Changes in Energy Intensity in Canada
Saeed Moshiri
STM College, University of Saskatchewan, Canada
moshiri.s@mail.usask.ca
Nana, Duah
University of Saskatchewan, Canada
nkd535@mail.usask.ca
Abstract
Canada is one of the top ranked energy intensive and CO2 emitters among the OECD countries.
However, energy intensity has been declining on average by about 1.1 percent since 1980. In this paper,
we use the Fisher Ideal Index to determine the contributions of changes in economic activity and
efficiency to a decline in energy intensity in Canada at national, provincial, and industry levels. We also
apply panel data estimation methods to further investigate the factors driving energy intensity, efficiency
and activity indexes for the period 1980-2008. We test for endogeneity as well as the cross-section
dependency in the provincial data and control for factors such as climate, policy, and energy endowment.
The national and provincial decomposition results suggest that most of the reduction in energy intensity
have occurred mainly due to improvements in energy efficiency as compared to shifts in economic
activities. Within the industry, while manufacturing experienced a significant decline in energy intensity
mostly due to an improvement in efficiency, energy intensity has remained stable in transportation,
utilities, and construction, and increased significantly in mining. The provincial panel regression results
indicate that energy intensity is higher in provinces with higher income, faster population growth, colder
climate, and higher capital-labour ratio, and lower in provinces with higher energy prices and higher
investment ratio. The industry panel regression results show that investment has contributed to energy
efficiency in utilities and mining and to moving away from energy intensive activities in manufacturing
and transportation industries. Technological advances have been most effective in increasing energy
efficiency in construction and utilities and in moving to less energy intensive activities in manufacturing
industries. The results indicate that although efficiency contributes to a reduction in energy intensity in
Canada, increasing activities in energy intensive industries, such as oil and mining, partially offsets the
efficiency gains in other industries.
Key words: Energy intensity, efficiency, economic activity, Canada
JEL Classification: Q40, Q43, Q48
1
Introduction
Canada is one of the top ranked energy users in the world with its total energy use growing on average by
1.1 percent since 1980. The energy intensity, energy consumed per unit of output and measured by the
ratio of energy consumption to GDP, in Canada is 1.3 and 2.4 times greater than that in United States and
Germany, respectively. Canadian energy intensity has been declining recently, but Canada is still one of
the top ranked energy intensive and CO2 emitters among the OECD countries (Figures 1 and 2). As
emission control has become one of the key global issues in addressing environmental problems and
sustainability of economic growth, and more than 80 percent of Canada’s greenhouse gas emissions are
generated from energy production and consumption, Canada may need to develop more aggressive
policies to curb its energy consumption1. Therefore, understanding the factors driving the changes in
energy intensity is vital to any policy designs addressing high energy consumption.
[Figure1 and Figure 2 here]
It is important to note that a fall in energy intensity does not necessary mean total energy
consumption is falling. The ratio of energy per GDP can still fall even if total energy use is rising
because the percentage increase in GDP can be greater than the percentage increase in total energy
consumption. This has been the case in Canada for the past 30 years as total energy consumption has risen
on average by 1.1 percent annually, whereas GDP has been growing at an average of 2.5 percent.
Changes in energy intensity also reflect changes in either technology or economic activities. For instance,
lower energy intensity in Canada may have been caused by either an improvement in technology or
moving away from energy intensive sectors. This paper seeks to investigate the underlying factors driving
energy intensity changes in Canada. Specifically, the study intends to estimate the contributions of energy
efficiency and changes in economic activity to the declining energy intensity in Canada using the Fisher
Ideal Index decomposition method. Although the decomposition method is an established tool to isolate
1
Canada signed the Copenhagen Accord, the first international agreement to include all major emitting countries,
in 2009, thereby committing to reducing its greenhouse gas emissions 17% below 2005 levels by 2020
(Environment Canada, 2010).
2
two major sources of energy intensity changes, efficiency improvement and changes in economic activity,
it does not allow for an analysis of socio-economic forces influencing energy intensity, such as price and
income. This is particularly important in our study as Canada is a country of vast distances with high
income, distinct economic activities and climate across its provinces, and significant natural resources,
each of which contributes to the growing energy demand. We, therefore, apply econometric methods to
further investigate the determinants of changes in energy intensity in Canada using
provincial and
industry panel data.
The study is conducted in three levels; national, industry, and provinces. The national level gives
a general understanding of the changes in aggregate energy intensity. The aggregate data depicts a large
picture of the changes in energy intensity, but might lead to misleading outcomes as it hides away
individual responses to changes in economic and energy market conditions, particularly in Canada as a
large country with diverse economic activities. The provincial study enables us to take a closer look at
variations across provinces and time. We also delve into the industry level for a deeper understanding on
energy intensity changes in the individual industries. As far as the authors know, this is a first study on
energy intensity changes in Canada using both the decomposition and regression techniques at different
aggregation levels. The paper is organized as follows: Section 2 reviews previous studies; section 3
describes the decomposition analysis and presents the results. In section 4, we report and discuss the
results from the regression analysis and in section 5, we present the concluding remarks.
2. Review of Previous Studies
Two decomposition and econometrics approaches have been used to ascertain factors driving changes in
energy intensity; with most studies based on China and the United States. The decomposition approach
uses two main techniques: structural decomposition analysis (SDA) and index decomposition analysis
(IDA). Both the SDA and IDA have their respective advantages and disadvantages. Hoekstra and Van der
Bergh (2003) show that SDA produces a more refined decomposition because it uses the input-output
framework and also measures the indirect effects which emerge from spillover effects from increase in
3
demand of one sector. However, as they point out, the IDA, which uses aggregate sector information, has
been extensively used in decomposition studies because of its low data requirement. IDA has a wide
range of indexing techniques, but the two most popular methods are the Divisia Index and the Laspeyres
Index, with each method having their respective sub-techniques. The main difference between the
Laspeyres and Divisia Index methods is that the former is based on the concept of percentage change
while the latter deals with logarithmic change. Ang (2004) notes that the choice of Index method
generally depends on factors such as theoretical foundation, adaptability, ease of use, and ease of
understanding and result presentation.
Gardner (1993) uses the Divisia index approach to analysis the change in energy intensity of
Ontario, Canada, industrial sector for the period 1962-1984. He finds that changes in the energy
efficiency played an important role in the pre-oil shock period (1962-1973), but structural change had
more significant effect in the post-oil shock period (1973-1984). The study does not explain the causes of
structural changes and efficiency changes. Wing (2007) shows that structural change was the principal
driver of the overall decline in aggregate energy intensity in the U.S., but efficiency improvement
contributed most to the reduction in energy intensity in the post-1980 period. Howarth et al. (1990) also
conclude that structural change lead to modest reductions in energy use in the manufacturing sector of
some OECD countries. Fisher-Vanden et al. (2004) argue that the use of aggregate data, usually the twodigit industry level, can lead to the overstating of the impact of subsector energy productivity
improvements on energy intensity reduction. Many previous studies have used Laspeyres or Paache
indexes to decompose the energy intensity, but those indexes may produce different outcomes and leave
unexplained residuals. Boyd and Roop (2004) are the first to use the Fisher Ideal index to perfectly
decompose changes in energy intensity into structural and intensity effects. The Fisher Ideal index is the
geometric average of the Laspeyres and Paasche indexes, which produce perfect decomposition whereby
no unexplained residual term appears in the results. The need to have a perfect decomposition is critical as
the residual term could sometimes be even larger than the estimated effects (Ang, 2004).
4
Decomposition method is a standard method to isolating two factors, efficiency and economic
activity, comprising the energy intensity. However, it does not provide any economic explanations on
forces deriving changes in energy intensity. Regression analysis is, therefore, used to explain the
relationships between energy intensity and socio-economic variables. For instance,
Wing (2007)
attributes the decline in energy intensity in the U.S. to adjustments in quasi-fixed inputs—particularly
vehicle stocks
and disembodied autonomous technological progress, and shows that price-induced
substitution of variable inputs generate transitory energy savings, while innovation induced by energy
prices has only a minor impact. Howarth et al. (1990) note that rising energy prices and technological
advances reduce energy intensity. Using a firm-level data set, Fisher-Vanden et al. (2004) conclude that
China's declining energy intensity is being driven by rising relative energy prices, research and
development expenditures, ownership reform in the enterprise sector, as well as shifts in China’s
industrial structure. Gardner and Elkhafif (1998) also analyze the changes in industry structure and energy
intensity in Ontario for the period 1962-1992. They find economic growth and trend as major forces to
improving the efficiency of energy use within individual industries, thus reducing the intensity indexes.
