Energy Policy 30 (2002) 151–163
Aggregating physical intensity indicators: results of applying the
composite indicator approach to the Canadian industrial sector
Mallika Nanduri*, John Nyboer, Mark Jaccard
School of Resource and Environmental Management, Energy Research Group, Simon Fraser University, Burnaby, BC, Canada V5A 1S6
Received 13 July 1999
Abstract
Issues surrounding the development, application and interpretation of energy intensity indicators are a continuing source of
debate in the field of energy policy analysis. Although economic energy intensity indicators still dominate intensity/efficiency studies,
the use of physical energy intensity indicators is on the rise. In the past, physical energy intensity indicators were not employed since
it was often impossible to develop aggregate (sector-level or nation-wide) measures of physical energy intensity due to the difficulties
associated with adding diverse physical products. This paper presents the results of research conducted specifically to address this
‘‘aggregation’’ problem. The research focused on the development of the Composite Indicator Approach, a simple, practical,
alternative method for calculating aggregate physical energy intensity indicators. In this paper, the Composite Indicator Approach is
used to develop physical energy intensity indicators for the Canadian industrial and manufacturing sectors, and is then compared to
other existing methods of aggregation. The physical composite indicators developed using this approach are also evaluated in terms
of their reliability and overall usefulness. Both comparisons suggest that the Composite Indicator Approach can be a useful, and
ultimately suitable, way of addressing the aggregation problem typically associated with heterogeneous sectors of the economy. r
2002 Elsevier Science Ltd. All rights reserved.
Keywords: Physical; Intensity; Aggregation
1. Introduction
In the past, changes in energy intensity have been used
mainly to track energy efficiency progress at various
levels of a country’s economy. Some are developed at
the economy-wide level, and account for intensity
changes in all the major sectors of the economyFindustry, residential, commercial, transportation and
agriculture. Others are constructed at the aggregate
level (to monitor intensity changes in each of the
aforementioned economic sectors), the sector level
(usually corresponding to the 2-digit SIC or ISIC
category), the sub-sector level (usually corresponding to the 3-digit SIC/ISIC category) and the industry
level (typically the 4-digit SIC/ISIC category). Quantitative assessment of the variables that drive energy
*Corresponding author. Tel.: +1-604-291-5756; fax: +1-604-2915473.
E-mail address: mnanduri@sfu.ca (M. Nanduri).
intensity change at these various levels, most
notably economic structure and technological change,
has also been used to forecast future energy demand
and measure the performance of energy-related
policies.
Energy intensity indicators continue to be used
for monitoring purposes and, increasingly, as a basis
for policy-making despite continued debate surrounding the development of both physical energy intensity
and economic energy intensity indicators. Economic
intensity indicators measure the energy used per
dollar of GDP produced by some sector, sub-sector,
industry or product. They are also relied on to provide estimates of aggregate or economy-wide energy
intensity change. Such indicators reflect changes
in technical (‘‘true’’) energy efficiency as well as
changes in the economic structure (process or product
mix) at the aggregate, sector, sub-sector, industry
or product level. Many publications have empirically
evaluated a variety of decomposition methodologies,
which are used to remove or account for shifts
0301-4215/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved.
PII: S 0 3 0 1 - 4 2 1 5 ( 0 1 ) 0 0 0 8 3 - 0
152
M. Nanduri et al. / Energy Policy 30 (2002) 151–163
in economic structure, and have reported on the effect
of their use in estimating energy intensity change (Ang
and Choi, 1997; Ang and Lee, 1994; Ang, 1994; Boyd
et al., 1988; Eichhammer, 1998; Greening et al., 1997;
Nanduri, 1998).
Others publications have focused on physical energy
intensity indicators, which measure the energy used per
physical unit of output produced by some sector, subsector, industry or product (Farla et al., 1997; Phylipsen
et al., 1997; Worrell et al., 1997; Nanduri, 1998). Many
of these same publications encourage reliance on
physical indicators rather than economic indicators,
based in part on the prevailing belief that changes in
physical intensity provide more reliable estimates of
changes in technical energy efficiency (Phylipsen et al.,
1996, 1997; CIEEDAC, 1996; Farla et al., 1997) since
they are relatively less affected by shifts in economic
structure.
Physical energy intensity indicators are typically only
constructed for industries (such as pulp), sub-sectors
(such as pulp and paper) or sectors (such as pulp and
paper and allied products) whose outputs are in
common physical units (such as tonnes). They are
seldom developed at the aggregate (all industry or all
manufacturing) or economy-wide level, since the diversity of products at this level typically means their output
units are not additive.
The problem of aggregating across diverse units of
measurement in order to derive a single, meaningful
index or indicator is not unique to physical energy
intensity indicators. In the field of monetary economics,
economists regularly try to aggregate diverse financial
assets such as currency, deposits and other financial
assets, into a single monetary aggregate (Berndt, 1985).
Various methods are also used to overcome difficulties
related to aggregating diverse forms of energy. Although
a single indicator may not always be appropriate for
obtaining a comprehensive understanding or accurate
estimation of energy efficiency change in a country
(Phylipsen et al., 1996), the search for a way to deal with
this ‘‘aggregation problem’’, and develop aggregate
physical intensity indicators, continues.
This paper presents the results of research aimed at
expanding on a simple, alternative methodology for
developing aggregate physical intensity indicators. First,
brief reviews of the aggregation problem, and of existing
aggregation methodologies, are presented. Then, an
alternative aggregation method entitled the Composite
Indicator Approach is introduced and used to develop
aggregate and sector-level physical energy intensity
indicators (referred to hereafter as physical composite
indicators or composites) for the Canadian industrial
sector. Finally, the general reliability of the composites,
and the overall usefulness of the Composite Indicator
Approach, are evaluated relative to the other aggregation methods/indicators.
2. Methodology and data
Several existing methods of aggregation are reviewed using four criteria: international comparability,
data availability, ease of use/interpretation and the
overall usefulness of the methodology. International
comparability, in this context, refers to how well or
how poorly the aggregate physical intensity indicator
developed by a particular method can be compared
to those developed by other countries. Data availability is linked primarily to the ease/difficulty
associated with acquiring the necessary data. Ease
of use, as well as ease of interpretation, are also key.
