Discretionary foods have notable environmental and expenditure relevance regardless of preference for meat or plant-based protein sources

Discretionary foods have notable environmental and expenditure relevance regardless of preference for meat or plant-based protein sources | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Discretionary foods have notable environmental and expenditure relevance regardless of preference for meat or plant-based protein sources Jelena Meinilä, Rachel Mazac, Henna Vepsäläinen, Juha-Matti Katajajuuri, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6463928/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and objectives: To understand food groups’ contribution to nutrition, environmental impacts, and expenditure requires self-selected food consumption data. We analyzed implications of a hypothetical transition in protein sources on these sustainability dimensions considering total food consumption. Methods The clusters were derived from food purchase data of 22,901 loyalty card holders by sequence analysis of purchases over 12 months. In a cross-sectional setting, we performed between-cluster comparisons of energy adjusted purchases’ expenditure, LCA-based environmental impacts, and nutrient content. Results Relative to 2500kcal, members of Plant-based and Fish clusters spent the most money on food (9.0-9.8€) and members of Red meat cluster the least (7.4€). The main contributors to the between-cluster differences were not the protein sources themselves. Greenhouse gas emissions were similar in Red meat, Red meat mixed, and Red meat & poultry clusters, but 27–28% lower in Plant-based cluster. Freshwater eutrophication and consumptive water use were the highest in Fish cluster (67% and 25% higher than in Plant-based cluster, respectively). The improvement of micronutrient supplies towards Fish and Plant-based clusters were explained by other foods than protein sources. Discretionary foods had a large contribution to expenditure (22%) and all environmental impacts (17–32%) in all clusters. Conclusions A sustainability transition in protein sources seems affordable for an average Finnish household. Partial replacement of red meat with poultry would offer minimal environmental gains. While fish consumption is nutritionally beneficial, the environmental trade-offs should be carefully considered. Reducing discretionary food consumption could yield notable environmental benefits while reducing household food budgets and improving nutritional quality. sustainability sustainability transition food system change planetary health food budget food affordability Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Food systems are responsible for about a third of all greenhouse gas emissions (GHGE, primarily carbon dioxide, methane, and nitrous oxide) (1), and agriculture is the main contributor to land and water use, biodiversity loss, and use of nitrogen and phosphorus (2). On average, the production of animal-based foods have larger environmental impacts than plant-based foods (3). Therefore, there is a growing global need to shift from animal-based diets to more plant-based diets (4). Animal-based foods (meat, dairy, fish, egg) have a notable role as a protein source, as in high-income countries in North America and Europe they provide 60-70% of protein intake (5,6). Thus, protein sources are central in the transition towards more ecologically sustainable diets, although environmental impact assessment beyond GHGE are still necessary. Protein source foods also provide numerous nutrients beyond protein such as iron and saturated fatty acids (SFA) from meat, vitamin B12 from meat and dairy, vitamin D from fish, and folate and fiber from legumes (https://fineli.fi/fineli/en/index). Some of the essential nutrients lost through a shift from one protein source to another can be compensated by other food groups, whereas vitamin B12 is only naturally available from animal-based foods. Therefore, estimation of overall implications for nutrition of the total food consumption pattern is essential when analysing the nutritional consequences of food transition towards a more plant-based diet. Protein sources also vary in cost, influencing their accessibility and affordability. Several studies have been conducted, in which considerations on cultural acceptability are limited or are based on subjective decisions on how much the modelled diet is allowed to differ from the current diet (7–10). Previous work in this area has suggested that transitioning towards more sustainable protein sources is a gradual process and is more likely to occur between similar protein sources: from red meat to poultry, from poultry to fish, and from fish to plant-based protein sources (11). In aarlier analyses, we have also identified six clusters based on the main protein sources of the purchases (11). We believe that these real, self-selected consumption patterns are more appealing to adjacent consumer groups than hypothetical patterns based primarily on assumptions about sustainability or artificial acceptability criteria. We built upon and used the six previously identified clusters based on selected protein sources (11) as cross-sectional data to mimic a longitudinal, stepwise transition towards more plant-based diets. We examined: 1) would a hypothetical transition from cluster to another affect food expenditure? 2) what are the environmental impacts of total food purchases and specific food groups? 3) how would the hypothetical transition affect nutritional composition of the food purchases? Methods Purchase data and participants Data originate from the loyalty card holders of the largest food retail chain in Finland, namely the S Group (12). The retailer held a market share of 46% of the Finnish food retail sector in 2018 when the data was collected. During the data collection period, 2.4 million households in Finland possessed the S Group's customer loyalty card, constituting 88% of all households in the country. All individuals aged 18 years or older with loyalty card and an email address in the retailer’s database were contacted via email to consent to the release of their purchase data for the research. Consenting respondents were also invited to complete an electronic questionnaire, collecting additional background information such as household income, highest educational attainment, and self-reported loyalty (i.e. how much (%) of the food purchases the participant buys from the S Group retailer). Of the 47,066 consenting loyalty-card holders, we included those who completed the background questionnaire (n=36,621). From the questionnaire, we derived how much and what proportion of their total food purchases the participant bought from the retailer. We selected those who made at least 50 kg of purchases during the year and who reported buying 61% or more of their food purchases from the retailer. We have previously shown that the purchases associated more strongly with the respondent’s self-reported food intake frequency (measured using a food frequency questionnaire) among the most loyal customers (loyalty >60%) (13). Based on the above exclusions, the final number of participants was 22,901. All food purchases from the year 2018 were included in the data (nutritional supplements, such as vitamin and mineral supplements, were not included). To better serve nutrition and environmental research purposes, the retailer’s food categories were recategorized, using principles described elsewhere (14). In this study, we used the 12-month purchase data aggregated to annual purchase volume (kg) and expenditure (€) per 2500 kcal (10.5 MJ) of purchased energy to represent population average daily energy requirement (15). Background characteristics of the participants Participants’ highest educational attainment and household’s monthly income were collected by questionnaire in four categories from primary school or below to master's degree or higher. Participants reported their household's monthly income by selecting one of seven predefined categories. The average value for each income category was divided by the square root of the household size to calculate the adjusted monthly household income (OECD square root scale). The resulting income was categorized into five groups. The age and sex of the primary card holder were derived from the retailer’s database. Greenhouse gas emission assessment The methodology for evaluating GHGE has been extensively described elsewhere (16). In summary, 3435 product groups were ascribed a GHGE coefficient (measured in kilograms of CO2-equivalent) with the functional unit of 1 kg of food purchased at retail. Indicator products were selected to represent these product groups, with one indicator product usually standing in for multiple product groups. Approximately 100 different indicator products were chosen based on available and suitable Life Cycle Assessment (LCA) studies in the Finnish retail context (16). The GHGE coefficient of an indicator product was computed as the weighted average of the GHGEs of the most sold foods within the corresponding product group. The primary life cycle phases considered in the assessment encompassed the production of inputs for agriculture, agricultural primary production, food processing, packaging, storage (pre-retail), and transportation, with exclusions of food waste, land use changes, and alterations in soil carbon stocks due to insufficient data. Data on storage, packaging, transportation, and their GHGE were derived from Finnish and international databases (16). The purchase volume (in kilograms) of each product group of each loyalty card holder was multiplied by the corresponding indicator product’s GHGE coefficient to calculate customers' product group-specific and total purchase GHGE. Land use, consumptive water use, and freshwater and marine water eutrophication assessment We utilized LCA data for specific food products, aligned with ingredient-level food groups, to assess eutrophication, water use, and land use impacts (expressed per kilogram of food product). This information was sourced from the Agri-footprint database (Blonk Consultants) and the Agribalyse 3.0 database (French Agency for Ecological Transition, 2020) and analysed using OpenLCA 1.10.3 software (GreenDelta, 2007). Following previously published methods for assessing impacts of self-selected diets in Finland (17), Agribalyse, a comprehensive French Life Cycle Inventory (LCI) Analysis database featuring information on over 2500 products produced in France, was employed for its multi-indicator nature (Koch & Salou 2020). To adapt to products with a significant import-to-export ratio (>1), indicating substantial importation into Finland, product inventory data originally based on French average electricity use were modified to reflect electricity use in Europe, excluding Switzerland, as per UN FAO STAT data. Conversely, products with an import ratio <1, such as livestock products and grains for livestock feed, were considered 'produced in Finland,' with their inventory data adjusted to Finnish average electricity use. Information on livestock feed cultivated in Finland was derived from Finnish grains included in the Agri-footprint life cycle inventory database (see Supplementary Description 1 for additional information). Following the verification and updating of product information, LCAs were conducted for each item. The ReCiPe Midpoint (H) method (National Institute for Public Health and the Environment Netherlands, 2011) provided characterization factors for calculating land use in square metres of arable crop land equivalents, consumptive water use in cubic metres, marine eutrophication (in kilograms of nitrogen equivalents), and freshwater eutrophication (in kilograms of phosphorus equivalents). Marine eutrophication in this context refers to the extent to which emitted nutrients, with nitrogen as the limiting factor in marine waters, reach the marine end compartment. Similarly, freshwater eutrophication refers to the