Metcalf (2008) builds on Boyd and Roop's (2004) work focusing on energy indexes at the state level
and estimating the impact of changes in economic and climate factors on the energy intensity indexes in
the United States. The sectors included in this study are residential, commercial, industrial, and
transportation. The decomposition results show that the decline in energy intensity were driven more by
efficiency improvements and the regression results indicate that rising energy prices and income drove
changes in energy intensity in the United States. Song and Zheng (2012) also conducted decomposition
and econometric analysis of the driving forces behind China’s changing energy intensity path using a
provincial-level panel data for the period from 1995 to 2009. They concluded that rising income has
contributed to the reduction of energy intensity while the effect of energy price is relatively limited.
In this paper, we examine the driving forces behind changes in Canada's energy intensity at the
national, provincial and industry levels. We contribute to existing literature by employing the Fisher Ideal
Index to decompose the national energy intensity at the two- and the three-digit NAICS industry level
5
data for each industry. Our decomposition at the provincial level comprises of seven sectors: Agriculture,
Mining and Oil and gas extraction, Construction, Manufacturing, Transportation, Public administration
and other service sectors. We further employ a panel data analysis to examine socio-economic and
climate factors driving changes in energy intensity in Canadian provinces and industries.
3. Decomposition Method
Energy intensity can be written as the weighted average of sectoral energy intensity, where weights are
the output share of the sectors. That is,
𝑒𝑡 =
𝐸𝑡
𝑌𝑡
𝐸 𝑌𝑖𝑡
𝑖𝑡 𝑌𝑡
= ∑𝑖 𝑌𝑖𝑡
= ∑ 𝑒𝑖𝑡 𝑠𝑖𝑡
(1)
where e is energy intensity, Eit and Yit are the total energy consumption and GDP for sector i in time t,
respectively. Equation (1) indicates that the aggregate energy intensity is equal to the sum of the products
of energy intensity within a particular sector (eit) and changes in economic activity (sit) across sectors.
The energy intensity index (It) is then constructed by dividing the energy intensity in year t (et) by the
energy intensity in a base year (e0).
𝐼𝑡 = 𝑒𝑡 ⁄𝑒0 =
∑𝑖 𝑒𝑖𝑡 𝑠𝑖𝑡
∑𝑖 𝑒𝑖𝑜 𝑠𝑖0
The energy intensity index can be decomposed into two factors: The efficiency index and the
activity index. The efficiency index attributes energy intensity to efficiency change holding economic
activity constant, and activity index attributes energy intensity to change in the mixture of economic
activity holding efficiency within a sector constant. The decomposition can be done by either the
Laspeyres index, which uses a base period fixed weight, or the Paasche index, which uses an end period
fixed weight as follows.
Laspeyres Indexes
∑ 𝑒
𝑠
𝑒𝑓𝑓
𝐿𝑎𝑐𝑡
= ∑𝑖 𝑒𝑖𝑜 𝑠 𝑖𝑡
𝑡
𝐿𝑡
𝑖 𝑖𝑜 𝑖0
6
∑ 𝑒 𝑠
= ∑ 𝑖 𝑒𝑖𝑡 𝑠𝑖𝑜
𝑖 𝑖𝑜 𝑖0
Paasche Indexes
∑ 𝑒𝑖𝑡 𝑠𝑖𝑡
𝑖 𝑒𝑖𝑡 𝑠𝑖0
𝑒𝑓𝑓
𝑃𝑡𝑎𝑐𝑡 = ∑𝑖
𝑃𝑡
∑ 𝑒𝑖𝑡 𝑠𝑖𝑡
𝑖 𝑒𝑖𝑜 𝑠𝑖𝑡
= ∑𝑖
These indexes produce different decompositions as they use different base years and the decomposed
indices might not add up to the total energy intensity index. The Fisher Ideal Index is the weighted
average of Laspeyres and Paasche Indexes, which perfectly decomposes energy intensity into two
𝑒𝑓𝑓
efficiency (𝐹𝑡
) and activity (𝐹𝑡𝑎𝑐𝑡 ) elements with no residuals2. The Fisher Ideal indexes for efficiency
and activity are given by
𝑒𝑓𝑓
𝑎𝑐𝑡
𝐹𝑡𝑎𝑐𝑡 = √𝐿𝑎𝑐𝑡
,
𝑡 𝑃𝑡
𝐹𝑡
𝑒𝑓𝑓 𝑒𝑓𝑓
= √𝐿𝑡 𝑃𝑡 ,
and the total energy intensity index can be written as a product of the two efficiency and activity indexes
as follows
𝑒𝑓𝑓
𝐼𝑡 ≡ 𝑒𝑡 ⁄𝑒0 = 𝐹𝑡𝑎𝑐𝑡 𝐹𝑡
(2)
The energy savings can be allocated between efficiency and activity using the equation below:
ln(𝐹𝑡𝑎𝑐𝑡 )
)+
ln(𝐼𝑡 )
∆𝐸𝑡 = 𝐸𝑡 − 𝐸̂ = ∆𝐸𝑡 (
𝑒𝑓𝑓
∆𝐸𝑡 (
ln(𝐹𝑡
ln(𝐼𝑡 )
)
𝑒𝑓𝑓
) = ∆𝐸𝑡𝑎𝑐𝑡 + ∆𝐸𝑡
(3)
Et is the actual energy consumption and 𝐸̂ is the actual energy that would have been consumed had
energy intensity remained at its base year level.
3.1 Data
2
The idea is based on Fisher’s (1921) decomposition of an expenditure index into a price and quantity indexes
(Metcalf, 2008).
7
The energy consumption and economic activities data are obtained from Canadian Socio-economic
Information Management System of Statistics Canada (CANSIM). We first conduct the national level
analysis using the two-digit NAICS level industry data for the period 1981-2008. Due to the
inconsistency in the datasets, we regrouped the data into 17 industries as follows.
Agriculture, forestry, fishing and hunting
Utilities
Manufacturing
Retail trade
Information and cultural industries
Accommodation and food services
Professional, scientific and technical services
Finance, insurance, real estate, rental and leasing and
management of companies and enterprises
Other services
Mining and oil and gas extraction
Construction
Wholesale trade
Transportation and warehousing
Educational services
Health care services
Arts, entertainment and recreation
Public administration
The industry classifications for economic activities have to match with that for the energy use in
order to conduct the decomposition analysis. Thus, we use the real gross domestic product at industry
levels for which the energy consumption data is available3. Decomposition using the aggregate data may
generate misleading results as changes in economic activities within a sector are not accounted for and
therefore ascribed to efficiency. Although decomposition using disaggregate data is more desirable, the
exercise runs into the data availability problem. We, however, further construct a data set for some
selected industries at the three-digit NAICS level for the period 1981-2008. This data set allows us to
decompose the energy intensity index at the national level as well as at the selected industry levels. The
sectors are listed below.