An unduly complicated method of aggregation, or one
that is not ‘‘reader-friendly’’ or whose results are
difficult to understand, is not likely to be the first choice
for an analyst or policy-maker. The criterion of
usefulness relates to the actual advantages of using a
particular method to develop an aggregate physical
indicator, and refers primarily to differences in the type
and amount of information provided by one indicator
versus another. The methods reviewed are the Fixed
Basket Approach, the Laspeyres Physical Index Approach and the Actual SEC/Reference SEC Ratio
Approach.
The Composite Indicator Approach is then explained
and employed to develop physical composite indicators
using Canadian industrial sector data. Energy consumption and physical production data for a variety of
industrial sub-sectors and industries are used to apply
the methodology. All the data are found in the
Canadian Industrial Energy End-use Analysis Centre’s
(CIEEDAC) databases.1 Despite an extensive search for
more disaggregate data, CIEEDAC’s databases proved
to be the most comprehensive source of reliable,
available and consistent physical output and purchased
energy use data.
Selection of the industries and sub-sectors used to
develop the physical composite indicators was based
solely on data availability, and where required, higher
heating values were employed. The data set includes
both Tier 1 and 2 industries and sub-sectors2 and is
shown in Table 1.
1
The data originate from the publications Energy Intensity
Indicators for Canadian Industry 1990–1995 and 1990–1996 (Nyboer
et al., 1996a, 1997), and Analysis of Industrial Energy Use Data for
1994 and 1995 (Nyboer et al., 1996b; Nyboer and Bailie, 1997). Both
these publications rely extensively on several publicly available
Statistics Canada data sets, namely the Industrial Consumption of
Energy, Annual Survey of Manufacturers, Quarterly Report on
Energy Supply and Demand, and the Annual Census of Mines. For
more detailed information about the comparison and reconciliation
between these different data sources, please refer to these publications.
2
Tier 1 industries are responsible for approximately 80% of energy
consumption in the Canadian industrial sector, while Tier 2 industries
consume only about 20% of total industrial energy but account for
approximately 70% of the total industrial GDP.
M. Nanduri et al. / Energy Policy 30 (2002) 151–163
153
Table 1
Number of sub-sectors and industries (listed by SIC code) included in the development of aggregate and sectoral composite indicators
Standard industrial classification code
Industry (4-digit SIC), sub-sector (3-digit SIC) or sector (2-digit SIC)
611
612
614
616
617
619
61
622
623
624
625
629
62
101
104
1111
1131
2711
2712
2713
2714
2719
271
2919
2951
295
3231
3521
3581
3611
Gold mines
Nickel–copper–zinc mines
Silver–lead–zinc mines
Uranium mines
Iron mines
Other metal mines
Metal mining
Peat industry
Gypsum mines
Potash mines
Salt mines
Other non-metal mines
Non-metal mining
Meat and poultry products
Dairy products
Soft drinks
Brewery products
Pulp industry
Newsprint industry
Paperboard industry
Building board industry
Other paper industry
Pulp and paper
Primary steel industries
Primary aluminium production
Non-ferrous metal smelters and refineries
Motor vehicles industry
Hydraulic cement industry
Lime industry
Refined petroleum
The reliability of the physical composite indicators
is based predominantly on the quality of the physical
production data used in their development, mainly
because good energy consumption data are available
for a number of industries and sub-sectors, but good
quality physical production data are often lacking. The
focus on data quality is also because the basic index
methodology underlying the Composite Indicator Approach is fairly well established. The criteria used to
judge the composites are completeness and commodity
coverage.
The completeness of a composite refers to the number
of sub-sectors and/or industries included in its construction. A sector-level composite that is developed from all
the sub-sectors and industries associated with that sector
might be considered complete, for example. This
definition of completeness is ideal because it implies
that all the component sub-sectors and industries are
accounted for, and that energy intensity changes at all
necessary levels are captured by the physical composite
indicator. Given data constraints, however, and the fact
that the ultimate concern is to calculate a meaningful
aggregate, a working definition of completeness based
on whether or not all the major energy-consuming
industries/sub-sectors are represented in the composite
will be used instead.3
The second criterion is commodity (or goods) coverage. Physical output measures used for calculating
energy intensity in a specific industry should consist of
the major commodities or goods produced by that
industry. Suppose that an industry produces three major
commodities or goods which account for 80–100% of
that industry’s total physical output. If all three
commodities are included in the physical output
measure for that industry, then the energy intensity
ratio constructed at this level is likely to be adequately
representative of actual changes. Comparison of actual
commodity coverage in the data to the recommended
commodity coverage is therefore used to evaluate how
well the composites meet this criterion.
Each physical composite indicator is given a subjective rating for completeness and commodity coverage
and is shown in Table 2.
3
This re-definition of completeness is not of serious concern, in this
case, since changes in sub-sectors/industries with a relatively small
share of total energy consumption are not likely to have a large impact
on sector-level or aggregate energy intensity change.
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M. Nanduri et al. / Energy Policy 30 (2002) 151–163
Table 2
Evaluation criteria for physical composite indicators
Criteria
Completeness
Commodity (goods) coverage
Rating
Poor
Good
Excellent
Few sub-sectors or industries
included; energy-intensive
industries missing
Less than 50% of major
commodities represented
Many sub-sectors and industries
included; energy-intensive ones
represented
50–75% of major commodities
represented
Majority or all sub-sectors
and industries included; all
energy-intensive ones represented
75–100% of major commodities
are represented
A final rating is assigned to each of the composites
based on their score with respect to each criterion. The
number of ‘‘poor’’, ‘‘good’’ and ‘‘excellent’’ ratings
allocated to each one provides a general, overall
indication of its reliability in terms of measuring
aggregate physical energy intensity change.
3. The ‘‘Aggregation’’ problem
While indicators and indexes are terms that are often
used interchangeably, they are not necessarily the same.
An indicator may be interpreted as a series of
observations about a specific variable (or set of
variables) believed to represent the behavior of some
specific occurrence. Indicators are typically relied upon
to monitor and analyse changes in important variables,
and they most often take the form of a quantitative
index. An index number can be formally defined as
a number expressing the value of some entity, say
price or gross national product, at a given period of
time in absolute number form but related to a base
period which is arbitrarily set equal to 100 (Pearce,
1992).