extent to which emitted nutrients, with phosphorus as the limiting factor in freshwater, reach the freshwater end compartment. The assessment of land use in this study focuses on changes in soil organic carbon, measured in kilograms of carbon per square metre per year, rather than accounting for biodiversity impacts (18). Consumptive water use represents here the total amount of water consumed, calculated as the difference between the water extracted and the water returned to the environment, across all processes involved in a product's life cycle. Nutrient content of the purchases The retailer’s product groups (n=3435) were linked to nutrient content using the Finnish food composition database Fineli® (version 20, www.fineli.fi), maintained and constantly updated by the Finnish Institute for Health and Welfare. We selected a representative food item from Fineli®, with the assistance of the retailer's dataset of the most sold items within each food group (14). The purchase volume (kg) of the product groups were multiplied by the nutrient contents per 1 kg of the food products to obtain the total nutrient contents of the purchased foods. Statistical methods We used sequence analysis for deriving protein purchase clusters. For the analysis, we categorized protein sources into the following four groups: i) red meat and processed meat, which also encompassed processed white meat; ii) poultry and poultry dishes; iii) fish and seafood; and iv) plant-based foods, which included plant-protein rich products and vegetable dishes, excluding whole vegetables (11). Primary sources at a given time, i.e. the protein source that was the most purchased by the individual in that month, were used as states in the sequence analysis. The analysis identified six clusters with distinguishable purchase preferences for protein sources: Red meat, Red meat mixed (mainly red meat, occasionally poultry and fish), Red meat & poultry, Mixed (all protein sources equally), Fish, and Plant-based (11). Expenditure, nutrient content, and environmental impacts per 2500 kcal (10.5 MJ) in each cluster are reported as means and SD, and characteristics of the population either as numbers and percentages or means and SD. Distributions within and between clusters of food group-specific expenditure, nutrient content, and environmental impacts are presented graphically and using descriptive statistics (means and proportions). For the presentation of the food group-specific results, all purchases were aggregated into 17 food groups (see Figures 1-4 and disaggregation of discretionary foods in Supplementary Figures 1-3). In a large sample such as the current loyalty card data, even small differences become statistically significant. We argue that interpreting the sizes of the differences is more meaningful than their statistical significance. Therefore, we refrain from showing statistical tests when examining differences in food purchases among the clusters. We used the statistical software R (R Foundation for Statistical Computing, http://www.R-project.org/) for the analyses. Mean differences between the clusters are denoted as , where X and Y are mean values in any two clusters that are compared with each other. The differences are expressed as absolute or relative to Y. Results Characteristics of the participants by cluster The members of the Plant-based cluster were more often women than the members of the other clusters (Table 1 ). The members of the Fish cluster had master’s degree or higher the most often relative to the other clusters. The members of the Plant-based cluster were the youngest (mean 38 years) and those of the Fish cluster the oldest (56 years). The members of the Plant-based cluster had more often monthly scaled household income of less than 1000€ and the Fish cluster more often a monthly scaled household income of 4000€ or more compared with the others. Table 1 Characteristics of 22 901 loyalty-card holders by cluster. Red meat (N = 6554) Red meat mixed (N = 9580) Red meat & Poultry (N = 1980) Mixed (N = 3265) Fish (N = 464) Plant-based (N = 1058) Sex, n (%) Men 2487 (38) 3364 ( 35 ) 576 ( 29 ) 948 ( 29 ) 161 ( 35 ) 227 ( 22 ) Women 4067 (62) 6216 (65) 1404 (71) 2317 (71) 303 (65) 831 (79) Age, mean (SD) 51 ( 14 ) 48 ( 15 ) 43 ( 15 ) 47 ( 17 ) 56 ( 15 ) 38 ( 13 ) Highest education, n (%) Primary school or lower 668 ( 10 ) 584 ( 6 ) 61 ( 3 ) 136 ( 4 ) 14 ( 3 ) 22 ( 2 ) Upper secondary school 3026 (46) 3611 (38) 639 ( 32 ) 951 ( 29 ) 102 ( 22 ) 289 ( 27 ) Bachelor’s degree or equivalent 1908 ( 29 ) 3175 ( 33 ) 732 ( 37 ) 1031 ( 32 ) 141 ( 30 ) 358 ( 34 ) Master’s degree or higher 941 ( 14 ) 2183 ( 23 ) 546 ( 28 ) 1138 ( 35 ) 205 (44) 388 ( 37 ) Other or missing 11 (0.2) 27 (0.2) 20 (0.1) 9 (0.3) 2 (0.4) 1 (0.1) Loyalty, n (%) 61–80% 2099 ( 32 ) 3994 (42) 757 (38) 1489 (46) 189 (41) 470 (44) 81–100% 4455 (68) 5586 (58) 1223 (62) 1776 (54) 275 (59) 588 (56) Scaled household income (€/month) Less than 1000 430 ( 7 ) 759 ( 8 ) 191 ( 10 ) 349 ( 11 ) 34 ( 7 ) 209 ( 20 ) 1000–1999 1250 ( 19 ) 1379 ( 14 ) 246 ( 12 ) 363 ( 11 ) 31 ( 7 ) 133 ( 13 ) 2000–2999 2013 ( 31 ) 2751 ( 29 ) 590 ( 30 ) 866 ( 27 ) 125 ( 27 ) 295 ( 28 ) 3000–3999 1521 ( 23 ) 2206 ( 23 ) 437 ( 22 ) 692 ( 21 ) 99 ( 21 ) 202 ( 19 ) 4000 or more 919 ( 14 ) 1881 ( 20 ) 385 ( 19 ) 768 ( 24 ) 131 ( 28 ) 165 ( 16 ) Missing 421 ( 6 ) 604 ( 6 ) 131 ( 7 ) 227 ( 7 ) 44 ( 10 ) 54 ( 5 ) Purchase volumes of food per 2500 kcal by cluster As the clusters were identified based on the main protein source of the purchases, it is logical that they differed largely in purchase volumes of the protein sources (Fig. 1 ). In addition to the protein sources, the most important differences among the clusters were attributable to fruit, vegetable, and liquid dairy purchases. Fruit and vegetable purchases increased consistently, while red and processed meat purchases decreased from one cluster to another (∆% (Plant-based, Red meat): fruits and berries + 89%, vegetables + 107%, red meat − 89%, Supplementary Data 1 ). The members of the Plant-based cluster bought considerably less liquid dairy products than the others, in absolute terms (Fig. 1 ) and in proportion to the total purchases (13% vs. 20–25%, Supplementary Data 1 ). Discretionary foods’ purchase volume was slightly smaller in the members of the Fish and Plant-based clusters in absolute terms (Fig. 1 ) and in proportion to total purchases (Fish 19%, Plant-based 20%, others 23–27% from total purchases, Supplementary Data 1 ). Expenditure on food per 2500 kcal by cluster Members of the Fish cluster spent the most money on food (mean 9.8€), followed by the Plant-based (9.0€) and Mixed clusters (8.1€). Members of the Red meat cluster spent the least money on food (7.4€) (Table 2 and Fig. 2 ). Differences among the clusters in food expenditure were not attributable to the main protein source (Fig. 2 ). For example, members of the Plant-based and Red meat clusters spent approximately the same amount of money on the main protein sources (although protein content in the purchases was smaller as seen later in section Nutrient content of foods per 2500 kcal by cluster ). The main contributors to the differences were expenditures on fruits and vegetables (in fruits and berries maximum ∆% (Fish, Red meat): +143%, in vegetables maximum ∆% (Plant-based, Red meat): +139%) ( Supplementary Data 1 ). A large proportion of the total food expenditure was attributable to discretionary foods in all clusters (from 18% in Plant-based to 24% in Red meat) ( Supplementary Data 1 and Fig. 2 ). In all clusters the main food groups among discretionary foods in terms of expenditure were alcoholic and non-alcoholic beverages, cereal bakery products, and sweets ( Supplementary Fig. 1 ). Table 2 Expenditure, nutrient contents, and environmental impacts of total food purchases per 2500 kcal among 22 901 loyalty-card holders by cluster. Red meat (N = 6554) Red meat mixed (N = 9580) Red meat & Poultry (N = 1980) Mixed (N = 3265) Fish (N = 464) Plant-based (N = 1058) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Expenditure per 2500 kcal, € 7.4 (1.9) 8.1 (2.1) 8.5 (2.1) 8.1 (2.4) 9.8 (2.8) 9.0 (2.4) Nutrient content per 2500 kcal Protein, g 97 ( 14 ) 97 ( 16 ) 104 ( 19 ) 94 ( 18 ) 97 ( 19 ) 83 ( 17 ) Sucrose, g 67 ( 23 ) 68 ( 24 ) 65 ( 23 ) 69 ( 28 ) 64 ( 24 ) 69 ( 22 ) Fibre, g 20 ( 5 ) 22 ( 6 ) 24 ( 7 ) 26 ( 8 ) 30 ( 9 ) 34 ( 10 ) SFA, g 45 ( 8 ) 44 ( 9 ) 42 ( 9 ) 42 ( 10 ) 40 ( 10 ) 36 ( 10 ) Vitamin B12, µg 6.6 (2.4) 6.6 (2.6) 6.5 (2.3) 6.5 (3.3) 8.0 (3.5) 3.7 (2.1) Vitamin D, µg 10.1 (3.6) 9.4 (3.7) 8.7 (3.6) 9.0 (4.3) 11.7 (5.3) 6.3 (3.3) Folate, mg 244 (59) 265 (83) 287 (88) 296 (94) 326 (85) 347 (99) Calcium, mg 1330 (346) 1330 (347) 1310 (349) 1350 (393) 1460 (419) 1230 (451) Iron, mg 11.4 (2.22) 11.9 (2.34) 12.6 (2.59) 12.8 (2.91) 13.6 (2.85) 15.0 (3.37) Salt, g 7.2 (1.3) 6.8 (1.2) 6.5 (1.2) 6.3 (1.4) 6.3 (1.5) 5.8 (1.3) Environmental impact per 2500 kcal GHGE, kg CO 2 -eq. 4.9 (0.9) 4.9 (1.0) 5.0 (1.0) 4.5 (1.0) 4.1 (0.9) 3.6 (0.9) Marine eutrophication, g N-eq. 6.1 (1.0) 5.9 (0.9) 6.0 (1.0) 5.5 (1.0) 5.3 (1.1) 4.7 (1.0) Freshwater eutrophication, g P-eq. 2.0 (3.8) 2.0 (0.4) 2.0 (0.4) 2.0 (0.6) 2.5 (0.9) 1.5 (0.4) Land use, m 2 a crop-eq. 6.0 (1.0) 5.6 (0.9) 5.4 (1.0) 4.8 (1.0) 4.3 (0.8) 3.6 (0.8) Water use, m 3 0.23 (0.05) 0.24 (0.06) 0.26 (0.06) 0.26 (0.08) 0.30 (0.10) 0.24 (0.07) GHGE, greenhouse gas emission; N-eq., nitrogen equivalent; P-eq., phosphorus equivalent; crop-eq. arable cropland equivalent. Environmental impacts of foods per 2500 kcal by cluster For GHGE, land use, and marine eutrophication, the impacts of the purchases decreased from meat-dominant (Red meat, Red meat mixed, Red & poultry) to Plant-based clusters ( Table 2 and Fig. 3 ). GHGE of total purchases was similar in the purchases of Red meat, Red meat mixed, and Red meat & poultry clusters, after which the emissions diminished step-wise through Fish to the Plant-based clusters (maximum ∆% (Plant-based, Red meat & poultry): -28%). Although several food groups contributed to the GHGE, land use, and marine eutrophication, the major contributors to the differences among the clusters were the main protein sources (Fig. 3 ). Differences in freshwater eutrophication among the clusters were mainly explained by fish content and was the highest in the purchases of the Fish cluster (2.5 g P eq., from which 40% originated from fish, Supplementary Data 1 ), and the lowest in the Plant-based cluster (1.5 g P eq., from which 20% originated from fish) (Fig. 3 ) . A large portion of the freshwater eutrophication impact of fish came from fish that could not be classified as either wild-caught or farmed due to lack of information on the species (34–85% from total impact of fish, depending on the cluster) ( Supplementary Table 1) . Consumptive water use and content of fruits and fish tended to be associated across the clusters; water use was the highest for the Fish cluster and the lowest for the Red meat cluster (Table 2 and Fig. 3 ). Discretionary foods had a large contribution to all environmental impacts in all clusters (on average 17–32% from total purchases, depending on the environmental impact category) (Fig. 3 , Supplementary Data 1 ). For GHGE, the largest contributors within the discretionary foods were beverages, including both alcoholic and non-alcoholic drinks ( Supplementary Fig. 2 ). Nutrient content of foods per 2500 kcal by cluster Folate, fibre, and iron contents of the total purchases increased stepwise as red meat content of the cluster decreased (Table 2 and Fig. 4 ) . These differences in nutrient content were mainly attributable to differences in fruit, vegetable, and grain purchases (Fig. 4 ), with members of the Plant-based and Fish clusters purchasing fruits, vegetables, and high-fibre grains the most. The opposite was observed for salt and SFA contents, which decreased stepwise as red meat purchases in the cluster decreased. Although in all clusters most of the salt was derived from other food groups than the main protein source, the