3
Data on the industry gross domestic product (2002 constant prices) is available at CANSIM Table 379-0027
8
Manufacturing
Food
Clothing
Paper
Chemical
Primary metal
Computer and electronic product
Beverage and tobacco product
Leather and allied product
Printing and related support activities
Plastics and rubber products
Fabricated metal product
Electrical equipment, appliance and
component
Miscellaneous
Textile and textile product mills
Wood product
Petroleum and coal products
Non-metallic mineral product
Machinery
Transportation equipment
Rail transportation
Transit and ground passenger transportation
Postal service and couriers and messengers
Water transportation
Pipeline transportation
Warehousing and storage
Non-residential building construction
Engineering, repair and other
construction activities
Oil and gas extraction
Mining
Support activities for mining and oil
and gas extraction
Utilities
Electric power generation,
transmission and distribution
Natural gas distribution, water, sewage and
other systems
Furniture and related product
Transportation
Air transportation
Truck transportation
Other transportation services
Transportation and warehousing
Construction
Residential construction
Mining
Others
Wholesale and Retail Trade,
Information and Cultural
Industries, Education services,
Health care and social assistance,
Other services
Finance and Insurance, real estate, professional,
scientific and technical services, Administrative
support, waste management, Arts, entertainment
and recreation, Accommodation and food services
We construct the provincial data for seven sectors: (1) Agriculture (2) Mining and Oil and gas
extraction (3) Construction (4) Manufacturing (5) Transportation (6) Public administration(7) Other
service sectors. Due to the lack of consistent provincial real GDP by Industry that dates back to 1984, we
obtain real GDP by dividing the nominal GDP by an appropriate price index.4Specifically, we use
provincial farm product index to obtain the real GDP for the agriculture sector, national IPPI (total
4
Provincial energy use data is available at CANSIM 128-0009. Provincial nominal GDP data is available at
CANSIM Table 379-002. We replaced missing value for each sector by using their respective average share of GDP
throughout the years. Check CANSIM Table 002-0069 for farm product price index, Table 329-0056 for Industry
Product Price Index (IPPI) and Table 326-0021 for Consumer Price Index (CPI).
9
excluding petroleum and coal product) for manufacturing, IPPI (petroleum and coal product) for oil and
mining, CPI for transportation, and provincial CPIs for all the other service sectors.
3.2 Decomposition Results
3.2.1 National Level Analysis
We use the Fisher Ideal Index presented by equation (2) to decompose the energy index into efficiency
and activity indexes. We first conduct the decomposition at the two-digit NAICS using 17 sectors for the
1981-2008 period. The decomposition result for Canada, taking 1981 as the base year, has been illustrated
in Figure 3. Total energy intensity has declined by 26% between 1981 and 2008, that is, 1.1% annual
decline on overage. Moreover, activity index and efficiency index were 90% and 82% of their 1981 level,
respectively. That is, had energy efficiency remained unchanged at its 1981 level for all sectors, energy
intensity would have declined by 10%. Likewise, had composition of the economic activity remained
constant between 1981 and 2008, energy intensity would have declined by 18%.
[Figure 3 here]
Using Equation (3), we further compute the energy saved assuming energy intensity had
remained the same at its 1981 level. We work out the energy savings and allocate them between
efficiency and economic activity. From 1981 to 2008, a total of 27.8 x 106 tera joules of energy or 13
percent of total energy use has been saved due to the decline in energy intensity. Improvement in
efficiency accounted for 83% of the energy saved while changes in economic activity accounted for 17%
(Figure 4).
[Figure 4 here]
3.2.2 Industry Level Analysis
We decompose the energy intensity index into efficiency and activity indexes for the selected industries at
the three-digit NAICS level for the period 1981 to 2008. The industries included in the analysis are
10
Manufacturing, Transportation, Mining, Utilities, Construction, and Services.5 We use the gross domestic
product (2002 constant prices) appropriate for each energy use sector for different industries. Figure 5
shows the energy intensity trends and the decomposition into efficiency and activity for different
industries for the period (1981-2008).
[Figure 5 here]
Energy intensity in the manufacturing industry declined in the 1980s before increasing between
1988 and 1992. It further declined sharply from 1993-2000 and stabilized after 2000. On average, energy
intensity in the manufacturing industries has declined at an annual rate of 2% for the period 1981-2008,
and in 2008 is 63% of its level in 1981. Improvement in efficiency has played a dominant role in this
downward trend. Specifically, if energy efficiency had not changed in 2008, changes in economic activity
would have reduced energy intensity to just 99.8% of its 1981 level. The activity index depicts that
economic activity shifted to more energy intensive sectors between 1983 and 1994; however, this drift
reversed from 1994 to 2008.
Energy intensity was stable in the transportation industry in the early 1980s, before increasing in
the late 1980s and reaching its peak in 1993. Although there has been a steady improvement in efficiency,
economic activity has shifted to the energy intensive sectors. Thus, aggregate energy intensity in the
industry decreased at a very slow rate.
Mining is the only industry that has a relatively consistent upward trend in energy intensity for
most periods. Energy Intensity increased sharply after the late 1990s, reaching its peak in 2003 and
stabilizing afterward. Changes in economic activity in the mining industry have been moderately constant
mainly because the industry includes only two homogenous energy intensive activities (oil and mining).
The upward trend in energy intensity within this industry was driven by the decline in energy efficiency.
With efficiency worsening at average annual rate of 1.22%, energy intensity also increased at average
5
The energy use data is obtained from Statistics Canada. The data for the earlier period (1981-1989) is not available
at CANSIM. We obtained the data for that period through a direct request to Statistics Canada, and for the period
(1990-2008) from CANSIM Table 153-0032.
11
annual rate of 1.26%. Energy intensity in the utility industry has been stable until the mid-1990s, after
which it started to increase reaching its peak in 2001 when intensity was 134% of its 1981 level. The
energy intensity has been declining mostly due to efficiency improvement in the 2000s and almost
reached its 1981 level in 2008. Energy Intensity in the construction industry declined in the early 1980s
and remained rather stable throughout the remaining period. On average, energy intensity in the industry
has been declining on annual rate of 0.002%. In 2008, energy intensity was 83% of its 1981 level.
Efficiency improvement has been the main source of declining energy intensity in the industry.
3.2.3 Provincial Level Analysis
Due to data limitation, we divide total energy use into seven sectors: (1) Agriculture (2) Mining and Oil
and gas extraction (3) Construction (4) Manufacturing (5) Transportation (6) Public administration (7)
Other sectors.6 We use the real gross domestic product appropriate for each energy use sector for the
period 1984-2008. Since there is no consistent provincial real GDP by Industry that dates back to 1984,
we obtain real GDP by dividing the nominal GDP by appropriate price indexes.
[Figure 6 here]
As Figure 6 shows, all ten provinces have a downward trend in energy intensity with most of it
happening in the late 1990s and 2000s. Newfoundland, with an average annual decline rate of 2.3%,
experienced the most declines in energy intensity followed by Ontario with average annual decline rate of
1.9%. Saskatchewan and Alberta have the lowest average annual decline rates of 0.3% and 0.4%,
respectively. In general, Saskatchewan is the most energy intensive province while Ontario has the lowest
energy intensity. The gap between energy intensity in Saskatchewan and Ontario has been widening since
the 1984. Energy intensity in Saskatchewan was 27% and 81% higher than Ontario’s in 1984 and 2008,
respectively.
[Table 1 here]
6
Other sectors include Wholesale and Retail Trade, Utilities, Information and Cultural Industries, Education
services, Health care and social assistance and any other services not listed. We excluded utility industry from this
group, but the results did not alter.
12
Table 1 shows the decomposition results from Canadian provinces in 2008. The intensity index generally
measures the change in energy intensity over years. Newfoundland and Labrador has the lowest Intensity
Index (0.54) followed by Ontario (0.63) and Saskatchewan has the highest intensity index (0.90) followed
by Alberta (0.89). The trends for changes in intensity, activity and efficiency indexes are displayed for
each province over time in Figure 7.
[Figure 7 here]
In general, for most provinces, energy intensity was stable in the 1980’s, but has been fast declining after
the mid 1990’s. There are also variations in energy intensity across provinces, with these variations
increasing over time. The coefficients of variation almost quadrupled between 1985 and 2008. While
energy intensity in New Brunswick and Prince Edward Island has generally remained stable.
Saskatchewan and Alberta have experienced more increases in energy intensity than other provinces. In
Newfoundland and Labrador, Ontario, Quebec, Nova Scotia, British Columbia and Manitoba, energy
intensity has been declining for most periods. Both changes in the economic activity and energy
efficiency improvement have played a role in reducing energy intensity in provinces, but the impact of the
latter has been much stronger that the former. Newfoundland and Labrador is an outlier showing a greater
than one activity index and less than 0.5 efficiency index. High activity index in Newfoundland perhaps
reflect the structural change from fishing to oil and gas industry in the mid-1990s.
