fuel use may also contribute to the large variation with SEDS. A complete state-bystate comparison of the two data sets is included in Appendix B.

MECS provides the most detailed comparison possible with a publicly available industrial 

energy data source and the data foundation. To make this comparison, we aggregated the IET 

foundational data to the census region and by MECS NAICS code. Note that MECS does 

not report energy data at the six-digit NAICS level for all manufacturing industries due to 

confidentiality concerns. As a result, the 364 six-digit manufacturing NAICS codes provided by 

the data foundation are aggregated to match the 45 six-digit NAICS, 1 five-digit NAICs, 14 fourdigit NAICs, and 21 three-digit NAICS codes used by MECS. 

Table 7 summarizes the relative differences between the IET foundational data and MECS 2014. 

We have identified the smallest and largest relative differences for each dimension of data 

detail—geographic and operational (industry and fuel use)—based on mean relative difference. 

We note that this table does not capture instances where the data foundation reports a non-zero 

value for energy use and MECS data are either withheld or reported as zero. A full comparison 

of the IET and MECS—including absolute and percent difference by all NAICS codes reported 

by MECs—is provided in the NREL Data Catalog.
26

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

Table 7. Summary of Smallest and Largest Relative Differences between Data Foundation 

and MECS 2014 (%)

Dimension Geographic Operational: Industry Operational: Fuel Use

South Midwest Paper (Except 

Newsprint 

Mills)

Other Aluminum 

Rolling, Drawing 

and Extrudinga

Diesel Coal

Mean 21 45 0.5 1,680 4 126

Variance 68 107 8 5 89 179

Range [-91, 582] [-44, 663] [-10, 13] [1,680, 1,695] [-95, 570] [-100, 

2,800]

a MECS reports Other Aluminum Rolling, Drawing, and Extruding for only the South Census Region and 

the entire United States.
27

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

3 IET Components

The IET is currently composed of four components in addition to its data foundation: stock 

turnover, energy efficiency, fuel switching, and emissions. The stock turnover component uses 

projections of industry value of shipments by census region as a proxy to capture capital 

equipment stock retirements and new investment. The energy efficiency component implements 

changes to the UEC by end use, industry, and census region to account for improvements to 

energy efficiency. Fuel switching is conducted by substituting an alternate energy source 

(currently limited to electricity) in new capital stock investment. The IET’s emissions component 

(currently limited to GHG emissions) calculates emissions associated with projected energy use 

after accounting for any changes to fuel switching or energy efficiency. 

The IET does not include construction industries in its stock turnover, energy efficiency, 

fuel switching, and emissions calculations because of the lack of data on energy end-use 

disaggregation. However, county-level construction industry energy use can be found in the 

IET’s foundational data.

3.1 Stock Turnover

The stock turnover component enables the IET to account for the retrofit of existing and 

purchase of new industrial capital stock. This feature is used to project future energy use and 

to capture the effects of installing energy-efficient equipment and fuel switching. As discussed 

in detail below, the IET does not use physical stock data, but instead uses changes in industrial 

value of shipments as proxies. 

IET uses a top-down modeling approach similar to portions of the Industrial Demand Module 

(IDM) of the National Energy Modeling System (NEMS) (EIA 2014e). Future energy use is 

projected by census region and industry using the AEO 2017 Reference Case industry value of 

shipments (EIA 2017a). The IET projects energy use and emissions on five-year increments due 

to the computational requirements of its county-level calculations.29 Industry stock is partitioned 

in two categories: pre-2014 (or, existing) and post-2014 (or, new) stock. For simplicity, the 

initial version of the IET assumes both pre-2014 and post-2014 stock across all industries and 

end uses are replaced linearly at default lifetimes of 20 years and 30 years respectively. All 

existing stock is assumed to begin retiring at 5% per year starting in 2015. The IET assumes new 

stock is added to replace retired existing stock and to add capacity when value of shipments 

increases from the previous year. Future IET versions could include more sophisticated 

retirement behavior (e.g., using probability distributions or logistic functions, retirement 

behavior as a function of industry sector, or a capital vintage model).

 

29 We note that there are additional opportunities to improve the performance of the IET (e.g., code optimization or 

refactoring, including the use of relational databases), but these were not implemented in this version of the tool. 
28

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

3.2 Energy Efficiency

Energy efficiency is represented by a change in the UECs of pre- and post-2014 stock calculated 

by the IET’s stock turnover component. We implement changes to UECs with an assumed 

constant annual change in the form of technology possibility curves (TPCs), an approach used by 

the Industrial Demand Module of NEMS to capture the relative differences in energy use 

between existing technologies and future, more-efficient technologies, as well as incremental 

improvement to the efficiency of existing technologies (EIA 2018d). TPCs are defined for 

existing and new equipment by industry and end use by fuel type for electricity, steam, and 

natural gas. We assumed an average TPC for missing fuel types. Examples of TPCs are shown in 

Table 8 along with energy efficiency potentials estimated by DOE bandwidth studies, which are 

described in the following paragraph. 

Energy efficiency beyond the baseline assumptions is implemented as efficiency bounds defined 

by DOE bandwidth studies (DOE n.d.). Energy consumption bounds (bandwidths) in these 

studies are defined consistently as:

• State-of-the-art: the minimum energy required assuming adoption of best technologies 

and practices available worldwide

• Practical minimum: the minimum energy required assuming deployment of the applied 

R&D technologies under development worldwide

• Thermodynamic minimum: the minimum energy theoretically required under ideal 

conditions. 

