What’s on the Menu? Quantifying the Greenhouse Gas Emissions and Water Footprint of the Food Provision within Elite Male & Female Football Teams

What’s on the Menu? 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Quantifying the Greenhouse Gas Emissions and Water Footprint of the Food Provision within Elite Male & Female Football Teams Ollie Turner, Nigel Mitchell, Andrew Jenkinson, Hans Louis, Colin Oakley, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8924973/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 7 You are reading this latest preprint version Abstract Background: The global food system contributes approximately 34% of anthropogenic greenhouse gas emissions (GHGE) and exerts substantial pressure on freshwater resources, yet the environmental impact of food provision within professional sport remains unexplored. Addressing this evidence gap, this study quantified the environmental footprint of food service provision across four professional football clubs in England, representing the English Premier League (EPL), English Football League Championship (EFL Champ), English Football League One (EFL L1), and Women’s Super League (WSL). The primary aims were to determine total and relative GHGE, water footprints, and the contribution of individual food categories to each environmental metric. Methods: A seven-day cross-sectional design was employed using reference-standard life cycle assessment methodology via the Nutritics Foodprint Carbon Footprint Functionality . A bottom-up analytical approach assessed 289 individual food and beverage items across 52 categories. Data were analysed descriptively, with comparisons made using 95% confidence limits to preserve interpretive transparency. Results: Marked inter-team variation was observed in relative nutritional provision and environmental impact. The EFL L1 team demonstrated the highest energy (2,272 ± 27 kcal·d⁻¹), carbohydrate (228 ± 3 g·d⁻¹), protein (138 ± 3 g·d⁻¹), and fat (90 ± 2 g·d⁻¹) provision per cover per day, as well as the GHGEs (7.17 ± 0.14 kg·CO₂·eq) and water footprint (6.01 ± 0.11 kL·eq). The EPL, EFL Champ, and WSL teams followed in descending order for both energy and environmental measures. Across all clubs, meat - particularly beef - was the largest contributor to total GHGE (~ 38%) and water footprint (~ 36%), with dairy (~ 16%) and vegetables (~ 18%) representing notable secondary contributors. Beverages, primarily bottled water, accounted for 22% of total GHGE in the EFL L1 team, underscoring how both menu composition and operational practices influence environmental outcomes. Conclusion: This study provides the first evidence that professional football food services differ markedly in their environmental impact, with higher energy and protein provision - particularly from ruminant meat and dairy - driving greater GHGEs and water use. Practical strategies such as moderating overall protein provision, particularly from red meat procurement, while increasing plant-based protein inclusion, and replacing bottled water and dairy with filtered and plant-based alternatives could substantially reduce emissions without compromising performance nutrition. Embedding these actions within FIFA, UEFA, and EPL sustainability frameworks would align football with global climate goals and position the sport as a leader in advancing sustainable nutrition practices. Sustainability nutrition sport football soccer 1.0 Introduction Anthropogenic greenhouse gas emissions (GHGE) are the primary drivers of contemporary climate change, with human-induced global warming estimated to have reached 1.1°C above pre-industrial levels in 2017. 1 Without substantial mitigation, current GHGE trajectories project that human-induced global warming will exceed 1.5°C above pre-industrial levels by the early 2030s, leading to severe environmental, economic, and health related consequences. 