differences between the clusters were mainly related to the main protein sources of the purchases. Vitamins B12 and vitamin D content were the lowest in the purchases of members of the Plant-based cluster and the highest in the Fish cluster. Protein content was lower in the Plant-based cluster than in the other clusters. Discretionary foods were responsible for a large amount of energy (average 18% of total purchases) in all clusters ( Supplementary Data 1 and Fig. 4 ) and contributed markedly to sucrose contents in all (Fig. 4 , Supplementary Fig. 3 ). Among discretionary foods, the sucrose content of purchases was the highest in the Red meat cluster. Discussion A notable finding was that expenditure for the protein sources was similar across the clusters, independent of the choice of the main protein source. The mean protein supply was lower in the Plant-based cluster than in the others but was well within the recommended intake range (10-20E%, which is 63–125 g in 2500 kcal intake) ( 19 ). Another main finding was that the step from the Red meat cluster to the Poultry cluster would bring little, if any, environmental benefit, with better environmental and nutritional gains achieved by transitioning from any meat-dominant cluster to a Plant-based cluster. Notably, discretionary foods played a large role across all clusters, comprising a substantial proportion of food expenditure, while having significant negative impacts on both environmental footprints and nutrient content of food purchases. Expenditure Because of minimal differences in expenditure (per energy unit) in protein source purchases between the clusters, we infer that a shift from meat to fish or plant-based protein sources does not seem to be an economic obstacle for the average Finnish household. However, it is possible that the perception of price is more important than the actual price in food choice preferences ( 20 ). In previous studies, both meat-eaters and those consuming plant-based foods or transitioning to plant-based food consumption have considered the price motive to be important ( 21 – 24 ). Motives also seem to vary depending on the food group in question. Independent of the cluster, the amount of money spent on discretionary foods was large, at least one-fifth of the total food expenditure. Food serves purposes beyond nutritional value, such as regulating feelings, social influences, and constructing cultural identity ( 25 ), which complicates the idea of substituting food choices across different food groups, e.g. substituting fruits and vegetables for discretionary foods. Environmental impacts Unexpectedly, those choosing more unprocessed poultry and less red and processed meat (including both processed red meat and processed poultry) did not have smaller GHGE from protein sources or from total purchases than those who bought mainly red and processed meat. This may have been because a large part of the “replaced” red and processed meat (by poultry) was processed red meat, which may contain ingredients with low GHGE per kg, e.g. potato extracts in many sausages, reducing the overall GHGE of the red meat product. These findings support the meat recommendation of the food-based dietary guidelines of the Nordic nutrition recommendations 2023, which emphasize not replacing red meat with poultry but replacing it with plant-based protein sources and sustainable fish products ( 15 ), and a recent Finnish study suggesting poultry intake as one of the primary foods in to contribute to global biodiversity loss ( 26 ). Although many of the foods included in discretionary foods have relatively low environmental impact per kg of product compared to, for example, meats, the high consumption volume makes them a significant food group. In previous Nordic studies, discretionary foods have played a relatively large role in environmental impacts ( 27 – 29 ). Thus, the results suggest a potential for reduction of environmental impacts through reducing consumption of discretionary foods in all population groups. The finding is noteworthy, as many discretionary foods have small environmental impacts per mass unit, which may take attention away from their overall impact. The relatively large contribution of fruits and vegetables to consumptive water use has also been found before, e.g. in a Swedish study ( 30 ). However, the measure of consumptive water use does not directly inform about water overconsumption because it does not consider water scarcity in the production area. Currently, lack of data hinders research on water-scarcity in Finnish food consumption ( 31 ). In a US study, large scarcity-weighted water footprints were found for meat, especially beef, and some fruits, nuts, and seeds ( 32 ). The study is, however, not fully comparable with Finnish food consumption because of different agricultural conditions of some foods. In Finland, which is highly self-sufficient in beef production, most of the agricultural production is rainfed ( 33 ). Therefore, consumptive water use for beef production in Finland is minimal, and water scarcity is generally low ( 34 ). In contrast, certain imported foods, such as some fruits, rely on irrigation, notably contributing to consumptive water use ( 35 ). Some of the fruits may come from water-scarce areas. Nutrient contents The purchases of the members of the Fish and Plant-based clusters had overall the most favorable nutrient contents, in accordance with earlier results ( 9 , 10 ). Although we did not consider dairy products as one of the main protein sources in our analysis, they are an important food group in the sustainability transition because of their significant role in the food culture, in environmental impact, and as a source of many nutrients. In this study, a trade-off of lower environmental impacts of the purchases in the Plant-based cluster was the relatively low vitamin D intake because of lower dairy content than in the other clusters. According to our results, Finland’s otherwise successful fortification program of dairy products and fat spreads ( 36 ) might not be sufficient to cover vitamin D intake of the members of the Plant-based cluster. The Finnish Nutrition Recommendations ( 19 ) recommend vitamin D supplementation to individuals who do not regularly consume fish or products fortified with vitamin D. Strengths and limitations One of the main strengths of this study was the use of food purchase data with exceptionally large dataset in the context of nutrition research and larger sample size and longer timespan than could be collected using traditional methods. Loyalty card data are less reliant on self-reporting than traditional dietary intake data. The self-selected data brought realism to the analysis in terms of acceptability, which is difficult to evaluate in studies based on theoretical diets. Another strength is the data on the observed individual-level expenditure on food, which in food consumption research is usually self-reported or approximations based on statistics. We were unable to calculate absolute daily purchases of individuals because the card holder usually buys food for the whole household. In addition, not all the household’s food is purchased from the retailer in question. Moreover, the household members’ diets may also differ from each other. However, we have shown that when comparing the food purchases of the cardholders with food intake data derived from FFQs collected for the same individuals, the relative validity for most food groups is acceptable ( 13 ). The reliability of the magnitudes of eutrophication from fish is affected by the species and production method for some of the products not being identified and therefore being classified as “uncategorized fish products”. The environmental impact coefficients for "uncategorized fish products," which accounted for a significant share of the freshwater eutrophication impact, are therefore more uncertain than those of other captured and farmed fish. The large contribution of fish to freshwater eutrophication was, however, similar in another study of Finnish diets ( 17 ). Another consideration is that captured fish from Finnish lakes and the Baltic Sea removes N and P from the water system, thereby reducing eutrophication ( 37 ), which was not considered in the impacts. In addition, the French environmental impact data probably did not rigorously reflect the environmental impacts of Finnish food consumption. However, the directions and patterns of at least the most established environmental impacts of foods in different studies across several countries point in the same direction ( 4 , 8 , 17 , 29 ). The environmental impacts reported in this study should not be considered as absolute impacts but as indications of relative differences between the clusters and food groups. Although our idea in the study was that the purchase profiles of the protein preference clusters could indicate a realistic transition pathway from meat-dominant to more sustainable food purchase profiles, whether the whole purchase profile would change following an individual’s shift from one protein source to another remains unknown. Conclusions The hypothetical protein source transition path that was derived from a cross-sectional self-selected purchase data produced stepwise improvements in fibre, SFA, folate, iron, and salt content and in some environmental impacts including GHGE when moving towards plant-based protein source purchases. The step from red meat to poultry would bring little environmental benefit; better nutritional gains were achieved by moving from red meat dominance to fish or plant-based dominant purchases. A notable potential for reducing environmental impact lies in the reduction of discretionary food purchases across all population groups. According to our results, a sustainability transition is not an affordability issue, apart from some expensive sustainable fish products. Instead, the obstacles for transition of individuals/households lie among other determinants of behaviour. Additionally, it is crucial not to place the burden of change solely on individual consumers but to prioritize systemic transformations, including shifts towards more sustainable agricultural practices, value chain adjustments, improved food distribution systems, and policy incentives that make healthy and sustainable choices more accessible and affordable for all. The Nordic Nutrition Recommendations, in line with the results of this study, provide a crucial framework for policymakers and other stakeholders towards a healthier and more sustainable food system. Declarations Ethics approval and consent to participate The study obtained ethical clearance from the ethical board of the University of Helsinki Review Board in humanities and social and behavioural sciences. Consent for publication Each participant provided electronic consent for the collection and use of their purchase data and questionnaire data for research. Availability of data and materials Supplementary data 1 contains volume, expenditure, environmental impacts, and energy contents of the purchases by food group as unit per 2500kcal and as mass percentage from total purchases. The program codes and their output files are available from [email protected] . Data can be analyzed in collaboration with the research team on a reasonable request. Competing interests MF is a member of the S-group's advisory group for social responsibility. This is an unpaid advisory position in a commercial organization. The authors declare no other competing interests. Both the research group and the retailer signed a contract on data transfer, ensuring the independence of the research and scientific publishing from business interests. Funding The work was funded by Juho Vainio foundation (JM: #202100202), Yrjö Jahnsson foundation (JM: #20207300), and Research Council of Finland (MF and JN: #350852, #350863). The funding sources had no role in study design, data collection, analysis, interpretation of data, writing the report, or in the decision publish the findings. Authors' contributions J.M., R.M., H.V., J.