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The German energy audit program for firms—a cost-effective way to improve energy efficiency?

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Abstract

In 2008, a program was established in Germany to provide grants for energy audits in small- and medium-sized enterprises. It aims to overcome barriers to energy efficiency, like the lack of information or a lack of capacity, and is intended to increase the adoption of energy efficiency measures. We evaluate the program's impact in terms of energy savings, CO2 mitigation, and cost-effectiveness. We find that firms adopt 1.7–2.9 energy efficiency measures, which they would not have adopted without the program. Taking a firm's perspective, the program shows a net present value ranging from −0.4 to 6 €/MWh saved, which very likely implies a net benefit. For the government, each ton of CO2 mitigated costs between 1.8 and 4.1 €. Each euro of public expenditure on audit grants led to 17–33 € of private investment. The cost-effectiveness of the program for firms and the low share of public expenditure underline its value for the German energy efficiency policy mix and suggest that it should be expanded in Germany. Further, the good experiences with the program in Germany should encourage countries which have not yet established an audit program to do so.

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Notes

  1. German title: "Sonderfonds Energieeffizienz in KMU".

  2. For a more process-oriented assessment of the role of the energy auditors and the regional partners, we refer to Gruber et al. (2011)

  3. Defined as firms with less than 250 employees

  4. This figure also considers omitted firms that did not provide an e-mail address or that objected to data processing. Further, in the case where a firm conducted both audits, it was only asked about the comprehensive audit.

  5. Another approach would be to calculate energy savings based on a comparison of firms’ energy bills before and after the audit. However, due to the relatively low share of energy savings compared to firms’ energy demand, and the fluctuating energy demand as a result of production variation, the success of such an approach is very uncertain. A similar approach, that is, comparing energy bills of audited companies against those of a control group, is hardly possible because of the heterogeneity among firms. Furthermore, we had to rely on a survey and energy audit reports as our main data sources, because metering in firms would have been too costly.

  6. In contrast to the CO2 factor for fuel oil and natural gas, CO2 emissions of electricity generation vary over time, depending on the dynamics in the generation mix. While we are not able to consider hourly variations, the data allows the consideration of annual variations. See Vine et al. (2012) for a more detailed discussion of this aspect.

  7. Question in survey: Was the implementation of measures already planned before the audit?—“certainly planned” (20.9 %), “considered” (42.3 %), “no” (36.8 %)

  8. List of four EEMs not adopted followed by the question in survey: “Do you plan to implement these measures in the near future?”—“certainly” (33.5 %), “possibly” (35.9 %), “rather not” (30.6 %)

  9. Question: “How many measures were recommended in total?” Question: “How many measures did you implement?”

  10. Question in survey: "Would you have conducted the audit even without the program?"—"Yes, with the same level of comprehensiveness" (9.1 %), "Yes, but less comprehensive" (42.6 %), and "No, rather not" (48.3 %).

  11. As the response rate of the control group was very low, we were not able to cross-check this statement by control group comparison.

  12. This mainly results from the fact that many firms consume energy only for building-related end-uses. In this case, single measures like insulation or replacement of heating systems show high impacts when compared to the firm’s total energy consumption.

  13. The weighted mean energy savings are calculated as \( {\text{MeanSaving}}{{\text{s}}^w} = \frac{{\sum\nolimits_{{i = 1}}^n {{\text{Saving}}{{\text{s}}_i}} }}{{\sum\nolimits_{{j = 1}}^m {{\text{EnergyDeman}}{{\text{d}}_j}} }} \) while the nonweighted savings are calculated as \( {\text{MeanSaving}}{{\text{s}}^{\text{nw}}} = \frac{{\sum\nolimits_{{i = 1}}^n {\frac{{{\text{Savings}}_i^j}}{{{\text{EnergyDeman}}{{\text{d}}^j}}}} }}{n} \), where i = EEM and j = firm

  14. Very long payback times are mainly observed for measures related to building renovation (see “Technology characteristics” section).

  15. The corresponding shares of audit costs paid by the company are more than 20 % (initial audit) and 40 % (detailed audit) that were defined as maximum in the program. This is due to the fact that the program provides grants only for 2 days for initial audits and 10 days for detailed audits and several audits exceeded these limits.

  16. Particularly, firms with energy management show a higher share of free riders (15.4 %), whereas this is lower for firms without (7.2 %).

  17. Measured as energy efficiency progress for industry using the ODEX indicator to correct for structural change.

  18. About 42 % of firms used KfW soft loans for financing, while 47 % used own funds and 11 % other types of loans

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Acknowledgments

We would like to thank our colleagues for intensive discussions and their valuable input. We are particularly thankful to Birgit-Jo Frahm, Joachim Schleich, Patrick Plötz, Christian Stenqvist, Christine Mahler-Johnstone and Vicki Duscha as well as two anonymous reviewers. Part of the work was conducted within the framework of a project for the German Federal Ministry of Economics and Technology.

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Authors and Affiliations

  1. Fraunhofer Institute for Systems and Innovation Research ISI, Breslauer Str. 48, 76139, Karlsruhe, Germany

    Tobias Fleiter & Wolfgang Eichhammer

  2. Institute for Resource Efficiency and Energy Strategies IREES GmbH, Schoenfeldstr. 8, 76131, Karlsruhe, Germany

    Edelgard Gruber

  3. Copernicus Institute of Sustainable Development, Utrecht University, Heidelberglaan 2, 3584, Utrecht, The Netherlands

    Ernst Worrell

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  1. Tobias Fleiter
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  2. Edelgard Gruber
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  4. Ernst Worrell
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Correspondence to Tobias Fleiter.

Appendix

Appendix

Table 8 Definition of EEM types and assumed average lifetime
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Table 9 Energy price development as assumed for the NPV calculation (Euro/MWh)
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Fleiter, T., Gruber, E., Eichhammer, W. et al. The German energy audit program for firms—a cost-effective way to improve energy efficiency?. Energy Efficiency 5, 447–469 (2012). https://doi.org/10.1007/s12053-012-9157-7

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