Fuzzy Cognitive Mapping (FCM) is a widely used participatory modelling methodology in which stakeholders collaboratively develop a ‘cognitive map’ (a weighted, directed graph), representing the perceived causal structure of their system. This can be directly transformed by a workshop facilitator into simple mathematical models to be interrogated by participants by the end of the session. Such simple models provide thinking tools which can be used for discussion and exploration of complex issues, as well as sense checking the implications of suggested causal links. They increase stakeholder motivation and understanding of whole systems approaches, but cannot be separated from an intersubjective participatory context. Standard FCM methodologies make simplifying assumptions, which may strongly influence results, presenting particular challenges and opportunities. We report on a participatory process, involving local companies and organisations, focussing on the development of a bio-based economy in the Humber region. The initial cognitive map generated consisted of factors considered key for the development of the regional bio-based economy and their directional, weighted, causal interconnections. A verification and scenario generation procedure, to check the structure of the map and suggest modifications, was carried out with a second session. Participants agreed on updates to the original map and described two alternate potential causal structures. In a novel analysis all map structures were tested using two standard methodologies usually used independently: linear and sigmoidal FCMs, demonstrating some significantly different results alongside some broad similarities. We suggest a development of FCM methodology involving a sensitivity analysis with different mappings and discuss the use of this technique in the context of our case study. Using the results and analysis of our process, we discuss the limitations and benefits of the FCM methodology in this case and in general. We conclude by proposing an extended FCM methodology, including multiple functional mappings within one participant-constructed graph.
11Effective risk management within environmental policy making requires knowledge on 12 natural, economic and social systems to be integrated; knowledge characterised by 13 complexity, uncertainty and ambiguity. We describe a case study in a (UK) central 14 government department exploring how risk governance supports and hinders this challenging 15 integration of knowledge. Forty-five semi-structured interviews were completed over a two 16 year period. We found that lateral knowledge transfer between teams working on different 17 policy areas was widely viewed as a key source of knowledge. However, the process of 18 lateral knowledge transfer was predominantly informal and unsupported by risk governance 19 structures. We argue this made decision quality vulnerable to a loss of knowledge through 20 staff turnover, and time and resource pressures. Our conclusion is that the predominant form 21 of risk governance framework, with its focus on centralised decision-making and vertical 22 knowledge transfer is insufficient to support risk-based, environmental policy making. We 23 discuss how risk governance can better support environmental policy makers through 24 systematic knowledge management practices. 25 26
SummaryIndustrial ecology (IE) has recognized the relevance of space in various areas of the field. In particular, industrial symbiosis has argued for proximity and the colocation of firms to reduce emissions and costs from transport. But, space is also relevant for industrial ecosystems more widely. These spatial principles have rarely been spelled out analytically and this article does so. From economic geography, we now have frameworks and analytical tools to undertake this kind of analysis. Using the example of ports and their hinterland, we argue for spatial analyses in IE.
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