Index numbers make very useful indicators since they
are both an effective means of summarizing observations
about a variable, and an easy way to obtain necessary
information about trends in a significant variable.
Furthermore, different indexes can often be combined
into a single, composite index capable of summarizing
observations and information about many different
variables. However, an indicator need not be an index.
Simple energy intensity indicators, such as energy
consumed per unit of gross domestic product, are often
used to indicate the level of energy efficiency in a
particular industry or sector, but do not need to be
constructed using an index.
4. Problems aggregating indicators
As mentioned in the introduction, difficulties in
aggregating different indicators and/or indexes into a
single measure exist. Methods of aggregation do exist,
however; the true difficulty lies in aggregating indicators
or indexes into a single, meaningful measure. According
to Patterson (1996), each of the three main types of
energy intensity indicators that can be used to track
progress in energy efficiency is subject to various
aggregation-related problems.
Since most end-use services and human activities can
be logically expressed in physical terms, analysts often
depend on physical intensity indicators to approximate
changes in energy efficiency. Physical energy intensity
indicators are ratios where energy consumption or
energy input is expressed in energetic units such as
Joules, and output produced is expressed in physical
units such as tonnes or litres. Since it is relatively easy to
understand the relationship between the amount of
energy needed to produce one physical unit of some
good, changes in physical indicators are thought to
provide reliable estimates of changes in energy efficiency
(Phylipsen et al., 1996, 1997; Nyboer et al., 1996a, 1997;
Farla et al., 1997).
However, physical energy intensity indicators can
only be constructed using disaggregate data due to the
diverse output of different sectors, sub-sectors and
industries. In other words, when there are numerous
outputs or services produced by many different industries, it becomes difficult to develop an aggregate
measure of energy intensity. This aggregation problem
is especially true of the industrial sector and its largest
sub-sector, manufacturing. The heterogeneity of the
manufacturing sub-sector makes the development of a
single physical indicator virtually impossible since
diverse output units such as tonnes, cubic metres or
litres are not additive. Even if the goods produced by a
sub-sector can be measured in like units (tonnes, for
example), it is not always meaningful to add tonnes of
one product to tonnes of another, especially if the
energy-consuming processes required for their production are very different. Although a single indicator may
not be appropriate for obtaining a comprehensive
understanding of energy efficiency change in a sector
(Phylipsen et al., 1996), the search for a method to
aggregate different physical intensity indicators continues.
M. Nanduri et al. / Energy Policy 30 (2002) 151–163
Since human activities are also commonly expressed
in financial terms, monetary or economic intensity
indicators are also used extensively. The use of
monetary measures of value solves the aggregation
problem described above by relying on a common
unit of output specification, dollars. On a national
or economy-wide level, the energy–GDP ratio is
frequently used as a broad indicator of aggregate
energy efficiency. Economic intensity indicators can
provide policy-makers with a single number that
reflects the state of energy use in the economy
in a way that physical energy intensity indicators
cannot. The preference for a monetary proxy, however, appears to be somewhat arbitrary. Some analysts
use GDP to avoid the double-counting of goods
inherent in Gross Output, while others maintain
that any double-counting in Gross Output figures
is minimal, and rely on it because it is thought to
track changes in physical production levels and inventory more closely (U.S. Department of Energy and
Energy Information Administration, 1995). However,
the accuracy with which a particular proxy tracks
physical production levels differs from country to
country (Worrell et al., 1997), and can also vary from
data set to data set.
A more significant problem concerns the relationship
between energy consumption and the value of output, as
represented by a monetary proxy like GDP, which is
thought to be weaker than the relationship between
physical production and energy consumption. This is
believed to be due, at least in part, to structural effect,
which can change the numerical value of an economic
intensity indicator. Consequently, it is often more
difficult to interpret the value given by an economic
intensity indicator. Indeed, many analysts view the
energy–GDP ratio as a measure of economic efficiency
rather than energy efficiency (Phylipsen et al., 1996).
Interpretation diverges even more if such indicators are
used in cross-country comparisons, since definitions of
monetary values may vary from country to country.
Thermodynamic indicators rely exclusively on measurements derived from thermodynamics, which is
defined as the science of energy and its processes
(Patterson, 1996). The main advantage of this indicator,
which defines energy efficiency (energy intensity) relative
to either first-law energy efficiencies (the heat content of
inputs and outputs of a process or device) or second-law
energy efficiencies (the theoretical minimum amount of
energy required for a task relative to the heat content of
inputs of the process or device), is that it can provide
‘‘an objective measure for a given process in a particular
environment’’ (Patterson, 1996). Also, a common unit of
output measurement can be used at all levels of
aggregation, thus solving the aggregation problem.
However, such indicators focus on the work done by a
process (the physical definition of energy), which implies
155
that they do not adequately consider the specific output
service provided by the process or device (units of
product or type of service). Furthermore, the use of
thermodynamic indicators ignores the value ascribed to
different forms of energy through the market (Zarnikau,
1999).
5. Problems aggregating indexes
The classical index number problem is also one of
aggregating different variables into a single measure.
Particular problems arise when trying to compare an
aggregated ‘‘single measure’’ at different points in time.
While there are many ways to combine separate indexes,
agreement on the best way to do so does not exist.
Furthermore, although most index number formulations appear to be very similar, they tend to produce
slightly different numbers.
The simplest way of combining several different
variables into a single index is to calculate a baseweighted index, also known as a Laspeyres index.
The central problem with the Laspeyres and other
fixed-weight indexes is that by anchoring themselves to one period only (the base period), the growth
or change in the variable over time tends to be
overstated (Diewert 1989, 1992). Nevertheless, the
Laspeyres index is widely used because of its simplicity. An alternative is to calculate an index based
on current weights, also known as a Paasche index.
Many argue that the Fisher ideal index, which is
the geometric mean of the Laspeyres and Paasche
indexes, has more attractive theoretical properties,
and should be used instead of the others (Diewert,
1989, 1992). Diewert (1989, 1992), who is a strong
proponent of the Fisher ideal index, has used an
axiomatic approach to examining various index
number formulations. While the Fisher ideal index
passes all 20 tests required to formulate a ‘‘good’’ index
number, the Laspeyres index fails a few of these tests.
