Life cycle inventory and assessment data for quantifying the environmental impacts of a wide range of food products belonging to the same food category: A case study of 80 pizzas representatives of the French retail market

• Published in: Data in brief Vol. 41; p. 107950
• Main Authors: Cortesi, Adeline, Pénicaud, Caroline, Saint-Eve, Anne, Soler, Louis-Georges, Souchon, Isabelle
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.04.2022; Elsevier
• Subjects: case studies; computer software; Data; data collection; Ecology, environment; energy; Food and Nutrition; Food industry; Life cycle assessment (LCA); life cycle inventory; Life Sciences; pizza; Processed food; ready-to-eat foods; Recipe; retail marketing; Supply chain; wastes; Supply chain; Life cycle assessment (LCA); Processed food; Recipe; Food industry; LCAINRAE
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2022.107950

Most of the time, Life Cycle Assessments (LCA) of food products are performed only on one representative of a food category. This doesn't allow us to understand the possible variations of environmental impacts within a food product category and the responsible factors for these variations. For this reason, LCAs were conducted for 80 different industrial pizzas representative of the French retail market. The LCAs were performed using the “EF 3.0 Method (adapted) V1.00/EF 3.0 normalization and weighting set” on SimaPro software. Most of the data used were taken from the AGRIBALYSE 3.0 and Ecoinvent 3.6 databases. The system perimeter goes from the production of the ingredients to the pizza consumption. The functional unit used was 1 kg of ready-to-eat pizza. Life cycle inventories were made to include the different flows in the LCA (materials, transport, energy, water, waste, etc.). The dataset contains details on products, life-cycle inventories (LCI) and LCIA results. These data can enrich the discussion on the need to study the environmental impacts of different products belonging to the same food category and not only one representative in order to avoid erroneous conclusions.