Structural decomposition analysis applied to energy and emissions: Some methodological developments

doi:10.1016/j.eneco.2011.10.009

Energy Economics

Volume 34, Issue 1, January 2012, Pages 177-188

https://doi.org/10.1016/j.eneco.2011.10.009 Get rights and content

Abstract

The only comprehensive study comparing structural decomposition analysis (SDA) and index decomposition analysis (IDA) was conducted around 2000. There have since been new developments in both techniques in energy and emission studies. These developments have been studied systematically for IDA but similar studies for SDA are lacking. In this paper, we fill the gap by examining the new methodological developments in SDA. A new development is a shift towards using decomposition methods that are ideal. We compare four such SDA methods analytically and empirically through decomposing changes in China's CO₂ emissions. We then provide guidelines on method selection. Finally, we discuss the similarities and differences between SDA and IDA based on the latest available information.

Highlights

► We review the new methodological developments in SDA in energy and emission studies. ► A shift towards using decomposition methods that are ideal is revealed. ► Four ideal methods are compared analytically and empirically using China's data. ► We provide guidelines on decomposition method selection for SDA. ► We discuss the similarities and differences between SDA and IDA based on the latest available information.

Introduction

Decomposition analysis has been widely used to study the driving forces of changes of an aggregate indicator over time. Two popular decomposition techniques are the index decomposition analysis (IDA) and the structural decomposition analysis (SDA). They have been developed independently and applied extensively in energy and emissions.¹ IDA is often adopted by energy researchers who wish to have a better understanding of the drivers of energy use and energy-related emissions in a specific energy consumption sector, such as transportation or industry. SDA is used primarily by researchers who are familiar with input–output (I–O) analysis and wish to extend it to study changes in energy consumption or emissions in the economy. Reviews of IDA can be found in Ang, 1995, Ang, 2004, Ang and Zhang, 2000, and of SDA in Rose and Casler, 1996, Miller and Blair, 2009.

There are similarities as well as differences between IDA and SDA in terms of study scope, method formulation, data requirements and the results given. Hoekstra and van den Bergh (2003) is the first and only comprehensive study that compares SDA and IDA. It deals with the developments and state of the two techniques up to 2000 or thereabouts. In the past ten years or so, there have been new developments in both decomposition techniques. These have been studied systematically for IDA. See, for example, Ang, 2004, Ang et al., 2009, Ang et al., 2010. Guidelines on IDA application, including method selection, have been proposed. Similar studies, however, have not been reported for SDA. At the same time, the number of SDA studies on energy and emissions has grown rapidly.

The objective of this study is to fill the gap that exists between SDA and IDA with a focus on methodological developments. We begin with a literature survey on SDA by concentrating on studies published from 1999 onwards in the application area of energy and emissions. Studies prior to 1999 are already reported in a number of earlier studies including Hoekstra and van den Bergh (2002) and will therefore be dealt with in less detail. A new development is the inroads made by SDA methods that are ideal in decomposition. Arising from this development, we focus on four such methods and propose a general additive SDA decomposition framework. We confine our study to additive decomposition since this framework has been overwhelmingly applied in SDA.²

To illustrate the differences among the four methods, we conduct an empirical study using the CO₂ emission data of China and compare the results obtained. An issue that researchers and practitioners will face is method selection since more than one method can be adopted. We therefore propose guidelines on method selection. Finally we discuss the similarities and differences between SDA and IDA based on the latest available information, which is an update on the study by Hoekstra and van den Bergh (2003). It is shown that conceptually and methodologically the linkages between the two techniques remain strong.

Section snippets

Review of SDA literature

We give an overview of SDA studies reported prior to 2000 and provide a more detailed analysis of the studies from 1999 onwards. The objective is to identify trends and summarize key developments in the past three decades with a focus on methodology.

Methodological developments and issues

As the most widely used SDA methods in the past decade, the main contribution of Dietzenbacher and Los (1998) is revealing the existence of n ! equivalent exact decomposition forms based on the concept of reordering the sequence of n factors in product for particular exact decomposition. Assuming no preference of any particular form, D&L takes the full average of all the forms to address the non-arbitrary issues with respect to the factor-sequence chosen. It has been shown that this approach is

General additive decomposition framework

Several ideal SDA decomposition methods have been adopted by researchers. We shall propose a general additive decomposition framework to establish the linkages of these methods. We show that the four ideal decomposition methods, namely D&L, LMDI-I, LMDI-II and MRCI are special cases of the general framework.

Empirical study on China's CO₂ emissions

The choice of method affects the numerical results of a decomposition study. We present an empirical study to illustrate the differences between the results given by the four ideal decomposition methods. This is studied at both the aggregate and sub-category levels. We use the 2002 and 2007 data of China and Table 3 shows the basic emission and socio-economic indicators.⁹ The I–O tables of

Guidelines on SDA method selection

In a study on the desirable properties of IDA methods, Ang (2004) points out the following criteria for method evaluation: (a) theoretical foundation, (b) adaptability, (c) ease of use, and (d) ease of result interpretation. On this basis, we do not recommend MRCI due to the potential “cancel-out” issue in constructing the “mean-rate-of-change” index. Our focus will therefore be the choice between D&L and LMDI.

On theoretical foundation, D&L is based on a concept similar to that of the Shapley

Comparison between SDA and IDA

Hoekstra and van den Bergh (2003) compare SDA and IDA in a number of aspects. It is pointed out that data requirements lead to fundamental differences between the two techniques, e.g. SDA relies on the I–O model framework while IDA does not. As a result, SDA can account for the indirect effect while IDA can only deal with direct effect. It is also pointed out that two indicator forms (absolute and intensity) and two decomposition forms (additive and multiplicative) are in IDA, while only

Conclusion

This paper deals with methodological developments in SDA. The focus is the developments in the last decade or so. It also serves as an update of the study by Hoekstra and van den Bergh (2003). From the literature survey of 43 recent SDA studies, the most significant development observed was the switch from ad hoc methods to methods that are ideal in decomposition. We compare four such methods analytically and empirically. Guidelines on method selection between D&L and LMDI are provided to

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Structural decomposition analysis applied to energy and emissions: Some methodological developments

Abstract

Highlights

Introduction

Section snippets

Review of SDA literature

Methodological developments and issues

General additive decomposition framework

Empirical study on China's CO2 emissions

Guidelines on SDA method selection

Comparison between SDA and IDA

Conclusion

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Empirical study on China's CO₂ emissions