Impacts of changing coniferous and non-coniferous wood supply on forest product markets: a German scenario case study

Abstract

Management strategies which encourage the conversion of coniferous forests to mixed and deciduous stands potentially increase the share of non-coniferous timber in wood supply over the next decades. The objective of this study is to examine possible market impacts from changing wood supply modeled in the German Forest Development and Timber Volume Model (WEHAM) Scenario. Special emphasis is paid to decreasing coniferous timber availability and the ramifications this development might have on the wood-based industry in Germany. For this purpose, our study introduces the GFPMCNC, a modified version of the Global Forest Products Model (GFPM) which distinguishes coniferous and non-coniferous industrial roundwood as different raw materials and coniferous and non-coniferous sawnwood as additional products on global level. In the GFPMCNC, wood-based panels and pulp could be made from a mix of two roundwood commodities instead of one single input factor. The base period for this study is 2012. Results are reported by 2015 and in the following in mid-period intervals at 5-year steps until 2050. The WEHAM Scenario impact analysis reveals that limited coniferous raw materials lead to lower wood manufacturing activities and declining exports of coniferous sawnwood at the same time as German imports of coniferous industrial roundwood and wood pulp increase over time.

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Notes

  1. 1.

    Several alternative WEHAM Scenarios were modeled in the last 15 years. However, the scenario used for this paper was first published as “Baseline Scenario” (see Rock et al. 2016b). Since this common term can be misleading in international usage, the scenario is here referred to as WEHAM Scenario.

  2. 2.

    The version referred to here is the GFPM version 2016.01.03 published by Buongiorno and Zhu (2016).

  3. 3.

    E.g., in GFPM version 2016.01.03, GFPM version 2015.11.27, GFPM version 2014.11.24. The latest version of the GFPM together with user manuals and further specifications can be downloaded from http://labs.russell.wisc.edu/buongiorno/welcome/gfpm/.

  4. 4.

    GFPM version 2016.01.03.

  5. 5.

    Wood species groups as in NFI.

  6. 6.

    Here 0.736 based on Mantau (2012).

  7. 7.

    Here \(r_{\text{N}}\) = 0.190 and \(r_{\text{L}}\) = 0.486 as average ratio from 2002 to 2013 based on Jochem et al. (2015).

  8. 8.

    Tree age classes as in NFI.

  9. 9.

    Method described in Kramer and Krüger (1981) and applied in Dieter et al. (2001).

  10. 10.

    Method described in Kramer and Krüger (1981) and applied in Dieter et al. (2001.

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Acknowledgements

The authors express their appreciation to Matthias Dieter for providing valuable comments; to Hermann Englert and Nils Ermisch for technical and methodological advice regarding the NFI (German National Forest Inventory (NFI)) and WEHAM (German Forest Development and Timber Volume Modeling) data; to Dominik Jochem for supplying updated data on wood removals in Germany; to Przemko Döring, Kristin Gerber, Sebastian Glasenapp, Susan Klatt, Udo Mantau and Katja Öhmichen for their collaboration in scenario data handling. The authors would like to thank two anonymous reviewers for their very valuable comments.

Funding

The study is part of the project “WEHAM-Szenarien” (WEHAM Scenarios) funded through project grants of the “Waldklimafonds” (Forest Climate Fund) under the auspices of the Federal Ministry of Food and Agriculture (BMEL) and the Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB).

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Correspondence to Franziska Schier.

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Communicated by Martin Moog.

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Schier, F., Morland, C., Janzen, N. et al. Impacts of changing coniferous and non-coniferous wood supply on forest product markets: a German scenario case study. Eur J Forest Res 137, 279–300 (2018). https://doi.org/10.1007/s10342-018-1111-6

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Keywords

  • Forest sector modeling
  • Forest product markets
  • Model calibration
  • Spatial price equilibrium model
  • Scenario impact analysis