Energy efficiency improvement is currently implemented in the IET as a constant annual 

decrease in UEC from 2015 to 2050. The default efficiency assumptions are based on TPCs 

defined for AEO 2014 (EIA 2014a). IET users can choose to implement energy efficiency 

beyond these default assumptions by scaling bandwidth efficiency assumptions for state-of-theart and practical minimum levels by industry. For instance, a user may implement one energy 

efficiency scenario that assumes 50% of state-of-the-art level by 2050 and another scenario that 

assumes 100% of practical minimum level for chemicals and primary metals industries. Table 8

summarizes the bandwidth studies used by the IET (DOE 2007, 2015a, 2015b, 2015c, 2015d, 

2017a, 2017b, 2017c, 2017d, 2017e, 2017f, 2017g) and the corresponding TPC equivalent range; 

a detailed listing of industries and efficiency bounds is provided in Appendix C. Note that unlike 

TPCs, which in many instances are defined by industry, end use, and fuel type, the bandwidth 

efficiencies are specified only by sector.
29

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

Table 8. Summary of Energy Efficiency Potentials from Industry Bandwidth Studies and TPCs

Sector Reduction from 

Current Typical to 

State-of-the-Art (%)

Reduction from 

Current Typical to 

Practical Minimum (%)

TPC (New 

Equipment)a (%)

Food 8–64 20–75 6–33

Beverage and Tobacco 10–18 20–23 6–33

Paper 44–46 61–74 5–68

Chemicals 6–40 37–64 14–61

Plastics and Rubber 15–37 25–47 19–61

Petroleum and Coal Products 13 40 not definedb

Primary Metals 3–67 25–79 0–66

Nonmetallic Minerals 18–49 32–53 4–21

Mining (except oil and gas) 20–34 55–70 not definedb

a TPC values represent the energy use of new equipment stock relative to 2010.

Values have been linearly extrapolated to 2050 from 2040 values. b TPCs are not defined for this sector.

3.3 Fuel Switching

The IET adopts a simplified approach to representing user-defined substitution of existing fuel 

use with an alternate fuel. The current version of the IET limits fuel switching to electrification, 

but the capability to substitute other fuels could be added to the tool. Fuel switching is integrated 

with stock turnover calculations; users may specify the industry, end use, temperature band, and 

portion of fuel use switched by 2050. The IET assumes a constant annual fraction of new stock 

fuel use is substituted with the alternate fuel type. Other representations of fuel switching 

behavior (e.g., logistic or S-curve adoption) are not included in this version of the IET, but

they have been identified as potential future tool expansions. 

Unlike other models that provide fuel switching functionality (e.g., EnergyPATHWAYS30) the

IET does not account for the relative efficiency of incumbent and substitute technologies at this 

time. Energy use does not change in fuel switching scenarios as a result. The IET does not limit 

the types of end uses that can be selected for fuel switching scenarios.

3.4 Emissions

GHG emissions associated with base-year and projected energy use are calculated by fuel type 

(i.e., coal, coke and breeze, natural gas, diesel, LPG NGL, residual fuel oil, and “other” fuel 

types) using EPA emissions factors for carbon dioxide (CO2), methane (CH4), and nitrous oxide 

(N2O) (EPA 2018). For “other” fuel, which includes fuels ranging from agricultural residues 

to used vehicle tires, we assigned the third quartile of the emission factors of all the other fuel 

types. Emission factors for each fuel were converted into CO2 equivalent.

 

30 See “EnergyPATHWAYS”, GitHub, https://github.com/energyPATHWAYS/EnergyPATHWAYS
30

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

GHG emissions associated with electricity purchases reflect regional generation fuel mixes by 

year and are calculated using either EPA data for 2014 or AEO 2017 data for 2015–2050. Baseyear (2014) emissions are calculated using EPA’s Emissions and Generation Resource Integrated 

Database (eGRID) Power Profiler Emissions Tool (EPA 2014). The tool identifies electricity 

generation emission factors by ZIP code and eGRID region; ZIP codes associated with more than 

one eGRID region are assigned an emission factor equal to the average of the regions. These ZIP 

code emission factors are then mapped to the IET’s county FIPS codes. Regional electricity 

emission factors are projected for 2015 to 2050 using annual generation fuel mix data from AEO

2017, which are mapped to eGRID region and county FIPS codes.

We have introduced functionality that enables users to specify a percentage-point increase in 

renewable electricity penetration that is achieved by 2050. The IET proportionally reduces 

electricity generation emissions factor for each year.31

 

31 In the future, this functionality could be complemented by integrating renewable energy penetration assumptions 

from AEO side cases and NREL’s Standard Scenarios. For information about the latter, see “Annual Technology 

Baseline and Standard Scenarios,” NREL, https://www.nrel.gov/analysis/data-tech-baseline.html. 
31

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

4 Case Studies

We present two case studies as illustrative examples of applications of the IET and two baseline 

scenarios. These four sets of results can be considered bounding cases for the IET. The baseline 

scenarios—one that assumes no stock turnover and the other that assumes stock turnover with 

baseline efficiency—represent projections that assume constant 2014 efficiency or that mimic 

AEO Reference Case TPCs respectively. The two case studies—one for fuel switching and one 

for energy efficiency—achieve the maximum values for fuel switching and energy efficiency by 

2050 and represent the technical potential for each of these considerations. We note that although 

using the maximum values for fuel switching and efficiency are not intended to be realistic 

assumptions and projections of possible future scenarios, the case studies are useful for 

presenting bounds for the current version of the IET.