The most important test it fails is the time reversal
test, which indicates that a base-weighted index will be
sensitive to the choice of, and changes in, the base
year (Diewert, 1989). For the most part, then, while
there are different ways to get around the aggregation
problem, the choice of a method still appears to be
largely subjective.
6. Review of existing aggregation methods
6.1. The fixed basket approach
The concept of defining a physical intensity indicator
in terms of a fixed basket of goods was emphasized in
Phylipsen et al. (1996). The goods making up the fixed
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M. Nanduri et al. / Energy Policy 30 (2002) 151–163
basket would be, for example, one tonne of each of the
most energy-intensive industrial products in a given
economy.4 The formula for calculating aggregate
physical energy intensity in such a case is
n
X
wi PEIi ;
ð1Þ
PEIagg ¼
significant issue. Finally, analysts may want to track
more than just the energy-intensive goods in a sector.
This may be particularly true in Canada, where energyintensive goods consume nearly 80% of total industrial
energy but contribute to less than 40% of the nation’s
GDP (CIEEDAC, 1996).
i¼1
where PEIagg refers to aggregate physical energy
intensity, wi is a weight factor equivalent to the
energy consumption of sector i divided by the total
energy consumption of the basket (i.e., Ei =E basket ) and
PEIi is the physical energy intensity of sector i:
Expanding on Eq. (1) gives
X E i En
PEIagg ¼
ðPEIi Þ þ ? þ
ðPEIn Þ;
Ebasket
Ebasket
ð2Þ
where all the energy-intensive goods are included in the
basket. This method could presumably be used to
develop baskets at various levels of disaggregation if
necessary, thereby allowing for estimation of aggregate
physical energy intensity values for sectors, sub-sectors
and industries alike.
This method is appealing when compared against the
four criteria outlined previously. The method is intuitively simple and easy to calculate (providing data are
available), which makes it easy to use. The resulting
aggregate physical indicator is also easy to interpret,
since it would simply reflect aggregate physical energy
intensity for a given year. This method also allows for
the weight factor to be something other than an energy
share. Lastly, tracking changes in the fixed basket from
one year to the next would make monitoring energy
intensity in certain key industrial sectors easier.
Nevertheless, the Fixed Basket Approach has several
challenges. First, not all countries produce the same
energy-intensive goods. Consequently, this method may
not be suitable for international or cross-country
comparisons. Second, if lower-level indicators were
aggregated using the above method, then fixed baskets
would have to be defined at these more disaggregate
levels as well. Defining a fixed basket of goods at the 4digit SIC level, at the 3-digit level and then finally at the
2-digit level might be cumbersome, especially since a
basket at the 4-digit SIC level is likely to consist of
individual goods whose production may change more
often that of an entire industry or sub-sector. Third, the
basket of fixed goods might not really be ‘‘fixed’’ and
may need frequent revisions in order to be representative. Further to this, disagreement on what actually
constitutes a representative set of goods might be a
4
Ultimately, the analyst must decide which goods to include in the
basket. Instead of including only energy-intensive goods, for example,
goods that consume a great deal of energy in general (but not per unit
of output) might also be included in the basket.
6.2. The Laspeyres physical index approach
The Laspeyres index method, a fixed-weight index
that uses base year weights to track the change in a
variable between two periods of time, is typically used
for calculating economic intensity indicators. The first
step in formulating a Laspeyres physical index is to
develop aggregate physical intensity indicators for a
fixed basket of energy-intensive goods for both a
reference year, 0, and a comparison year, t: The
Laspeyres physical index is expressed as
Pn
ðPi;0 ÞðPEIi;t Þ
P
LASP ¼ ni¼1
;
ð3Þ
ðP
i;0 ÞðPEIi;0 Þ
i¼1
where Pi;0 refers to the physical output of sector i during
the reference year, and PEI is the aggregate physical
energy intensity during the comparison and reference
years respectively. In this sense, it is also a Fixed Basket
Approach, although the weights relate to output rather
than energy. The denominator essentially becomes the
minimum energy requirement for the fixed basket of
goods in the reference year (E0 ). The numerator also
represents the energy needed for the fixed basket of
goods, but accounts for aggregate physical energy
intensity in the comparison year. The generalized
Laspeyres physical index then becomes
LASP ¼
PEIagg
i
:
PEIagg
0
ð4Þ
This index gives full weight to the base year (reference
year), which means that the value of the index in year 0
equals one. For subsequent years, index values greater
than one are suggestive of increasing energy intensity for
the basket of goods (relative to the reference year), and
vice versa.
The Laspeyres Physical Index Approach suffers from
the same problems as the Fixed Basket Approach. They
also share many of the same appealing attributes,
namely both are easy to calculate. This ease of
calculation and ease of interpretation make them both
useful to an analyst.
6.3. Actual SEC/reference SEC ratio approach
Some recent research (Phylipsen et al., 1996, 1997;
Farla et al., 1996, 1997; Worrell et al., 1997) has
advocated the development of physical energy intensity
indicators at various levels of aggregation. These
researchers generally designate physical energy intensity
M. Nanduri et al. / Energy Policy 30 (2002) 151–163
as specific energy consumption (SEC), and the same
terminology is used here with specific reference to this
method. Similarities with the Fixed Basket Approach
and the Laspeyres Physical Index Approach include the
development of a weighted index to represent major
industrial products, the aggregation of disaggregatelevel physical intensity indicators (i.e., SECs at the 4- or
3-digit SIC level) to get an aggregate indicator, and the
construction of an index which reflects how much
energy efficiency has improved/not improved compared
to some reference level of efficiency.
It is also different in some major ways. The method
was developed primarily for constructing sector (2-digit
SIC level) level physical intensity indicators and has
mainly been applied to the pulp and paper sector (Farla
et al., 1997) and the iron and steel sector (Worrell et al.,
1997). Re-aggregation at the aggregate level is possible
however (Phylipsen et al., 1996), and is the focus here.