The overall trend of the indexes has been further accentuated in the provincial average in Figure
8. The average energy intensity in provinces has been declining at a 1.2% annual rate for the period 1984
- 2008. As the Figure shows efficiency improvement plays a dominant driving force in the decline in
energy intensity throughout the period. Specifically, had the economic activity remained unchanged
between 1984 and 2008, energy intensity would have declined by 23%. Changes in economic activity
have not contributed much to the decline in energy intensity as compared to improvement in efficiency
for most of the period. Changes in economic activity actually increased energy intensity in the 1980s and
the late 1990s mostly due to rising activity indexes in Newfoundland and Labrador and Alberta.
13
[Figure 8 here]
Comparing the provincial average to the two-digit NAICS analysis (Fig. 3), although the overall
energy intensity indexes show similar trends, the contribution of efficiency and changes in economic
activity differs. The impact of efficiency is greater in the provincial average as compared to the two-digit
NAICS, whereas the opposite is true for the changes in activity. This may be explained by the fact that
decomposition method using aggregate data overestimates the efficiency index because it does not allow
for the within sector changes from high energy intensive economic activities to low energy-intensive
activities. In this case, any within sector activity changes in the provincial sectors, such as manufacturing,
would be attributed to efficiency rather than economic activity.
[Table 2 here]
Table 2 shows the total amount of energy saved throughout the 1985- 2008 period. Due to decline in
energy intensity, all provinces, with the exception of Alberta, experienced a reduction in energy
consumption (saved energy). Efficiency improvement was a major contributor to the reduction in energy
use, whereas changes in economic activity increased energy use in six provinces. Alberta was the only
province in which both changes in economic activity and decline in energy efficiency increased energy
consumption. This is mainly because of huge investments in the Alberta oil sands, which is a high energy
and capital intensive industry.
4. Socio-economic Drivers of the Changes in the Energy Intensity Indexes
The decomposition analysis ascribes the changes in energy intensity to either efficiency or changes in
activity. However, the decomposition method cannot explain the socio-economic factors driving changes
in energy intensity. To analyze the underlying forces driving changes in these indexes, we employ the
regression analysis for the three indexes using the provincial and industry data. We set up a panel data
model as follows;
14
𝑦𝑖𝑡 = 𝑥𝑖𝑡 𝛽 + 𝑢𝑖 + 𝑣𝑡 + 𝜀𝑖𝑡
(4)
where yit is the intensity index for province i at time t. 𝑥it consists of all the economic and weather related
variables, ui is the province fixed effect controlling for the non-measurable province-specific
characteristics, vt is the time fixed effect controlling for macroeconomic and business cycles effects that
affect all provinces to the same degree, and 𝜀𝑖𝑡 is random error term with zero mean and constant
variance.
4.1 Provincial Analysis
The specification of the energy intensity model is similar to that of energy demand model, with energy
price as a main driver. By theory, a higher energy prices should reduce energy intensity through the
efficient use of energy and moving away from energy intensive sectors. We use the energy price index
(2002=100) at the provincial level provided by Statistics Canada. This energy index includes: electricity,
natural gas, fuel oil and other fuels, gasoline, and fuel, parts and supplies for recreational vehicles. The
energy price varies across provinces and through time, and its coefficient would capture the degree to
which energy intensity responds to price changes in Canadian provinces. The independent variables also
include real per capita income, weather, population growth, investment ratio, capital-labour ratio and
control variables for policy.
Income can have contrary effects on energy intensity. An increase in income may stimulate people to
spend more and live a more energy-consuming lifestyle, which will lead to an increase in energy
intensity. On the other hand, it can also increase environmental conscious consumers, which will lead to
adapting energy-saving technology. It can be postulated that at lower levels of income, the former effect
will dominate but as income rises, the latter effect will take over. Therefore, we use per capita income and
its squared term to take into account the non-linear response of energy use to income. To control for the
effect of weather on energy intensity, we use the heating degree days (HDD) and cooling degree days
(CDD). Degree-days for a given day denote the number of Celsius degrees that the mean temperature is
above or below a given base. For example, heating degree-days are the number of degrees below 18° C
15
while cooling degree days are the number of degrees above 18° C. If the temperature is equal to or greater
than 18, then the number of heating degrees will be zero. Values above or below the base of 18° C are
used primarily to reflect the demand of energy required to cool or heat buildings and fuel consumption.
There are several weather stations in each province due to Canada’s vast geography and varying weather.
We obtained the degree days by weather stations within a province in order to compute the degree days.
We first extracted the monthly degree day’s data by stations for each province from the Environment
Canada database. We average the degree days of all stations within a province to obtain monthly degree
days. We summed up the monthly degree days to obtain the annual degree days for each province for
each year.
Increasing population can have a positive or negative effect on energy intensity. Fast growing
provinces may be adding more energy efficient infrastructure as compared to slow growing provinces. In
contrast, if infrastructure does not keep up with growth, provinces growing at a fast pace may be less
energy efficient because of greater utilization of old and inefficient infrastructure and traffic congestion.
We also use investment ratio as a proxy for the turn over cycle of capital stock. Increasing investment
may indicate the usage of improved and energy efficient capital which will lead to lower energy intensity.
Thus provinces with higher investment are likely to have lower energy intensity as compared to provinces
with lower investment (Metcalf, 2008). There has been conflicting evidence in the relationship between
energy and capital. Some studies suggest the relationship to be substitutes while other studies postulate
the relationship to be complimentary. Apostalakis (1990) noted that time series data tends to categorize
capital and energy as complements because it reflects the short-term relationship. On the other hand,
pooled cross-section studies capture the relationship as substitutes because it reflects the long-term
relationship. We use data on capital and labour force to estimate the effect of capital-labour ratio on
energy intensity for each province.
Government energy policies and regulations can influence the energy consumption of its populace
and consequently affect energy intensity. We use reign of three distinct political parties (Conservatives,
Liberal and the New Democratic Party, NDP) in Canada as a proxy for energy policy. Using the proxy of
16
reign of political parties for energy policy variable implicitly assumes the principles and agenda of
political parties are the same across the provinces and do not change over time. Our model also includes a
time fixed effect to capture the effects of technological progress and business cycles on energy intensity
index over time. The summary statistics of the explanatory variables are presented in Table 3.The data
are obtained from CANSIM, Bank of Canada, and Environment Canada.
[Table 3 here]
The three measures of standard deviation indicate the variation in the data. The overall standard
deviation reports the variations in the panel data. The between standard deviation measures the variation
on the average across provinces whereas the within standard deviation measures the variation from the
province-specific means. The weather variables provide a good example of the variation across and
within provinces. Heating and cooling degree days show a more variations between provinces as
compared to variation within provinces. This is a representative of the weather pattern in Canada. We
conjecture that the fixed effects regressions will work well because the within province standard
deviations indicate there is sufficient variation within provinces across time. The fixed effects control for
unobserved province-specific factors that affect energy intensity.
We use a panel data model to estimate the energy intensity index across Canadian provinces for
the period 1984-2008. We first test for fixed effects using the Hausman test. The test result with a chisquare (χ2) of 103.50 and p value of 0.000 indicates that fixed effect method generates consistent
estimation for this data set. One econometric issue is that energy prices and income may be endogenously
determined. At the national level, energy (oil and oil products) prices are exogenous as they are
determined internationally. However, at the provincial level, energy price may be simultaneously
determined by supply and demand. We construct separate endogeneity test for energy price and income
using average energy price of adjacent provinces and lag of income respectively. We fail to reject null
hypothesis of price and income exogeneity at p-value of 0.533 and 0.215 respectively.
17
Table 4 reports the results of the fixed effects regression of energy intensity indexes. Column (1)
shows that the coefficient of price is negative and significant and the income effect is positive and
significant but slightly declining as income rises. A standard deviation increase in the log of per capita
income is associated with 0.95 percentage point increase in the intensity index at the mean of log of per
capita income. The coefficient of capital-labour ratio is positive and significant indicating that energy and
capital are complementary in Canada. The heating degree day shows a positive and highly significant
effect, implying that colder provinces have higher energy intensity. A standard deviation increase in the
heating degree days is associated with a 2.6 percentage point increase in energy intensity. The investment
ratio is negative but not significant. The population growth coefficient shows that faster growing
provinces have higher energy intensity. This could be due to the fact that faster growing provinces suffer
from congestion or attract energy intensive infrastructure. The policy coefficients are positive but not
significant.