Figure 11 (page 32) shows projections for total industry GHG emissions of the four IET 

scenarios, the AEO 2017 Reference Case, and EIA historical emissions from 1990 to 2017 (EIA

2018e). Without incorporating stock turnover behavior, emissions follow a trajectory set by the 

industry value of shipments projections and electricity grid mix assumptions of AEO 2017. With 

stock turnover behavior (with baseline energy efficiency assumptions from AEO TPCs) 

included, emissions in 2050 decrease to 50% of the baseline scenario without stock turnover and 

to about 40% of the AEO 2017 Reference Case. The fuel switching and energy efficiency 

scenarios build on the baseline stock scenario by assuming the same stock turnover behavior and 

achieving maximum fuel switching or energy efficiency potential by 2050. 

The emissions and energy results point to large differences in assumptions and application 

of energy efficiency and stock turnover in the IET and NEMS. For example, by the release of 

AEO 2017, NEMS had implemented process flow models—which do not use TPCs to represent 

energy efficiency—for the paper, glass, cement and lime, iron and steel, and aluminum

industries. UEC in NEMS may also be defined in terms of physical output (i.e., tons) in addition 

to monetary output (i.e., value of shipments). 

Regarding stock turnover modeling, NEMS includes a third equipment vintage and it assumes 

logistic retirement. Stock turnover occurs much more slowly in NEMS than in the IET, which 

may reflect assumptions regarding a larger prevalence of equipment retrofitting. Faster 

retirement assumptions—particularly of existing stock—introduces much more efficient stock 

more quickly, compounding the projected reduction in energy use. The IET defaults for existing 

and new stock lifetimes are equivalent to annual retirement rates of 5% and 3.3% respectively. 

These are nearly four times as large as NEMS annual retirement rate assumptions of 1.3%–1.7% 

per year (EIA 2017h).

32 However, researchers have found that expected equipment lifetimes are 

not significant inputs into capital investment decisions without policy or market incentives 

(Lempert et al. 2002). 

 

32 These NEMS retirement rate assumptions are for the industries represented by end use models (i.e., food products, 

bulk chemicals, metal-based durables, wood products, plastics and rubber products, and balance of manufacturing). 

Industries represented by process flow models (i.e., paper, glass, cement and lime, iron and steel, and aluminum) 

assume a stock lifetime of 20 or 30 years. 
32

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

Figure 11. National industry historicala total greenhouse gas emissions and projections by 

scenario

a Historical emissions include feedstock emissions.

baseline_nostock = baseline assumptions with no stock turnover, baseline assumptions with stock turnover, full_ee =

energy efficiency case study, and full_elect = fuel switching case study

Figure 12 compares industry energy projections by fuel type for the AEO 2017 Reference Case 

and the IET baseline stock turnover scenario. As with GHG emissions, the IET projects 

decreasing energy use until 2035, after which energy use gradually increases through 2050. 

The fuel type composition of each set of projections also exhibits differences. For instance, the 

IET begins with a larger share of coal use that increases over time. Conversely, the relative use 

of coal in AEO 2017 decreases over time. 
33

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

(a)

(b)

Figure 12. Comparison of (a) absolute and (b) relative national industry fuel energy projections 

from AEO 2017 and IET Baseline Stock scenario

The national results by scenario shown in Figure 11 belie large variation that exists from county 

to county. The following sections discuss the fuel switching and energy efficiency case studies in 

detail and demonstrate the IET’s analysis resolution by presenting results at the county level.
34

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

4.1 Fuel Switching

We assume for our fuel switching case study that 100% of new stock additions for all end uses in 

all industries are electrified by 2050 following a linear annual increase beginning in 2015. This 

leads to near-zero use of coal and natural gas, and a proportional increase in net electricity use 

by 2050, as shown in Figure 13. Note again that the IET’s fuel switching calculations do not 

currently account for relative differences in energy efficiency between existing technologies and 

substitute technologies. 

Figure 13. IET national projected coal, natural gas, and electricity use in baseline and fuel 

switching scenarios

The GHG impacts of the fuel switching scenario are a function of the difference in emissions 

intensity of the incumbent combustion fuel and grid electricity. As shown by Figure 14, when 

eGRID emission factors are extrapolated based on annual AEO 2017 grid mix projections to 

2050, census region electricity emission factors are greater than direct natural gas combustion. 

The electricity emission factor for the Midwest and South Census Regions is greater than direct 

coal combustion. We also note that the IET does not consider supply-side behavior related 

to changes in electricity demand or the implications for electricity generation emissions rates. 

