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Tracking economy-wide energy efficiency using LMDI: approach and practices

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Abstract

Various national and international agencies have developed and reported accounting systems to track progress in energy efficiency improvements. Most of these energy efficiency accounting systems (EEAS) are based on index decomposition analysis and the logarithmic mean Divisia index (LMDI) has emerged as the main decomposition method used. We discuss the fundamentals of LMDI with specific reference to energy efficiency analysis and its application to national EEAS development. The main design dimensions and elements of an EEAS are explained. The flexibility of the LMDI approach has allowed analysts and national agencies to tailor the EEAS to suit their national needs and policy purposes. We conduct a literature survey of the implemented and proposed EEAS and summarise their key features. In view of its growing importance, the extension of the EEAS to an energy-related emissions accounting system to track progress towards climate mitigation targets is introduced. Finally, the strengths and limitations of an LMDI-based EEAS for the tracking of energy efficiency trends are discussed.

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Notes

  1. In the literature, there are two widely reported decomposition analysis techniques—index decomposition analysis and structural decomposition analysis. For simplicity, in this paper, decomposition refers to index decomposition analysis.

  2. The European Union also uses an alternative approach, the unit consumption approach, to compute an energy efficiency indicator known as ODEX. Based on Ang et al. (2010), ODEX can be treated as a special case of the index decomposition analysis approach.

  3. Only publications in the English language are included in the literature survey.

  4. Examples of methods that are not perfect in decomposition are the Laspeyres index method and Paasche index method. The former was widely used by researchers and analysts before the introduction of LMDI in the late 1990s (Ang 2015).

  5. Other methods that are perfect in decomposition such as the Shapley/Sun method (Sun, 1998) and the generalised Fisher ideal index method (Ang et al. 2004) have formulae that increase in complexity with the number of factors in the decomposition. In terms of ease of use, LMDI is superior to these decomposition methods.

  6. A third dimension is type of aggregate indicator (energy consumption or energy intensity). For further details, see Ang (2015).

  7. The energy transformation sector’s “energy consumption” includes energy losses from electricity and heat generation and the energy sector’s own use. It may also include energy losses and own use pertaining to transformations of other energy sources. In most countries, these other transformations generally account for a small proportion of the total “energy consumption” in the sector. For simplicity, in this study, the energy transformation sector is taken as the electricity sector.

  8. Although LMDI-II’s weights are more complicated, they sum to unity. This may be a desirable property for some analyses (Ang 2015).

  9. LMDI-II is more commonly used multiplicatively as the decomposition results do not depend on the indicator chosen. The effects remain the same regardless of whether energy consumption or intensity is decomposed. The LMDI-I and LMDI-II formulae presented here are referred to as LMDI model 1 and LMDI model 3 respectively in Ang (2015). They are two out of the eight LMDI models listed in the reference.

  10. Decomposition results using more realistic or real data can be found in Ang et al. (2010) and Stanwix et al. (2015) and Office of Energy Efficiency (2016).

  11. Economic activity in the services sector does not influence energy consumption as much as floor area. The intensity effects derived based on the two different activities have to be interpreted differently. For the same country over the same time period, the energy intensity effect for the services sector can be negative when the activity for the sector is value added while it can be positive when floor area is the activity. At times, the choice of activity depends on the agency—economic agencies tend to be more interested in monetary measures of activity while energy and environment agencies prefer physical measures.

  12. A list of recommended activity indicators for each sector can be found in IEA (2014).

  13. The Divisia index is based on a line integral over a period of continuous time. Due to the discreteness of real data, approximations have to be made. Chaining analysis provides better approximations to the path of the line integral as annual data is used to estimate the trends over time. Numerical examples showing the difference between chaining and non-chaining decomposition analyses can be found in Ang and Liu (2007).

  14. If there is more than one publication for a single country from the same agency, the latest publication is listed.

  15. The Laspeyres index method is the most intuitive decomposition method; the contribution of a particular factor to the total change in energy consumption is estimated by keeping all factors in the base year and changing the factor of interest.

  16. The refined Laspeyres index method used is very similar to the Shapley/Sun method which is an improvement to the Laspeyres index method. The residual from the Laspeyres method is reallocated to various factors to achieve perfect decomposition. See Sun (1998) for more details on the method.

  17. The emission intensity Uij is dependent on the fuel mix of energy consumption in the sub-sector and the fuel emission factors, and further decomposition may be conducted if needed to analyse the emission intensity.

  18. Emissions from the energy transformation sector can be classified under each end-use sector to simplify the decomposition. For instance, the emissions from the use of electricity can be distributed to end-use sector based on the share of electricity used by each sector.

  19. In the Kaya identity, CO2 emissions are expressed as the product of four factors—population, GDP per capita, energy consumption per unit of GDP output and CO2 emissions per unit of energy consumed.

  20. In Australia, it was observed that the energy-GDP ratio increased in the early years as the EEI decreased. Subsequently, the energy-GDP ratio decreased more slowly in comparison to the EEI in the 1990s and decreased more rapidly in the later years in comparison to the EEI. A similar trend is observed in Canada. This pattern is not surprising. In the earlier years of development, due to shifts to more energy-intensive industries and rapid development, the energy-GDP ratio is likely to decrease more slowly or increase in comparison to the intensity effect. As a country develops, shifts to less energy-intensive industries results in a rapidly decreasing energy-GDP ratio.

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  1. Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore, Singapore

    Tian Goh & B. W. Ang

  2. Energy Studies Institute, National University of Singapore, Singapore, Singapore

    B. W. Ang

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Goh, T., Ang, B.W. Tracking economy-wide energy efficiency using LMDI: approach and practices. Energy Efficiency 12, 829–847 (2019). https://doi.org/10.1007/s12053-018-9683-z

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