[Table 4 here]
The regression in column (1) assumes energy intensity responds immediately to change in
economic variables. Realistically, economic variables are likely to affect energy intensity with some lag
because of timely capital and structural adjustments. Thus, the regression in column (2) results from a
partial adjustment model which includes the lag of intensity index. Let y*it be the desired energy intensity
in province i in year t and assume that it is a function of the variables included in the regression equation
(4).
′
𝑦𝑖𝑡∗ = 𝑥𝑖𝑡
𝛽 + 𝑣𝑖𝑡
(5)
where 𝑣𝑖𝑡 = 𝑢𝑖 + 𝜀𝑖𝑡 includes a province fixed effect. The adjustment process is defined as follows
𝑦𝑖𝑡 − 𝑦𝑖,𝑡−1 = ƛ(𝑦𝑖𝑡∗ − 𝑦𝑖,𝑡−1 )
(6)
where ƛ is measure of the adjustment factor in moving from desired to actual energy intensity. The
combination of equation (5) and (6) leads to equation (7) below.
18
′ ̃
𝑦𝑖𝑡 = 𝑥𝑖𝑡
𝛽 + (1 − ƛ)𝑦𝑖,𝑡−1 + 𝜀̃𝑖𝑡
(7)
Where 𝛽̃ = ƛ𝛽 is the short run and 𝛽̃ /ƛ the long-run impacts impact of changes in x on y. We use the
Arellano and Bond estimator in reporting the estimates in column (2) because the lagged dependent
variable will cause the standard fixed effect regression to produce biased estimates (Arellano and Bond,
1991). The signs of the coefficients in column (1) and column (2) remain the same, except for the policy
variables, which are now negative and significant. The reign of NDP and the liberal party is associated
with a fall in energy intensity as compared to the conservative party. The coefficient of the investment
ratio has also become significant with a larger value. A 1% rise in investment ratio is associated with a
0.05 percentage point decrease in energy intensity.
Columns (3) and (4) report the static and dynamic estimation results for efficiency index and
columns (5) and (6) for activity index. They show that the negative effect of price is fully explained by
changes in economic activity, but positive effect of income mostly by efficiency index. The positive
effect of capital-labour ratio on energy intensity is due to changes in efficiency and economic activity,
indicating that Canada has been employing higher energy intensive capital. The positive effect of
population growth on energy intensity index is fully explained by the efficiency index, implying that
higher population growth has put more pressure on energy intensive infrastructures. The effect of heating
degree days can mostly be explained by efficiency index. Similarly, the results for policy effects show
that both NDP and liberals, relative to conservatives, have contributed to increasing energy efficiency
and, at the same time, to encouraging more energy intensive activities in provinces. The positive
coefficients for the activity index might be surprising, particularly for NDP, which is known as a proregulation and pro-environment party and their reign is expected to be associated with lower energy
intensive activity. However, NDP is also a big supporter of unions, which have a strong presence in the
energy-intensive industries such as manufacturing and mining.
19
4.2 Industry Analysis
We use the three-digit NAICS data to estimate the energy intensity indexes for Canadian industries. The
industries included in the estimation are manufacturing, mining, construction, utility, transportation, and
services. The estimation model includes energy prices, capital-labour ratio, investment ratio, and TFP
growth as a proxy for technological changes7. Similar to the provincial case, we estimate static and
dynamic models using panel data with industry and time fixed effects. Table 5 reports the estimation
results for the six industries. Energy prices are mostly negative but not significant. Capital-labour ratio’s
effect on energy intensity is positive and the effect is fully explained by the efficiency index, implying
that capital and energy are complementary and that employing more capital intensive technologies have
been associated with lower energy efficiency. Investment ratio and TFP growth both have negative effects
on energy intensity, which are due to efficiency improvement.
[Table 5 here]
To shed more light on the individual industry effects, we re-estimate the industry models
allowing for heterogeneity in the major variable effects such as energy prices, capital-labour ratio,
investment ratio, and the TFP growth. The results are presented in Table 6. Overall, the efficiency factor
is a dominant determinant in reducing energy intensity through changes in energy prices, investment, and
technological advancement, and in increasing energy intensity through changes in capital-labour ratio.
Specifically, the higher energy prices have led to a reduction in energy intensity through efficiency and
changes in activity in manufacturing and through changes in activity in services. The positive effect of
capital-labour ratio on energy intensity comes mostly from the mining and transportation industry, where
capital and energy are complementary. The investment ratio has mostly negative and significant effects in
manufacturing, utilities, mining, and transportation. The effects are due to changes in economic activity in
manufacturing, but to efficiency improvement in the other three industries. Finally, the TFP growth has
7
R&D investment may be a better proxy for technological advances, but the data is not long enough for having a
meaningful estimation at the industry level.
20
contributed to lowering energy intensity in manufacturing and construction, but the effect is the strongest
in utilities for the efficiency index.
[Table 6 here]
4.3. Alternative Specifications and Estimation methods
Cross-Sectional Dependency
The fixed effect results above may be biased in the presence of cross-sectional correlation (Pesaran,
2004). Although Canada has a federal system with its provinces having a great deal of autonomy, it is still
likely that the provinces respond similarly to common shocks implying that their economic performance
and energy consumption are correlated. Thus we conduct a test for cross-sectional correlation using the
Frees and Friedman tests. The Frees’ test value is 0.65 (critical value of alpha at 0.1 = 0.11), and the
Friedman’s test is 24.39 (Prob.=0.004), indicating that the null of cross-sectional independency is strongly
rejected by both tests. We, therefore, use the Driscoll-Kraay estimator (1998) which is robust to very
general forms of cross-sectional and temporal dependence (Hoechle, 2007). As the results in Table 7
show, the magnitudes and signs of the coefficient estimates by the Driscoll-Kraay (1998) estimator are
very similar to the fixed effect model but the standard errors are slightly greater with no major effects on
statistical inferences.
[Table 7 here]
Energy Endowment Effect
The regression in Table 4 assumes the same effects of the variables on energy intensity index in all
provinces. Since the energy intensity in general is higher in oil-abundant countries, one might suspect that
the energy intensity and its determinants in the oil-abundant Canadian provinces might also be different
from those in the rest of country. We, therefore, study whether energy intensity responds differently to the
explanatory variables in oil-abundant provinces: Alberta, Saskatchewan, and Newfoundland and
Labrador. Two separate regressions are estimated for two groups of provinces and the results are reported
21
in Table 8. Energy prices are not significant in energy-endowed provinces, but significant in less energyendowed provinces. Income is significant in both groups, but with much stronger effect in less energyendowed provinces. The coefficient of capital-labour ratio indicates that energy and capital are
complementary in energy-endowed provinces, but substitute in the less energy-endowed provinces, which
can be explained by the fact that energy-endowed provinces use more energy intensive capital. The
investment ratio is insignificant in both groups. The population growth coefficient remains positive for
both groups but with much stronger effect in energy-endowed provinces, implying investment in high
energy intensive infrastructures in those provinces. The coefficient of heating degree days is positive and
significant in both groups of provinces, but the magnitude of the coefficient is higher in energy-endowed
provinces. This might not be surprising as the two of the three energy-endowed provinces (Saskatchewan
and Alberta) have the coldest temperature in Canada. The policies of NDP, compared to Conservatives,
tend to increase energy intensity in energy-endowed provinces but reduce energy intensity in less
endowed provinces. This might reflect the fact that the long serving NDP government in Saskatchewan
encouraged investment in its oil fields to boost economic growth of the province during the period 19902006.8
[Table 8 here]
Alternative Energy Prices
Our aggregate measure of energy prices might hide away the true effects of different energy prices in
provinces. To examine the price impact of individual energy types on energy intensity, we re-estimate the
model using the provincial electricity and natural gas prices. Although electricity price data are available
for all provinces, the natural gas price data are available for only six provinces in our sample period (the
missing data are for Maritime Provinces: NF, PEI, NB, and NS) reducing our sample size to 146. The
8
During the 1984-2008 period and in 10 provinces, NDP has been in power 45 province-year (20%), liberal party 80
province-year (34%), and conservative party 108 province-year (46%). About 35% of the NDP province-year has
been in Saskatchewan, but liberal and conservatives’ province-years have been more evenly distributed across the
provinces, so the reign of long serving Conservatives in Alberta does not have the same effect as that of NDP in
Saskatchewan.