Meeting the 300% increase in electricity demand by 2050 would likely require new generation 

to be built, which would affect emissions rates.
35

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

Figure 14. IET electricity generation emission factor by census region based on 

AEO 2017 projections

kg = kilograms

Without assuming additional renewable electricity penetration beyond AEO 2017 assumptions,

33

the fuel switching scenario projects that due to the electricity grid mix and prevalence of direct 

combustion of natural gas most counties would increase emissions by 2050 (i.e., have negative 

emissions savings) as shown in Figure 15. The largest exception is San Patricio County, Texas,

where emissions are projected to decrease in 2050 relative to baseline by nearly 350%. This 

indicates that industrial facilities in the county are selling or otherwise transferring more 

electricity for use by other facilities than they are using on site.34

Although it is not shown in Figure 15, the fuel switching scenario changes the location of 

emissions from facilities to power generating station. This change is not important for the 

impacts of GHG emissions, but it would affect the distribution of impacts from criteria air 

pollutants, for example. 

 

33 The range of renewables and nuclear portion of electricity generation by NEMS Electricity Market 

Module Region in 2050 is from 22% (Northeast Power Coordinating Council) to 75% (Western Electric 

Coordinating Council)

34 This large negative net electricity value may demonstrate the limitation of using EIA Form-923 data for facilitylevel electricity use.
36

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

Figure 15. IET fuel switching scenario: Relative emissions savings by county in 2050 

4.2 Energy Efficiency

The energy efficiency case study assumes all practical minimum energy efficiency

improvements are achieved by 2050. This means food industries reduce their UEC (energy per 

value of shipments) by 20%–75% of 2014 UEC values by 2050. Figure 16 shows the GHG 

emissions implications of these efficiency levels by energy end use. Under the baseline 

efficiency assumptions, all end uses experience strong GHG reductions until 2035; at that point,

emissions from process heating and other end uses begin to increase through 2050. This is likely 

the result of a smaller annual improvement in baseline energy efficiency that occurs after all 

existing stock has been replaced by more energy-efficient new stock. At the same time, the 

annual value of shipments growth from 2035 to 2050 is similar to projections for 2014–2035 for 

many industries.

The practical minimum efficiency levels have the largest impact on emissions from CHP and/or 

cogeneration, conventional boilers, and process heating. We note that the changes to end-use 

emissions are not a function of bandwidth efficiencies defined at the end-use level; instead, 

bandwidth efficiency is defined only at the industry level. Consequently, industries that have 

large GHG emissions from CHP and/or cogeneration also have large practical minimum 

energy reduction.
37

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

Figure 16. IET baseline and energy efficiency scenario projected emissions by end use

CHP = combined heat and power, EE = energy efficiency, MMTCO2e = million metric tons of carbon dioxide 

equivalent, and ST_enduse = energy end use category

Figure 17 depicts the projected emissions savings by county of achieving practical minimum 

efficiency levels by 2050. These results reflect the industrial composition of each county and are 

analogous to technical potential (i.e., no consideration given to costs) estimates. We note that 

counties showing very low emissions savings are likely to have a higher portion of industries, 

such as agriculture and some mining subsectors, not covered by DOE bandwidth studies. A 

closer inspection of these counties could help identify gaps in current efforts to improve energy 

efficiency, although the counties may be less important in terms of their absolute energy use and 

GHG emissions. 
38

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

Figure 17. IET energy efficiency scenario emissions savings in 2050 relative to baseline scenario
39

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

5 Data Improvements and Tool Expansion

The IET is an open-source, transparent, and flexible tool for exploring energy and emissions 

scenarios for the industrial sector. To make the IET and its data more useful, we have identified 

several important areas for improved data and expanded functionality. Addressing these areas 

would provide additional confidence in the IET’s foundational data, an improved user 

experience, and the ability to analyze a larger set of factors that affect industrial energy use and 

associated emissions.

1. IET Foundational Data 

o Investigate further the large differences with SEDS, 2014 MECS, and other 

publicly available data sets. Additional analysis is needed to determine where the 

estimation approaches may be inaccurate and where they capture energy use 

missed by EIA fuel use and consumption surveys.

o Improve coverage for end uses in construction and agriculture to enable the IET 

to capture these industries by expanding the search for data sources and contact 

additional subject matter experts. 

o Update foundational data to use 2014 MECS data in its regional average 

calculation approach for manufacturing. This update may result in a closer 

approximation to publicly available energy data.

2. IET Components

o Create a graphical user interface to simplify the user experience and make the IET 

more approachable and usable for a wider audience.

o Automate results visualization to provide several standard visual outputs, 

including energy and emissions projections on the county, census region, end-use, 

and industry levels. Current visualizations must be created manually, which can 

be time consuming.

o Expand fuel switching capabilities by including additional fuel types 

(e.g., biomass). Fuel switching is currently limited to electrification and adding 

fuel types would enable users greater flexibility for assessing strategies that affect 

industrial energy use and emissions.

o More closely examine the drivers behind differences with AEO 2017 emissions 

and energy projections. We believe that these are related to assumptions for stock 

lifetime and energy efficiency projection. Establishing how exactly these 

assumptions lead to different results would provide a better understanding of how 

the IET relates to existing industry energy models.

o Integrate the hybrid dynamic IO model within the IET framework, providing 

users with the ability to explore potential energy and emissions implications of 

material efficiency strategies.

o Develop CHP functionality to better represent that end use and to address the 

boundary issues created by quantifying net electricity and not total electricity use.
40

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

6 Conclusions 

Studies of industrial energy use typically mention the sector’s heterogeneity and complexity as 

modeling barriers. One of the more significant impediments to analyzing and building models of 

U.S. industrial energy use is the lack of current disaggregated data. The IET addresses some of 

these barriers by building an open-source, transparent, and flexible tool that operates on 

geographically and operationally disaggregated data. The IET’s energy and emissions scenarios 

are based on a simplified approach that allows users to modify pre-populated inputs for 

achieving energy efficiency, rate of equipment stock turnover, and future penetration of 

renewable electricity. 

