WP7: Locating development in the “smart” bioeconomy
Currently technological development across the globe tends to occur in ‘clusters’ where resources and organisations are geographically located in the same area in order to minimise costs and maximise efficiency. Referring to bioeconomic transformations, Kearnes (2013: 545) observes that “the spatiality of these developments is striking.” In a “smart” bioeconomy where bio-sectors work together the clustering of such activities around multiple factors such as knowledge, resources, supply chains, and transportation links could assist in optimising the use of resources.
Aims and objectives: The aim of this WP is to identify possible and best areas for development of bioeconomic clusters by exploring locational preferences, requirements, major obstacles and bottlenecks for desirability of location. The results from this WP can be used as decision support by industry and government. Based on information from the individual foresight (WP2a) we will use a two-step approach to answer the following questions:
(1) where are the best locations for bio-economic clusters and how should this be assessed, (
2) what are the tradeoffs, how can they be mapped and described, and are there any general major obstacles, and
(3) what are the conditions that would aid in developing a smart bioeconomy and what are the spatial implications of different developments.
Method: First a cartographic GIS model (Tomlin and Berry, 1979) will be used to identify candidate locations for the most promising bioeconomic clusters. We will combine several thematic map layers of the same area and perform weighted overlay analyses. We must capture and explore relevant spatial datasets of resources (from WP6). Input criteria must be operationalised in a manner that relates to bioeconomy. The result will be a suitability map of possible cluster locations.
When possible cluster locations have been found using the geographic models, optimization methods will determine which of these candidates have the best locations given certain assumptions and conditions, and what the composition of each location should be. The optimization model will be based on input data from other WPs and the geographic model. Parameters will be determined in the first round of foresight analysis (WP2a) but should include factors such as transportation distances (raw material and manufactured products), demand, and supply and production costs. Depending on the chosen perspective, different components can be assigned dissimilar weights to analyse different perspectives and give more information of consequences of decisions (e.g. waste recycling). Advanced optimization methods allow for simultaneously identifying the best overall suggestions for developing bioeconomic clusters but also including detailed information for each industry at each location. The optimization model developed in this work package will be based on other models made by SINTEF in previous projects (see e.g. Nørstebø and Johansen, 2013; Schütz et al., 2010).
Outputs: Early results from this WP will be used in WP2b to facilitate discussions among stakeholders on locating integrated bioeconomic development. The final integrated scenarios will be re-examined for the effects of variations in different parameters on optimal location.
WP leader: Dr Wenche Dramstad, NIBIO. Additional participants: NIBIO, SINTEF.