22
results reported in Table 9 show that electricity prices are not significant, but natural gas prices are
negative and significant in all regressions. With regard to other variables, the sings of all coefficients,
except for liberal, remain unchanged, but the magnitudes of the effects are smaller for income and
population growth and larger for capital-labour ratio, and investment ratio.
[Table 9 here]
4.4 Elasticites
Using the estimated coefficients, we can obtain price and income elasticities for energy intensity index
and energy demand. The price and income elasticities for energy intensity index are
𝛽1
⁄𝐼 and
𝑡
(𝛽2 + 𝛽3 ln 𝑦𝑡 )
⁄𝐼 , where 𝛽′𝑠 are the coefficients of price, per capita income, and per capita income
𝑡
squared, 𝑦𝑡 is the per capita income, and 𝐼𝑡 is the energy intensity index. The implied price and income
elasticities for energy demand can also be obtained from the energy intensity index regression equation,
which are
(𝛽 + 𝛽2 ln 𝑦𝑡 )
𝛽1
⁄𝐼 + 1, respectively. Table 10 shows the short-run and long-run
⁄𝐼 and 1
𝑡
𝑡
elasticities for energy demand using the estimated coefficients from the dynamic regression equations
evaluated at the average values for energy intensity index and log of per capita income.
[Table 10 here]
The price elasticities are negative for intensity index and activity index and positive for efficiency
index, but none is significant. The income elasticities are all positive and significant (except for efficiency
in the long-run), meaning that higher income will lead to higher energy demand. However, the greater
than one income elasticities for the activity index indicate that higher energy demand will mostly come
from the energy-intensive activities, such as oil and mining extraction.
We calculate separate ealsticities for two energy-endowed and less energy-endowed provinces.
The price elasticities are positive and significant for efficiency index in energy-endowed provinces, but
negative and significant for intensity and efficiency in less energy-endowed provinces. The income
23
elasticities are positive and significant in both groups and they are greater for activity index, particularly
in energy-endowed provinces. Finally, the bottom part of the Table 10 shows the price elasticities for
electricity and natural gas obtained from the regression which included individual energy prices. The
electricity price elasticities of demand for energy are not significant, but the natural gas price elasticities
are significant in the energy intensity and efficiency index regressions. As previous results, the income
elasticities are significant and greater than one in activity index regression. The finding that Canadian
provinces do not respond to electricity price changes but react to natural gas price changes may be
explained by the fact that electricity is produced locally and most provinces do have excess capacity, but
natural gas is imported by most central and eastern provinces and its provision is subject to transportation
and weather condition constraints. Further investigation shows that a rise in electricity prices will increase
energy demand due to lower efficiency and decrease energy demand due to changes in activities in
energy-endowed provinces. However, electricity price elasticities are not significant in less energyendowed provinces, most of which generate electricity using hydro or nuclear plants and have significant
excess capacity.9
5. Conclusion
This paper provides a comprehensive understanding of the forces driving changes in energy intensity in
Canada since 1980. We employ the Fisher Ideal Index to perfectly decompose energy intensity at the
national, provincial, and industry, and use econometric methods to identify underlying factors driving the
changes in energy intensity in Canada.
The decomposition results, both at the national and provincial levels, suggest that most of the
reduction in energy intensity have occurred mainly due to improvements in energy efficiency as
compared to shifts from energy intensive to less energy intensive economic activities. Energy efficiency
improvement accounted for more than 82% of the decline in energy intensity. Energy intensity was
mostly stable in the 1980’s but has been fast declining after the mid 1990’s. Additionally, variation in
9
To save space, the regression results and elasticities for electricity and natural gas prices for energy-endowed and
less energy-endowed provinces are not reported here, but they are available upon request.
24
energy intensity across provinces has been increasing over time. Within the industry, while energy
intensity increased significantly in mining, it experienced a significant decline in manufacturing mostly
due to an improvement in efficiency. The energy intensity has remained rather stable in other industries.
The panel data regression results also indicate that on average higher energy prices have led
Canadian economic structure to move away from energy intensive activities, while rising income has
been the most significant factor in increasing energy intensity. Even though population growth is
relatively low in Canada, it has positive and significant effect on energy intensity. Energy intensity is
higher in provinces with colder climate, but the effect of warmer climate on energy intensity is relatively
limited. The provincial and industry level study show that capital and energy are complementary on
average across provinces and industries. Investment ratio, which captures the turnover of capital stock,
has also contributed to the declining in energy intensity in provinces. The industry regression results also
confirm the investment effect and shows that it has contributed to energy efficiency in utilities and mining
and to changes to less energy intensive activities in manufacturing and transportation industries.
Technological advances have been most effective in increasing energy efficiency in construction and
utilities industries and in switching to less energy intensive activities in manufacturing industries. The
regression analysis for the two energy-endowed and less energy-endowed provinces also reveals
heterogeneous responses of energy intensity indexes to explanatory variables. Specifically, energy prices
and income have stronger negative and positive effects in less energy-endowed provinces, respectively.
Also, policy effects are different in the two groups with liberals having to increase energy intensity in
energy-endowed provinces and to decrease it in less energy-endowed provinces. The energy demand
elasticities results also indicate that energy is price inelastic and changes in energy prices will reduce
energy demand only in less energy-endowed provinces. However, breaking down the energy prices into
electricity and natural gas prices in the regression reveals that while all provinces respond significantly to
changes in natural gas prices, the electricity price elasticity is only significant in the less energy-endowed
provinces. Furthermore, a rise in income will increase energy demand mostly due to a rise in high energy
intensive activities particularly in energy-endowed provinces.
25
Our study shows that Canada is slowly reducing its high energy intensity with a focus on
increasing energy efficiency through economic forces such as investment and technological advances.
However, increasing activities in energy intensive sectors, such as oil and mining, will partially offset the
efficiency effects gained in other industries. This is particularly true as about 50 percent of the greenhouse
gas produced in Canada is concentrated in oil and gas and transportation industries and in two oil
producing provinces: Saskatchewan and Alberta. Thus, the pace of energy intensity reduction will
increase rapidly, should efficiency improve significantly in the energy intensive industries, or they move
to less energy intensive activities. Since the latter is not a realistic option for Canada as a major oilexporting country, the government policy to encourage R&D in those energy intensive industries will
help meet the CO2 reduction targets in due course.
26
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28
Figure 1: Energy Intensity, Energy consumption. and GDP in Canada (1980-2011)
18000
16000
14000
12000
10000
8000
6000
4000
2000
GDP
Energy Consumption
Energy Intensity
Energy intensity is Total Primary Energy Consumption per Dollar of GDP (Btu per Year 2005 U.S. Dollars
(Purchasing Power Parities)). GDP is Constant 2005 US$ (×100,000,000). Energy is Total Primary Energy
Consumption (Trillion BTU). Data Source: EIA and WDI.
