Elsevier

Energy

Volume 25, Issue 12, December 2000, Pages 1149-1176
Energy

A survey of index decomposition analysis in energy and environmental studies

https://doi.org/10.1016/S0360-5442(00)00039-6Get rights and content

Abstract

Index decomposition methodology was a technique first used in the late 1970s to study the impact of changes in product mix on industrial energy demand. A survey in 1995 listed a total of 51 studies. Since then, many new studies and several new decomposition methods have been reported and the methodology has been increasingly used in energy-related environmental analysis. We trace these new developments, discuss method formulation using an index number framework, and classify more than one hundred studies based on application area, aggregate indicator, and decomposition scheme. Application issues useful to researchers undertaking new studies and possible areas for future research are presented.

Introduction

Not long after the 1973/74 world oil crisis, energy researchers began to look for ways to quantify the impact of structural shift in industrial production on total industrial energy demand in order to have a better understanding of the mechanisms of change in energy use in industry. They realized that changes in industrial production mix can have a major impact on the aggregate energy intensity given by the ratio of total industrial energy demand to total industrial output. They developed several simple techniques to single out this impact through decomposing changes in the aggregate energy intensity over time. In addition to the impact associated with industrial activity composition (i.e. structural effect), the decomposition results obtained often include the impact associated with changes in sectoral energy intensity (i.e. intensity effect). Sectoral energy intensity, which was considered a better measure of energy efficiency than the aggregate energy intensity, is the amount of energy consumption that is required to yield a given level of output at the sectoral level.

This line of research has continued until today with more and more reported studies every year. Decomposition methodology has become a useful and popular tool not only in industrial energy demand analysis but also in energy and environmental analysis in general. This survey found a total of 124 studies [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51] and they are listed in Table 1. The study by Huntington and Myers [59] in 1987 listed only eight studies and the survey by Ang [6] in 1995 found a total of 51. Since 1995, the number of studies has increased by 1.5 times and there have been important new developments both in the methodological and application fronts. Several new and more defined methods have been proposed. They include methods that do not leave a residual term in the results (the methods reported prior to 1995 always contain a residual term). Almost all the studies reviewed in the 1995 survey dealt with industrial energy demand analysis. In the last five years, there have been substantially more studies dealing with other aspects of energy and environmental analysis and the scope of application has expanded significantly. Arising from these developments, the 1995 review is no longer able to provide a comprehensive and up-to-date assessment of the current status of index decomposition analysis in energy and environmental studies.

This paper deals primarily with the new developments in the last five years and brings the 1995 survey up to date. We describe the new application areas, particularly those related to energy-related gas emissions, and the key features of the 124 surveyed studies. We also assess the new methods that have been proposed and compare them with the methods proposed in the earlier years. Decomposition methods are classified using a new framework based on index numbers in economics. Several tests for identifying desirable properties of decomposition methods are presented. Finally application issues useful to researchers undertaking new studies and possible areas for future research are presented.

Section snippets

Historical overview

The decomposition formulae used by researchers prior to the mid 1980s are straightforward and intuitive. The impact of structural change was singled out by computing the hypothetical aggregate energy intensity that would have been for a target year had sectoral energy intensities for all industrial sectors remained unchanged at their respective values in a base year. The difference between this hypothetical aggregate energy intensity in the target year and the observed aggregate energy

Basic approaches and index decomposition methodology

In the last decade, the Laspeyres and the arithmetic mean Divisia index methods have been the two most often used decomposition methods. We shall use the decomposition of the aggregate energy intensity of industry to introduce these two methods and the concept of multiplicative and additive decomposition. Assume that the aggregate energy consumption in industry is the sum of consumption in m different sectors (e.g. food, textiles, metal products, etc.). Define the following variables for time t

Main features of past studies

The 124 survey studies listed in Table 1 were conducted based primarily on the concepts described in the earlier sections although they differ in terms of decomposition method and scheme, application area, indictor type, level of sector disaggregation, etc. The third column of Table 1 shows that the 124 survey studies cover a wide spectrum of countries. To study possible changes over time, we classified the studies into three time periods that are roughly equal in time span: 1978–1984,

Recent developments in ID methodology

Ang [6] used a general parametric Divisia index framework to classify the methods proposed before 1995 and pointed out two major problems in these methods. The first is the issue of imperfect decomposition, i.e. all the methods leave a residual after decomposition. They mentioned that if a large part of the change in the aggregate appears as a residual and is left unexplained, the purpose of decomposition, namely to divide the change of a chosen energy or environmental indicator into

Classification and properties of ID methods

Ang [6] proposed a framework for classifying decomposition methods. It was developed with reference to the decomposition of industrial energy demand. Decomposition methods were classified with respect to two general parametric Divisia index methods originally proposed in Liu et al. [74]. With the proper assignment of parameter values of the parametric methods, i.e. the weights for variables in year 0 and year T, it is possible to use the framework to classify the methods proposed by researchers

Method selection and related issues

Since many ID techniques are available, researchers undertaking new decomposition studies will face the problem of having to decide the variable type to be decomposed, the choice between multiplicative and additive decomposition, and the specific decomposition method to apply. In most past studies, these choices were rather arbitrary and no reasons were given. Although the guidelines given in Ang [6] remain generally valid, an updated summary of these guidelines with particular reference to the

Areas for future studies and conclusion

During the past two decades, ID analysis has been an area in which extensive research has been conducted. A variety of methods have been proposed and empirical studies for a wide spectrum of countries have been reported. While industrial energy demand was the main focus in the earlier years, there have been an increasing number of studies dealing with energy-induced greenhouse gases and other harmful gas emissions in the last 5 years. Some researchers have also extended the application to areas

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