2 Recognising the urgency of this challenge, the United Nations has mobilised a global response through frameworks such as the Intergovernmental Panel on Climate Change (IPCC) and Paris Agreement, 3,4 which aim to stabilise human-induced global warming well below 2.0°C, and pursue efforts to limit human-induced global warming to below 1.5°C preindustrial levels by the end of the century. 4 The global food system accounts for approximately 34% of anthropogenic GHGE, 5 with agricultural activities alone occupying nearly 40% of the Earth’s terrestrial surface. 6 Consequently, the global food system substantially contributes to climate change, 7 and is recognised as a major driver in the transgression of several environmental planetary boundaries, 8 thresholds established to maintain a stable Earth system and a safe operating space for humanity. 9 As the global population is projected to rise from 7.7 billion to between 9.4 and 10.2 billion by 2050, 10 the pressure on food systems and natural resources is expected to intensity. 11 Modelling studies suggest that even if all non-food related emissions were held at net zero from 2020 to 2100, global food system trends alone could result in global warming exceeding 1.5°C above pre-industrial levels by 2051 to 2063 and surpassing 2.0°C within the next 80 years. 12 In addition to its role in climate change, the global food system exerts substantial stress on freshwater resources. 13 human-induced global warming, 14 population growth, 10 and rapid urbanisation, projected to increase urban populations from 5.0 billion to 6.3 billion by 2050, 15 are compounding drivers of freshwater scarcity. 16 Freshwater supports essential ecosystem processes such as drinking water provision, 17 ecosystem functioning, 18 and carbon sequestration via aquatic systems. 19 Currently, an estimated four billion people experience severe water scarcity for at least one month annually, 20 with recent planetary boundary assessments indicating that both blue and green water boundaries have already been surpassed. 21,22 Agriculture remains a major contributor to this imbalance, with irrigation responsible for approximately 70% of global blue water use (freshwater found in rivers, lakes, and underground sources used for drinking, farming, and industry). 23 The concept of a ‘sustainable diet’ encompasses dietary patterns that promote optimal health, while minimising environmental impacts, thereby supporting food systems that operate within established environmental planetary boundaries. 24 Advances in environmental assessment methodologies, particularly the life-cycle assessment, have enabled the quantification of the GHGE and water footprint of various foods. 25 Evidence from life cycle assessment studies consistently demonstrate that red meats such as beef and lamb exhibited the greatest GHGE (beef = 28.73 kg⋅CO 2 -eq⋅kg; lamb = 27.91 kg⋅CO 2 -eq⋅kg), while poultry (4.12 kg⋅CO 2 -eq⋅kg), and pork (5.85 kg⋅CO 2 -eq⋅kg) produce substantially lower emissions. 26 Plant based foods such as legumes (0.66 kg⋅CO 2 -eq⋅kg), cereals (0.53 kg⋅CO 2 -eq⋅kg), fruits (0.50 kg⋅CO 2 -eq⋅kg), and vegetables (0.47 kg⋅CO 2 -eq⋅kg) demonstrate the lowest GHGE. 26 When standardised to a 2000 Kcal⋅d − 1 diet, GHGE are markedly higher among high meat eater consumers (> 100 g⋅d − 1 meat; 7.28 kg⋅CO 2 -eq⋅d − 1 ), compared with medium (50–100 g⋅d − 1 meat; 5.34 kg⋅CO 2 -eq⋅d − 1 ), and low meat eaters (< 50 g⋅d − 1 meat; 4.21 kg⋅CO 2 -eq⋅d − 1 ). Dietary patterns of pescatarians (3.81 kg⋅CO 2 -eq⋅d − 1 ), vegetarian (3.33 kg⋅CO 2 -eq⋅d − 1 ), and vegan diets (2.16 kg⋅CO 2 -eq⋅d − 1 ) reduce GHGE further. 27 Water use within food systems is complex: red meat, dairy products, nuts, and grains are typically associated with high blue water usage, in comparison to legumes, oats, and plant-based milks. 28 Collectively, these findings underscore environmental benefits of transitioning towards a ‘plant forward’ dietary model. 11 A cultural shift towards more sustainable practices is urgently required across all sectors, including sport. 29 Football has increasingly positioned itself as a platform for sustainably-driven initiatives under the broader ‘Sport for Development’ agenda. 