Figure 2: Energy Intensity in Selected OECD Countries (1980-2011)
19000
17000
15000
13000
11000
9000
7000
5000
3000
Canada
United Kingdom
United States
Finland
Australia
Japan
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1000
Germany
Total Primary Energy Consumption per Dollar of GDP (Btu per Year 2005 U.S. Dollars
(Purchasing Power Parities)). Data Source: EIA
29
Figure 3. Energy Intensity Indexes in Canada (Two-digit Industry Level)
1.05
1.00
0.95
0.90
0.90
0.85
0.82
0.80
0.75
0.74
Activity
Efficiency
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
0.70
Intensity
3500000
3000000
2500000
Terajoules
Figure 4. Energy Savings Due to a Declining Energy Intensity in Canada (1981-2008)
2000000
1500000
1000000
0
-500000
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
500000
Savings due to efficiency
Savings due to activity
30
Figure 5: Energy Intensity Decomposition results at three-digit Industry level
Intensity Index
1.38
1.18
0.98
0.78
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
0.58
Minning
Utilities
Construction
Transporation
Services
Manufacturing
1.5
Efficiency Index
1.4
1.3
1.2
1.1
1
0.9
0.8
0.7
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
0.6
Minning
Utilities
Construction
Transporation
Services
Manufacturing
1.28
Activity Index
1.23
1.18
1.13
1.08
1.03
0.98
0.93
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
0.88
Minning
Transporation
Utilities
Services
31
Construction
Manufacturing
Figure 6: Energy Intensity by Canadian Provinces (1984-2008)
13
NL
12
11
Terajoules per million dollar
SK
10
9
MB
NB
AB
NS
8
7
PE
QC
6
ON
5
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
4
NL
PE
NS
NB
QC
ON
MB
SK
AB
BC
Figure 7: Decomposition Results for Canadian Provinces (1984-2008)
1.3
Intensity Index
AB
1.2
SK
1.1
MB
1
0.9
QC
0.8
PE
NB
BC
NS
0.7
ON
0.6
NL
0.5
ON
MB
SK
32
AB
BC
2008
2007
2006
2005
QC
2004
2003
2002
NB
2001
2000
1999
NS
1998
1997
1996
PE
1995
1994
1993
NL
1992
1991
1990
1989
1988
1987
1986
1985
1984
0.4
Efficiency Index
SK
1.2
AB
1.1
MB
1
0.9
NS
0.8
BC
NB
PE
QC
ON
0.7
0.6
NL
0.5
0.4
NL
PE
NS
NB
1.2
QC
ON
MB
SK
AB
BC
Activity Index
NL
1.15
1.1
AB
NB
1.05
MB
QC
1
NS
0.95
SK
PE
ON
0.9
BC
0.85
33
2008
2007
2006
QC
BC
2005
2004
2003
2002
NB
AB
2001
2000
1999
1998
NS
SK
1997
1996
1995
1994
PE
MB
1993
1992
1991
1990
NL
ON
1989
1988
1987
1986
1985
1984
0.8
Figure 8: Energy Intensity Indexes in Canada (Provincial average)
1.05
1
0.96
0.95
0.9
0.85
0.8
0.77
0.75
0.73
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
0.7
Intensity Index
Efficiency
34
Activity
Table 1- Provincial Energy Intensity and Decomposition Results (1984-2008)
Province
Energy Intensity
(1984)
Energy Intensity
(2008)
Activity
Index
(2008)
1.17
Efficiency Index
(2008)
6.64
Intensity
Index
(2008)
0.54
Newfoundland
12.24
Prince Edward
Island
Nova Scotia
8.02
6.30
0.79
0.94
0.83
9.61
6.44
0.67
0.94
0.71
New Brunswick
9.91
7.34
0.74
0.93
0.80
Quebec
8.38
5.98
0.71
0.95
0.75
Ontario
8.24
5.20
0.63
0.92
0.69
Manitoba
8.52
6.37
0.75
0.97
0.77
Saskatchewan
10.50
9.40
0.90
0.97
0.92
Alberta
8.98
8.01
0.89
0.97
0.92
British Columbia
8.54
5.92
0.69
0.85
0.82
0.46
Note: Energy intensity is measured in terajoules per 1,000,000 dollars. Source: CANSIM and authors’ calculation
Table 2- Energy Savings Relative to 1984 Intensity (terajoules)
Energy Saved due to
Efficiency
Energy Saved
Activity
Newfoundland
927299
-212514
1139814
Prince Edward Island
41877
8902
32975
Nova Scotia
1076650
85439
991210
New Brunswick
404991
-149363
554355
Quebec
5402804
-468094
5870898
Ontario
17963398
3262355
14701043
Manitoba
628499
-17497
645996
Saskatchewan
15270
-222325
237595
Alberta
-780827
-715326
-65502
British Columbia
3662433
Source: CANSIM and authors’ calculation
1161317
2501117
35
Table 3 - Provincial Data Summary Statistics (1984-2008)
Mean
Variable
Std. Dev.
Overall
Between
Within
Min
Max
Intensity Index
0.89
0.13
0.094
0.09
0.48
1.195
Population growth
0.006
0.01
0.008
0.005
0.030
Energy Price Index
0.92
0.26
0.040
0.256
0.02
0.49
Log of per capita income
10.23
0.25
0.21
0.148
9.64
10.99
Log of capital-labour ratio
11.38
0.28
0.289
0.052
10.9
12.13
Heating degree days
8.48
0.14
0.134
0.061
8.15
8.804
Cooling degree days
4.43
0.68
0.594
0.388
2.17
5.750
New Democratic Party (NDP)
0.22
0.42
0.258
0.335
0
1
Liberal Party (Liberal)
0.32
0.47
0.247
0.404
0
1
Conservative party
0.46
0.5
0.269
0.430
0
1
1.760
Sources: Energy Price Index data: CANSIM Table 326-0021, Climate data: Environment Canada’s website:
(http://climate.weather.gc.ca/prods_servs/cdn_climate_summary_e.html), population: CANSIM Table 051-0001,
Investment and Capital stock: CANSIM Table 031-0002, Labour force data: CANSIM Table 282-0087.
Table 4- Provincial Regression Results (1984-2008)
Intensity
(1)
Intensity
(2)
Efficiency
(3)
Efficiency
(4)
Activity
(5)
Activity
(6)
Price
-0.071*
-0.027
-0.009
0.016
-0.111***
-0.017
Per Capita Income
5.144***
2.467***
3.467***
1.626*
1.605**
1.046
Per Capita Income2
-0.281***
-0.145***
-0.204***
-0.103**
-0.067*
-0.048
Capital-labour ratio
0.388***
0.186***
0.240***
0.062
0.128**
0.099**
Investment ratio
-0.002
-0.049***
0.053
-0.007
-0.071***
-0.030
Population growth
2.002***
1.865***
2.541***
2.452***
-0.392
-0.618
Heating Degree Days
0.198***
0.167***
0.137**
0.120***
0.063*
0.051*
Cooling Degree Dayhs
0.000
0.010**
-0.002
0.011*
0.003
-0.001
NDP
0.004
-0.011*
-0.015
-0.020***
0.035***
0.013**
liberal
0.004
-0.014***
-0.017*
-0.026***
0.025***
0.013***
Energy Intensity Index(-1)
0.375***
0.498***
0.746***
Constant
-28.440***
-13.189***
-17.052**
-7.247
-10.365**
-6.940
N
240
220
240
220
240
220
adj. R-sq
0.785
0.72
0.388
Notes:Dependent variable is the energy intensity index (columns 1&2), efficiency index (columns 3 &4), and
activity index (columns 5 &6). All variables (except for energy index) are in log form, NDP and liberal are dummies
for corresponding provincial political parties in power. The regressions are estimated using fixed province and
fixed time effects. The dynamic regression is estimated using the Arellano-Bond estimator. * p<0.10, ** p<0.05,
and *** p<0.01.
