The Impact of Practitioner Decisions on LCA for Marine Energy Converters

The LCA methodology is designed to be used for a wide range of applications, but this flexibility introduces considerable scope for variation in results. This is a particular issue in the marine renewable energy industry, where estimates of the Global Warming Potential (GWP) and energy return on investment inform policy maker and investor decisions. Although existing papers have identified the specific limitations of LCA [1-4], few have attempted to quantify the effects of individual assumptions and methodological choices.

A number of carbon and energy audits of Marine Energy Converters (MECs) have been carried out at the University of Edinburgh, and recent work has taken the raw data from some of these studies and expanded them to full LCAs with comprehensive sensitivity analyses [5-8]. Access to the original calculations, each carried out by a different person, allows the impact of different practitioner choices to be examined in detail and quantified. One such review was presented in [9] and further refined in [8], using the Pelamis Wave Energy Converter as a case study. This paper further expands this work to include a second case study of the Seagen tidal current turbine [5, 7], identifying the key sources of variation in the results, with particular reference to LCA methodology and selection of life cycle impact assessment method. The principal aim of this work is to provide recommendations on best practice for LCA of MECs, and potentially inform LCA studies of all types of renewable energy converter.

A preliminary review of the results focuses on the GWP and energy intensity, finding that variations between studies are typically 17 to 33%, but as much as 1027% in the case of the GWP of the Seagen [5, 7]. The most significant variations are expected to be due to the applied recycling allocation method, differences in LCI data source, and chosen characterisation factors. This work will quantify the contribution of each of these key choices to the variation in results, examine variation in other impact categories, and provide recommendations for future analyses to maximise comparability.

References

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