Expert Elicitation Methods in Quantifying the Consequences of Acoustic Disturbance from Offshore Renewable Energy Developments

  • Carl Donovan
  • John Harwood
  • Stephanie King
  • Cormac Booth
  • Bruno Caneco
  • Cameron Walker
Conference paper

DOI: 10.1007/978-1-4939-2981-8_27

Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 875)
Cite this paper as:
Donovan C., Harwood J., King S., Booth C., Caneco B., Walker C. (2016) Expert Elicitation Methods in Quantifying the Consequences of Acoustic Disturbance from Offshore Renewable Energy Developments. In: Popper A., Hawkins A. (eds) The Effects of Noise on Aquatic Life II. Advances in Experimental Medicine and Biology, vol 875. Springer, New York, NY

Abstract

There are many developments for offshore renewable energy around the United Kingdom whose installation typically produces large amounts of far-reaching noise, potentially disturbing many marine mammals. The potential to affect the favorable conservation status of many species means extensive environmental impact assessment requirements for the licensing of such installation activities. Quantification of such complex risk problems is difficult and much of the key information is not readily available. Expert elicitation methods can be employed in such pressing cases. We describe the methodology used in an expert elicitation study conducted in the United Kingdom for combining expert opinions based on statistical distributions and copula-like methods.

Keywords

Expert elicitation Renewable energy Noise 

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Carl Donovan
    • 1
  • John Harwood
    • 1
  • Stephanie King
    • 2
  • Cormac Booth
    • 2
  • Bruno Caneco
    • 3
  • Cameron Walker
    • 4
  1. 1.Centre for Research into Ecological and Environmental Modelling (CREEM), The Observatory, University of St. AndrewsSt. Andrews, FifeUK
  2. 2.Sea Mammal Research Unit (SMRU) Marine, University of St. AndrewsSt. AndrewsUK
  3. 3.DMP Statistical Solutions UK Ltd.St. AndrewsUK
  4. 4.Department of Engineering ScienceUniversity of AucklandAucklandNew Zealand

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