Abstract
The resource reuse and recycling practice of industrial symbiosis (IS) has been shown to create significant economic and environmental benefits for both the businesses involved in a synergy and the wider communities in which they reside. In the United Kingdom IS has been largely facilitated by the National Industrial Symbiosis Programme (NISP) on a demand-led ‘work with the willing’ basis. It is proposed, however, that the systematic and directed identification of opportunities for IS would allow NISP to facilitate the delivery of greater economic and environmental benefits than those achieved to date. The goal of this project was to develop a framework for the proactive identification of opportunities for IS. Drawing on ecological theory the framework and supporting tools were built on the concept of a symbiosis ‘conducive environment’ (i.e. areas of mature industrial activity that - through the forces and effects of system succession - are prime for eco-industrial development). The framework’s theoretical platform informed the development of a spatial analysis tool which drew on the conservation biology concept and practice of Habitat Suitability Indices and Habitat Suitability Mapping. The tool characterised (through multi-criteria-evaluation) a baseline of high symbiosis potential and identified comparable geographic areas of IS suitability that are prime for context sensitive eco-industrial development. Industrial diversity was identified as being an important criterion within the identification and facilitation of IS opportunities. This finding led to an analysis of the role diversity plays in the development of industrial ecosystems and an analysis of the role geospatial industrial diversity plays in the facilitation of regional resource efficiency. By mapping the geospatial industrial diversity of England, it was found that regions with high potential for local IS could be identified. The framework and supporting tools provide NISP with a model for the delivery of proactive intelligence-based regional resource efficiency.