The IET is built on an industry energy data foundation that estimates detailed county-level 

industrial energy use in 2014 at the four- to six-digit NAICS code level. This data set is based on 

facility-level calculations of energy use for the largest energy-using industries. We have further 

improved the operational characterization of industrial energy use by identifying temperature 

requirements for boiler and process heating energy use by industry. 

We also demonstrated the functionality of the IET by providing results of baseline assumptions 

and two illustrative case studies that explore the IET’s upper bounds of energy efficiency and 

fuel switching assumptions. By comparing these results with NEMS and AEO 2017, we find that 

the IET’s current baseline assumptions result in larger reductions in energy use and emissions 

than AEO 2017 projections; the combination of the IET’s current stock turnover and energy 

efficiency assumptions is the likely source of these differences.

The open-source nature of the IET tool and its data foundation means they are intended to 

be revised and improved over time by a community of users. Identified improvements to the IET 

would improve its foundational data, provide new functionality, and create a better user 

experience. In combination with the tool’s current capabilities, the improvements would offer

more ways for analysts and policymakers to understand energy use in the U.S. industrial sector. 
41

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46

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Appendix A. Dynamic Hybrid Input-Output Module

The purpose of the input-output (IO) module is to evaluate the effects of stock turnover, change 

in material efficiency, and fuel switching on interindustry trade, emissions, and energy use 

within the U.S. economy.

The IO model is based on the USEEIO Model published by the EPA (Yang et al. 2017; 

“USEEIO Input-Output Accounts” 2017), which specifies detailed commodity outputs and use

on a level of six-digit NAICS code for 2013 in monetary units (389 x 389 matrix). The six-digit 

NAICS codes were mapped to the U.S. Bureau of Economic Analysis (BEA) IO codes, and the 

values having the same BEA IO codes were summed to obtain new make and use tables (78 x 78 

matrices). The fractional distribution of outputs and use for all the sectors was calculated, and the 

resulting value could then be used to obtain the make and use tables for 2014.

The BEA prepared statistics for make and use tables after redefinitions data from the IO accounts 

for 1997–2015. These data are available in the form of 71 x 71 matrices. The data for 2014 were 

then multiplied by the fractions to obtain a more detailed version of the 78 x 78 matrix.

The interindustry transaction matrix can be obtained as follows (Miller and Blair, 2014):

If U is considered parallel to Z, the matrix B parallel 

to A can be obtained as:

BB = UU ∙ (xx_cccccc) − 1

Similarly, if V is considered parallel to Z, the matrix 

parallel to A can be obtained as:

DD = VV ∙ (qq_cccccc)−1

AA = BB ∙ DD

ZZ = AA ∙ (xx_cccccc)−1

LL = (II − AA)−1

ff = xx − mm

Where:

V = make matrix

U = use matrix

Z = interindustry transactions matrix

x = total industry outputs vector

f = total final demands vector

q = total commodity output vector

A = direct input coefficients

L = Leontief inverse

x_cap,q_cap,f_cap =diagonal 

matrices having x, q and f vectors as 

diagonal elements

A.1 Hybrid IO Model

Defining energy use and production in physical units instead of monetary units maintains an 

energy balance and helps avoid issues associated with heterogenous fuel prices (Miller and Blair 

2009). For example, commodities are sold at wholesale and retail rates depending on the 

consumer. Consider electricity: it is sold at lower rates to the industrial sector than to the 

residential sector. Thus, measuring the value of such commodities in dollars (or any other 

currency) would imply overestimation in the retail sector and underestimation in the wholesale 

sector. So, we used a hybrid interindustry transaction matrix with energy sectors measured in 

physical units and all other sectors in monetary units.
47

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The approach was adopted from Miller and Blair (2014). In this approach, we tried to create 

corresponding hybrid matrices to account for the interindustry transactions (Z_hybrid), direct 

input coefficients (A_hybrid) and the total requirements (L_hybrid). Z_hybrid matrix is obtained

by taking the original interindustry transactions matrix and replacing the rows representing the 

energy flows in dollars with the rows representing the same commodities in the energy flow

matrix, E. Thus Z-hybrid has energy flows represented in energy units and the flow of other 

commodities represented in dollars. Similarly, the vectors corresponding to total output

(x_hybrid) and final demand (f_hybrid) are obtained in hybrid units. The matrix notations

are summarized as:

ZZhyyyyyyyyyy = �zz ii� = �

zz ii wwheeeeee ii iiii nnnnnn − ee eeeeeeee ss

eekk wwheeeeee kk iiii ee eeeeeeee ss

xxhyyyyyyyyyy = [xx ] = �

xx wwheeeeee ii iiii nnnnnn − ee eeeeeeee ss

kk wwheeeeee kk iiii ee eeeeeeee ss

ffhyyyyyyyyyy = [ff ] = �

ff wwheeeeee ii iiii nnnnnn − ee eeeeeeee ss

qqkk wwheeeeee kk iiii ee eeeeeeee ss

hyyyyyyyyyy = [ ] = � 0 wwheeeeee ii iiii nnnnnn − ee eeeeeeee ss

kk wwheeeeee kk iiii ee eeeeeeee ss

Total energy used by fuel types for all the sectors was obtained from AEO 2017 (EIA 2017a). 

