World Energy Projection System (WEPS): 

Industrial Demand Module

Independent Statistics & Analysis 

www.eia.gov

U.S. Department of Energy

Washington, DC 20585

Last Updated June 2022
U.S. Energy Information Administration   |   World Energy Projection System: Industrial Demand Module i

This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and

analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are

independent of approval by any other officer or employee of the United States Government. The views

in this report therefore should not be construed as representing those of the U.S. Department of Energy

or other federal agencies.
U.S. Energy Information Administration   |   World Energy Projection System: Industrial Demand Module 1

Contents 

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

2. Description of the Projection Method ...................................................................................................... 4

3. Fuel Switching and Efficiency Improvement............................................................................................. 6

4. Steel Industry ............................................................................................................................................7

Figures 

Figure 1: Log Trend Example......................................................................................................................... 5

Figure 2: Exponential Decline Example......................................................................................................... 5

Figure 3: Mean/Median Reverting Example ................................................................................................. 6

Tables 

Table 1: Industrial Sector Industry Categories.............................................................................................. 3

 
U.S. Energy Information Administration   |   World Energy Projection System: Industrial Demand Module 2

1. Introduction 

The WEPS Industrial Demand Module (World Industrial Model, or WIM) projects the amount of energy

that is directly consumed as a fuel or as a feedstock by industrial processes and activities. The projection

includes consumption by energy‐intensive manufacturing industries, non‐energy intensive

manufacturing industries, and nomanufacturing industries.  

WIM projects energy consumption for 16 industry categories for all WEPS regions and fuels over the

projection period. Table 1 lists each covered industry category, some representative industries for each,

and their respective Nomenclature of Economic Activities (NACE2) codes which are the European

statistical classification of economic activities. By clearly defining the boundaries for each industry

category using NACE2, we ensure that the energy demand and macroeconomic activity are matched and

represented as accurately as possible.

We assume that the following factors determine industry energy consumption changes over time:

 Changes in industrial gross output, defined as the total dollar value of services provided by a

given industrial sector, adjusted to reflect purchasing power parity

 Changes in energy intensity (EI), defined as thousands of British thermal units (Btu) of energy

consumed per U.S. dollar of gross output

 Sustained changes in industrial energy prices

Increased industry category gross output generally leads to higher consumption, and sustained fuel

price increases will encourage some fuel switching and efficiency improvements. Energy intensities are

generally expected to have a downward slope as manufacturers replace older capital stock with newer,

more efficient technology; however, this assumption has limits because many mature industries in

advanced economies will already employ the most efficient means of production currently available.

This logic does not apply to fuels consumed as feedstocks, such as several petroleum‐based fuels and

natural gas plant liquids in the basic chemicals industry, which are not subject to efficiency

improvements.  

WIM uses an array of energy intensity (EI) models to find the best fit for each region‐industry based on

historical trends, statistical measures, and analyst judgement. The WIM EI models use historical energy

intensity data to estimate coefficients, which they then apply to compute projections for the projection

period. WIM then ranks the EI model results using several statistical indicators; selects the top ranked

energy intensity model result, subject to analyst override; and calculates the energy consumption for

each region‐industry as gross output multiplied by the projected energy intensity. Finally, WIM shares

out the total region‐industry consumption to individual fuels.
U.S. Energy Information Administration   |   World Energy Projection System: Industrial Demand Module 3

Table 1: Industrial Sector Industry Categories for the World Energy Projection System (WEPS)

NACE 2     Industry Category Representative Industries

       

C10     Food Food, beverage, and tobacco product manufacturing

C17‐18     Paper Paper manufacturing, printing and related support activities

C20.1     Basic chemicals Inorganic chemicals, organic chemicals (for example, ethylene,

propylene), resins, and agricultural chemicals. Includes chemical

feedstocks

C19     Refining Petroleum refineries and coal products manufacturing to include

coal and natural gas used as feedstock

C24.1‐3     Iron and steel Iron and steel manufacturing, including coke ovens

C24.4     Non‐Ferrous metals Primarily aluminum and other non‐ferrous metals such as copper,

zinc, and tin, including carbon anode production for primary

aluminum smelting

C23     Non‐Metallic minerals Primarily cement and other non‐metallic minerals such as glass,

lime, gypsum, and clay products

C11‐16,

22,31‐33

    Other industrial All other manufacturing

C20.2‐6,21     Other chemicals Pharmaceuticals, medicinal and botanical, paint and coatings,

adhesives, detergents, and other miscellaneous chemical products.