30 Major governing bodies such as the Federation Internationale de Football Association (FIFA), the Union of Football Associations (UEFA), and the English Premier League (EPL) have all implemented sustainability strategies aimed at reducing GHGE. 31–33 In alignment with the United Nations ‘Sports for Climate Change Action’ Framework, these organisations have committed to reducing GHGE by 50% by 2030, and achieving net zero emissions by 2040. 34,35 The EPL further reinforced this objective through its ‘Environmental Sustainability Commitment’ (2024), establishing a minimum standard of actions across all member clubs. 36 Despite these advances, food-related emissions remain disappointedly absent from most football sustainability frameworks. 31–33 For example, FIFA’s reported sustainability actions have primarily focused on food waste reductions during events, 31 while UEFA’s initiatives target improved access to ‘healthy’ food at events and waste minimisation (e.g. food waste and single use items), 32 and the EPL’s ‘Primary Stars’ initiative emphasises sustainability education for children. 33 The relative neglect of wider sustainable nutrition practices likely reflects the novelty of this concept within elite sport. 37,38 Indeed, the UEFA Expert Group Statement on Nutrition in Elite Football acknowledged that “ further work is required to understand the interplay between sports nutrition and sustainability and how principles can be incorporated within best practice nutrition recommendations”. 39 Elite football occupies a unique position to drive societal change due to its global influence, visibility, and cultural reach. 40 Players and clubs can create momentum through their sporting performances and media profile, utilising this as a strategic vehicle to communicate, implement, and drive nonsporting development goals such as sustainability. 40 This presents an important opportunity to promote awareness and behavioural change regarding sustainable diets through football. However, current dietary sustainable frameworks such as the Planetary Health Diet proposed by the EAT-Lancet Commission are designed for the general population, 41 and may not meet the health and performance demands of elite footballers. 42–52 The Planetary Health Diet assumes an average energy intake of approximately 2400 Kcal⋅d − 1 , increasing to 3008 Kcal⋅d − 1 and 2451 Kcal⋅d − 1 for ‘very active’ adult men and women respectively. 41 These estimates remain well below the average energy requirements of elite adolescent and senior male (~ 3,250-3,600 Kcal⋅d − 1 ) and female (2,600–2900 Kcal⋅d − 1 ) footballers. 42–52 Moreover, football-specific nutritional guidelines recommend individualised “nutrition periodisation”, with carbohydrate intakes adjusted between 3–8 g⋅kg⋅d − 1 depending on training intensity, and protein intakes of 1.6–2.2 g⋅kg⋅d − 1 to optimise adaptation and recovery. 39 Consequently, the elevated energy and protein requirements of footballers may predispose them to diets with higher associated GHGE and water footprints. Elite football clubs operate continuous, large-scale food service operations throughout both the competitive and off-season periods, typically providing at least two daily meals, such as pre- and post-training provisions, and employing multiple performance chefs to support the footballer’s nutrition. 53 Although previous research has assessed GHGE and water footprints within institutional catering contexts, such as healthcare settings, 54 and explored emissions linked with football club operations, 55 travel, 56 and fan activites, 57 no study to date has quantified the environmental impacts associated with food service provision in elite male or female football. This absence of data limits the development of integrated, evidence-based strategies capable of aligning nutritional performance goals with environmental sustainability objectives. Calculating the GHGE and water footprint of the food services of elite football teams will therefore help inform football clubs on the sustainability of their food