The fractional distribution of the energy values was assumed equal to the fractional distribution 

of monetary values for a given sector. The total energy use of a sector was then multiplied by the 

fractional values to obtain the energy used by fuel type on a six-digit NAICS code level (389 

industries). This was again mapped to the BEA IO codes (78 industries). The energy commodity 

rows in the Z matrix were then replaced by these values to obtain Z_hybrid matrix.

The matrices A_hybrid and L_hybrid can be obtained as follows:

AAhyyyyyyyyyy = ZZhyyyyyyyyyy. (xx�hyyyyyyyyyy)−1

LLhyyyyyyyyyy = (II − AAhyyyyyyyyyy)−1

A.2 RAS Algorithm

The construction of an IO table for any country requires detailed surveys to calculate the input

and the output of each sector. The results of these surveys are not available immediately (and 

sometimes not for five years) due to the need to process the information, and debug the 

inconsistencies. As such, constructing and updating the IO table can be time consuming. 

However, a parameter estimation technique can be used to predict the values of a new IO matrix 

using an old IO matrix if partial information such as the final demands of commodities for the 

current year are known. One such technique to achieve this is the RAS algorithm (Miller and 

Blair 2014).

The approach taken with the IET was adopted from Miller and Blair (2014). Consider a detailed

IO table for a past year (“0”) and that we want to apply the parameter estimation technique to it 
48

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to obtain the IO table for current year (“1”); that is, we have A(0) and we want A(1). To apply 

the parameter estimation technique, we would require some information for the current year. The 

RAS algorithm requires “3n” pieces of information for the current year for a “n” sector 

economy:

• Total gross output, xj

• Total interindustry sales by sector

• Total interindustry purchases by sector.

The procedure can be explained with a 3x3 matrix. The potential usefulness of the technique is in 

real-world applications, where the number of sectors is much larger than three and hence the 

difference between n2 and 3n is large. In the IET, we consider a 78-sector economy, n2 =6,084, 

whereas 3n=234. The usefulness of RAS can be seen from the fact that to update 6,084 elements 

we need information for just 234 elements.

For the general 3 × 3 case, assume that base-year coefficients are known.

AA(0) = �

11(0) 12(0) 13(0)

21(0) 22(0) 23(0)

31(0) 32(0) 33(0)

�

For the target year 1, following data are known:

xx(1) = �

xx1(1)

xx2(1)

xx3(1)

� uu(1) = �

uu1(1)

uu2(1)

uu3(1)

� vv(1) = �

vv1(1)

vv2(1)

vv3(1)

�

Initially it is assumed A(0) = A(1).

ZZ = AA(0). xx(1)_cccccc

ZZ = �

11(0). xx1(1) 12(0). xx2(1) 13(0). xx3(1)

21(0). xx1(1) 22(0). xx2(1) 23(0). xx3(1)

31(0). xx1(1) 32(0). xx2(1) 33(0). xx3(1)

�

The vector u for this matrix is the row sum and the vector v is the column sum. For most of the 

cases, these values will not be equal to u(1) and v(1) respectively.

11(0). xx1(1) + 12(0). xx2(1) + 13(0). xx3(1) = uu1 ≠ uu1(1)

21(0). xx1(1) + 22(0). xx2(1) + 23(0). xx3(1) = uu2 ≠ uu2(1)

31(0). xx1(1) + 32(0). xx2(1) + 33(0). xx3(1) = uu3 ≠ uu3(1)

Premultiplying the matrix A(0) by a matrix R such that:
49

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RR = �

uu1(1)/uu1 0 0

0 uu2(1)/uu2 0

0 0 uu3(1)/uu3

�

Now, considering A(1) = R.A(0) and Z = A(1).x(1)_cap, the row sum of Z matrix is equal to the 

u(1) vector. However, the column sum is not equal to the v(1) vector.

11(1). xx1(1) + 21(1). xx1(1) + 31(1). xx1(1) = vv1 ≠ vv1(1)

12(1). xx2(1) + 22(1). xx2(1) + 32(1). xx2(1) = vv2 ≠ vv2(1)

13(1). xx3(1) + 23(1). xx3(1) + 33(1). xx3(1) = vv3 ≠ vv3(1)

Postmultiplying the current A(1) matrix by a matrix S such that:

SS = �

vv1(1)/vv1 0 0

0 vv2(1)/vv2 0

0 0 vv3(1)/vv3

�

Now,

AA(1) = RR

The Z matrix obtained from these coefficients will have a column sum equal to the v(1) vector. 

However, the row sum is disturbed and is not equal to the u(1) vector. The procedure is repeated 

until the column sum and row sum obtained from the Z matrix are equal to the v(1) and u(1) 

vectors respectively. We considered convergence of the order of 0.001 instead of to zero to speed 

the calculations.
50

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Appendix B. Comparison of IET Foundational Data 

and EIA State Energy Data System (SEDS)

The following figure and table aggregate the IET county-level industrial energy use data to the 

state level to compare against EIA SEDS. The IET fuel types have also been aggregated to match 

the categories used by SEDS. Negative values indicate an IET value less than SEDS; positive 

values indicate an IET value greater than SEDS.

