B.W. Ang

Decomposition analysis for policymaking in energy:: which is the preferred method?

Abstract Although a large number of energy decomposition analysis studies have been reported in the last 25 years, there is still a lack of consensus among researchers and analysts as to which is the “best” decomposition method. As the usefulness of decomposition analysis has now been firmly established in energy studies and its scope for policymaking has expanded greatly, there is a need to have a common understanding among practitioners and consistency on the choice of decomposition methods in empirical studies. After an overview of the application and methodology development of decomposition analysis, the paper attempts to address the above-mentioned issues and provide recommendations.

A survey of index decomposition analysis in energy and environmental studies

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.

A survey of data envelopment analysis in energy and environmental studies

Data envelopment analysis has gained great popularity in energy and environmental (E&E) modeling in recent years. In this paper, we present a literature survey on the application of data envelopment analysis (DEA) to E&E studies. We begin with an introduction to the most widely used DEA techniques, which is followed by a classification of 100 publications in this field. The main features observed are summarized. Issues related to the selection of DEA models in E&E studies are discussed.

A new energy decomposition method: perfect in decomposition and consistent in aggregation

A new energy decomposition method, called the Log-Mean Divisia Index Method I (LMDI I), is presented. It has the desirable properties of perfect decomposition and consistency in aggregation. Perfect decomposition ensures that the decomposition results obtained do not contain a residual term. Consistency in aggregation allows estimates for sub-groups to be aggregated in a consistent manner. The formulation of the new method and the usefulness of aggregation consistency in energy decomposition studies are described. Two case studies on energy-related CO2 emissions are presented.

Factorizing changes in energy and environmental indicators through decomposition

We introduce a decomposition method for factorizing changes in energy demand or gas emissions over time. This method has the advantage of giving perfect decomposition. It can also handle cases with zero values in the data set. We compare this new method with three existing methods and summarize the respective decomposition formulae for various applications. Three application studies using data for Singapore, China, and Korea are presented. In each case, the change of a different energy or environmental indicator is decomposed using the four methods and the results obtained are compared. Our new method is superior to any of the three existing methods and may be generally applied in energy and environmental decomposition studies.

A NON-RADIAL DEA APPROACH TO MEASURING ENVIRONMENTAL PERFORMANCE

Data envelopment analysis (DEA) has gained great popularity in environmental performance measurement because it can provide a synthetic standardized environmental performance index when pollutants are suitably incorporated into the traditional DEA framework. Past studies about the application of DEA to environmental performance measurement often follow the concept of radial efficiency measures. In this paper, we present a non-radial DEA approach to measuring environmental performance, which consists of a non-radial DEA-based model for multilateral environmental performance comparisons and a non-radial Malmquist environmental performance index for modeling the change of environmental performance over time. A case study of OECD countries using the proposed non-radial DEA approach is also presented. It is found that the environmental performance of OECD countries as a whole has been improved from 1995 to 1997.

Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach

This paper presents a non-radial directional distance function approach to modeling energy and CO2emission performance in electricity generation from the production efficiency point of view. We first define and construct the environmental production technologies for the countries with and without CHP plants, respectively. The non-radial direction distance function approach is then proposed and several indexes are developed to measure energy and CO2 emission performance of electricity generation. The directional distance functions established can be computed by solving a series of data envelopment analysis models. We then conduct an empirical study using the dataset for over one hundred countries. It is found that OECD countries have better carbon emission performance and integrated energy-carbon performance than non-OECD countries in electricity generation, while the difference in energy performance is not significant.

Perfect decomposition techniques in energy and environmental analysis

Abstract In a recent article, Albrecht et al. (Energy Policy 30 (2002) 727) presented a new decomposition technique based on the Shapley value and used it to study CO 2 emissions in four OECD countries. This technique makes it possible to present decomposition without residuals, a very desirable property in decomposition analysis. We show that their proposed technique and the method by Sun (Energy Economics 20 (1998) 85) are exactly the same. As there has been a great deal of interest in decomposition analysis in energy policy studies, we extend the work by Albrecht et al. (Energy Policy 30 (2002) 727) by giving a more complete and up-to-date overview of perfect decomposition techniques and their role in energy demand and related analysis.

Decomposition of industrial energy consumption: Some methodological and application issues

Abstract Several methodological and application issues related to the technique of the decomposition of industrial energy consumption are discussed. It is shown that several decomposition methods reported in past studies are special cases of two general parametric methods based on the Divisia index and that the formulation of these methods can be treated in a unified framework. Decomposition, as a result, can be performed in an infinite number of ways for a given set of energy and production data. Five specific methods are considered, their differences are highlighted, and it is explained how to interpret the results obtained from a specific method. The differences between periodwise decomposition and time series decomposition are then discussed. The decomposition results for Singapore and Taiwan are presented throughout to illustrate the issues raised.

Decomposition methodology in industrial energy demand analysis

We discuss some methodological and application issues related to decomposing national industrial energy consumption into changes associated with aggregate industrial production level, production structure and sectoral energy intensity. Past studies are classified and reviewed with respect to study scope and decomposition technique. A framework for decomposition method formulation which incorporates three different approaches is presented. Several specific methods are described and their applications are illustrated with an example. Relevant application issues, such as method selection, periodwise vs time-series decomposition, significance of levels of sector disaggregation, and result interpretation are discussed.