service and can implement appropriate adjustments if required, whilst also putting sustainable nutrition practices to the forefront of major stakeholders’ attention, such as governing bodies and organisations, football clubs, and football players. Accordingly, the present study aimed to quantify both the GHGE and water footprint associated with food service provision in four professional football clubs in the United Kingdom, representing the EPL, the Women’s Super League, the English Football League Championship, and English Football League One, as well as determining the contribution of individual food categories to the overall environmental impact within each club. 2.0 Methods 2.1 Study Design This cross-sectional investigation included four professional football clubs based in England, representing the English Premier League (Team One), English Football League Championship (EFL Champ; Team Two), English Football League One (EFL L1; Team Three), and Women’s Super League (WSL; Team Four). Food service procurement data were collected for a typical in-season weekly microcycle, inclusive of at least one competitive match, during the final two months of the 2024–2025 competitive season. Data acquisition was facilitated through the professional networks of the authorship team and was conducted with the full cooperation of participating clubs. Team One (EPL) provided 150 daily covers across four training days (600 total covers, due to two match-days and one rest day); Team Two (EFL Champ) had 200 daily covers across four training days (800 total covers, due to one match day and two rest days); Team Three (EFL L1) had 135 daily covers across four training days (540 total covers, due to one match day and two rest days); and Team Four (WSL) had 120 daily covers over four training days (480 total covers, due to two match days and one rest day). Each football team provided two meal services per day (pre-training meal: breakfast; and post-training meal: lunch). Ethical approval for the study was obtained through Leeds Beckett University ethics committee (reference number: 139424). 2.2 Procedures Using the food service procurement data obtained from each club, the GHGE (expressed as kilogram [kg] CO 2 equivalents per kilogram of food) and water footprint (kL⋅eq) were quantified. Data were classified into food categories following the framework of Poore & Nemecek (2018), 28 which outlines 42 distinct food groups (e.g. beef [beef herd], groundnuts, potatoes, etc.). Some food and beverage items could be directly mapped into existing categories (e.g. olive oil into olive oil ), whereas others could not (e.g. mushrooms). Consequently, ten additional food categories were created: other milks; other oils; other vegetables; other fruits; herbs; fungi; spices; condiments; confectionary and beverages , resulting in 52 total categories. Details of these classifications and the categorisation of each individual food and beverage item are provided in Supplementary Material 1. Notably, fruit juices such as apple juice or orange juice were classified under fruits rather than beverages due to Poore & Nemecek (2018) having categories for apples and citrus, whereas beverages was an additional category created by the authorship. Each item was entered into Nutritics nutrition analysis software (Nutritics Version 6.14, Nutritics Ltd, Ireland) by the lead author (OT). 58 Individual datasets were created for each team using the Nutritics Meal Plan function, with all food and beverage items being entered as raw weights unless otherwise specified. Missing or unclear information (e.g. portion sizes, or item specification) was clarified with the club head chef and lead sport nutritionist to ensure accuracy and completeness. The GHGE and water footprint for each food and beverage item was derived using the Nutritics Foodprint Carbon Footprint Functionality (Nutritics Version 6, Nutritics Ltd, Ireland), 59 which estimates environmental impact values based on peer reviewed published life cycle assessment research across five databases. 