-M.K., H.L.T., M.F., M.E., and J.N. designed the study; J.-M.K. provided the carbon footprint data; RM produced the land use, water use, and eutrophication data; J.N., M.F., and M.E. obtained the purchase data; J.M. and J.N. processed the purchase data; J.M. analysed the data and performed the data analysis; J.M. and R.M. wrote the paper; J.M., J.N., R.M., and J.-M.K. had primary responsibility for the final content. All the authors reviewed and approved the final version of the manuscript. Acknowledgements The authors thank Hanna Hartikainen, Hannele Heusala, Eric Harrison and Frans Silvenius from Natural Resources Institute Finland who have contributed to producing the carbon footprint database utilised in this study and Satu Kinnunen from the Department of Food and Nutrition at the University of Helsinki for processing the food purchase data before the analysis. References Crippa M, Solazzo E, Guizzardi D, Monforti-Ferrario F, Tubiello FN, Leip A. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat Food. 2021;2(3):198–209. Campbell BM, Beare DJ, Bennett EM, Hall-Spencer JM, Ingram JSI, Jaramillo F et al. Agriculture production as a major driver of the Earth system exceeding planetary boundaries. Ecology and Society [Internet]. 2017 [cited 2025 Feb 12];22(4). Available from: https://www.jstor.org/stable/26798991 Xu X, Sharma P, Shu S, Lin TS, Ciais P, Tubiello FN, et al. Global greenhouse gas emissions from animal-based foods are twice those of plant-based foods. Nat Food. 2021;2(9):724–32. Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet. 2019;393(10170):447–92. Pasiakos SM, Agarwal S, Lieberman HR, Fulgoni VL. Sources and Amounts of Animal, Dairy, and Plant Protein Intake of US Adults in 2007–2010. Nutrients. 2015;7(8):7058–69. Valsta L, Kaartinen N, Tapanainen H, Männistö S, Sääksjärvi K, Ravitsemus Suomessa. FinRavinto 2017 -tutkimus [Internet]. 2018 [cited 2024 Oct 8]. Available from: https://www.julkari.fi/handle/10024/137433 Mazac R, Meinilä J, Korkalo L, Järviö N, Jalava M, Tuomisto HL. Incorporation of novel foods in European diets can reduce global warming potential, water use and land use by over 80. Nat Food. 2022;3(4):286–93. Springmann M, Wiebe K, Mason-D’Croz D, Sulser TB, Rayner M, Scarborough P. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet health. 2018;2(10):e451–61. Wilson N, Cleghorn CL, Cobiac LJ, Mizdrak A, Nghiem N. Achieving Healthy and Sustainable Diets: A Review of the Results of Recent Mathematical Optimization Studies. Adv Nutr. 2019;10:S389–403. Saarinen M, Heikkinen J, Ketoja E, Kyttä V, Hartikainen H, Silvennoinen K et al. Soil carbon plays a role in the climate impact of diet and its mitigation: the Finnish case. Front Sustain Food Syst [Internet]. 2023 Sep 5 [cited 2025 Feb 12];7. Available from: https://www.frontiersin.org/journals/sustainable-food-systems/articles/ 10.3389/fsufs.2023.904570/full Erkkola M, Kinnunen SM, Vepsäläinen HR, Meinilä JM, Uusitalo L, Konttinen H, et al. A slow road from meat dominance to more sustainable diets: An analysis of purchase preferences among Finnish loyalty-card holders. PLOS Sustain Transformation. 2022;1(6):e0000015. Vuorinen AL, Erkkola M, Fogelholm M, Kinnunen S, Saarijärvi H, Uusitalo L, et al. Characterization and Correction of Bias Due to Nonparticipation and the Degree of Loyalty in Large-Scale Finnish Loyalty Card Data on Grocery Purchases: Cohort Study. J Med Internet Res. 2020;22(7):e18059. Vepsäläinen H, Nevalainen J, Kinnunen S, Itkonen ST, Meinilä J, Männistö S, et al. Do we eat what we buy? Relative validity of grocery purchase data as an indicator of food consumption in the LoCard study. Br J Nutr. 2022;128(9):1780–8. Kanerva N, Kinnunen S, Nevalainen J, Vepsäläinen H, Fogelholm M, Saarijärvi H, et al. Building nutritionally meaningful classification for grocery product groups: the LoCard Food Classification process. Br J Nutr. 2024;132(6):770–81. Blomhoff R, Andersen R, Arnesen EK, Christensen JJ, Eneroth H, Erkkola M et al. Nordic Nutrition Recommendations 2023: Integrating Environmental Aspects [Internet]. Nordisk Ministerråd; 2023 [cited 2024 Dec 20]. Available from: https://urn.kb.se/resolve?urn=urn:nbn:se:norden:org:diva-12891 Meinilä J, Hartikainen H, Tuomisto HL, Uusitalo L, Vepsäläinen H, Saarinen M, et al. Food purchase behaviour in a Finnish population: patterns, carbon footprints and expenditures. Public Health Nutr. 2022;25(11):3265–77. Mazac R, Hyyrynen M, Kaartinen NE, Männistö S, Irz X, Hyytiäinen K, et al. Exploring tradeoffs among diet quality and environmental impacts in self-selected diets: a population-based study. Eur J Nutr. 2024;63(5):1663–78. Koch P, Salou T. AGRIBALYSE®:Rapport Méthodologique - Version 1.3. Angers France: ADEME; 2016 Nov. p. 335. National Nutrition Council and Finnish Institute for Health and Welfare. Kestävää terveyttä ruoasta - kansalliset ravitsemussuositukset 2024 [Sustainable health from food - Finnish national nutrition recommendations 2024]. Helsinki: PunaMusta Oy; 2024. Giskes K, Van Lenthe FJ, Brug J, Mackenbach JP, Turrell G. Socioeconomic inequalities in food purchasing: The contribution of respondent-perceived and actual (objectively measured) price and availability of foods. Prev Med. 2007;45(1):41–8. Neff RA, Edwards D, Palmer A, Ramsing R, Righter A, Wolfson J. Reducing meat consumption in the USA: a nationally representative survey of attitudes and behaviours. Public Health Nutr. 2018;21(10):1835–44. Graça J, Truninger M, Junqueira L, Schmidt L. Consumption orientations may support (or hinder) transitions to more plant-based diets. Appetite. 2019;140:19–26. Nevalainen E, Niva M, Vainio A. A transition towards plant-based diets on its way? Consumers’ substitutions of meat in their diets in Finland. Food Qual Prefer. 2023;104:104754. Vainio A, Niva M, Jallinoja P, Latvala T. From beef to beans: Eating motives and the replacement of animal proteins with plant proteins among Finnish consumers. Appetite. 2016;106:92–100. Klink U, Härtling V, Schüz B. Perspectives on Healthy Eating of Adult Populations in High-Income Countries: A Qualitative Evidence Synthesis. Int J Behav Med. 2023. Kyttä V, Hyvönen T, Saarinen M. Land-use-driven biodiversity impacts of diets—a comparison of two assessment methods in a Finnish case study. Int J Life Cycle Assess. 2023;28(9):1104–16. Trolle E, Nordman M, Lassen AD, Colley TA, Mogensen L. Carbon Footprint Reduction by Transitioning to a Diet Consistent with the Danish Climate-Friendly Dietary Guidelines: A Comparison of Different Carbon Footprint Databases. Foods. 2022;11(8):1119. Saxe H. The New Nordic Diet is an effective tool in environmental protection: it reduces the associated socioeconomic cost of diets123. Am J Clin Nutr. 2014;99(5):1117–25. Moberg E, Karlsson Potter H, Wood A, Hansson PA, Röös E. Benchmarking the Swedish Diet Relative to Global and National Environmental Targets—Identification of Indicator Limitations and Data Gaps. Sustainability. 2020;12(4):1407. Hallström E, Carlsson-Kanyama A, Börjesson P. Environmental impact of dietary change: a systematic review. J Clean Prod. 2015;91(Journal Article):1–11. Usva K. Assessing water scarcity impact of food products applying AWARE method within LCA. Helsinki: University of Helsinki; 2024. 96 p. (Dissertationes Universitatis Helsingiensis). Heller MC, Willits-Smith A, Mahon T, Keoleian GA, Rose D. Individual US diets show wide variation in water scarcity footprints. Nat Food. 2021;2(4):255–63. Peltonen-Sainio P, Jauhiainen L, Alakukku L. Stakeholder perspectives for switching from rainfed to irrigated cropping systems at high latitudes. Land Use Policy. 2015;42:586–93. Kummu M, Varis O. The world by latitudes: A global analysis of human population, development level and environment across the north–south axis over the past half century. Appl Geogr. 2011;31(2):495–507. Sandström V, Kauppi PE, Scherer L, Kastner T. Linking country level food supply to global land and water use and biodiversity impacts: The case of Finland. Sci Total Environ. 2017;575:33–40. Raulio S, Erlund I, Männistö S, Sarlio-Lähteenkorva S, Sundvall J, Tapanainen H, et al. Successful nutrition policy: improvement of vitamin D intake and status in Finnish adults over the last decade. Eur J Public Health. 2017;27(2):268–73. Groenroos J, Seppaelae J, Silvenius F, Maekinen T. Life cycle assessment of Finnish cultivated rainbow trout. Boreal Environment Research [Internet]. 2006 Jul 1 [cited 2024 Oct 10];11. Available from: https://www.osti.gov/etdeweb/biblio/20874290 Additional Declarations Competing interest reported. MF is a member of the S-group's advisory group for social responsibility. This is an unpaid advisory position in a commercial organization. The authors declare no other competing interests. (Both the research group and the retailer signed a contract on data transfer, ensuring the independence of the research and scientific publishing from business interests.) Supplementary Files supplementarymaterialajcn.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6463928","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444401620,"identity":"e475b4b7-6863-45b4-b8fc-115b309de905","order_by":0,"name":"Jelena Meinilä","email":"data:image/png;base64,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","orcid":"","institution":"University of Helsinki","correspondingAuthor":true,"prefix":"","firstName":"Jelena","middleName":"","lastName":"Meinilä","suffix":""},{"id":444401622,"identity":"184f66a7-640e-4c92-ae24-7469a93c495c","order_by":1,"name":"Rachel Mazac","email":"","orcid":"","institution":"Stockholm University","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Mazac","suffix":""},{"id":444401623,"identity":"365d3e7e-cce2-4245-8183-9898f9b7f564","order_by":2,"name":"Henna Vepsäläinen","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Henna","middleName":"","lastName":"Vepsäläinen","suffix":""},{"id":444401624,"identity":"f9bf6d29-dbe3-4255-9c2d-5c4c47e79892","order_by":3,"name":"Juha-Matti Katajajuuri","email":"","orcid":"","institution":"Natural Resources Institute Finland","correspondingAuthor":false,"prefix":"","firstName":"Juha-Matti","middleName":"","lastName":"Katajajuuri","suffix":""},{"id":444401625,"identity":"29008dfc-e781-428c-98a3-29b01c644829","order_by":4,"name":"Hanna L. 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MF is a member of the S-group's advisory group for social responsibility. This is an unpaid advisory position in a commercial organization. The authors declare no other competing interests. \n(Both the research group and the retailer signed a contract on data transfer, ensuring the independence of the research and scientific publishing from business interests.)","formattedTitle":"Discretionary foods have notable environmental and expenditure relevance regardless of preference for meat or plant-based protein sources","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFood systems are responsible for about a third of all greenhouse gas emissions (GHGE, primarily carbon dioxide, methane, and nitrous oxide) (1), and agriculture is the main contributor to land and water use, biodiversity loss, and use of nitrogen and phosphorus (2). On average, the production of animal-based foods have larger environmental impacts than plant-based foods (3). Therefore, there is a growing global need to shift from animal-based diets to more plant-based diets (4). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnimal-based foods (meat, dairy, fish, egg) have a notable role as a protein source, as in high-income countries in North America and Europe they provide 60-70% of protein intake (5,6). Thus, protein sources are central in the transition towards more ecologically sustainable diets, although environmental impact assessment beyond GHGE are still necessary.