The food service and restaurant industry play a crucial role in promoting sustainable food consumption by offering sustainable meal options, shaping consumer preferences, and introducing eco-friendly practices. To enable the food services operating in Finland to provide more sustainable options, we created a climate impact dataset of 1233 typical ingredients used in Finnish food services. The dataset was created using Life Cycle Assessment (LCA) to assess the climate impacts of ingredients from cradle to wholesale. The climate impacts in the dataset were assessed using the characterisation factors from the IPPC's sixth assessment report, using a functional unit of 1 kg of ingredient. The final climate impacts of the ingredients include both impacts from domestic and imported products, aggregated by calculating the degree of domestic origin -weighted average. The climate impacts of Finnish plant production were assessed based on data derived from ProAgria's field plot database, and the impacts of animal and fish production were derived from recent Finnish LCA studies. The post-farm processing was assessed using data from the Agribalyse database, by changing the energy inputs of the processes to Finnish energy and modifying the transport profile to reflect Finnish conditions. The impacts of imported products were also assessed using the Agribalyse database and changing the energy inputs and transport profiles accordingly to better reflect average European production. In addition, for imported products, transportation to Finland was added. The data presented in this dataset can be utilised in other LCA studies to assess the impacts of food ingredients used in Finland in menu, or diet level assessments. As the dataset is compatible with the Finnish Food Composition Database Fineli®, it enables simultaneous assessment of nutritional value, which is crucial in achieving emission reductions without weakening the nutritional quality of food consumed.
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http://dx.doi.org/10.1016/j.dib.2024.111143 | DOI Listing |
Proc Natl Acad Sci U S A
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Center for Environmental Economics - Montpellier (Univ Montpellier, CNRS, INRAE, Institut Agro), Montpellier 34000, France.
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Research Applications Laboratory, NSF National Center for Atmospheric Research, Boulder, CO 80301.
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Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France.
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School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
Stock trend prediction is a significant challenge due to the inherent uncertainty and complexity of stock market time series. In this study, we introduce an innovative dual-branch network model designed to effectively address this challenge. The first branch constructs recurrence plots (RPs) to capture the nonlinear relationships between time points from historical closing price sequences and computes the corresponding recurrence quantifification analysis measures.
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State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China.
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