26,60–63 The system prioritises primary life cycle assessment data where available, locking it as the definitive value, while secondary data are extended to incorporate outbound transportation and packaging stages. Each individual ingredient’s name, description, and country of origin were inputted to further improve accuracy where possible. Each assigned GHGE and water footprint value was manually verified to ensure the most appropriate match between the item and corresponding life cycle assessment dataset. This methodology has previously been utilised in the literature to quantify the GHGE of school lunches for children attending school nurseries. 64 In total, 289 individual foods and beverages (Team One, EPL = 154 individual foods or beverages; Team Two, EFL Champ = 201 individual foods or beverages; Team Three, EFL L1 = 154 individual foods or beverages; Team Four, WSL = 117 individual foods or beverages). Following verification, the data were exported from Nutritics into Microsoft Excel (Excel Version 16.99, Microsoft, Redmond, Washington, USA) for data cleaning and preparation prior to analysis. 2.3 External Funding No funding was received to support the design, data collection, analysis, interpretation, or writing of this study. The research was conducted independently by the authors without financial support from an organisation or sponsor. 2.4 Statistical Analysis Data are presented as mean ± standard deviation, with 95% confidence limits (CLs). Given the small sample size ( n = four), no formal inferential statistical analyses were conducted. Instead, a descriptive analytical approach was employed to enable meaningful interpretation of the environmental impact data while avoiding inappropriate statistical inference. Comparisons between teams and food groups were made by examining whether mean values fell outside the respective 95% CLs of other teams or food groups. A mean value was interpreted as higher or lower when it lay outside another team’s or food groups 95% CLs, and comparable when within these bounds. This approach was selected to preserve the integrity of interpretation. Descriptive comparison using 95% CLs allows for transparent presentation of potential differences while appropriately reflecting the exploratory and observational nature of the study. All analyses were conducted using Microsoft Excel (Excel Version 16.99, Microsoft, Redmond, Washington, USA). To calculate GHGE per cover per day, the overall GHGE was divided by the number of days food service was provided for, by the number of covers per day: $$\:\frac{\frac{GHGE\:of\:food}{Number\:of\:days\:of\:food\:service}}{Number\:of\:covers}$$ Equation 1. Calculation of GHGE per cover per day. To calculate GHGE per cover per food service, this was further divided by the number of food services per day: $$\:\frac{\left(\frac{\frac{GHGE\:of\:food}{Number\:of\:days\:of\:food\:service\:}}{Number\:of\:cover}\right)}{Number\:of\:food\:services\:per\:day}$$ Equation 2. Calculation of GHGE per cover per service. 3.0 Results 3.1 Energy & Macronutrient Breakdown 3.1.1 Energy provision Table 1 summarises the total and relative energy and macronutrient provision across the four football team’s food services. Team Two (EFL Champ) provided the greatest total energy (1,097,459 ± 10,261 kcal; 95% CL: 1,096,040–1,098,878 kcal]), followed by Team One (EPL) (1,069,587 ± 14,601 kcal; 95% CL: 1,067,281–1,071,983 kcal), Team Three (EFL L1) (863,256 ± 10,319 kcal; 95% CL: 861,626–864,886 kcal) and Team Four (WSL) (464,203 ± 9,4334 kcal; 95% CL: 462,494–465,912 kcal). When expressed relative to service volume, Team Three (EFL League One) demonstrated the greatest energy provision per cover per day (2,272 ± 27 kcal; 95% CL: 2,267–2,276 kcal) and per cover per service (1,136 ± 14 kcal; 95% CL: 1,134–1,138 kcal). Mean energy provision was higher for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs. Team One (EPL) (per day = 1,783 ± 24 kcal; 95% CL: 1,779–1,787 kcal; per service = 891 ± 12 kcal; 95% CL: 889–893 kcal) also exhibited higher per-cover energy provision than Team Two (EFL Champ) (per day = 1,373 ± 13 kcal; 95% CL: 1,370–1,374 kcal; per service = 686 ± 6 kcal; 95% CL: 685–687 kcal) and Team Four (WSL) (per day = 967 ± 20 kcal; 95% CL: 964–971 kcal; per service = 484 ± 10 kcal; 95% CL: 482–485 kcal). Team Two exhibited higher per-cover energy provision than Team Four . 