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Industrial electricity demand and energy efficiency policy: the case of the Swedish mining industry

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Abstract

The purpose of this paper is to analyze long-run electricity demand behavior in the Swedish mining industry with special emphasis on the impact of energy prices and private research and development (R & D) on electricity use. Methodologically, we estimate a generalized Leontief variable cost function using a panel data set of nine mining operations over the time period 1990–2005. Since the lower boundary of a set of short-run cost functions confines the long-run cost function, we can compute the long-run own- and cross-price elasticities of electricity demand. The empirical results indicate that long-run electricity demand in the mining industry is sensitive to changes in the own price, and already in a baseline setting Swedish mining companies tend to allocate significant efforts towards improving energy efficiency, in part through private R & D. From a policy perspective, the results imply that taxes (and tax exemptions) on electricity can have significant long-run impacts on electricity use. Moreover, future evaluations of so-called voluntary energy efficiency programs must increasingly recognize the already existing incentives to reduce energy use in energy-intensive industries.

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Notes

  1. Energy management systems are a means by which organizations establish the systems and processes necessary to achieve operational control and continual improvement of energy performance (e.g., International Energy Agency 2012).

  2. This differs, for instance, from the more commonly used Translog function (Christensen et al. 1973) for which iterative numerical techniques have to be employed. On occasion, this has caused practical difficulties (e.g., Berndt and Hesse 1986).

  3. Due to data availability reasons, we have not been able to address the role of public R & D expenses.

  4. It should be noted that our data set includes two firms and nine plants, and the data on private R & D are reported at the firm level. For this reason, the analysis builds on the assumption that the outcomes of the firms’ investments in private R & D immediately spill over to all plants and mining operations owned by a specific firm.

  5. Similar specifications can be found in the recent learning curve literature in which the cost reductions of, for instance, new energy technology are analyzed (e.g., Klaassen et al. 2005; Ek and Söderholm 2010).

  6. An important caveat is that knowing the short-run and the long-run elasticities does not provide us with any information about the adjustment path towards equilibrium.

  7. We also tested one specification with plant-specific dummy variables, but this violated many of the regularity conditions. Moreover, a plant dummy procedure implies that all cross-plant variance in the input–output equations is removed and the estimations would be based solely on within-firm variations. The estimated price elasticities would in this case therefore best be interpreted as measuring short-run price responses (Baltagi 1995).

  8. As a robustness check, we also employed a Translog cost function approach to the data, but this model specification performed considerably worse in terms of the monotonicity and concavity conditions.

  9. The standard errors for the elasticities were calculated using the ANALYZ command and the option NDRAW in the TSP software; this command computes asymmetric confidence intervals for nonlinear functions by drawing n parameter vectors.

  10. These results were confirmed when employing a depreciation rate for the knowledge stock of 15 %. It can be noted that the knowledge stock elasticity of electricity demand is not statistically significant in our alternative model with plant-size dummies. This is in part explained by the fact that the size dummies are correlated with the R & D-based variable. Bigger companies typically invest more in R & D.

  11. Hammar and Löfgren (2006, 2010) also show that the share of environmental protection (including energy efficiency) investments has been comparably low in the Swedish mining industry (compared to other energy-intensive sectors). One explanation for this can be that the mining industry over a long time period (1985–2002) experienced depressed output prices and small corporate profit margins.

  12. See also Henriksson et al. (2012) for a related approach, however using another model specification (i.e., a variable Translog cost function) and focusing solely on the pulp and paper industry.

  13. These interviews were conducted by the Swedish Energy Agency in late 2006 (thus during the second year of the program), and covered companies in the entire manufacturing industry. The questions on PFE concerned the program’s impact on the identification of energy efficiency measures as well as on energy use behavior in general. See Hammes (2006) for a detailed questionnaire.

  14. In this simulation exercise, we use the short-run factor demand equations in (5), primarily since the time period investigated is too short to justify an assumption that the industry has had the time to adjust its capital stock to changes in relative prices.

  15. Clearly, our baseline estimate is uncertain and should be interpreted with this in mind. When assuming a knowledge depreciation rate of 15 % (instead of 3 %) we obtained a slower increase in the R & D-based knowledge stock over the relevant period. This led thus to a marginally slower reduction in electricity use (from 5 % to roughly 4 %).

  16. Such an extended assessment should also take into account the possibility that the payback time for energy efficiency investments used in PFE (i.e., 3 years) can be higher than that typically employed by industrial firms. One may note, though, that the previous research suggests that larger industrial companies often consider 3-year paybacks to be acceptable (e.g., Anderson and Newell 2004; Lefley 1996).

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Acknowledgments

Financial support from the Swedish Energy Agency and the Hjalmar Lundbohm Research Centre (HLRC) is gratefully acknowledged as are helpful comments from Chris Gilbert, Bo Jonsson, Magnus Lindmark, Robert Lundmark, David Maddison, Mats Nilsson, Eva Samakovlis, Joachim Schleich, two anonymous reviewers, as well as the journal’s associate editor. Any remaining errors, however, reside solely with the authors.

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  1. Economics Unit, Luleå University of Technology, 971-87, Luleå, Sweden

    Eva Henriksson, Patrik Söderholm & Linda Wårell

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  1. Eva Henriksson
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  2. Patrik Söderholm
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Correspondence to Patrik Söderholm.

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Henriksson, E., Söderholm, P. & Wårell, L. Industrial electricity demand and energy efficiency policy: the case of the Swedish mining industry. Energy Efficiency 7, 477–491 (2014). https://doi.org/10.1007/s12053-013-9233-7

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