\u003c/p\u003e\n\u003cp\u003eProtein source foods also provide numerous nutrients beyond protein such as iron and saturated fatty acids (SFA) from meat, vitamin B12 from meat and dairy, vitamin D from fish, and folate and fiber from legumes (https://fineli.fi/fineli/en/index). Some of the essential nutrients lost through a shift from one protein source to another can be compensated by other food groups, whereas vitamin B12 is only naturally available from animal-based foods. Therefore, estimation of overall implications for nutrition of the total food consumption pattern is essential when analysing the nutritional consequences of food transition towards a more plant-based diet. Protein sources also vary in cost, influencing their accessibility and affordability.\u003c/p\u003e\n\u003cp\u003eSeveral studies have been conducted, in which considerations on cultural acceptability are limited or are based on subjective decisions on how much the modelled diet is allowed to differ from the current diet (7\u0026ndash;10). Previous work in this area has suggested that transitioning towards more sustainable protein sources is a gradual process and is more likely to occur between similar protein sources: from red meat to poultry, from poultry to fish, and from fish to plant-based protein sources (11). In aarlier analyses, we have also identified six clusters based on the main protein sources of the purchases (11). We believe that these real, self-selected consumption patterns are more appealing to adjacent consumer groups than hypothetical patterns based primarily on assumptions about sustainability or artificial acceptability criteria.\u003c/p\u003e\n\u003cp\u003eWe built upon and used the six previously identified clusters based on selected protein sources (11) as cross-sectional data to mimic a longitudinal, stepwise transition towards more plant-based diets. We examined: 1) would a hypothetical transition from cluster to another affect food expenditure? 2) what are the environmental impacts of total food purchases and specific food groups? 3) how would the hypothetical transition affect nutritional composition of the food purchases? \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003ePurchase data and participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData originate from the loyalty card holders of the largest food retail chain in Finland, namely the S Group (12). The retailer held a market share of 46% of the Finnish food retail sector in 2018 when the data was collected. During the data collection period, 2.4 million households in Finland possessed the S Group\u0026apos;s customer loyalty card, constituting 88% of all households in the country. All individuals aged 18 years or older with loyalty card and an email address in the retailer\u0026rsquo;s database were contacted via email to consent to the release of their purchase data for the research. Consenting respondents were also invited to complete an electronic questionnaire, collecting additional background information such as household income, highest educational attainment, and self-reported loyalty (i.e. how much (%) of the food purchases the participant buys from the S Group retailer).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf the 47,066 consenting loyalty-card holders, we included those who completed the background questionnaire (n=36,621). From the questionnaire, we derived how much and what proportion of their total food purchases the participant bought from the retailer. We selected those who made at least 50 kg of purchases during the year and who reported buying 61% or more of their food purchases from the retailer. We have previously shown that the purchases associated more strongly with the respondent\u0026rsquo;s self-reported food intake frequency (measured using a food frequency questionnaire) among the most loyal customers (loyalty \u0026gt;60%) (13). Based on the above exclusions, the final number of participants was 22,901. All food purchases from the year 2018 were included in the data (nutritional supplements, such as vitamin and mineral supplements, were not included). To better serve nutrition and environmental research purposes, the retailer\u0026rsquo;s food categories were recategorized, using principles described elsewhere (14). In this study, we used the 12-month purchase data aggregated to annual purchase volume (kg) and expenditure (\u0026euro;) per 2500 kcal (10.5 MJ) of purchased energy to represent population average daily energy requirement (15).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBackground characteristics of the participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; highest educational attainment and household\u0026rsquo;s monthly income were collected by questionnaire in four categories from primary school or below to master\u0026apos;s degree or higher. Participants reported their household\u0026apos;s monthly income by selecting one of seven predefined categories. The average value for each income category was divided by the square root of the household size to calculate the adjusted monthly household income (OECD square root scale). The resulting income was categorized into five groups. The age and sex of the primary card holder were derived from the retailer\u0026rsquo;s database.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGreenhouse gas emission assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp id=\"_Toc122073990\"\u003eThe methodology for evaluating GHGE has been extensively described elsewhere (16). In summary, 3435 product groups were ascribed a GHGE coefficient (measured in kilograms of CO2-equivalent) with the functional unit of 1 kg of food purchased at retail. Indicator products were selected to represent these product groups, with one indicator product usually standing in for multiple product groups. Approximately 100 different indicator products were chosen based on available and suitable Life Cycle Assessment (LCA) studies in the Finnish retail context (16). The GHGE coefficient of an indicator product was computed as the weighted average of the GHGEs of the most sold foods within the corresponding product group.\u003c/p\u003e\n\u003cp\u003eThe primary life cycle phases considered in the assessment encompassed the production of inputs for agriculture, agricultural primary production, food processing, packaging, storage (pre-retail), and transportation, with exclusions of food waste, land use changes, and alterations in soil carbon stocks due to insufficient data. Data on storage, packaging, transportation, and their GHGE were derived from Finnish and international databases (16).\u003c/p\u003e\n\u003cp\u003eThe purchase volume (in kilograms) of each product group of each loyalty card holder was multiplied by the corresponding indicator product\u0026rsquo;s GHGE coefficient to calculate customers\u0026apos; product group-specific and total purchase GHGE.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLand use, consumptive water use, and freshwater and marine water eutrophication\u003c/em\u003e\u003cem\u003e\u0026nbsp;assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized LCA data for specific food products, aligned with ingredient-level food groups, to assess eutrophication, water use, and land use impacts (expressed per kilogram of food product). This information was sourced from the Agri-footprint database (Blonk Consultants) and the Agribalyse 3.0 database (French Agency for Ecological Transition, 2020) and analysed using OpenLCA 1.10.3 software (GreenDelta, 2007).\u003c/p\u003e\n\u003cp\u003eFollowing previously published methods for assessing impacts of self-selected diets in Finland (17), Agribalyse, a comprehensive French Life Cycle Inventory (LCI) Analysis database featuring information on over 2500 products produced in France, was employed for its multi-indicator nature (Koch \u0026amp; Salou 2020). To adapt to products with a significant import-to-export ratio (\u0026gt;1), indicating substantial importation into Finland, product inventory data originally based on French average electricity use were modified to reflect electricity use in Europe, excluding Switzerland, as per UN FAO STAT data. Conversely, products with an import ratio \u0026lt;1, such as livestock products and grains for livestock feed, were considered \u0026apos;produced in Finland,\u0026apos; with their inventory data adjusted to Finnish average electricity use. Information on livestock feed cultivated in Finland was derived from Finnish grains included in the Agri-footprint life cycle inventory database (see \u003cstrong\u003eSupplementary Description 1\u003c/strong\u003e for additional information).\u003c/p\u003e\n\u003cp\u003eFollowing the verification and updating of product information, LCAs were conducted for each item. The ReCiPe Midpoint (H) method (National Institute for Public Health and the Environment Netherlands, 2011) provided characterization factors for calculating land use in square metres of arable crop land equivalents, consumptive water use in cubic metres, marine eutrophication (in kilograms of nitrogen equivalents), and freshwater eutrophication (in kilograms of phosphorus equivalents).\u003c/p\u003e\n\u003cp\u003eMarine eutrophication in this context refers to the extent to which emitted nutrients, with nitrogen as the limiting factor in marine waters, reach the marine end compartment. Similarly, freshwater eutrophication refers to the extent to which emitted nutrients, with phosphorus as the limiting factor in freshwater, reach the freshwater end compartment. The assessment of land use in this study focuses on changes in soil organic carbon, measured in kilograms of carbon per square metre per year, rather than accounting for biodiversity impacts (18). Consumptive water use represents here the total amount of water consumed, calculated as the difference between the water extracted and the water returned to the environment, across all processes involved in a product\u0026apos;s life cycle.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNutrient content of the purchases\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe retailer\u0026rsquo;s product groups (n=3435) were linked to nutrient content using the Finnish food composition database Fineli\u0026reg; (version 20, www.fineli.fi), maintained and constantly updated by the Finnish Institute for Health and Welfare. We selected a representative food item from Fineli\u0026reg;, with the assistance of the retailer\u0026apos;s dataset of the most sold items within each food group (14). The purchase volume (kg) of the product groups were multiplied by the nutrient contents per 1 kg of the food products to obtain the total nutrient contents of the purchased foods.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical methods\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used sequence analysis for deriving protein purchase clusters. For the analysis, we categorized protein sources into the following four groups: i) red meat and processed meat, which also encompassed processed white meat; ii) poultry and poultry dishes; iii) fish and seafood; and iv) plant-based foods, which included plant-protein rich products and vegetable dishes, excluding whole vegetables (11). Primary sources at a given time, i.e. the protein source that was the most purchased by the individual in that month, were used as states in the sequence analysis. The analysis identified six clusters with distinguishable purchase preferences for protein sources: Red meat, Red meat mixed (mainly red meat, occasionally poultry and fish), Red meat \u0026amp; poultry, Mixed (all protein sources equally), Fish, and Plant-based (11).