The physical energy intensity indicator for some good
x is defined as
SECx ¼
Ex
;
Px
ð5Þ
where E is the specific amount of energy consumed by
activity or good x; and P is the physical output
produced by activity or good x: The SEC (physical
energy intensity indicator) for some sub-sector i (pulp
and paper, for example) then becomes
Pn
Ex
SECi ¼ Px¼1
;
ð6Þ
n
x¼1 Px
where the numerator is the summation of the energy
consumed by all goods produced by sub-sector i; and the
denominator is the summation of all goods produced by
the sub-sector. This methodology assumes that goods in
the sub-sector are expressed in common physical units
(number of cars, number of computers, tonnes of pulp,
etc.). This is necessary since the quantities of goods
produced must be added in the denominator, although
this is not always the case in practice. A physical
production index (PPI) is then constructed to account
for any changes in the composition of output from the
sub-sector (Worrell et al., 1997; Farla et al., 1997). This
is expressed as
PPI ¼
n
X
ðPi Þðwi Þ;
ð7Þ
i¼1
where the output of each sub-sector i is weighted by
some factor w that reflects the energy needed to produce
all of the goods in that sub-sector. In this method, the
weighting factors ‘‘must be chosen to indicate appropriately the amount of energy needed to produce the
output of the sub-sector’’ (Farla et al., 1997). These
factors should therefore reflect one of the following
(Phylipsen et al., 1997):
*
*
*
*
157
best practices observed, which represents the practices of a complete production plant with the lowest
SEC already in full operation;
best practical means, which represents a production
plant with the lowest SEC that can be realized using
proven technologies at reasonable costs;
best available technologies, which represents a
production plant with the lowest SEC that can be
realized using proven technologies; and/or
an average SEC, which represents an average energy
efficiency for a set of comparable countries/regions
that can be used as a benchmark.
Such weighting factors can be used to create reference
SECs for a number of sub-sectors. Actual SEC in a
sector can then easily be compared to the reference SEC
for the sector (once they have been aggregated),
providing a measure of energy efficiency change. The
weighting factor is expressed as
Eref;i
wi ¼ SECref;i ¼
;
ð8Þ
Pi
where Eref;i is the reference energy consumption across
all goods in sub-sector i derived from one of the
definitions outlined above. Substituting this into the PPI
equation yields
X
PPI ¼
Eref;i :
ð9Þ
The PPI is used when aggregating to create an energy
efficiency indicator. The aggregated sectoral indicator
(2-digit SIC level) is then calculated as
P
P
P
ðPi ÞðSECi Þ
Ei
Ei
P
P
¼
¼
;
sectoralindicator ¼
PPI
ðPi ÞðSECref;i Þ
ref;i
ð10Þ
where the summation is across all the goods (x; y; n) in
all sub-sectors (i ¼ 1; y; n). The indicator is ultimately a
ratio similar to that derived in the Physical Laspeyres
Index Approach. If the indicator equals 1, then the
sector is producing at an SEC equal to the reference
SEC, meaning that energy is being used as efficiently as
possible given the definition of energy efficiency used to
construct the reference figure. A ratio greater than 1
suggests that energy efficiency can still be improved.
Obtaining a single, all industry indicator is also possible
using Eq. (10). This is expressed as
P1 SEC1 þ ? þ Pn SECn
E1 þ ? þ En
¼
;
P1 SECref;1 þ ? þ Pn SECref;n Eref;1 þ ? þ Eref;n
ð11Þ
where the numerator sums actual energy consumption
for all the sectors being added, and the denominator
sums reference energy consumption for the same sectors.
The reference energy consumption for each of the
sectors would presumably be made up of the reference
energy consumption for each sub-sector and so forth.
158
M. Nanduri et al. / Energy Policy 30 (2002) 151–163
The result would be a single number greater than, equal
to or less than 1, reflecting energy efficiency at the
aggregate (in this case all industry) level.
One of the most positive and useful aspects of this
approach is that it provides energy analysts and policymakers with a way to judge, using a single ratio, how
well various sub-sectors and sectors are using energy. It
also gives them a way to aggregate these sub-sectoral
and sectoral indicators, and derive a measure of how
energy efficiency is changing at different levels of the
economy without defining a fixed basket of goods. It
gives analysts an idea of the gap that exists between
actual energy use and a potential minimum energy use,
which can then be further analysed. The methodology is
also easy to apply and, like the other two methods, easy
to interpret. Furthermore, one can account for structural changes using this method. Farla et al. (1997) and
Worrell et al. (1997) both used these indicators in studies
where energy consumption in different sectors was
decomposed into its related activity, structural and
efficiency effects. The use of weighting factors is also
valuable, because they can help reflect differences
between countries if a global average SEC is used as a
reference. This facilitates international comparisons of
physical energy intensity indicators.
There are, however, two disadvantages to this
method. First, it may be difficult to find suitable data,
particularly for the formulation of weighting factors.
Currently, ‘‘best plant’’, ‘‘best practice’’ and other types
of similar data may be not readily available, making it
difficult to calculate a realistic reference SEC. Secondly,
international consensus on how to define ‘‘best practice
observed’’, for example, is problematic. Other challenges
include the fact that the weighting factors change over
time, sometimes from year to year, and would require
regular updating. Even if the data needed to formulate
the weighting factors were available, they may not be on
a regular or annual basis. This approach is fundamentally designed to construct an ‘‘ideal’’ aggregate physical
intensity indicator. At the moment, however, given the
lack of suitable data, this approach may be somewhat
less than ideal.
7. An alternative aggregation method: the composite
indicator approach
The Composite Indicator Approach, originally developed by the U.S. Department of Energy and Energy
Information Administration (1995), combines many
aspects of the previous approaches. By definition, a
composite is something made up of, or amalgamated
from, distinct parts. This concept lends itself nicely to an
aggregate indicator that must be developed from energy
intensity values which use different units of physical
output. This method was first created to develop an
economy-wide intensity indicator based on physical
energy intensity. According to the U.S. Department of
Energy and Energy Information Administration (1995),
the composite index is constructed by first developing
energy intensity indicators for the major economic
sectors (industrial, commercial, residential, transportation and agriculture), which are subsequently combined
to form an economy-wide measure of energy intensity.