Figure 18. Relative difference between state-aggregated IET foundational data and SEDS, by fuel

Values above 300% and where SEDS values are zero are excluded.
51

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Table 9. Comparison of State-Level Aggregated IET Foundational Data and SEDS

State Petroleum

Products

Natural Gas Coal Net Electricity Total

TBtu % TBtu % TBtu % TBtu % TBtu %

Alabama -16 -40% -17 -8% -7 -17% -18 -15% -39 -7%

Alaska -28 -68% -31 -12% 1 7,756% 4 89% -44 -14%

Arizona -6 -16% 8 34% -2 -39% -8 -16% 4 4%

Arkansas -22 -45% 61 63% 9 166% 7 12% 90 32%

California -229 -65% -252 -29% 10 30% 39 21% -190 -13%

Colorado -5 -11% -48 -25% -1 -15% -5 -10% -39 -13%

Connecticut 4 57% 1 3% 1 3 26% 9 17%

Delaware -19 -72% 3 9% 0 -2 -29% -2 -4%

District of 

Columbia

0 52% 1 0 0 -60% 1 48%

Florida 3 4% 37 38% 27 169% 32 58% 107 32%

Georgia -20 -35% 26 16% 12 57% 9 9% 4 1%

Hawaii -12 -66% 4 955% 0 1% -11 -86% -12 -32%

Idaho -1 -6% 9 32% 1 12% -5 -15% 17 16%

Illinois -170 -73% 15 5% 31 47% 9 6% 50 7%

Indiana -68 -64% -46 -12% 44 94% 23 14% 96 13%

Iowa -55 -54% 48 27% 24 41% 3 4% 38 9%

Kansas -34 -50% -2 -1% 1 27% 11 28% 21 9%

Kentucky -67 -63% 57 45% 0 2% -1 -1% 49 13%

Louisiana -879 -96% -506 -44% 3 109% -18 -15% -1,001 -43%

Maine 2 34% -8 -31% 2 292% -1 -8% -18 -17%

Maryland 4 22% 17 109% -5 -35% 14 106% 36 52%

Massachuset

ts

8 67% 1 3% 3 196% -1 -2% 19 20%

Michigan -18 -31% 30 16% 6 22% 54 49% 117 26%

Minnesota -46 -45% -15 -9% 4 17% 11 14% -24 -6%

Mississippi -41 -68% -35 -29% 3 106% -10 -19% -49 -17%

Missouri -13 -27% 42 62% -9 -38% 4 7% 48 24%
52

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State Petroleum

Products

Natural Gas Coal Net Electricity Total

TBtu % TBtu % TBtu % TBtu % TBtu %

Montana -19 -50% -7 -28% -1 -18% 0 3% 2 3%

Nebraska -2 -6% 7 8% -1 -4% 0 1% 21 12%

Nevada -5 -22% 6 37% 4 57% -29 -61% -17 -18%

New 

Hampshire

0 8% 6 63% 0 4 66% 9 37%

New Jersey -75 -74% 2 3% 9 13 49% -2 -1%

New Mexico -14 -46% 0 0% 10 682% -5 -18% 6 4%

New York 17 43% 57 65% 3 20% 12 19% 99 44%

North 

Carolina

0 -1% 89 80% 21 131% 18 19% 209 61%

North 

Dakota

-54 -65% -7 -15% -82 -88% -13 -52% -132 -52%

Ohio -82 -60% 59 18% 17 53% 21 12% 111 16%

Oklahoma -57 -61% -3 -1% 6 43% 15 25% 30 6%

Oregon -2 -7% 2 3% -1 -36% 12 27% 23 14%

Pennsylvania -94 -55% -148 -35% 3 9% -40 -25% -175 -21%

Rhode Island 0 14% 0 6% 0 1 48% 3 23%

South 

Carolina

-13 -40% 65 77% 9 61% -17 -17% 64 20%

South Dakota 3 18% -7 -15% 1 30% 12 124% 19 26%

Tennessee -35 -57% 68 56% 2 3% 24 29% 64 17%

Texas -2,486 -94% -377 -18% 17 63% -23 -6% -1,938 -37%

Utah -21 -50% 6 8% 6 41% 6 17% 22 14%

Vermont 0 0% 8 420% 1 0 9% 16 137%

Virginia 8 27% 34 36% 6 22% 15 25% 66 24%

Washington -71 -66% -1 -2% 8 294% -20 -21% -44 -12%

West Virginia -22 -48% 18 22% -4 -18% 0 0% 8 4%

Wisconsin -10 -20% 26 18% 17 52% 48 59% 67 18%

Wyoming -42 -75% 49 50% 14 45% -21 -60% 20 9%
53

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Appendix C. Energy Efficiency Assumptions

Table C-1 lists all efficiency assumptions used by the IET. Efficiency is expressed as the 

reduction in 2050 energy use relative to 2014 energy use. So, for example, the first row of the 

table identifies that the state-of-art opportunity (SOA_opp) for NAICS code 31212 in 2050 is 

a reduction of 17.9% from 2014 energy use. The assumptions may be referred to in slightly 

different fashions in the bandwidth reports:

• SOA_opp: the efficiency improvement achieved by reducing current typical energy 

consumption (i.e., 2014 baseline energy use) to state-of-the-art energy consumption;

also referred to as current opportunity by bandwidth studies

• R&D_opp: the energy efficiency improvement achieved by reducing state-of-the-art 

energy consumption to practical minimum energy consumption 

• PM_opp: the efficiency improvement achieved by reducing current typical energy 

consumption to practical minimum energy consumption; PM_opp is the sum of 

SOA_opp and R&D_opp. Certain bandwidth studies included estimates for low and 

high practical minimum energy consumption, which are captured as efficiency 

improvements in Table B1 as PM_low and PM_high respectively. 

