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Comprehensive economy-wide energy efficiency and emissions accounting systems for tracking national progress

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Abstract

Energy efficiency accounting systems (EEAS) have been widely used to track progress in economy-wide energy efficiency. In the literature, there is general agreement on the approach for handling energy consumption in end-use sectors. However, this is not the case for the energy sector which captures losses arising from energy transformation, transmission and distribution. The energy sector constitutes between 25 and 40% of total primary consumption in most countries. How it is handled in an EEAS greatly affects quantification of progress in economy-wide energy efficiency and its accompanying policy implications. This study systematically compares the various approaches used to incorporate energy losses from the power sector in an EEAS and discusses interpretation and implications of the results in the context of different energy systems. A new approach, which quantifies the contribution of changes in the share of renewable energy and separates transmission and distribution losses in the power sector, is recommended and illustrated through a case study of Canada. The approach can also accommodate energy systems with electricity trade. Extensions from an EEAS to an energy-related emissions accounting system (EMAS) are also presented in light of the increasing interest in emissions accounting and climate mitigation.

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Notes

  1. IDA can also be used to analyse energy efficiency of primary or final energy separately (Landwehr and Jochem 1997), or examine specific sectors individually (Sorrell et al. 2009). These applications usually focus on more in-depth analyses of specific factors in comparison to the EEAS where the main aim is to develop an economy-wide composite energy efficiency indicator from sectoral trends.

  2. The losses take the form of losses during the transformation of primary energy sources into secondary energy sources, the energy sector’s own use and losses in the transmission and distribution of secondary energy sources to final consumers.

  3. The energy consumption of the five end-use sectors refers to final energy consumption while the energy consumption of the energy sector refers to the energy losses from transformation, transmission and distribution and the energy sector’s own use. Therefore, the sum of the final energy consumption of the five end-use sectors and the energy losses from the energy sector is theoretically equivalent to the TPEC.

  4. In additive decomposition, the contribution of factors can be expressed directly in the units of the indicator (i.e. energy units for energy consumption) and the results can be interpreted more easily by policymakers. Multiplicative procedures have other merits and analysts may choose either procedure in their analysis. Further details about the commonly used IDA methods and the basis of making an appropriate choice can be found in Ang (2015).

  5. The inclusion of generation systems where there are multiple outputs (e.g. combined heat and power plants) in an EEAS is complicated as the loss intensity may not reflect actual changes in efficiency accurately. How the changes in efficiency can be captured more accurately at the economy-wide level through an EEAS is an area that requires further research.

  6. Canada’s EEAS for the period 1990 to 2004 incorporates the electricity generation sector but the intensity effect of the electricity sector is not included in the economy-wide energy intensity index.

  7. Primary energy consumption of the electricity sector is decomposed instead of the energy losses during transformation in this study.

  8. When a monetary activity indicator such as gross value-added is chosen (Kerimray et al. 2017; Román and Colinet 2018; Shahiduzzaman and Alam 2013), the energy sector is often classified as one of the sub-sectors of the industry sector. A disadvantage of using monetary activity indicators for the energy sector is that the resultant intensity effect may not be a good reflection of changes in energy efficiency.

  9. Assume that an electricity system requires an input of 2.4 million tonnes of oil equivalent (Mtoe) of energy to generate 1 Mtoe of electricity (i.e. the energy loss is 1.4 Mtoe). If a sub-sector consumes 1000 GWh of electricity, its source energy consumption will be 2400 GWh of electricity or 2.4 times the electricity consumed.

  10. While the first three terms in Identity C are the same as those in Identity A, the corresponding sub-sectoral effects that are obtained based on the two identities are different due to the different weights assigned. Based on the additive LMD-I method, the weights assigned in Identity C are based on the source energy, \( {E}_{ij}^s \), while the weights assigned in Identity A are based on the final energy consumed, Eij. The impact of this difference depends on the scale of the energy losses from the energy sector and the share of electricity consumed in each end-use sub-sector.

  11. Renewable energy and nuclear power plants have their own actual conversion efficiencies. For example, the conversion efficiency of a solar panel is a measure of the fraction of solar energy that can be converted to electricity. However, it does not have an impact on supply security as the energy source is renewable and no fuel needs to be imported or extracted.

  12. The primary energy form of geothermal and solar thermal is heat.

  13. If Identity C is selected, the energy sector is only accounted for under the SSR effect and no further breakdown is available. The impact of renewables, nuclear energy and T&D losses are all subsumed under the SSR effect and do not influence the aggregate economy-wide intensity effect.

  14. Losses include the energy sector’s own consumption but this is not shown explicitly in the formulae in Table 2 as it is unlikely that this will result in significant differences across the different cases.

  15. The loss fraction is computed as the ratio of the T&D losses in kilowatt hour to the electricity output in kilowatt hour (i.e. Qi).

  16. This problem is unique to the energy sector. It does not occur when renewables such as wood and wood waste are consumed directly in end-use sectors as the energy is consumed directly and not transformed into electricity.

  17. If geothermal energy or biomass is converted into electricity, it can be included as an additional energy source j.

  18. For example, Bashmakov and Myshak (2014) decompose T&D losses by classifying them in a separate sector.

  19. The data were collected from Energy Use Data Handbook (1990–2013) (Natural Resources Canada 2016b) and its accompanying comprehensive Energy Use Database (Natural Resources Canada 2016a). For simplicity, agriculture is included in the industry sector and street lighting is included in the services sector. Net exports of electricity from non-fossil based sources were obtained from the IEA world energy balance database (IEA 2017d). Electricity consumption by pipelines which constitutes less than 1% of total electricity consumption is excluded from end-use electricity consumption as there is no activity data.