\u003c/p\u003e\n\u003cp\u003eExpenditure, nutrient content, and environmental impacts per 2500 kcal (10.5 MJ) in each cluster are reported as means and SD, and characteristics of the population either as numbers and percentages or means and SD. Distributions within and between clusters of food group-specific expenditure, nutrient content, and environmental impacts are presented graphically and using descriptive statistics (means and proportions). For the presentation of the food group-specific results, all purchases were aggregated into 17 food groups (see Figures 1-4 and disaggregation of discretionary foods in Supplementary Figures 1-3). In a large sample such as the current loyalty card data, even small differences become statistically significant. We argue that interpreting the sizes of the differences is more meaningful than their statistical significance. Therefore, we refrain from showing statistical tests when examining differences in food purchases among the clusters. We used the statistical software R (R Foundation for Statistical Computing, http://www.R-project.org/) for the analyses.\u003c/p\u003e\n\u003cp\u003eMean differences between the clusters are denoted as\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"101\" height=\"20\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e, where X and Y are mean values in any two clusters that are compared with each other. The differences are expressed as absolute or relative to Y.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the participants by cluster\u003c/h2\u003e \u003cp\u003eThe members of the Plant-based cluster were more often women than the members of the other clusters (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The members of the Fish cluster had master\u0026rsquo;s degree or higher the most often relative to the other clusters. The members of the Plant-based cluster were the youngest (mean 38 years) and those of the Fish cluster the oldest (56 years). The members of the Plant-based cluster had more often monthly scaled household income of less than 1000\u0026euro; and the Fish cluster more often a monthly scaled household income of 4000\u0026euro; or more compared with the others.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of 22 901 loyalty-card holders by cluster.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed meat (N\u0026thinsp;=\u0026thinsp;6554)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed meat mixed\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;9580)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRed meat \u0026amp; Poultry\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1980)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;3265)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFish\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;464)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePlant-based\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1058)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2487 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3364 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e576 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e948 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e161 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e227 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4067 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6216 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1404 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2317 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e303 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e831 (79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest education, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e668 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e584 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3026 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3611 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e639 (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e951 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e289 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1908 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3175 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e732 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1031 (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e358 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u0026rsquo;s degree or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e941 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2183 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e546 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1138 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e205 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e388 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther or missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoyalty, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u0026ndash;80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2099 (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3994 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e757 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1489 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e189 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e470 (44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e81\u0026ndash;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4455 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5586 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1223 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1776 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e275 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e588 (56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScaled household income (\u0026euro;/month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e759 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e349 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e209 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u0026ndash;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1250 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1379 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e363 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e133 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2013 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2751 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e590 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e866 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e295 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u0026ndash;3999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1521 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2206 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e437 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e692 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e202 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4000 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e919 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1881 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e385 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e768 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e165 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e604 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePurchase volumes of food per 2500 kcal by cluster\u003c/h2\u003e \u003cp\u003eAs the clusters were identified based on the main protein source of the purchases, it is logical that they differed largely in purchase volumes of the protein sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition to the protein sources, the most important differences among the clusters were attributable to fruit, vegetable, and liquid dairy purchases. Fruit and vegetable purchases increased consistently, while red and processed meat purchases decreased from one cluster to another (∆% (Plant-based, Red meat): fruits and berries\u0026thinsp;+\u0026thinsp;89%, vegetables\u0026thinsp;+\u0026thinsp;107%, red meat \u0026minus;\u0026thinsp;89%, \u003cb\u003eSupplementary Data 1\u003c/b\u003e). The members of the Plant-based cluster bought considerably less liquid dairy products than the others, in absolute terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and in proportion to the total purchases (13% vs. 20\u0026ndash;25%, \u003cb\u003eSupplementary Data 1\u003c/b\u003e). Discretionary foods\u0026rsquo; purchase volume was slightly smaller in the members of the Fish and Plant-based clusters in absolute terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and in proportion to total purchases (Fish 19%, Plant-based 20%, others 23\u0026ndash;27% from total purchases, \u003cb\u003eSupplementary Data 1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExpenditure on food per 2500 kcal by cluster\u003c/h3\u003e\n\u003cp\u003eMembers of the Fish cluster spent the most money on food (mean 9.8\u0026euro;), followed by the Plant-based (9.0\u0026euro;) and Mixed clusters (8.1\u0026euro;). Members of the Red meat cluster spent the least money on food (7.4\u0026euro;) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Differences among the clusters in food expenditure were not attributable to the main protein source (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For example, members of the Plant-based and Red meat clusters spent approximately the same amount of money on the main protein sources (although protein content in the purchases was smaller as seen later in section \u003cem\u003eNutrient content of foods per 2500 kcal by cluster\u003c/em\u003e). The main contributors to the differences were expenditures on fruits and vegetables (in fruits and berries maximum ∆% (Fish, Red meat): +143%, in vegetables maximum ∆% (Plant-based, Red meat): +139%) (\u003cb\u003eSupplementary Data 1\u003c/b\u003e). A large proportion of the total food expenditure was attributable to discretionary foods in all clusters (from 18% in Plant-based to 24% in Red meat) (\u003cb\u003eSupplementary Data 1 and\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In all clusters the main food groups among discretionary foods in terms of expenditure were alcoholic and non-alcoholic beverages, cereal bakery products, and sweets (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExpenditure, nutrient contents, and environmental impacts of total food purchases per 2500 kcal among 22 901 loyalty-card holders by cluster.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed meat (N\u0026thinsp;=\u0026thinsp;6554)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed meat mixed\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;9580)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRed meat \u0026amp; Poultry\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1980)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;3265)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFish\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;464)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePlant-based\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1058)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eExpenditure per 2500 kcal, \u0026euro;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.4 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8.1 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.8 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.0 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNutrient content per 2500 kcal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein, g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e94 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSucrose, g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e69 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibre, g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e26 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA, g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e42 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B12, \u0026micro;g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.5 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.0 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.7 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D, \u0026micro;g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.1 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.7 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9.0 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.7 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.3 (3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolate, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265 (83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e287 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e296 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e326 (85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e347 (99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1330 (346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1330 (347)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1310 (349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1350 (393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1460 (419)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1230 (451)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4 (2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9 (2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.6 (2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e12.8 (2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.6 (2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.0 (3.