Table C-1. Bandwidth Efficiency Data Assumptions by NAICS Code

(Assumed Reduction in 2050 Energy Use Relative to 2014)

Description of 

Three-Digit NAICS

NAICS 

Code 

SOA_opp R&D_opp PM_opp PM_low PM_high

Beverage and 

Tobacco

31212 0.179 0.051 0.231 nd nd

Beverage and 

Tobacco

31213 0.100 0.100 0.200 nd nd

Chemical 325110 0.208 nd nd 0.210 0.430

Chemical 325120 0.061 nd nd 0.060 0.370

Chemical 325180 0.300 nd nd 0.310 0.450

Chemical 325192 0.063 nd nd 0.060 0.530

Chemical 325193 0.087 nd nd 0.090 0.640

Chemical 325199 0.282 nd nd 0.250 0.530

Chemical 325211 0.178 nd nd 0.180 0.470

Chemical 325311 0.399 nd nd 0.400 0.590

Chemical 325312 0.086 nd nd 0.070 0.460

Chemical 325998 0.062 nd nd 0.062 0.467

Food 3112 0.349 0.000 0.349 nd nd

Food 311221 0.088 0.176 0.265 nd nd

Food 31131 0.450 0.281 0.732 nd nd

Food 3114 0.283 0.055 0.338 nd nd
54

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Description of 

Three-Digit NAICS

NAICS 

Code 

SOA_opp R&D_opp PM_opp PM_low PM_high

Food 3115 0.355 0.125 0.480 nd nd

Food 3116 0.289 0.096 0.386 nd nd

Food 3121 0.178 0.053 0.230 nd nd

Food 311 0.271 0.111 0.382 nd nd

Food 311224 0.349 0.000 0.349 nd nd

Food 311314 0.642 0.107 0.749 nd nd

Food 311313 0.347 0.376 0.723 nd nd

Food 311421 0.250 0.100 0.350 nd nd

Food 311411 0.302 0.029 0.331 nd nd

Food 311511 0.436 0.145 0.582 nd nd

Food 311512 0.600 0.200 0.800 nd nd

Food 311513 0.083 0.083 0.167 nd nd

Food 311514 0.400 0.000 0.400 nd nd

Food 31152 0.000 0.000 0.000 nd nd

Food 311612 0.281 0.063 0.344 nd nd

Food 311613 0.357 0.286 0.643 nd nd

Food 311615 0.200 0.000 0.200 nd nd

Mining (except Oil 

and Gas)

2121 0.204 0.371 0.575 nd nd

Mining (except Oil 

and Gas)

2122 0.244 0.458 0.702 nd nd

Mining (except Oil 

and Gas)

2123 0.339 0.211 0.550 nd nd

Nonmetallic Mineral 3272 0.325 0.086 0.410 nd nd

Nonmetallic Mineral 327211 0.218 0.105 0.323 nd nd

Nonmetallic Mineral 327212 0.289 0.099 0.388 nd nd

Nonmetallic Mineral 327213 0.489 0.042 0.531 nd nd

Nonmetallic Mineral 327993 0.180 0.139 0.319 nd nd

Nonmetallic Mineral 327310 0.338 0.036 0.373 nd nd

Paper 322110 0.465 nd nd 0.470 0.742

Paper 32212 0.445 nd nd 0.445 0.608

Petroleum and 

Coal Products

324110 0.132 nd nd 0.140 0.400

Plastics and Rubber 326140 0.200 0.051 0.251 nd nd

Plastics and Rubber 326211 0.149 0.136 0.285 nd nd
55

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Description of 

Three-Digit NAICS

NAICS 

Code 

SOA_opp R&D_opp PM_opp PM_low PM_high

Plastics and Rubber 3262 0.149 0.135 0.284 nd nd

Plastics and Rubber 326150 0.371 0.102 0.473 nd nd

Plastics and Rubber 326220 0.145 0.136 0.282 nd nd

Plastics and Rubber 326291 0.145 0.136 0.282 nd nd

Plastics and Rubber 326299 0.145 0.136 0.282 nd nd

Plastics and Rubber 326 0.315 0.083 0.398 nd nd

Primary Metal 331410 0.028 0.221 0.249 nd nd

Primary Metal 331110 0.388 nd nd 0.390 0.630

Primary Metal 331313 0.383 0.403 0.786 nd nd

Primary Metal 331314 0.667 0.037 0.704 nd nd

Primary Metal 331315 0.143 0.123 0.266 nd nd

Primary Metal 331318 0.143 0.123 0.266 nd nd

nd: Data not defined by bandwidth analysis.