Risk frames and multiple ways of knowing: Coping with ambiguity in oil spill risk governance in the Norwegian Barents Sea

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The opening of new areas for offshore drilling in the Arctic is highly controversial. As ice cover in the region is melting at an alarming rate, new areas have been opened for petroleum industry in the Norwegian Barents Sea. Our qualitative analysis examines risks related to the petroleum operations in the newly opened areas and provides insight into the complex and socially constructed nature of the risks. With the use of visual influence diagram- based mental modelling approach, we demonstrate the multiple ways in which the risks are understood and defined. We also analyse the type of knowledge that the risk frames are based on. The influence diagrams present the risk frames in a clear, visual, form. The study indicates that the existing governance framework fails to treat the ambiguity around oil spill risks: the current risk assessments and risk management do not reflect on the multiple ways in which the participants in this study 1) frame the problem situation, 2) how they identify different measures to manage risks, and 3) what are considered as key knowledge needs and knowledge producers by the participants. We suggest that social learning and collaborative knowledge production are needed to move towards developing shared understanding of the problem situation. Finally, we suggest that the rigorous examination and the unveiling of ambiguity may help developing deliberative risk governance measures and moving towards sustainability transformations.
Original languageEnglish
JournalEnvironmental Science & Policy
Volume98
Pages (from-to)95-111
Number of pages17
ISSN1462-9011
DOIs
Publication statusPublished - Aug 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 1172 Environmental sciences
  • maritime governance
  • oil spills
  • offshore petroleum industry
  • risk frames
  • mental models
  • influence diagrams
  • knowledge production

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