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalt, g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.3 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.3 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.8 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnvironmental impact per 2500 kcal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHGE, kg CO\u003csub\u003e2\u003c/sub\u003e-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4.5 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.1 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.6 (0.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarine eutrophication, g N-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.5 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.3 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.7 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreshwater eutrophication, g P-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.0 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use, m\u003csup\u003e2\u003c/sup\u003ea crop-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4.8 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.3 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.6 (0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater use, m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.26 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24 (0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eGHGE, greenhouse gas emission; N-eq., nitrogen equivalent; P-eq., phosphorus equivalent; crop-eq.\u0026nbsp;arable cropland equivalent.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEnvironmental impacts of foods per 2500 kcal by cluster\u003c/h3\u003e\n\u003cp\u003eFor GHGE, land use, and marine eutrophication, the impacts of the purchases decreased from meat-dominant (Red meat, Red meat mixed, Red \u0026amp; poultry) to Plant-based clusters \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). GHGE of total purchases was similar in the purchases of Red meat, Red meat mixed, and Red meat \u0026amp; poultry clusters, after which the emissions diminished step-wise through Fish to the Plant-based clusters (maximum ∆% (Plant-based, Red meat \u0026amp; poultry): -28%). Although several food groups contributed to the GHGE, land use, and marine eutrophication, the major contributors to the differences among the clusters were the main protein sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDifferences in freshwater eutrophication among the clusters were mainly explained by fish content and was the highest in the purchases of the Fish cluster (2.5 g P eq., from which 40% originated from fish, \u003cb\u003eSupplementary Data 1\u003c/b\u003e), and the lowest in the Plant-based cluster (1.5 g P eq., from which 20% originated from fish) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. A large portion of the freshwater eutrophication impact of fish came from fish that could not be classified as either wild-caught or farmed due to lack of information on the species (34\u0026ndash;85% from total impact of fish, depending on the cluster) (\u003cb\u003eSupplementary Table\u0026nbsp;1)\u003c/b\u003e. Consumptive water use and content of fruits and fish tended to be associated across the clusters; water use was the highest for the Fish cluster and the lowest for the Red meat cluster (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiscretionary foods had a large contribution to all environmental impacts in all clusters (on average 17\u0026ndash;32% from total purchases, depending on the environmental impact category) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eSupplementary Data 1\u003c/b\u003e). For GHGE, the largest contributors within the discretionary foods were beverages, including both alcoholic and non-alcoholic drinks (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNutrient content of foods per 2500 kcal by cluster\u003c/h2\u003e \u003cp\u003eFolate, fibre, and iron contents of the total purchases increased stepwise as red meat content of the cluster decreased (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These differences in nutrient content were mainly attributable to differences in fruit, vegetable, and grain purchases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with members of the Plant-based and Fish clusters purchasing fruits, vegetables, and high-fibre grains the most.\u003c/p\u003e \u003cp\u003eThe opposite was observed for salt and SFA contents, which decreased stepwise as red meat purchases in the cluster decreased. Although in all clusters most of the salt was derived from other food groups than the main protein source, the differences between the clusters were mainly related to the main protein sources of the purchases. Vitamins B12 and vitamin D content were the lowest in the purchases of members of the Plant-based cluster and the highest in the Fish cluster. Protein content was lower in the Plant-based cluster than in the other clusters.\u003c/p\u003e \u003cp\u003eDiscretionary foods were responsible for a large amount of energy (average 18% of total purchases) in all clusters (\u003cb\u003eSupplementary Data 1 and\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and contributed markedly to sucrose contents in all (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Among discretionary foods, the sucrose content of purchases was the highest in the Red meat cluster.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eA notable finding was that expenditure for the protein sources was similar across the clusters, independent of the choice of the main protein source. The mean protein supply was lower in the Plant-based cluster than in the others but was well within the recommended intake range (10-20E%, which is 63\u0026ndash;125 g in 2500 kcal intake) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Another main finding was that the step from the Red meat cluster to the Poultry cluster would bring little, if any, environmental benefit, with better environmental and nutritional gains achieved by transitioning from any meat-dominant cluster to a Plant-based cluster. Notably, discretionary foods played a large role across all clusters, comprising a substantial proportion of food expenditure, while having significant negative impacts on both environmental footprints and nutrient content of food purchases.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExpenditure\u003c/h2\u003e \u003cp\u003eBecause of minimal differences in expenditure (per energy unit) in protein source purchases between the clusters, we infer that a shift from meat to fish or plant-based protein sources does not seem to be an economic obstacle for the average Finnish household. However, it is possible that the perception of price is more important than the actual price in food choice preferences (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In previous studies, both meat-eaters and those consuming plant-based foods or transitioning to plant-based food consumption have considered the price motive to be important (\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Motives also seem to vary depending on the food group in question.\u003c/p\u003e \u003cp\u003eIndependent of the cluster, the amount of money spent on discretionary foods was large, at least one-fifth of the total food expenditure. Food serves purposes beyond nutritional value, such as regulating feelings, social influences, and constructing cultural identity (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), which complicates the idea of substituting food choices across different food groups, e.g. substituting fruits and vegetables for discretionary foods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental impacts\u003c/h2\u003e \u003cp\u003eUnexpectedly, those choosing more unprocessed poultry and less red and processed meat (including both processed red meat and processed poultry) did not have smaller GHGE from protein sources or from total purchases than those who bought mainly red and processed meat. This may have been because a large part of the \u0026ldquo;replaced\u0026rdquo; red and processed meat (by poultry) was processed red meat, which may contain ingredients with low GHGE per kg, e.g. potato extracts in many sausages, reducing the overall GHGE of the red meat product. These findings support the meat recommendation of the food-based dietary guidelines of the Nordic nutrition recommendations 2023, which emphasize not replacing red meat with poultry but replacing it with plant-based protein sources and sustainable fish products (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), and a recent Finnish study suggesting poultry intake as one of the primary foods in to contribute to global biodiversity loss (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Although many of the foods included in discretionary foods have relatively low environmental impact per kg of product compared to, for example, meats, the high consumption volume makes them a significant food group. In previous Nordic studies, discretionary foods have played a relatively large role in environmental impacts (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Thus, the results suggest a potential for reduction of environmental impacts through reducing consumption of discretionary foods in all population groups. The finding is noteworthy, as many discretionary foods have small environmental impacts per mass unit, which may take attention away from their overall impact.