The general scientific background for the concept of forest and wood-processing industry clusters was mostly formulated by Porter (1998) in the context of gen- eral economics. Whereas extensive literature is available for various industry
branches (Armstrong and Taylor, 2000; Brenner, 2002; Maier and Tödtling, 2002; Scherer and Bieger, 2003; Schiele, 2003; Sölvellet al., 2003), the general scientific background for cluster organization in forestry is very limited.
A relatively broad definition of the forest and wood-processing industry clus- ter was defined for the European Union (EU) by the European Commission (Kommission der Europäischen Gemeinschaften, 1999; Bundesrat, 2001).
Examples of the early application of the cluster concept based on this EU defini- tion can be found in Austria (TMG, 2005), Finland (Ministry of Agriculture and Forestry Finland, 2001) and Sweden (Anon., 2001).
The concept of forest and wood-processing industry clusters has seen further development in recent years. The modified and extended cluster definition inte- grates all industry branches relevant to the field of forestry. The different industry branches are presented in some detail in order to develop a conceptual frame- work for forest and wood-processing industry clusters (see Results below).
The method of cluster analysis allows cluster structures to be identified at different spatial scales (ranging from international, national, sub-national or regional to local levels). It includes data collection, analysis and assessment con- cerning all relevant aspects of forest resources, forest management as well as the utilization of timber and non-timber products. Cluster management can support the optimization of economic performance within a cluster and therefore contri- bute to the sustainable development of forestry-based regions (Schulte, 2003a, b; Mrosek and Schulte, 2004; Mroseket al., 2005; Schulte and Mrosek, 2006).
To improve information and knowledge transfer within the cluster, a transfer concept was developed and tested within both cluster analysis and management.
This transfer concept was based on methods of general communication science (e.g. stakeholder participation, public relations, marketing), but also supported by methods specific to the general cluster concept. Among these methods were the approaches of corporate networking and cooperation (Howaldtet al., 2001;
Initiative für Beschäftigung OWL e. V., 2004; Stahl and Schreiber, 2004).
The NRW forest and wood-processing industries cluster case study used to illustrate the potential of the concept within this chapter is currently the only exam- ple of a large-scale cluster analysis in forestry within Germany (other studies at dif- ferent spatial levels are in progress) (Mroseket al., 2005). This study, which took place from November 2001 to January 2003, applied the modified and extended EU definition of the forest and wood-processing industries cluster and methods of cluster analysis and management (Schulte, 2003a, b; Schulte and Mrosek, 2006).
Within the cluster analysis, data collection involved specific business sur- veys, expert interviews and general statistics from governmental institutions and industry associations. Although these statistics provided a suitable basic data- base, there was a lack of data in some areas (e.g. certain industry branches were not included in the existing NRW statistics) and the standards for data collection were not applicable to all variables (e.g. small companies with<21 employees relevant to this study were not included in the existing NRW statistics). Addi- tional surveys were conducted in order to identify companies in the forest and wood products industry cluster, with specific attention paid to small companies.
Socio-economic data were collected for all identified companies, focusing on the total number of employees and the corporate annual revenue. Where it was not
possible to collect this information on an individual company basis for a parti- cular industry branch, estimates were derived from interview-based industry expert assessments (Schulte, 2003b).
In addition to socio-economic data analysis, a SWOT analysis (SWOT= strengths, weaknesses, opportunities and threats) (Fleisher and Bensoussan, 2003) was conducted to support the overall assessment of the cluster. Following a participatory research approach, this analysis also involved industry experts and other forest stakeholders.
Case study area
North-Rhine/Westphalia (Nordrhein-Westfalen (NRW)) is one of 16 states (Länder) in the Federal Republic of Germany. Located in the western central part of Germany, it borders the Netherlands and Belgium and covers an area of 34,082 km2. Figure 8.1 shows the overall area of NRW with selected land cover features, as well as its geographical location in the larger contexts of Germany and Europe.
With about 18 million citizens, NRW is the state with the highest popula- tion in Germany. Accounting for 22% of the German gross domestic product
0 510 20 30 40 50 km N
North-Rhine/Westphalia
Urban area Forest area Europe
Germany
Fig. 8.1. Map of urban and forest land cover areas in NRW and physical location within contexts of Germany and Europe. (Data source: ESRI and Corine Landcover; GIS layout by Kies, 2005.)
(466.9 billion euros in 2004), NRW is also a region of major economic impor- tance within Germany and the EU (Landesamt für Datenverarbeitung und Statistik NRW, 2003).
Sustainable forestry has a long tradition in NRW, as it does in Germany in general. Forest land (915,800 ha) covers 27% of the total land area in NRW.
While 52.7% of the forest consists of deciduous stand types and corresponding tree species, the remaining 47.3% consists of coniferous stand types and tree species. The dominating tree species are spruce (Picea abiesL.), 36%; beech (Fagus sylvatica L.), 16%; and oak species (Quercus robur L. and Quercus petraea Liebl.), 15%. The age class distribution of the forest is biased towards younger stands, especially in the coniferous stands, which is mostly a result of afforestation following the effects of the Second World War (degradation and deforestation). Forest landownership is dominated by private forest owners (64%), followed by municipal (20%), state (13%) and federal (3%) forest owner- ship. The privately owned forest area is characterized by a very large number of owners (>150,000) and mostly small (≥200 ha) and very small parcels of forest land (<200 ha). Concerning forest productivity, the merchantable timber volume (under bark) is 221 m3/ha on average and 194.4 million m3in total.
The current total annual timber harvest is 3.9 million m3. Considering that the mean annual increment is 9.1 m3/ha on average, the current timber harvest level is significantly smaller than the sustainable harvest level based on the annual allowable cut volume. Forest management is based on the principles of sustainability, multiple forest use and nature-oriented silviculture (e.g. Nature Oriented Forest Management Programme) (Ministerium für Umwelt und Naturschutz, Landwirtschaft und Verbraucherschutz des Landes NRW 2003a, b;
Schulte, 2003a).
NRW is characterized by a high concentration of wood-processing indus- tries, covering both the primary and secondary level and all types of wood pro- cessing. For example, the concentration of the wood furniture industry in NRW is of nationwide and international relevance.