This method overcomes the aggregation problem by
focusing on the percentage change in physical energy
intensity that occurred in each of the major sectors. The
percent changes are energy-weighted; each intensity
indicator is weighted by that sector’s (i.e., industrial,
residential, etc.) percent share of total energy consumption in year t. Summing these values results in an
aggregate physical intensity indicatorFa physical composite indicatorFthat reflects changes in economy-wide
energy intensity (between 2 specified periods of time),
but still reflects the uniqueness of the physical output
measure used for each sector (U.S. Department of
Energy and Energy Information Administration, 1995).
So, even if one sector’s energy intensity (at the 2-digit
SIC level, for example) is measured in TJ/m3 and
another’s is measured in TJ/tonnes, the percentage
change in both these physical intensity indicators can
be weighted by each sector’s share of aggregate energy
consumption, and then added to form a composite.
8. Applying the composite indicator approach
In this study, the above method is used to develop
physical composite indicators for a number of typically
heterogeneous Canadian industrial sectors, and for the
Canadian manufacturing/industrial sector as a whole.
This type of indicator is built up from lower-level
changes in the physical energy intensities of industries
and sub-sectors which are weighted by their respective
shares of total industrial energy consumption. Physical
composite indicators are constructed for all available 3and 2-digit SIC/ISIC levels in order to maintain
consistency and uniformity in the application of this
method. The simple numeric example in Table 3 uses
SIC 10 (food and food products) to illustrate the
application of this methodology.
In this example, data for SIC 10 include 2 sub-sectors:
101 (meat and poultry products) and 104 (dairy
products). Given that physical energy intensity for SIC
101 is expressed in TJ/tonnes of meat, and physical
energy intensity for SIC 104 is computed using TJ/kl, a
physical composite indicator is needed in order to obtain
a physical intensity value for SIC 10. The second and
third columns of Table 3 show each sub-sector’s
respective physical energy intensity in the base year
(1990) and the comparison year (1995). The fifth column
depicts the percent change in these indicators from 1990
159
M. Nanduri et al. / Energy Policy 30 (2002) 151–163
Table 3
Example of aggregation methodology applied to develop physical composite indicators for the Canadian industrial sector
SIC
code
Physical energy
intensity 1990
Physical energy
intensity 1995
Change in physical
energy intensity
% Change in physical
energy intensity
1990–1995
% Energy share of
SIC in 1995
Energy-weighted
physical energy
intensity
101
104
5.69
1.62
4.57
1.57
1.12
0.05
24.51
3.18
0.16
0.13
3.92
0.41
Composite for SIC 10=(0.16)(3.92)+(0.13)(0.41)=0.68%
Table 4
Physical composite indicators for the Canadian industrial sector for the periods 1990–1994, 1990–1995 and 1990–1996
SIC
Sector
% D 90–94
% D 90–95
% D 90–96
6
61
62
10
11
27
29
32
35
36
All industries
Manufacturing industries
Mining
Metal mines
Non-metal mines
Food and food products
Beverage industries
Pulp and paper and allied products
Primary metals industry
Transportation equipment industry
Non-metallic mineral products industries
Petroleum refining and products
3.28
2.98
8.29
0.73
9.02
2.18
0.26
7.91
0.65
4.37
4.04
0.62
3.45
3.66
0.25
1.75
4.4
3.17
7.99
9.03
0.67
2.82
2.53
0.92
3.14
3.08
4.29
1.67
9.87
4.25
7.85
2.56
8.97
6.72
6.25
4.19
to 1995, while the sixth column indicates each subsector’s share of total sectoral energy consumption in
the comparison year. Using SIC 101 as an example, this
was calculated as
E101 14; 210
¼ 0:16:
ð12Þ
energy share ðSIC 101Þ ¼
¼
90; 358
E10
Lastly, the values for each SIC under the energyweighted PEI heading are the physical energy intensity
values which are then added together to form the
composite for SIC 10.
These calculations can be summarized in a simple,
generic equation:
PCI90295 ¼ ð%DA90295 Þð%DEA;95 Þ þ ð%B90295 Þð%EB;95 Þ;
ð13Þ
where PCI is the physical composite indicator to be
computed between specified periods of time and
expressed in percent changes, the term (%DA90295 )
refers to the percentage change in physical energy
intensity for some sub-sector (A in this case), the term
(%EA;95 ) is the energy share of that sub-sector in the
final year, and the PCI may be made up of sub-sectors
A; B; C; y; N: Table 4 depicts the physical composite
indicators developed for the Canadian industrial sector.
Each composite represents the percentage change in
physical energy intensity, measured initially in units
specific to the industry/sub-sector, between 1990 and the
comparison years. Eq. (13) is also applied to get an
indicator for manufacturing: each sectoral (2-digit SIC)
composite (except SIC 6, mining) is multiplied by that
sector’s share of total manufacturing energy consumption for year t and then summed. The aggregate (all
industry) indicator is computed by adding the energyweighted manufacturing composite and the energyweighted mining composite. For the sake of comparison, a set of production-weighted (e.g., Y10 =Y man )
composites is developed for 1990–1996, where
‘‘production’’ is GDP in millions of 1986 constant
dollars. These are shown in Table 5.
9. Results
All the aggregate indicators in Table 4 suggest that
industrial physical intensity declined during the periods
1990–1994, 1990–1995 and 1990–1996. The actual size of
the reduction in physical energy intensity appears to
have remained constant for each of the three periods.
The physical composite indicator for manufacturing
exhibits the same trend. Energy intensity in most of the
energy-intensive sectors (pulp and paper, petroleum
refining, non-metallic mineral industries and primary
metals) also declined, although each experienced their
biggest reductions in different periods. An exception is
the mining industry, where energy intensity rose from
1990 to 1995. The overall decline in aggregate intensity
for 1990–1994 appears to have been significantly
160
M. Nanduri et al. / Energy Policy 30 (2002) 151–163
Table 5
Comparison of physical composite indicators and CIEEDAC’s monetary and physical indicators for the period 1990–1996 (in % change)
SIC
Ind
Man
6
61
62
10
11
27
29
32
35
36
Composites
Composites
CIEEDAC-production
CIEEDAC-monetary
Energy-weighted
Production-weighted
E/P
E/GDP
3.14
3.08
4.29
1.67
9.87
4.25
7.85
2.56
8.97
6.72
6.25
4.19
0.67
0.27
8.7
8.52
9.57
4.5
8.2
1.81
4.77
4.17
1.54
na
na
na
2.22
2.78
9.79
na
na
na
na
na
na
na
2.5
3.35
13.39
16.06
13.26
6.2
34.59
13.05
4.08
3.44
12.02
7.58
influenced by large intensity reductions in the mining
(8.3%) and pulp and paper (8%) sectors. The same
cannot be said for the period 1990–1995. Although the
9% intensity decline in the pulp and paper sector
probably contributes to the overall decrease in aggregate
energy intensity, overall declines in other energyintensive industries seem more moderate. The exception
is SIC 11 (beverages), which experiences an 8% decrease
in energy intensity. However, this industry only represents about 0.5% of total industrial energy consumption.