36
Table 5- Energy Intensity Estimation Results for Canadian Industries (1981-2008)
Intensity
(1)
Intensity
(2)
Efficiency
(3)
Efficiency
(4)
price
-0.053
-0.062
0.013
Capital-labour ratio
0.404***
0.162***
Investment ratio
-0.095**
TFP growth
-0.916***
Energy Intensity Index (-1)
constant
N
Activity
(5)
Activity
(6)
-0.054
-0.066
0.002
0.423***
0.164***
-0.025
-0.004
-0.070***
-0.114**
-0.085***
0.015
0.006
-0.694***
-1.010***
-0.720***
0.070
-0.005
0.743***
0.772***
0.903***
-4.194***
-1.972***
-4.556***
-2.095***
1.411***
0.175
157
151
157
151
157
151
adj. R-sq
0.186
0.178
-0.051
Notes: Dependent variables are energy intensity index (columns 1 & 2), efficiency index (columns 3 & 4), and
activity index (columns 5 & 6). Price, capital-labour ratio, and investment ratio are in log form. The regressions are
estimated using fixed province and fixed time effects. The dynamic regression is estimated using the Arellano-Bond
estimator. * p<0.10, ** p<0.05, and *** p<0.01
37
Table 6- Estimation Results for Industries with Industry Specific Coefficients (1981-2008)
Intensity
(1)
Intensity
(2)
Efficiency
(3)
Efficiency
(4)
Activity
(5)
Activity
(6)
Manufacturing
price
-0.403***
-0.122
-0.239**
-0.078
-0.195***
-0.026
Capital- labour ratio
0.392*
0.191
0.307
0.164
0.116
0.032
Investment ratio
-0.033
-0.017
0.003
0.029
-0.031
-0.084**
TFP growth
-0.248
-0.557
-0.158
-0.295
-0.131
-0.453***
price
0.031
-0.021
0.101
-0.009
-0.075
0.034
Capital- labour ratio
0.163
0.353*
0.359
0.458**
-0.226*
-0.082*
Investment ratio
-0.130
-0.218
-0.238
-0.283**
0.120
0.051
TFP growth
-0.254
-0.748**
-0.319
-0.779**
0.076
0.085
price
0.151
0.056
0.352***
0.143*
-0.196***
0.000
Capital- labour ratio
-0.318
0.050
-0.402
0.017
0.066
-0.015
Investment ratio
-0.204***
-0.112**
-0.303***
-0.167***
0.084***
0.010
TFP growth
-1.390***
-1.221***
-1.708***
-1.454***
0.247
0.087
price
-0.063
-0.181*
0.066
-0.104
-0.114*
-0.022
Capital- labour ratio
0.781***
0.473***
0.780***
0.493***
-0.015
0.015
Investment ratio
-0.322***
-0.170**
-0.265***
-0.173**
-0.052
0.016
TFP growth
-0.604
-0.531
-0.592
-0.558
-0.004
0.044
Price
-0.151
-0.029
-0.100
-0.007
0.007
-0.032*
Capital- labour ratio
0.728
0.101
0.036
-0.174
0.806***
0.176
Investment ratio
-0.116
-0.082
-0.029
-0.037
-0.093**
-0.024*
TFP growth
-0.943*
-0.551
-0.659
-0.439
-0.238
0.002
price
-0.093
-0.038
-0.003
-0.011
-0.103**
0.000
Capital- labour ratio
-0.704
-0.038
-0.409
0.016
-0.328
-0.018
Investment ratio
-0.108
-0.023
-0.133
-0.041
0.028
-0.001
TFP growth
0.217
-0.249
0.259
-0.362
-0.116
0.353
Construction
Utilities
Mining
Transportation
Services
Energy Intensity Index(-1)
0.634***
0.572***
0.941***
Constant
-1.473
-2.187
-0.641
-1.775
0.057
-0.210
Number of observations
157
151
157
151
157
151
R-sq
0.635
0.715
0.535
adj. R-sq
0.544
0.644
0.420
Notes: Dependent variables are energy intensity index (columns 1 & 2), efficiency index (columns 3 & 4), and
activity index (columns 5 & 6). Price, capital-labour ratio, and investment ratio are in log form. The regression
include province and time fixed effects. The dynamic regression with adjustment parameter (lag of intensity index) is
estimated using the Arellano-Bond estimator. NDP and liberal are dummies for corresponding provincial political
parties in power. * p<0.10, ** p<0.05, and *** p<0.01.
38
Table 7- Estimation Results for Provinces with Cross Sectional Dependence (1984-2008)
Intensity
Efficiency
Activity
Price
-0.071*
-0.009
-0.111***
Per Capita Income
5.144**
3.467*
1.605
-0.281***
-0.204**
-0.067
Capital-labour ratio
0.388***
0.240**
0.128**
Investment ratio
-0.002
0.053
-0.071*
Population growth
2.002***
2.541***
-0.392
Heating Degree Days
0.000
-0.002
0.003
Cooling Degree Days
0.004
-0.015
0.035***
NDP
0.004
-0.017
0.025**
liberal
-0.010**
-0.004
-0.002
Constant
-28.440**
-17.052
-10.365*
Per Capita Income
2
N
240
240
240
Notes: Dependent variable is energy intensity index. All variables are in log form and regressions control for
individual fixed province and time effects and correct standard errors for cross-section dependency using DriscollKraay (1988) method. NDP and liberal are dummies for corresponding provincial political parties in power.
* p<0.10, ** p<0.05, and *** p<0.01.
Table 8- Energy Endowment Effect (1984-2008)
Price
Energy-endowed
Intensity
(1)
Intensity
(2)
Less Energy-endowed
Intensity
(3)
0.007
0.133
-0.083***
-0.087***
Intensity
(4)
Per Capita Income
5.552**
5.995***
18.821***
11.121***
Per Capita Income2
-0.309**
-0.318***
-0.952***
-0.566***
Capital-labour ratio
0.593**
0.247
-0.126*
-0.095
-0.048
-0.001
0.042
0.015
Investment ratio
Population growth
3.247***
2.155*
0.798
1.020**
Heating Degree Days
0.296***
0.243***
0.174***
0.184***
Cooling Degree Days
-0.005
0.002
-0.003
0.002
NDP
-0.005
-0.013
-0.004
0.004
liberal
0.043
0.040**
-0.019***
-0.015***
Energy Intensity Index(-1)
Constant
N
R-sq
0.349***
0.374***
-33.014***
-32.412***
-91.941***
-54.461***
72
66
168
154
0.89
0.889
adj. R-sq
0.863
0.875
Notes: Dependent variable is energy intensity index. Energy-endowed provinces are Saskatchewan, Alberta, and
Newfoundland. All variables are in log form, and regressions control for individual fixed province and time effects.
The dynamic regression is estimated using the Arellano-Bond estimator. NDP and liberal are dummies for
corresponding provincial political parties in power. * p<0.10, ** p<0.05, and *** p<0.01.
39
Table 9 - Provincial Regression Results with Individual Energy Prices (1984-2008)
Intensity
Intensity
Efficiency
Efficiency
Activity
Activity
(1)
(2)
(3)
(4)
(5)
(6)
-0.005
0.003
-0.006
0.000
-0.001
0.003
-0.003***
-0.001***
-0.003***
-0.002***
-0.001*
0.00
4.173*
2.72*
-1.453
-0.529
2.831**
2.034*
Price of Electricity
Price of Natural Gas
lnI
-0.238**
-0.155**
0.033
0.002
-0.138**
-0.098*
0.656***
0.262***
0.396***
0.116
0.074
0.052
-0.004
0.043
0.044
0.074*
-0.048
-0.01
Investment ratio
1.929**
1.039**
1.775**
1.357**
0.309
-0.068
Population growth
0.194***
0.153***
0.132**
0.107***
0.05*
0.029
Heating Degree Days
-0.003
0.00
-0.006
-0.002
0.008*
0.005
Cooling Degree Days
liberal
0.015
0.008
0.006
0.00
0.028***
0.015***
0.056***
0.023**
0.049***
0.02*
0.015*
0.01
Per Capita Income
Per Capita Income
2
Capital-labour ratio
Energy Intensity Index (-1)
_cons
0.470***
-25.882**
-15.335*
146
133
N
adj. R-sq
0.488***
0.816
6.828
3.628
146
133
0.639***
0.717
-14.878**
-11.019*
146
133
0.434
Notes: Dependent variable is energy intensity index. Energy-endowed provinces are Saskatchewan, Alberta, and
Newfoundland. All variables are in log form, and regressions control for individual fixed province and time effects.
The dynamic regression is estimated using the Arellano-Bond estimator. NDP and liberal are dummies for
corresponding provincial political parties in power. * p<0.10, ** p<0.05, and *** p<0.01.
Table 10 – Price and Income Elasticities
Intensity
SR
Efficiency
LR
SR
Activity
LR
SR
LR
All Provinces
Price
Income
-0.03
-0.05
0.02
0.04
-0.02
-0.07
0.44***
1.01***
0.45***
-0.09
1.05***
1.21***
Energy-endowed Provinces
Price
Income
0.14
0.213
0.22**
0.42*
-0.04
-0.13
0.34***
0.06
0.48***
0.01
1.02***
1.06***
Less Energy-endowed Provinces
Price
-0.101***
-0.16***
-0.08**
-0.12**
-0.01
-0.01
0.53***
0.25***
0.49***
0.22**
0.92***
0.81***
All provinces
Electricity Price
0.003
0.005
0.0007
0.001
0.003
0.007
Natural gas price
-0.002***
-0.003***
-0.002***
-0.004***
-0.0003
-0.0008
0.49***
0.04
0.45***
-0.07
1.02***
1.06***
Income
Income
* p<0.10, ** p<0.05, and *** p<0.01.
40