Spatially explicit assessment of roundwood and logging residues availability and costs for the EU28

ABSTRACT

Competition for woody biomass between material and energy uses is expected to further increase in the future, due to the limited availability of forest resources and increasing demand of wood for material and bioenergy. Currently, methodological approaches for modeling wood production and delivery costs from forest to industrial gates are missing. This study combines forest engineering, geographically explicit information, environmental constraints and economics in a bottom-up approach to assess cost–supply curves. The estimates are based on a multitude of wood supply systems that were assigned according to geographically explicit forestry characteristics. For each harvesting and transportation system, efficiencies were modeled according to harvesting sites and main delivery hubs. The cost–supply curves for roundwood and logging residues as estimates for current time and for the future (2030) show that there are large regional differences in the potential to increase extraction in the EU28. In most EU Member States, the costs of logging residues extraction increase exponentially already for low levels of mobilization, while extraction of roundwood can be increased to a larger extent within reasonable costs (30–40 $/m³). The large differences between countries in their harvest potential highlight the importance of spatially explicit analyses.

KEYWORDS:

• Spatial availability
• cost–supply curves
• woody biomass
• supply systems

Acknowledgements

We are grateful to the experts involved in the global benchmarking exercise for a cross-border adaptation of forest operation’s costs. We would like to thanks our colleagues Dr Pekka Lauri and Dr Stefan Frank for their professional advices.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCiD

Ola Lindroos http://orcid.org/0000-0002-7112-4460

Notes

1 Harvesting of forest areas which are strictly protected according to WDPA (2004) was excluded and no conversion or use of protected forest was allowed. Forests that are not protected are considered as potential production forest. The G4M model allocates harvests to this area so that the demand for wood for material and energy purposes will be satisfied. Forests that are used in a certain period to meet the wood demand (so-called used forests) are modeled to be managed for woody biomass production. This implies a rotation time, thinning events and final harvest. Unused forest do currently not contribute to wood supply (due to economic reasons) and the model allows for conversion from used forests to unused, and unused to used forests. The historical geographical location of harvest within each EU Country has been initialized using a map of wood production from Verkerk et al. (2015), which was applied for sorting the economical harvest suitability of cell in the G4M.

2 The GLOBIOM is a global partial equilibrium model of the forest and agricultural sectors, where economic optimization is based on the spatial equilibrium modeling approach (Havlík et al. 2011). The demand is based on the interaction of four different drivers: population growth, income per capita growth, bioenergy growth, response to prices. Demand increases linearly with population in each of the 57 GLOBIOM regions (including the 28 EU countries). GDP per capita changes determine demand variation depending on income elasticity values. For the agricultural sector, the income elasticities area calibrated to mimic anticipated FAO projections of diets (Alexandratos & Bruinsma 2012). Income elasticities for the forest sector are taken from Buongiorno et al. (2003). The response of non-energy related uses to commodity prices is endogenously computed in GLOBIOM. Bioenergy demand projections are implemented based on PRIMES projections for forest biomass (EU 2013). Price elasticities for the agricultural commodities are taken from a global database from USDA (Muhammad et al. 2011) and for the forest sector from Buongiorno et al. (2003). Hence, demand for non-energy (material) wood use are competing for the wood resource with energy uses and are projected endogenously by GLOBIOM. An increase in biomass production prescribed by the output of the PRIMES biomass model is entirely reproduced in GLOBIOM.

3 The official exchange rates were collected from the World Bank & International Monetary Fund (IMF) (http://data.worldbank.org; www.imf.org). The exchange rates are affected by short time fluctuations. As we thrive to reflect the long-term exchange situation, we selected a five-year average exchange rate (2009–2013) (see Appendix 5 for online Supplemental Material).

4 The PPP conversion factor expresses the number of units of a country's currency required to buy the same amounts of goods and services in the domestic market as U.S. dollar would buy in the United States. The PPP conversion factor was obtained from The World Bank International Comparison Program database (http://data.worldbank.org ) (The World Bank 2014a), the average of last five available years was used (2009–2013), the PPP conversion factor is available for 199 countries (see Appendix 5 for online Supplemental Material).

5 The international trade tariffs for manufactured products are available from The World Bank (http://data.worldbank.org/ ) (The World Bank 2014c). The average value for period 2009–2013 was used: the data were available for 172 countries and measured as a percentage on the price of traded commodity (Appendix 5, see Supplemental Material).

6 Gross value added per capita from roundwood production (VAE) ($ per capita): the value added per capita was calculated by dividing the Gross Valued Added from roundwood production ($) by the number of employees in roundwood production. Both data were retrieved from FAO (2014) and were available for the year 2011 for 175 countries (Appendix 5, see Supplemental Material).

7 The International Country Risk Guide (ICRG) (https://www.prsgroup.com) is a five-year composite index forecast that expresses the overall concern for investing in a specific country, providing a combined rating for political, financial and economic risk factors for each country. This forecast was available for 139 countries. The index assumes values between 0 and 100, where higher values represent lower risk (Appendix 5, see Supplemental Material).

8 A global collection of gasoline and diesel prices was retrieved from GIZ (2013). The most recent update for GIZ was released for 165 countries (year 2012). Value added taxes (VAT) are commonly excluded from the computation of costs incurred by forest companies, therefore VATs were collected from The World Bank Group database (http://www.doingbusiness.org) and subtracted from the diesel price from GIZ, obtaining a net official fuel price (Appendix 5, see Supplemental Material).

9 The PPP level ratio, also referred to as the national price level (price level ratio of PPP conversion factor to market exchange rate), makes it possible to compare the cost of the bundle of goods that make up the gross domestic product (GDP) across countries. It tells how many dollars are needed to buy a dollar's worth of goods in the country as compared to the United States. The price level ratio of PPP was obtained from the World Bank International Comparison Program database (http://data.worldbank.org ) (The World Bank 2014b), the average of last five available years (2009–2013) was available for 182 countries (Appendix 5, see Supplemental Material).

10 Social charges on wages were obtained from The World Bank Group database (http://www.doingbusiness.org) (The World Bank Group 2014), the information was available for 174 countries and applied to the country borders (Appendix 5, see Supplemental Material).

11 The “extraction distance” is the distance from the stump to the roadside. It is set to 300 m for altitudes below 600 m and for wheeled machinery it increases for higher altitudes (classes) in the model:

Table

12 In the case of road transportation, the delays were included into the time-consumption model as conventional practice for this operation.

Additional information

Funding

The Kempe Foundation “Kempestiftelserna” of Sweden is acknowledged for financial support under the post-doctoral funding scheme SKM-1336. This study was financially supported also by the Research Programme Future Forests. Future Forests is a multi-disciplinary research program supported by the Foundation for Strategic Environmental Research (MISTRA), the Swedish Forestry Industry, the Swedish University of Agricultural Sciences (SLU), Umeå University, and the Forestry Research Institute of Sweden (Skogforsk).