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      Food purchase behaviour in a Finnish population: patterns, carbon footprints and expenditures

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          Abstract

          Objective:

          To identify food purchase patterns and to assess their carbon footprint and expenditure.

          Design:

          Cross-sectional.

          Setting:

          Purchase patterns were identified by factor analysis from the annual purchases of 3435 product groups. The associations between purchase patterns and the total purchases’ carbon footprints (based on life-cycle assessment) and expenditure were analysed using linear regression and adjusted for nutritional energy content of the purchases.

          Participants:

          Loyalty card holders ( n 22 860) of the largest food retailer in Finland.

          Results:

          Eight patterns explained 55 % of the variation in food purchases. The Animal-based pattern made the greatest contribution to the annual carbon footprint, followed by the Easy-cooking, and Ready-to-eat patterns. High-energy, Traditional and Plant-based patterns made the smallest contribution to the carbon footprint of the purchases. Animal-based, Ready-to-eat, Plant-based and High-energy patterns made the greatest contribution, whereas the Traditional and Easy-cooking patterns made the smallest contribution to food expenditure. Carbon footprint per euros spent increased with stronger adherence to the Traditional, Animal-based and Easy-cooking patterns.

          Conclusions:

          The Animal-based, Ready-to-eat and High-energy patterns were associated with relatively high expenditure on food, suggesting no economic barrier to a potential shift towards a plant-based diet for consumers adherent to those patterns. Strong adherence to the Traditional pattern resulted in a low energy-adjusted carbon footprint but high carbon footprint per euro. This suggests a preference for cheap nutritional energy rather than environment-conscious purchase behaviour. Whether a shift towards a plant-based pattern would be affordable for those with more traditional and cheaper purchase patterns requires more research.

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          Most cited references42

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          Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems

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            Principal component analysis: a review and recent developments.

            Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
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              The ecoinvent database version 3 (part I): overview and methodology

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                Author and article information

                Journal
                Public Health Nutr
                Public Health Nutr
                PHN
                Public Health Nutrition
                Cambridge University Press (Cambridge, UK )
                1368-9800
                1475-2727
                November 2022
                18 August 2022
                : 25
                : 11
                : 3265-3277
                Affiliations
                [1 ]Department of Food and Nutrition, University of Helsinki , PO Box 66, Helsinki 00014, Finland
                [2 ]Tampere University , Tampere, Finland
                [3 ]Natural Resources Institute Finland , Helsinki, Finland
                [4 ]Department of Agricultural Sciences, University of Helsinki , Helsinki, Finland
                [5 ]Helsinki Institute of Sustainability Science, University of Helsinki , Helsinki, Finland
                [6 ]Department of Teacher Education, University of Helsinki , Helsinki, Finland
                Author notes
                [* ] Corresponding author: Email jelena.meinila@ 123456helsinki.fi
                Author information
                https://orcid.org/0000-0002-6377-1377
                https://orcid.org/0000-0002-0177-3609
                https://orcid.org/0000-0002-6966-1523
                Article
                S1368980022001707
                10.1017/S1368980022001707
                9991547
                35979803
                bec6f172-38b8-4370-b7b7-e155cacbc7f3
                © The Authors 2022

                This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 October 2021
                : 19 May 2022
                : 08 August 2022
                Page count
                Figures: 4, Tables: 3, References: 49, Pages: 13
                Categories
                Research Paper
                Sustainability

                Public health
                nutrition,greenhouse gas emission,diet,food consumption,environmental impact
                Public health
                nutrition, greenhouse gas emission, diet, food consumption, environmental impact

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