  20. Due to data limitations, the activity measure for the industry sector in this study is GDP output. Monetary activity indicators are not ideal as the resulting intensity effect may not be an accurate proxy for energy efficiency. Physical activity measures are preferred and will lead to more accurate estimations of the contribution of energy efficiency to changes in final energy consumption. For more reliable results, further refinements to the decomposition of the industry sector can be made with a more detailed data set. For example, Office of Energy Efficiency (2016) uses physical output as the activity measure and capacity utilisation is also included as a fourth factor in their report on Canada.

  21. There are no differences in the results for the end-use sectors because Identity A is applied regardless of the identity used for the energy sector. This is an advantage of using Identity A or A1. Countries such as Australia, New Zealand and Canada that have developed EEAS for end-use sectors can simply include the energy sector as an additional sector using Identity A1.

  22. If T&D losses are zero, the structure effect from Identity A will be equivalent to the sum of the structure and share effects from Identity A1. Therefore, Identity A can be seen as a less refined version of Identity A1.

  23. The weights for renewables are based on the T&D losses which are a very small fraction of the generation losses from fossil fuels and nuclear energy.

  24. When the conventional EEAS is used, the structure effect is mainly determined by energy intensive activities. This means that a shift to renewables is quantified by a large negative structure effect attributable to fossil fuels and nuclear energy while a shift away from renewables is quantified by a large positive structure effect which is also attributed to fossil fuels and nuclear energy.

  25. In this study, CO2 emissions refer to energy-related CO2 emissions.

  26. For example, a shift from coal to natural gas in industrial energy use can generally lower CO2 emissions. An increase in the fraction of renewables in the electricity mix can also reduce emissions from electric vehicles. These changes represent different climate mitigation measures and can be quantified by separate factors in the decomposition.

  27. Since CO2 emissions data are usually estimated by multiplying energy consumption of each fuel type with their corresponding emission factors, the data used to derive the economy-wide CO2 emissions should be sufficient for the computation of the mix and emission intensity effects.

  28. Of the 140 countries with available CO2 emissions data for the year 2015, the shares of CO2 emissions from electricity and heat generation in residential sector were more than 80% for 43 countries (IEA 2017b).

  29. The emission intensity of electricity generation is of interest as emission reductions can be achieved more easily in this sector. IDA has been used by IEA (2017b) to decompose emissions from the electricity sector. (For more details on the identity that can be used to decompose CO2 emissions from electricity generation, see Xu and Ang (2013). For more details on two-step decomposition, see Xu and Ang (2014).)

  30. Data by fuel types for some industry sub-sectors are not available. They are estimated based on the proportion of fuels in the sub-sector’s end-use energy mix for an earlier year or based on a rough estimate if no data is available. Constant emission factors measured in tonnes of CO2 per tonne of oil equivalent are used and they are 3.99 for coal, 3.08 for oil and 2.33 for natural gas.

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Authors and Affiliations

  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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  1. Tian Goh
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Appendix

Appendix

Based on the LMDI-I decomposition formulae for the additive LMDI-I method (Ang 2015), the three effects for Identity A in the EEAS are given as

$$ {\Delta E}_{i,\mathrm{act}}={\sum}_jL\left({E}_{ij}^T,{E}_{ij}^0\right)\mathit{\ln}\left(\frac{Q_i^T}{Q_i^0}\right) $$
(7)
$$ {\Delta E}_{i,\mathrm{str}}={\sum}_jL\left({E}_{ij}^T,{E}_{ij}^0\right)\mathit{\ln}\left(\frac{S_{ij}^T}{S_{ij}^0}\right) $$
(8)
$$ {\Delta E}_{i,\operatorname{int}}={\sum}_jL\left({E}_{ij}^T,{E}_{ij}^0\right)\mathit{\ln}\left(\frac{I_{ij}^T}{I_{ij}^0}\right) $$
(9)

where the logarithmic mean is given as \( L\left(x,y\right)=\frac{x-y}{\ln x-\ln y} \) for x ≠ y and L(x, y) = x for x = y.

Similarly, the five effects for the identity used in the EMAS are given as

$$ {\Delta C}_{i,\mathrm{act}}={\sum}_{jk}L\left({C}_{ijk}^T,{C}_{ijk}^0\right)\mathit{\ln}\left(\frac{Q_i^T}{Q_i^0}\right) $$
(10)
$$ {\Delta C}_{i,\mathrm{str}}={\sum}_{jk}L\left({C}_{ij k}^T,{C}_{ij k}^0\right)\mathit{\ln}\left(\frac{S_{ij}^T}{S_{ij}^0}\right) $$
(11)
$$ {\Delta C}_{i,\operatorname{int}}={\sum}_{jk}L\left({C}_{ij k}^T,{C}_{ij k}^0\right)\mathit{\ln}\left(\frac{I_{ij}^T}{I_{ij}^0}\right) $$
(12)
$$ {\Delta C}_{i,\mathrm{mix}}={\sum}_{jk}L\left({C}_{ijk}^T,{C}_{ijk}^0\right)\mathit{\ln}\left(\frac{M_{ijk}^T}{M_{ijk}^0}\right) $$
(13)
$$ {\Delta C}_{i,\mathrm{emi}}={\sum}_{jk}L\left({C}_{ijk}^T,{C}_{ijk}^0\right)\mathit{\ln}\left(\frac{U_{ijk}^T}{U_{ijk}^0}\right) $$
(14)
Table 5 Activity and energy consumption data for Canada by energy sources, 1990–2013 (energy consumption in petajoules)
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Goh, T., Ang, B.W. Comprehensive economy-wide energy efficiency and emissions accounting systems for tracking national progress. Energy Efficiency 12, 1951–1971 (2019). https://doi.org/10.1007/s12053-019-09796-w

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