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First series of seafood datasets in ecoinvent: setting the pace for future development

  • SUSTAINABLE FOOD PRODUCTION AND CONSUMPTION
  • Published:
The International Journal of Life Cycle Assessment Aims and scope Submit manuscript

Abstract

Purpose

The number of life cycle assessment studies related to seafood has risen considerably in the past decade. Despite this proliferation, major life cycle inventory databases tend to lack information describing this sector. Hence, the main objectives of this study are to present the first effort to aggregate and standardize seafood-related datasets in the ecoinvent database and to explain the main data sources and methodological choices used in the building of the datasets.

Methods

A list of the main datasets included in this first series is presented with a brief description of the underlying modelling approaches. Seafood capture, production and processing activities were modelled as the use phase of the required infrastructure. The full life cycle of infrastructure was considered, from construction, through use and maintenance to end-of-life.

Results and discussion

Some of the most representative seafood industries in South America were modelled, namely Peruvian anchovy and hake fisheries, Andean trout, Brazilian tilapia and Peruvian fishmeal production, as well as the production of canned, frozen, cured and of a multi-ingredient fish-based product (fish sticks). Inventory data were found to be in line with those of seafood LCA literature and driven by the parameters widely known to be critical: fuel use intensity for fisheries, feed conversion ratio for aquaculture and energy intensity for seafood processing and reduction into fishmeal. The modelling approach was modular and intuitive, thus useful and reproducible by database users and data providers.

Conclusions

The datasets created constitute a robust basis for the use of seafood-related data in international databases. It is expected that this work will stimulate further efforts by practitioners and data providers to model their inventory data into ecoinvent and other life cycle inventory databases.

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Notes

  1. In the 2009 Technical Paper No. 517 (http://www.fao.org/docrep/011/i0625e/i0625e00.htm), FAO defined LSW as the weight of a vessel including its gear, but excluding all solid and liquid cargo.

  2. “Tunids” is an informal way to refer to tuna species from the Thunnus genus (i.e. “true” tunas), including albacore, yellowfin tuna, bluefins or bigeye tuna.

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Acknowledgements

The authors of this manuscript would like to thank Pedro Villanueva-Rey and Daniel Verán-Leigh for their support in building the datasets. María Teresa Moreira and Gumersindo Feijoo, from the Universidade de Santiago de Compostela, Pierre Fréon, from the French Research Institute for Development (IRD), and Jara Laso, María Margallo and Rubén Aldaco, from the Universidad de Cantabria, are all thankfully acknowledged for their time and expertise on fisheries and seafood processing. Matheus Medeiros (former EMBRAPA and INRA PhD student) is thanked for contributing to the project with aquaculture data for Brazil. Friederike Ziegler and Kristina Bergman (RISE) are thanked for the review efforts.

Funding

The Swiss State Secretariat for Economic Affairs (SECO) and the Sustainable Recycling Industries (SRI) programme partially financed this work.

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Authors and Affiliations

  1. CIRAD, UPR Recyclage et risque, 34398, Montpellier, France

    Angel Avadí

  2. Recyclage et risque, CIRAD, University of Montpellier, Montpellier, France

    Angel Avadí

  3. Peruvian Life Cycle Assessment Network (PELCAN), Department of Engineering, Pontificia Universidad Católica del Perú, Avenida Universitaria 1801, San Miguel, 15088, Lima, Peru

    Ian Vázquez-Rowe

  4. ecoinvent, Technoparkstrasse 1, 8005, Zurich, Switzerland

    Avraam Symeonidis & Emilia Moreno-Ruiz

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  1. Angel Avadí
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Correspondence to Ian Vázquez-Rowe.

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Avadí, A., Vázquez-Rowe, I., Symeonidis, A. et al. First series of seafood datasets in ecoinvent: setting the pace for future development. Int J Life Cycle Assess 25, 1333–1342 (2020). https://doi.org/10.1007/s11367-019-01659-x

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