Spatial analysis of regional industrial clusters in the German forest sector

被引:27
作者
Kies, U. [1 ]
Mrosek, T. [1 ]
Schulte, A. [1 ]
机构
[1] Univ Munster, Wald Zentrum, D-48149 Munster, Germany
关键词
forest sector; wood-based industries; industry agglomeration; exploratory spatial data analysis; Germany; GEOGRAPHIC CONCENTRATION; ASSOCIATION; STATISTICS; PATTERNS;
D O I
10.1505/ifor.11.1.38
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
The economic concept of the forest sector as a cluster of interlinked wood-based industries is contributing to a growing understanding of a large sector in national economies of Europe. Although national level surveys have demonstrated the forest sector's global impact, neither its role in regional economies nor its distribution in geographic space are well understood. Attempting a regionalized analysis of the forest sector, this paper explores an approach combining regional economics and spatial statistics. Standard concentration indices (Gini coefficient, location quotient) and geostatistical autocorrelation measures for regional clustering (Moran's I and Getis-Ord G) are combined in an exploratory spatial analysis of detailed county-level employment statistics for Germany. The case study reveals decisive impacts of the forest sector on regional employment especially in rural areas. Regional industrial clusters and pairwise patterns of co-agglomeration of sawmilling, wood-based panels, wood-based construction and furniture industries are identified in geographical space. The pronounced spatial variability within Germany's forest sector is linked to regional factors influencing geographic location, size and regional association of the industries under study. The research offers a suitable geostatistical approach for regional industrial targeting of the forest sector that can be supportive to informed rational decision-making in forest cluster development and policy.
引用
收藏
页码:38 / 51
页数:14
相关论文
共 59 条
[1]  
Abt K.L., 2002, SO FOREST RESOURCE A, P239
[2]  
Aguilar FX, 2008, FOREST SCI, V54, P242
[3]  
Aguilar FX, 2006, WOOD FIBER SCI, V38, P121
[4]  
[Anonymous], 2003, Innovation Clusters and Interregional Competition, DOI [10.1007/978-3-540-24760-9_6, DOI 10.1007/978-3-540-24760-9_6]
[5]   GeoDa:: An introduction to spatial data analysis [J].
Anselin, L ;
Syabri, I ;
Kho, Y .
GEOGRAPHICAL ANALYSIS, 2006, 38 (01) :5-22
[6]   LOCAL INDICATORS OF SPATIAL ASSOCIATION - LISA [J].
ANSELIN, L .
GEOGRAPHICAL ANALYSIS, 1995, 27 (02) :93-115
[7]  
Anselin L., 1988, SPATIAL ECONOMETRICS, V85, P310, DOI [10.1007/978-94-015-7799-1, DOI 10.1007/978-94-015-7799-1]
[8]  
Arbia Giuseppe., 2001, Journal of Geographical Systems, V3, P271, DOI [10.1007/PL00011480, DOI 10.1007/PL00011480]
[9]  
BATHELT H., 2003, WIRTSCHAFTSGEOGRAPHI, V2nd
[10]  
BRADEN R, 1998, 66 U WASH CINTRAFOR