Risk identification and perception in the fisheries sector: comparisons between the Faroes, Greece, Iceland and UK

Diana Tingley, J. Ásmundsson, Edward Borodzicz, A. Conides, Ben Drakeford, I. Eðvarðsson, D. Holm, K. Kapiris, S. Kuikka, B. Mortensen

    Research output: Contribution to journalArticlepeer-review

    Abstract

    This paper presents the findings of risk identification and risk perception research conducted in relation to the fisheries systems of four distinct and diverse European countries: Faroes, Iceland,Greece and the UK. Risk research traditionally attempts to quantify the potential threat or consequences from a range of risk events or hazards.This research, however, adopted a social sciences perspective and so assumed that a risk event or hazard can mean different things to different people and that these perceptions are also context and culturally dependent.Risk perceptions were examined and risk registers developed in each country for a range of stakeholder groups. A ‘mental modelling’ approach was adopted in a series of qualitative interviews. Findings were examined in terms of a wide range of psychological, social and cultural risk theories.Differences in risk perceptions were noted between stakeholder groups and countries and contextual influences were examined such as the widely differing fisheries management systems used in each country. This research provide some of the first attempts to systematically evaluate risks and perceptions across a range of fisheries-systems. The findings support social science theories which argue that risk is a subjective, as opposed to objective, concept and that this subjectivity will therefore affect our attempts to assess and manage those hazards we think we can potentially influence or control.
    Original languageEnglish
    Pages (from-to)1249-1260
    Number of pages12
    JournalMarine Policy
    Volume34
    Issue number6
    DOIs
    Publication statusPublished - Nov 2010

    Keywords

    • risk perception
    • risk identification
    • fisheries
    • mental modelling
    • clustering analysis

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