\u003c/p\u003e \u003cp\u003eThe relatively large contribution of fruits and vegetables to consumptive water use has also been found before, e.g. in a Swedish study (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). However, the measure of consumptive water use does not directly inform about water overconsumption because it does not consider water scarcity in the production area. Currently, lack of data hinders research on water-scarcity in Finnish food consumption (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In a US study, large scarcity-weighted water footprints were found for meat, especially beef, and some fruits, nuts, and seeds (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The study is, however, not fully comparable with Finnish food consumption because of different agricultural conditions of some foods. In Finland, which is highly self-sufficient in beef production, most of the agricultural production is rainfed (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Therefore, consumptive water use for beef production in Finland is minimal, and water scarcity is generally low (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In contrast, certain imported foods, such as some fruits, rely on irrigation, notably contributing to consumptive water use (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Some of the fruits may come from water-scarce areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNutrient contents\u003c/h2\u003e \u003cp\u003eThe purchases of the members of the Fish and Plant-based clusters had overall the most favorable nutrient contents, in accordance with earlier results (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Although we did not consider dairy products as one of the main protein sources in our analysis, they are an important food group in the sustainability transition because of their significant role in the food culture, in environmental impact, and as a source of many nutrients. In this study, a trade-off of lower environmental impacts of the purchases in the Plant-based cluster was the relatively low vitamin D intake because of lower dairy content than in the other clusters. According to our results, Finland\u0026rsquo;s otherwise successful fortification program of dairy products and fat spreads (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) might not be sufficient to cover vitamin D intake of the members of the Plant-based cluster. The Finnish Nutrition Recommendations (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) recommend vitamin D supplementation to individuals who do not regularly consume fish or products fortified with vitamin D.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eOne of the main strengths of this study was the use of food purchase data with exceptionally large dataset in the context of nutrition research and larger sample size and longer timespan than could be collected using traditional methods. Loyalty card data are less reliant on self-reporting than traditional dietary intake data. The self-selected data brought realism to the analysis in terms of acceptability, which is difficult to evaluate in studies based on theoretical diets. Another strength is the data on the observed individual-level expenditure on food, which in food consumption research is usually self-reported or approximations based on statistics.\u003c/p\u003e \u003cp\u003eWe were unable to calculate absolute daily purchases of individuals because the card holder usually buys food for the whole household. In addition, not all the household\u0026rsquo;s food is purchased from the retailer in question. Moreover, the household members\u0026rsquo; diets may also differ from each other. However, we have shown that when comparing the food purchases of the cardholders with food intake data derived from FFQs collected for the same individuals, the relative validity for most food groups is acceptable (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe reliability of the magnitudes of eutrophication from fish is affected by the species and production method for some of the products not being identified and therefore being classified as \u0026ldquo;uncategorized fish products\u0026rdquo;. The environmental impact coefficients for \"uncategorized fish products,\" which accounted for a significant share of the freshwater eutrophication impact, are therefore more uncertain than those of other captured and farmed fish. The large contribution of fish to freshwater eutrophication was, however, similar in another study of Finnish diets (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Another consideration is that captured fish from Finnish lakes and the Baltic Sea removes N and P from the water system, thereby reducing eutrophication (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), which was not considered in the impacts. In addition, the French environmental impact data probably did not rigorously reflect the environmental impacts of Finnish food consumption. However, the directions and patterns of at least the most established environmental impacts of foods in different studies across several countries point in the same direction (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The environmental impacts reported in this study should not be considered as absolute impacts but as indications of relative differences between the clusters and food groups.\u003c/p\u003e \u003cp\u003eAlthough our idea in the study was that the purchase profiles of the protein preference clusters could indicate a realistic transition pathway from meat-dominant to more sustainable food purchase profiles, whether the whole purchase profile would change following an individual\u0026rsquo;s shift from one protein source to another remains unknown.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe hypothetical protein source transition path that was derived from a cross-sectional self-selected purchase data produced stepwise improvements in fibre, SFA, folate, iron, and salt content and in some environmental impacts including GHGE when moving towards plant-based protein source purchases. The step from red meat to poultry would bring little environmental benefit; better nutritional gains were achieved by moving from red meat dominance to fish or plant-based dominant purchases. A notable potential for reducing environmental impact lies in the reduction of discretionary food purchases across all population groups.\u003c/p\u003e \u003cp\u003eAccording to our results, a sustainability transition is not an affordability issue, apart from some expensive sustainable fish products. Instead, the obstacles for transition of individuals/households lie among other determinants of behaviour. Additionally, it is crucial not to place the burden of change solely on individual consumers but to prioritize systemic transformations, including shifts towards more sustainable agricultural practices, value chain adjustments, improved food distribution systems, and policy incentives that make healthy and sustainable choices more accessible and affordable for all. The Nordic Nutrition Recommendations, in line with the results of this study, provide a crucial framework for policymakers and other stakeholders towards a healthier and more sustainable food system.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study obtained ethical clearance from the ethical board of the University of Helsinki Review Board in humanities and social and behavioural sciences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEach participant provided electronic consent for the collection and use of their purchase data and questionnaire data for research.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data 1 contains volume, expenditure, environmental impacts, and energy contents of the purchases by food group as unit per 2500kcal and as mass percentage from total purchases. The program codes and their output files are available from [email protected]. Data can be analyzed in collaboration with the research team on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMF is a member of the S-group\u0026apos;s advisory group for social responsibility. This is an unpaid advisory position in a commercial organization. The authors declare no other competing interests.\u0026nbsp;Both the research group and the retailer signed a contract on data transfer, ensuring the independence of the research and scientific publishing from business interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe work was funded by Juho Vainio foundation (JM: #202100202), Yrj\u0026ouml; Jahnsson foundation (JM: #20207300), and Research Council of Finland (MF and JN: #350852, #350863). The funding sources had no role in study design, data collection, analysis, interpretation of data, writing the report, or in the decision publish the findings.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJ.M., R.M., H.V., J.-M.K., H.L.T., M.F., M.E., and J.N. designed the study; J.-M.K. provided the carbon footprint data; RM produced the land use, water use, and eutrophication data; J.N., M.F., and M.E. obtained the purchase data; J.M. and J.N. processed the purchase data; J.M. analysed the data and performed the data analysis; J.M. and R.M. wrote the paper; J.M., J.N., R.M., and J.-M.K. had primary responsibility for the final content. All the authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Hanna Hartikainen, Hannele Heusala, Eric Harrison and Frans Silvenius from Natural Resources Institute Finland who have contributed to producing the carbon footprint database utilised in this study and Satu Kinnunen from the Department of Food and Nutrition at the University of Helsinki for processing the food purchase data before the analysis.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCrippa M, Solazzo E, Guizzardi D, Monforti-Ferrario F, Tubiello FN, Leip A. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat Food. 2021;2(3):198\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell BM, Beare DJ, Bennett EM, Hall-Spencer JM, Ingram JSI, Jaramillo F et al. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.osti.gov/etdeweb/biblio/20874290\u003c/span\u003e\u003cspan address=\"https://www.osti.gov/etdeweb/biblio/20874290\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sustainability, sustainability transition, food system change, planetary health, food budget, food affordability","lastPublishedDoi":"10.21203/rs.3.rs-6463928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6463928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and objectives:\u003c/h2\u003e \u003cp\u003eTo understand food groups\u0026rsquo; contribution to nutrition, environmental impacts, and expenditure requires self-selected food consumption data. We analyzed implications of a hypothetical transition in protein sources on these sustainability dimensions considering total food consumption.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe clusters were derived from food purchase data of 22,901 loyalty card holders by sequence analysis of purchases over 12 months. In a cross-sectional setting, we performed between-cluster comparisons of energy adjusted purchases\u0026rsquo; expenditure, LCA-based environmental impacts, and nutrient content.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eRelative to 2500kcal, members of Plant-based and Fish clusters spent the most money on food (9.0-9.8\u0026euro;) and members of Red meat cluster the least (7.4\u0026euro;). The main contributors to the between-cluster differences were not the protein sources themselves. Greenhouse gas emissions were similar in Red meat, Red meat mixed, and Red meat \u0026amp; poultry clusters, but 27\u0026ndash;28% lower in Plant-based cluster. Freshwater eutrophication and consumptive water use were the highest in Fish cluster (67% and 25% higher than in Plant-based cluster, respectively). The improvement of micronutrient supplies towards Fish and Plant-based clusters were explained by other foods than protein sources. Discretionary foods had a large contribution to expenditure (22%) and all environmental impacts (17\u0026ndash;32%) in all clusters.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA sustainability transition in protein sources seems affordable for an average Finnish household. Partial replacement of red meat with poultry would offer minimal environmental gains. While fish consumption is nutritionally beneficial, the environmental trade-offs should be carefully considered. Reducing discretionary food consumption could yield notable environmental benefits while reducing household food budgets and improving nutritional quality.\u003c/p\u003e","manuscriptTitle":"Discretionary foods have notable environmental and expenditure relevance regardless of preference for meat or plant-based protein sources","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 06:08:06","doi":"10.21203/rs.3.rs-6463928/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c488bfa-1b6b-4c6e-94f9-05df9ceda9c6","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T12:53:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-18 06:08:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6463928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6463928","identity":"rs-6463928","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}