Includes chemical feedstocks

A01‐03      Agriculture Agriculture, forestry, and fishing  

B05

B06                     

    Oil extraction

Coal extraction

Oil and natural gas extraction

Coal extraction

B07‐09     Other extraction

   

Metallic and non‐metallic minerals mining such as bauxite, iron,

lithium, sand, or gold

C29‐30     Motor vehicles Transportation equipment— motor vehicles and aircraft ‐ 

manufacturing

C24.5‐28     Other metal‐based durables Other metal based durables (OMBD) manufacturing, computer and

electronic products, machinery, and electrical equipment

appliances and components

F41‐43

  

    Construction Buildings (residential and commercial) construction, heavy and civil

engineering construction, industrial construction, and specialty

trade contractors

Data source: U.S. Energy Information Administration
U.S. Energy Information Administration   |   World Energy Projection System: Industrial Demand Module 4

2. Description of the Projection Method 

For each WEPS region‐industry combination (for example, Japan‐Paper), we project an energy intensity

(EI) curve that forms the basis for energy demand over the projection period. The interaction between a

given region‐industry’s projected energy intensity and projected gross output (provided by the WEPS

Global Activity Module) largely determines its energy demand:

EIൌ Consumption

Gross Output.

Calculation of alternative energy intenensity (EI) curves

For each region‐industry combination, the model uses historical data going back to 2010 and a two‐step

estimation process to project alternative EI curves.  The first stage is to estimate the underlying trend by

three methods:

1. An exponential decay model, if the historical EI slope is negative, or an inverted exponential

decay model if the slope is positive (Figure 1)

2. A logarithmic growth model (Figure 2)

3. A zero‐slope model equal to the mean if the model uses ordinary least squares (OLS) or median

if the model uses least absolute deviations (LAD) (Figure 3)

The first and second specifications listed above are competing theories of the way in which energy

intensity changes over time. Logarithmic growth or decline assumes that EI will grow or decline at a

continually diminishing rate into the foreseeable future according to the trend established by history,

subject to any upper bounds of reasonable intensity, unless acted upon by an exogenous force.

Exponential and inverted exponential decay assume older production methods and technologies are

replaced with newer at a continuous percentage rate,approaching an asymptote that represents

complete adoption of a newer technology. The third specification—no change in EI—assumes that WEPS

does not have enough information to expect that a meaningful trend in the data is discernable (that is,

slope equals 0), and therefore, energy intensity reverts to the mean (the OLS intercept) or median (the

LAD intercept).

The second stage in the estimation process is to use an autoregressive model with a one‐period lag

(AR(1)) on the residuals of the first estimation stage.  Currently, the model assigns a coefficient of 0.9 to

the autoregressive term, a choice that represents the assumption that the data will slowly converge

back to the trend.

   
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Figure 1: Log Trend Example

Data source: U.S. Energy Information Administration

The logarithmic trend example in Figure 1 shows a downward trend in the energy intensity of African

(AFR) agriculture.  Rather than just projecting a straight line, we project a line whose slope diminishes

over time and that is not bound by an explicit asymptote.

Figure 2: Exponential Decline Example

Data source: U.S. Energy Information Administration

0.2

0.25

0.3

0.35

0.4

0.45

0.5

2010 2015 2020 2025 2030 2035 2040 2045 2050

year

Energy Intensity (AFR Agriculture)

1000 Btu per GO dollar

Observations Long Run Projection

0

1

2

3

4

5

6

2010 2015 2020 2025 2030 2035 2040 2045 2050

year

Energy Intensity (CAN Chemicals)

1000 Btu per GO dollar

Observations Long Run Projection
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Figure 2. This exponential decline example shows a decline in the energy intensity of Canada’s (CAN)

basic chemical manufacturing.  Unlike the similar‐looking logarithmic decline, the exponential decline

approaches a horizontal asymptote close to 2.0.

Figure 3: Mean/Median Reverting Example

Data source: U.S. Energy Information Administration

Figure 3. In this mean/median reverting example, the energy intensity of Europe’s food production

oscillates with no discernable trend.  We can reasonably expect that, in the long run, the EI will tend to

revert to the mean.

Selecting the best EI curve

We use the Akaike Information Criterion (AIC) to rank the EI curves by goodness‐of‐fit and select the

best EI curve. We  also employ diagnostic flags to ensure that the EI models are reasonable at each stage

in the estimation process:

 When modelling growth using the inverted exponential decay method, we flag inconsistency

from the expected direction of the trend (up or down).

 We flag significant correlations between residuals and time in the first stage estimation.

 We flag increases in variance after first stage estimation.

