
INTRODUCTION
Canada is one of the chief energy users in the world with its total energy use growing steadily since 1980. Although energy intensity, i.e., energy consumed per unit of output and measured by the ratio of energy consumption to GDP, has been declining in Canada recently, it is still 1.3 and 2.4 times greater than that in United States and Germany, respectively (Figures 1 and 2). It is important to note that a fall in energy intensity does not necessarily mean total energy consumption is falling. The ratio of energy per GDP can still fall even if total energy use rises 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 percent annually, whereas GDP has been growing at an average of 2.4 percent.
Changes in energy intensity also reflect changes in either technology or the structure of economic activities. For instance, lower energy intensity in Canada may have been caused by either an improvement in technology or a shift away from energyintensive sectors. Understanding the factors driving the changes in energy intensity is vital to any policy designs addressing high energy consumption. This is particularly important in Canada as emission control has become one of the key global issues in addressing environmental problems and sustaining economic growth, and more than 80 percent of Canada's greenhouse gas emissions are generated through energy production and consumption, indicating that Canada may need to develop more aggressive policies to curb its energy consumption. (1) This paper seeks to investigate the underlying factors affecting energy intensity changes in Canada. Specifically, the study intends to estimate the contributions of energy efficiency and changes in economic structure to the declining energy intensity in Canada, using the Fisher Ideal Index decomposition method. Since the decomposition method does not allow for an analysis of socioeconomic forces influencing energy intensity, we also apply econometric methods to further investigate the determinants of energy intensity in Canada using provincial and industry panel data.
The study is conducted on three levels: national, industrial, and provincial. The national level provides a general understanding of the changes in aggregate energy intensity, but can lead to misleading outcomes since it hides individual responses to changes in economic and energy market conditions, particularly in Canada, a large country with diverse economic activities. The provincial study enables us to take a closer look at variations across space and time. We also delve into the industrial level for a deeper understanding of energy intensity changes in individual industries. As far as the authors know, this is a first study on energy intensity changes in Canada using both decomposition and regression techniques at different aggregation levels. Our findings show that although efficiency has contributed significantly to declining energy intensity, the development of energyintensive industries in the oil, mining, and transportations sectors has partially offset the efficiency gains in other industries. Our results also indicate that energy prices have limited impact on energy intensity, but income, population growth, and changes in climate contribute to higher energy intensity, and investment ratio and technological advancement improve energy intensity. 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.

REVIEW OF PREVIOUS STUDIES
Two decomposition and econometrics approaches have been used to ascertain factors driving changes in energy intensity, with a focus on the United States, some European countries, and China. Table 1 shows a summary of selected papers on energy intensity. Most of the decomposition studies have found efficiency a major component of the changes in energy intensity, although some studies have also reported a significant impact of structural changes on declining energy intensity, particularly in the 1980s (Gardner, 1993; Howarth et al., 1993l, Boyd and Roop, 2004; Wing, 2007; Metcalf, 2008, Natural Resources Canada, 2013). In the more recent studies, Huntington (2010) and Mulder (2015) also highlight the importance of structural changes in the U.S. and some OECD countries due to international trade. Regression analysis is also used to explain the changes in energy intensity as a function of various socioeconomic variables. These studies report economic growth, FDI, and income as major determinants of changes in energy intensity (Gardner et al., 1998; FisherVanden et al., 2004; Wing, 2007; Metcalf, 2008; Zheng et al., 2011; Song and Zheng, 2012).
We contribute to existing literature by employing the Fisher Ideal Index to decompose the Canadian energy intensity at the two and the threedigit NAICS industry level data for each industry. The decomposition method is also applied to provincial data comprising seven sectors. We further employ a panel data analysis including endogeneity and crosssection dependence tests to examine socioeconomic and climate factors that drive changes in energy intensity in Canadian provinces and industries. The findings of this study will shed more light on the dynamics of the changes in energy intensity in Canada, one of the highest energy intensive countries, which can be utilized in making energy and environmental policies.

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,
[e.sub.t] = [[E.sub.t]/[Y.sub.t]] = [[summation].sub.i][[E.sub.it]/[Y.sub.it]][[Y.sub.it]/[Y.sub.t]] = [summation][e.sub.it][s.sub.it] (1)
where e is energy intensity, [E.sub.it] and [Y.sub.it] 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 ([e.sub.it]) and changes in the structure of economic activity ([s.sub.it]) across sectors. (2) The energy intensity index ([I.sub.t]) is then constructed by dividing the energy intensity in year t ([e.sub.t]) by the energy intensity in a base year ([e.sub.0]):
[I.sub.t] = [e.sub.t]/[e.sub.0] = [[[summation].sub.i][e.sub.it][S.sub.it]/[[summation].sub.i][e.sub.io][s.sub.io]]
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 the economic activity constant, and the activity index attributes energy intensity to change in the mixture of economic activity keeping efficiency within a sector constant. The decomposition can be carried out 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: [L.sup.act.sub.t] = [[[summation].sub.i][e.sub.io][S.sub.it]/[[summation].sub.i]][e.sub.io][S.sub.i0]], [L.sup.eff.sub.t] = [[[summation].sub.i][e.sub.it][S.sub.io]/[[summation].sub.i][e.sub.io][S.sub.i0]]
Paasche Indexes: [mathematical expression not reproducible]
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 efficiency ([F.sup.eff.sub.t]) and activity ([F.sup.act.sub.t]) elements with no residuals. (3) That is,
[mathematical expression not reproducible]
and the total energy intensity index can be written as a product of the two efficiency and activity indexes as follows:
[I.sub.t] [equivalent to] [e.sub.t]/[e.sub.0] = [F.sup.act.sub.t][F.sup.eff.sub.t] (2)
The energy savings can be allocated between efficiency and activity using the equation below:
[mathematical expression not reproducible] (3)
where [E.sub.t] is the actual energy consumption and [??] is the actual energy that would have been consumed had energy intensity remained at its base year level.
3.1 Data
The energy consumption and economic activities data are obtained from Canadian Socioeconomic Information Management System of Statistics Canada (CANSIM). The details of the data sources are presented in Table A1 in appendix. We first conduct the national level analysis using the twodigit NAICS level industry data for the period 19812008. Due to the inconsistency in the datasets, we regroup the data into 17 industries, a list of which is presented in Table A2 in Appendix. (4)
The industry classifications for economic activities have to match those for 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 available. As FisherVanden et al. (2004) note, decomposition using the aggregate data may generate misleading results as the likely changes in economic activities within a sector are not accounted for and are, 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 threedigit NAICS level for the period 19812008. This data set allows us to decompose the energy intensity index at the national level as well as at the selected industry levels. The list of the industries is presented in Table A3 in appendix.
We construct the provincial data for seven sectors: agriculture, mining and oil and gas extraction...
Changes in Energy Intensity in Canada.
Author:  Moshiri, Saeed 
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COPYRIGHT GALE, Cengage Learning. All rights reserved.
COPYRIGHT GALE, Cengage Learning. All rights reserved.