Local perceptions of the QICS experimental offshore CO2 release: Results from social science research

This paper explores the social dimensions of an experimental release of carbon dioxide (CO2) carried out in Ardmucknish Bay, Argyll, United Kingdom. The experiment, which aimed to understand detectability and potential effects on the marine environment should there be any leakage from a CO2 storage site, provided a rare opportunity to study the social aspects of a carbon dioxide capture and storage-related event taking place in a lived-in environment. Qualitative research was carried out in the form of observation at public information events about the release, in-depth interviews with key project staff and local stakeholders/community members, and a review of online media coverage of the experiment. Focusing mainly on the observation and interview data, we discuss three key findings: the role of experience and analogues in learning about unfamiliar concepts like CO2 storage; the challenge of addressing questions of uncertainty in public engagement; and the issue of when to commence engagement and how to frame the discussion. We conclude that whilst there are clearly slippages between a small-scale experiment and full-scale CCS, the social research carried out for this project demonstrates that issues of public and stakeholder perception are as relevant for offshore CO2 storage as they are for onshore.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.