The Timber Footprint of the German Bioeconomy—State of the Art and Past Development
Abstract
:1. Introduction
2. Materials and Methods
2.1. General Approach
2.2. Forest Material Accounting for the Timber Footprint
2.2.1. Timber Footprint (TFP)
2.2.2. Roundwood Equivalents (RE)
2.2.3. Terminology of Forest Inventories for the Present Study
2.2.4. Terminology of Material Flow Analysis (MFA) for the Present Study
2.2.5. Terminology of Plant Ecology for the Present Study
2.3. MRIO Analysis
2.3.1. Footprint Calculation with Exiobase 3.4
2.3.2. Adaptation of Exiobase 3.4 for This Study by Nowcasting
- Production quantities of crops and wood on basis of FAOSTAT that are reported in the satellite accounts of the MRIO and that are key for the material flow analysis;
- GHG emissions—where possible, differentiated for industries—on basis of Eurostat and UNFCCC data that are also reported in the satellite accounts of the MRIO and that are key for the assessment of GHG footprints;
- Macro-economic indicators (e.g., the final consumption expenditures by household) and Non-Profit Institutes Serving Households (NPISH) on basis of Worldbank data for the prolongation of the final demand blocks of the MRIO;
- Gross output at current prices for the agricultural sub-sectors on the basis of FAOSTAT for the adjustment of MRIO rows for these sub-sectors.
2.4. Sustainability Assessment of the Timber Footprint
2.4.1. Net Annual Increment
2.4.2. Roundwood Removal Adjustment (RRA)
2.4.3. Sustainable Sourcing of Roundwood
2.4.4. Forest Growth Utilization Rate
2.4.5. Self-Sufficiency Rate
2.4.6. Deforestation Wood as Possible Source for the TFP
- 1.
- The annual global area of forest loss per year displayed as a raster layer with a high-resolution grid of 30 m was published by Hansen et al. [70]. The data are freely available for the period 2000–2019 on the website operated by the University of Maryland [46]. For this study, information is filtered by deforestation events that occur between 2010 and 2015. Cells without deforestation events are classified as . The layer is then aggregated to a resolution of 100 m using R Version 3.6.1. The result is a new raster layer X showing the share of deforestation area per 100-m cell in the observed period.
- 2.
- This layer X is then overlaid with a raster layer showing the growing stock volume per cell at a resolution of 100 m for the base year 2010, published by Santoro [47] using QGIS Version 3.14.16. By combining the two layers, we obtain a new layer Y showing the amount of wood procured through deforestation and clear-cut operations per 100-m cell during the observed period.
- 3.
- Curtis et al. [37] identified the dominant drivers of global forest loss from five different categories i (commodity-driven deforestation, shifting agriculture, forestry, wildfires and urbanization) for the time period 2001–2015. The information is available as a raster layer with a resolution of 10 km. For this study, the category wildfire is excluded from analysis based on the assumption that wood affected by forest fire is not subsequently used. The existing layer is resampled to the resolution of 5 arc-minutes (aggregation method = average, weight according to the area) using QGIS Version 3.16 resulting in the layer Z.
- 4.
- A global 5-arc-minutes point shapefile is used to link layer Y and Z information to each point coordinate of a respective country or region of origin c using the GRASS point sampling tool of QGIS Version 3.14.16. The result is then summed to a country or region resolution using R Version 3.0.4 and divided by six to obtain the annual total volume of roundwood procured on deforestation or clear-cutting areas or the driver-specific volume .
- 5.
- The resulting annual quantity of is then compared to the official RRA 22%-adjusted annual roundwood production of the respective country or region provided by FAOSTAT [44].
3. Results
4. Discussion
4.1. Implications for Germany
4.2. Implications of the German TFPcon for Countries Abroad
4.3. Limitations of Current Approach and Way Forward
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Footprint | Coverage | Source |
---|---|---|
Timber Footprint | Used Domestic Extraction of: | Exiobase 3.4 adapted by a nowcasting process |
Industrial Roundwood Coniferous | ||
Industrial Roundwood Non-Coniferous | ||
Woodfuel Coniferous | ||
Woodfuel Non-Coniferous |
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Egenolf, V.; Vita, G.; Distelkamp, M.; Schier, F.; Hüfner, R.; Bringezu, S. The Timber Footprint of the German Bioeconomy—State of the Art and Past Development. Sustainability 2021, 13, 3878. https://doi.org/10.3390/su13073878
Egenolf V, Vita G, Distelkamp M, Schier F, Hüfner R, Bringezu S. The Timber Footprint of the German Bioeconomy—State of the Art and Past Development. Sustainability. 2021; 13(7):3878. https://doi.org/10.3390/su13073878
Chicago/Turabian StyleEgenolf, Vincent, Gibran Vita, Martin Distelkamp, Franziska Schier, Rebekka Hüfner, and Stefan Bringezu. 2021. "The Timber Footprint of the German Bioeconomy—State of the Art and Past Development" Sustainability 13, no. 7: 3878. https://doi.org/10.3390/su13073878