Exploring the Regional Potential of Lignocellulosic Biomass for an Emerging Bio-Based Economy: A Case Study from Southwest Germany

The globally emerging concepts and strategies for a bioeconomy rely on the vision of a sustainable bio-based substitution process. Fossil fuels are scarce and their use contributes to global warming. To replace them in the value chains, it is essential to gain knowledge about quantities and spatial distributions of renewable resources. Decision makers specifically require knowledge-based models for rational development choices. In this paper, we demonstrate such an approach using remote sensing-derived maps that represent the potential available biomass of forests and trees outside forests (TOF). The maps were combined with infrastructure data, transport costs and wood pricing to calculate the potentially available biomass for a regional bioeconomy in the federal state of Baden-Wurttemberg in Southwest Germany. We estimated the spatially explicit regional supply of biomass using routable data in a GIS environment, and created an approach to find the most suitable positions for biomass conversion facilities by minimizing transport distances and biomass costs. The approach resulted in the theoretical, regional supply of woody biomass with transport distances between 10 and 50 km. For a more realistic assessment, we subsequently applied several restrictions and assumptions, compiled different scenarios, optimised transport distances and identified wood assortments. Our analysis demonstrated that a regional bioeconomy using only local primary lignocellulosic biomass is possible. There would be, however, strong competition with traditional wood-processing sectors, mainly thermal utilisation and pulp and paper production. Finally, suitable positions for conversion facilities in Baden-Wurttemberg were determined for each of the six most plausible scenarios. This case study demonstrates the value of remote sensing and GIS techniques for a flexible, expandable and upgradable spatially explicit decision model.