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Economic and operational factors in energy and climate indicators for the steel industry

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Abstract

European steel producers need to increase energy efficiency and reduce CO2 emissions to meet requirements set by European policies. Robust indicators are needed to follow up these efforts. This bottom-up analysis of traditional energy and climate indicators is based on plant-level data from three Swedish steel producers with different product portfolios and production processes. It concludes that indicators based on both physical and economic production are interlinked with aspects both within and outside the company gates. Results estimated with partial least squares regression confirm that steel production has complex relationships with markets, societal context, and operational character of the industry. The study concludes that (i) physical indicators (based on crude steel production) may be useful at the process level, but not at the industry-wide level, (ii) the value added is not a reliable alternative since it cannot be properly estimated for companies belonging to larger international groups, and (iii) structural shifts may influence the results significantly and veil improvements made at the process level. Finally, harmonised system boundary definitions are vital for making indicators comparable between companies. The use of traditional indicators, as defined today, may lead to uninformed decisions at the company as well as policy levels.

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Notes

  1. In an integrated steel mill, steel is produced from iron ore using a blast furnace and a basic oxygen furnace. An integrated mill covers the whole chain of production, including coke production, steel production as well as rolling and finishing processes.

  2. In contrast to correlation, which is a linear relationship between two variables, multi-collinearity indicates that a linear relationship exists between the variable and another variable or with a linear combination of a set of other variables (Alin 2010).

  3. Carbon leakage is the phenomenon of CO2 emissions increasing elsewhere as a result of implementing regional regulations to reduce emissions (i.e. the Kyoto Protocols mitigation policy’s pressure on Annex B countries that could result in emissions increasing in non-Annex B countries) (Peters and Hertwich 2008).

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Acknowledgments

The research was conducted independently by KTH Royal Institute of Technology but in close cooperation with Swedish steel industries. The authors would like to acknowledge the generous funding from the Swedish Energy Agency through the project “Robust Energy and Climate Indicators for the Steel Industry”.

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Authors and Affiliations

  1. Energy and Climate Studies unit, KTH Royal Institute of Technology, Stockholm, Sweden

    Johannes Morfeldt & Semida Silveira

  2. SSAB EMEA AB, Stockholm, Sweden

    Tomas Hirsch & Jan Pettersson

  3. Sandvik Materials Technology, Sandvik AB, Sandviken, Sweden

    Susanne Lindqvist

  4. Jernkontoret, Stockholm, Sweden

    Alena Nordqvist

  5. Höganäs AB, Höganäs, Sweden

    Magnus Pettersson

Authors
  1. Johannes Morfeldt
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  2. Semida Silveira
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  7. Magnus Pettersson
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Corresponding author

Correspondence to Johannes Morfeldt.

Appendix A

Appendix A

The appendix includes comparisons of results including coal and coke as energy carrier (denoted by “. Coal”) with the main results for the energy efficiency indicators presented in the paper. The variance explained by each PLSR model is given in the parenthesis. Factors marked in blue were chosen based on the VIP method. Factors marked in green were chosen based on the β-method.

Fig. 7
figure 7

Regression coefficients for SSAB (left axis). The variance explained by each PLSR model is given in the parenthesis. Factors marked in blue were chosen based on the VIP method (see scores—right axis). Factors marked in green were chosen based on the β-method

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Fig. 8
figure 8

Regression coefficients for HGS (left axis). VIP scores for HGS (right axis)

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Fig. 9
figure 9

Regression coefficients for SMT (left axis). VIP scores for SMT (right axis)

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Fig. 10
figure 10

Regression coefficients for SSAB (left axis). VIP scores for SSAB (right axis)

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Morfeldt, J., Silveira, S., Hirsch, T. et al. Economic and operational factors in energy and climate indicators for the steel industry. Energy Efficiency 8, 473–492 (2015). https://doi.org/10.1007/s12053-014-9296-0

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