During 1990–1996, large intensity declines in the nonmetallic mineral products sector (6.25%) and the
primary metals sector (9%), in addition to moderate
intensity declines in mining (4.3%), petroleum refining
(4.2%) and food products (4.25%) are likely to have
influenced the 3.14% decrease in aggregate intensity.
The physical composite indicators also show that
energy intensity increased for some significant sectors.
Energy intensity in the mining sector, for example,
increased from 1990 to 1995, albeit by a small amount.
The moderate rise (4.4%) in energy intensity in the nonmetal mining sub-sector may partially explain this
increase. However, energy intensity in the transportation equipment sector rose substantially in almost all
three periods.
Table 4 indicates that major intensity shifts occurred
during the periods 1990–1995 and 1990–1996, relative to
the 1990–1994 period. For example, SIC 6 shows a large
drop in intensity from 1990 to 1994, an increase during
1990–1995, and another drop for the period 1990–1996.
The magnitude of the variation in energy intensity
within this sector is striking, and begs the question of
what might have happened in 1995 and 1996 to produce
such different estimates of energy intensity. One possible
reason is that many changes occurred in the individual
sub-sectors contributing to the SIC 6 composite. In the
period 1990–1994, for instance, there were large intensity
declines in the gold, silver, iron and peat mining subsectors, several of which consume a relatively large share
of total mining energy. Although intensity in many of the
same sub-sectors declined during 1990–1995, others such
as uranium and other metal and non-metals mines, show
large increases. This may be why SIC 62 and SIC 6 show
an increase in physical energy intensity for this period.
Likewise, significant decreases in energy intensity occur
for the potash (the largest energy consumer of all nonmetals mines) and salt mine sub-sectors in 1996,
influencing the observed decline in physical energy
intensity for this period.
Missing data can also potentially affect the value of a
composite indicator. Two industries, soft drinks and
brewery products, are included in the physical composite indicator for SIC 11 in 1990–1994. However, data
for the soft drinks industry are unavailable for 1995 and
1996. Consequently, the SIC 11 composite only reflects
changes in the sector’s largest energy consumer, brewery
products, for the periods 1990–1995 and 1990–1996.
This may be affecting the value of the indicator to a
certain extent.
The adoption of a ‘‘periodwise’’ approach to indicator
development may also be influencing composite values,
since annual data between the base and comparison
years are ignored. If significant changes in industry or
sub-sector-level energy intensities occurred during the
missing years, these changes, if recognized, may help
interpret the varied results of table 4 by placing them in
a more appropriate context.
Table 5, which depicts GDP-weighted composites for
1990–1996, also produces interesting results. As in Table
4, the aggregate indicators for industry and manufacturing suggest that energy intensity decreased, although by
a smaller amount. Indeed, the majority of the GDPweighted composites show the same trends as the energyweighted physical composite indicators. Only the composite for SIC 27 suggests a contradictory trend, that
energy intensity during 1990–1996 rose. This is likely to
be because the GDP shares of several pulp and paper
sub-sectors and industries declined during this period.
M. Nanduri et al. / Energy Policy 30 (2002) 151–163
Table 5 also compares the composites with the
physical and economic intensity indicators developed
by CIEEDAC for 1995 and 1996. These are columns 4
and 5 respectively. The absence of physical intensity
indicators for virtually all major sectors as a result of the
aggregation problem is striking. In contrast, the physical
composite indicators, however simple, provide at least a
measurement of the percentage change in physical
energy intensity for all the key energy-consuming
sectors. Developing a physical composite indicator that
measures the percent change in energy required to
produce one unit of each of these goods is one way to
estimate whether or not energy efficiency in the sector is
improving, something not permitted by the use of simple
physical intensity indicators as they are.
In general, the numerical values of the indicators
diverge. With the exception of SIC 35, though, the
trends suggested by CIEEDAC’s economic intensity
indicators (E/GDP) are very similar to the trends
implied by the production-weighted composites. One
possible explanation for the value of SIC 35 might be
that despite an increase in the output produced by SIC
3521 (the major sub-sector of SIC 35), GDP for both the
sub-sector and the sector declined, while overall energy
consumption rose. Both of these might be contributing
to the intensity rise for SIC 35.
10. Evaluating the physical composite indicators
Determining the reliability of a set of indicators is a
difficult task. The composite indicator is an indexFa
way to combine and add things that normally cannot be
161
added. Whether or not an indicator is reliable depends
primarily on two factors: the validity of the methodology and the quality of the data. In this case, the
methodology itself is not entirely new. Although
developed by the U.S. Department of Energy and
Energy Information Administration (1995) in the context of energy intensity, weighted indexes are very
common to economics, finance and banking. As such,
it is likely that the reliability of the composites
developed in this paper depends more on the quality
of the data used in their construction than on the
methodology itself.
The first criterion used to establish reliability is
completeness. Each composite developed in this analysis
uses data from a different number of sub-sectors and
industries, based completely on the availability of
suitable, disaggregate data (see Table 1).
Intuitively, the physical composite indicators which
are comprised of many sub-sectors are likely to estimate
changes in physical energy intensity more accurately
than ones which do not. For example, the composites
for SIC 6, SIC 61 and SIC 62 can be considered the most
complete, since data for all the sub-sectors making up
these sectors are included. Based on the criteria in Table
2, the composites for SIC 6 and SIC 27 merit a
subjective rating of ‘‘excellent’’ in terms of completeness.
