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A proposal to combine classical and hypothetical extraction input–output methods to identify key sectors for the production and distribution of electricity

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Abstract

This analysis explores the possibility of merging into a ‘combined’ proposal two standard I-O methods identifying key sectors, the Classical Multiplier and Hypothetical Extraction. In the context of the latest revision of the European Union Energy Efficiency Plan, we use this proposal to single out key sectors that boost potential energy savings in the economic system—specifically, in the production and distribution of electricity. Using the main distinctions and complementarities of the two traditional I-O key-sector approaches, the combined formulation allows us to disaggregate the backward stimuli of the electricity sector into three indicators: total, internal and external backward indicators. This combined proposal provides additional insights into the structure of the industrial linkages that participate in the production and distribution of electricity. Our results reveal that the explanation for the intensity of the backward effects of the electricity sector depends not only on other energy sectors but also on some manufacturing industries. We put forward that these findings may be important for developing a more balanced and cost-effective design for energy efficiency policies.

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Notes

  1. These studies identify those coefficients within the Leontief inverse matrix that significantly affect electricity consumption. These relevant coefficients are identified through a sensitivity analysis that is based on Sherman and Morrison’s (1950) formula. To the best of our knowledge, the earliest works in applying this method to identify ‘important’ coefficients in energy terms were Bullard and Sebald (1977, 1988).

  2. Using, for instance, the original HEM formulation of Paelinck et al. (1965) where not only external linkages but also internal linkages are extracted, i.e. A K K = A K N ‐ K = A N ‐ K K = 0, would have based the distinction between the two traditional backward indicators on the size of its final demand. Therefore, the application of this alternative HEM formulation would not be appropriate for the context of the analysis here.

  3. Under Cella’s HEM formulation, the size of the “own” intermediate demand interdependencies does not have any direct impact in determining the hierarchical order of sectors. The question whether these internal linkages should be accounted for or not still remains a major source of debate in the HEM literature. This debate was first initialized by Miller (1966, 1969) in a multiregional context and later retaken in a parallel way by Guccione (1986) as a response to the extraction method suggested by Cella (1984) for addressing inter-sectoral analysis.

  4. This explains why previous analyses have found strong correlations across the two traditional I-O key-sector methods (Miller and Lahr 2001). This was so even for alternative HEM formulations.

  5. This data set was downloaded from the official website of this institution (http://www.ine.es/daco/daco42/cne00/cneio2000.htm) and refers to the latest update available in December 2012. The symmetric table is available from the author upon request.

  6. In our analysis, we have used the commodity by commodity industry technology assumption, i.e. the so-called Model D. We replicated the analysis using the industry technology assumption, i.e. Model B. While the numerical results were slightly different, the policy implications remained unaltered.

  7. A limitation of this analysis is that we have not added to the I-O data set in monetary terms any information about electricity consumption in physical units or any information on associated emissions levels. Therefore, we do not exploit techniques proposed by Su et al. (2010) to sort out aggregation problems that obtain when using environmental an energy extensions in I-O tables.

  8. This author evaluates the economy-wide gross output effects of each industry by extracting both its external and internal linkages. In addition, he goes a step further when hypothetically extracting a sector by nullifying its final demand. Following this severe extraction formulation, backward indicators under the HEM and the CMM turn out to be completely identical.

  9. We thank a referee for this tip.

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Acknowledgments

I am particularly grateful to Ferran Sancho, Michael Lahr, Mónica Serrano, Jan Oosterhaven, Erik Dietzenbacher and Tobias Kronenberg for all their helpful suggestions and comments that have substantially contributed in improving this paper. Very detailed comments by the referees are also gratefully acknowledged. Support from research grant MICINN-ECO2009-11857 is acknowledged too.

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  1. Department of International Economics, Faculty of Economics and Business Administration, University of Granada, Campus Universitario de La Cartuja, 18011, Granada, Spain

    Ana-Isabel Guerra

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Correspondence to Ana-Isabel Guerra.

Appendix

Appendix

Table 3 Sectoral disaggregation for the symmetric Spanish input–output table (2007)
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Guerra, AI. A proposal to combine classical and hypothetical extraction input–output methods to identify key sectors for the production and distribution of electricity. Energy Efficiency 7, 1053–1066 (2014). https://doi.org/10.1007/s12053-014-9272-8

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