 We flag decreases in log likelihood after the first stage estimation.

3. Fuel Switching and Efficiency Improvement 

To consider fuel‐switching, we categorize consumption into six broad fuel categories:

 Petroleum (liquid fuels)

 Natural gas

0.8

0.81

0.82

0.83

0.84

0.85

0.86

2010 2015 2020 2025 2030 2035 2040 2045 2050

year

Energy Intensity (EUR Food)

1000 Btu per GO dollar

Observations Long Run Projection
U.S. Energy Information Administration   |   World Energy Projection System: Industrial Demand Module 7

 Coal

 Electricity

 District heat

 Renewables

The model accounts for fuel switching between four fuel categories: Petroleum, Natural Gas, Coal, and

Electricity. We do not consider fuel switching between specific fuels within a category (for example,

diesel and gasoline, which are both in the Petroleum category). Switching one petroleum fuel for

another (say, diesel for gasoline) would, in most cases, not make sense given that a) their prices are

positively correlated, limiting possible cost savings; and b) any savings on fuel costs would not justify the

investment cost of switching, for example, from a diesel generator to one that runs on gasoline.  

Fuel switching in response to year‐to‐year changes in relative prices affects fuel shares. The model

represents the effect through a fuel‐switching algorithm that uses industrial sector cross‐price

elasticities for coal, natural gas, liquid fuels, and electricity. For example, for consecutive years y‐1 and y,

the incremental change in coal use due to natural gas price changes is calculated as:  

Coal Usey െ Coal Usey‐1 ൌ constant ൈ

Natural Gas Pricey െ Natural Gas Pricey‐1

Natural Gas Pricey‐1

ൈ Cross Price Elasticity.

The model performs a similar calculation for other pairs of fuels, such as coal‐electricity. In addition, we

introduced an algorithm in the International Energy Outlook 2021 (IEO2021) to project fuel‐efficiency

improvements based on longer‐term price trends, rather than simple changes across consecutive years.

Currently, this algorithm considers only petroleum products used for fuel (distillate, residual fuel oil,

liquid petroleum gas (LPG)). If, as in the IEO High Oil Price case, WIM encounters sustained fuel price

increases in petroleum products, the new algorithm will simulate efficiency improvements that decrease

energy intensity, on the assumption that manufacturers will upgrade to more fuel efficient equipment.  

4. Steel Industry 

We model steel industry energy consumption differently than other industries (described above). We

develop projections for steel (virgin and recycled) based on a combination of historical production

trends, government and industry policies, and analyst judgment.  

The model uses historical data available from the World Steel Association for producing steel, using

oxygen blown converters (OBC) and electric arc furnaces (EAF), and producing direct‐reduced iron (DRI).

The model currently estimates total crude oil steel production as a power function of the real gross

output, in dollars of value, of the iron and steel industry. For most regions, the model assigns a

coefficient of 1 to the gross output term, indicating that a 1% increase in gross output corresponds to a

1% increase in total steel production.

We project quantities of steel produced in OBCs and EAFs by dissagregating the projected total steel

produced, based on projected proportions for OBC and EAF. We use logarithmic trend models to

estimate the proportions. The logarithmic trend represents an assumed continuously diminishing
U.S. Energy Information Administration   |   World Energy Projection System: Industrial Demand Module 8

absolute change in the proportions over the projection period. Based on expert judgment, EIA analysts

may overwrite the model‐projected proportions for OBC and EAF.

Electric arc furnaces primarily take two types of material inputs: scrap and direct‐reduced iron.  So, the

model estimates the proportion of DRI in total EAF production, assuming that the complement is

substantially scrap.  As for OBC and EAF, the model uses a logarithmic trend to project the proportion of

DRI produced, and we can overwrite the model‐estimated proportions.

Unlike EAFs, oxygen‐blown converters typically don’t use a lot of scrap; instead EAFs primarily use iron

produced in a blast furnace, usually within the same integrated steel mill.  The model, therefore, doesn’t

disaggregate scrap, pig iron, and less common iron inputs.

With the projections from these three products (OBC steel, EAF steel, and DRI), we use a three‐factor

linear model to estimate the marginal consumption by each production process on each fuel type.  OBC

production is generally coal‐intensive but may also use natural gas enrichment. EAF steel production is

electricity‐intensive, and DRI production is natural gas‐intensive, due to the role of syngas in removing

oxygen from iron ore.

We initially calibrate the steel model to the energy‐intensity‐based model described above. We use the

EI model across industrial sectors, and it can accommodate judgment‐based adjustments to specific

factors.