Most physical composite indicators, with the exception
of SIC 35 and SIC 29, include the most important subsectors and industries, and are rated ‘‘good’’ in terms of
completeness. It is more difficult to rate the aggregate
industrial indicator in terms of completeness. Although
the majority of necessary sectors are included in the
manufacturing composite, physical composite indicators
Table 6
Comparison of actual and required commodity coverage for physical output measures used in the physical composite indicators
Standard industrial
classification codesa
Approximate % of commodities
represented
2711
2712
2713–2719
3611
611
612–619
617
622,623,629
624
625
2919
91
87
84
84
88
98
99
98
96
98
Unknown
2951
26
3521
3581
101
104
1131
3231
96
Unknown
75
100
100
100
Comments
Major commodities represented
Major commodities represented
Major commodities represented
Major commodities represented
Actually includes only raw steel production.
Major commodities missing
Only includes production of aluminium.
Other commodities missing
Major commodities represented
Includes beef and poultry, but should also include pork
Major commodities represented
Major commodities represented
Major commodities represented
162
M. Nanduri et al. / Energy Policy 30 (2002) 151–163
for two energy-intensive industries, wood products
(SIC 25) and chemical products (SIC 37) could not be
constructed for 1995 and 1996 since the required subsector- and industry-level data were poor and/or
unavailable. The lack of a composite for SIC 37 is
especially troublesome, since eliminating it from the
manufacturing composite in the period 1990–1994
changed its numerical value slightly. However, a reversal
in the overall declining trend in industrial/manufacturing energy intensity is unlikely since the chemical
products sector is responsible for about 11% of total
manufacturing energy consumption. A cautious rating
of ‘‘good’’ is therefore assigned to both aggregate
indicators. These are shown in Table 7.
Table 6, adapted from information in Informetrica
Limited (1995, 1996), depicts the requirements regarding
the criterion of commodity coverage.
The second column of Table 6 indicates the approximate percentage of commodities represented by the
industries/sub-sectors listed in the first column, and
shows that the majority of the data used for the
composites have very good commodity coverage.
Virtually all the composites can be rated ‘‘excellent’’
based on the numbers in Table 6. The exception to this is
SIC 29. The physical composite indicator for SIC 35 is
also dubious in this respect. Examining how each
composite fares in terms of the criteria makes it possible
to assign them ratings shown in Table 7.
The SIC 6 (mining) and SIC 27 (pulp and paper)
composites receive the highest ratings. In fact, most
composites receive ‘‘good’’ or ‘‘very good’’ overall
ratings, since they score well in terms of completeness
and commodity coverage. The exceptions, again, are
SIC 35 (non-metallic mineral products) and SIC 29
(primary steel industries). They are likely to be less
reliable in terms of the information they provide
regarding changes in physical energy intensity.
The original definition of completeness states that a
physical composite indicator can be deemed complete if
all necessary industries and/or sub-sectors are included
in its development. Despite the fact that including the
energy-intensive industries in a composite is sufficient
for the assignment of a ‘‘good’’ overall rating, the
original definition should still be used as a standard.
Efforts to get more, better quality physical data should
continue in order to achieve this end. It is only by
accounting for all industries and/or sub-sectors that a
composite can accurately reflect energy intensity
changes that occur at all levels of the economy. Such a
physical composite indicator is likely to be the most
reliable in terms of measuring aggregate changes in
physical energy intensity.
11. Conclusions
None of the four methods examined in this paper
were exclusively designed to develop an aggregate
physical intensity indicator. However, any one of them
can feasibly be used for this purpose. The Fixed Basket
Approach, perhaps the most intuitively appealing of the
four methods, does not provide the analyst with as much
information as the other three methods. It also makes
cross-country comparisons more difficult, since not all
countries produce the same basket of goods. The
Physical Laspeyres Index Approach is slightly better in
that it allows the analyst to compare physical intensity
in a given year with physical intensity in a reference
year, and does so using a single ratio. It does, however,
suffer from many of the same problems as the Fixed
Basket Approach.
The Actual SEC/Reference SEC Approach is probably the ideal physical intensity index. It allows for the
easy comparison of actual versus recommended energy
efficiency and can be readily used in a decomposition
analysis. Recent literature indicates that this method is
becoming more widely used, particularly with respect to
European Union energy intensity studies. International
comparisons using this method are also possible,
provided that both the reference SEC and the weight
Table 7
Ratings for physical composite indicators
Sector composite (SIC codes)
Completeness rating
Commodity coverage rating
Overall composite rating
Industrial
Manufacturing
6
61
62
10
11
27
29
32
35
36
Good
Good
Excellent
Excellent
Excellent
Good
Good
Excellent
Poor
Good
Poor
Good
Excellent
Excellent
Excellent
Excellent
Excellent
Good
Excellent
Excellent
Poor
Excellent
Poor
Excellent
Very good
Very good
Excellent
Excellent
Excellent
Good
Very good
Excellent
Poor
Good
Poor
Good
M. Nanduri et al. / Energy Policy 30 (2002) 151–163
factors used in an analysis can be clearly defined. That
is, agreement on what constitutes ‘‘best practices
observed’’ or ‘‘best available technologies’’ is necessary.
The question of whether or not achieving consensus on
such definitions is realistic still remains, however, and is
a formidable barrier to the ready adoption of this
methodology.
In contrast to the difficulties associated with the above
methods, and given the types of energy and production
data typically available to analysts, the Composite
Indicator Approach serves as a very useful way to
derive estimates of changes in aggregate physical energy
intensity. Despite the fact that most of the physical
composite indicators developed in this paper perform
well, the quality and reliability of the composites can
still be improved with more detailed, disaggregate-level
data. Most importantly, this method effectively gets
around the aggregation problem and permits the
computation of an aggregate physical intensity indicator
for the industrial sector, which analysts have been
seeking for a long time.
There are, at the moment, no other studies with which
to compare the composites developed in this report. This
makes their assessment in the context of energy policy
somewhat limited. Nevertheless, this method warrants
consideration since it can make full use of all available
physical output and energy demand data to provide an
estimate of industrial physical energy intensity change,
and, ultimately, because it can be used to develop a
single physical energy intensity indicator for any
heterogeneous sector.
Acknowledgements
The authors would like to thank Natural Resources
Canada and the Office of Energy Efficiency for their
financial support in this research. They would also like
to thank Louise Metivier at NRCan and the Energy and
Materials Research Group for their helpful comments.
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