3.1.2 Carbohydrate provision Team Two (EFL Champ) provided the greatest total carbohydrate quantity (121,088 ± 1,342 g; 95% CL: 120,902–121,274 g), followed by Team One (EPL) (101,747 ± 1,310 g; 95% CL: 101,540–101,954 g), Team Three (EFL L1) (86,805 ± 1,102 g; 95% CL: 86,631–86,979 g), and Team Four (WSL) (40,035 ± 699 g; 95% CL: 39,908–40,162 g). When expressed relative to service volume, Team Three (EFL L1) demonstrated the highest carbohydrate provision per cover per day (228 ± 3 g; 95% CL: 228–229 g) and per cover per service (114 ± 2 g; 95% CL: 114–115 g). Mean carbohydrate provision was higher for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs. Team One (EPL) (per day = 170 ± 2 g; 95% CL: 169–170 g; per service = 85 ± 1 g; 95% CL: 85–85 g) exhibited higher per-cover carbohydrate provision than Team Two (EFL Champ) (per day = 151 ± 2 g; 95% CL: 151–152 g; per service = 77 ± 1 g; 95% CL: 76–76 g) and Team Four (WSL) (per day = 83 ± 2 g; 95% CL: 83–84 g; per service = 41 ± 1 g; 95% CL: 42–42 g). Team Two exhibited higher per-cover carbohydrate provision than Team Four . 3.1.3 Protein provision Team One (EPL) provided the greatest total protein quantity (62,655 ± 1,202 g; 95% CL: 62,465–62,846 g), followed by Team Two (EFL Championship) (53,038 ± 925 g; 95% CL: 52,910–53,166 g), Team Three (EFL L1) (52,383 ± 1,017 g; 95% CL: 52,222–52,544 g), and Team Four (WSL) (23,594 ± 507 g; 95% CL: 23,502–23,686 g). When expressed relative to service volume, Team Three (EFL L1) demonstrated the highest protein provision per cover per day (138 ± 3 g; 95% CL: 137–138 g) and per cover per service (69 ± 1 g; 95% CL: 69–69 g). Descriptively, mean protein provision was higher for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs. Team One (EPL) (per day = 104 ± 2 g; 95% CL: 104–105 g; per service = 52 ± 1 g; 95% CL: 52–52 g) exhibited higher per-cover protein provision than Team Two (EFL Champ) (per day = 66 ± 1 g; 95% CL: 66–67 g; per service = 33 ± 1 g; 95% CL: 33–33 g) and Team Four (WSL) (per day = 49 ± 1 g; 95% CL: 49–49 g; per service = 25 ± 1 g; 95% CL: 25–25 g). Team Two exhibited higher per-cover protein provision than Team Four . 3.1.4 Fat provision Team One (EPL) provided the greatest total fat quantity (45,823 ± 1,332 g; 95% CL: 45,613–46,033 g), followed by Team Two (EFL Champ) (44,534 ± 766 g; 95% CL: 44,428–44,640 g), Team Three (EFL L1) (34,066 ± 665 g; 95% CL: 33,961–34,171 g), and Team Four (WSL) (23,286 ± 958 g; 95% CL: 23,112–23,460 g). When expressed relative to service volume, Team Three (EFL L1) demonstrated the highest fat provision per cover per day (90 ± 2 g; 95% CL: 89–90 g) and per cover per service (45 ± 1 g; 95% CL: 45–45 g). Descriptively, mean fat provision was higher for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs. Team One (EPL) (per day = 77 ± 2 g; 95% CL: 76–77 g; per service = 38 ± 1 g; 95% CL: 38–38 g) exhibited higher per-cover fat provision than Team Two (EFL Champ) (per day = 56 ± 1 g; 95% CL: 56–56 g; per service = 28 ± 1 g; 95% CL: 28–28 g) and Team Four (WSL) (per day = 49 ± 2 g; 95% CL: 48–49 g; per service = 24 ± 1 g; 95% CL: 24–24 g). Team Two exhibited higher per-cover fat provision than Team Four . 3.2 Greenhouse Gas Emissions (GHGE) 3.2.1 Greenhouse gas emissions per team Table 2 presents the GHGE of the four football teams, with detailed procurement and GHGE breakdowns provided in Supplementary Material 1. Team Three (EFL L1) demonstrated the greatest total GHGE (2,725 ± 54 kg·CO₂·eq; 95% CL: 2,717–2,734 kg·CO₂·eq), followed by Team One (EPL) (2,345 ± 42 kg·CO₂·eq; 95% CL: 2,338–2,352 kg·CO₂·eq), Team Two (EFL Champ) (2,231 ± 2 kg·CO₂·eq; 95% CL: 2,225–2,237 kg·CO₂·eq), and Team Four (WSL) (832 ± 18 kg·CO₂·eq; 95% CL: 829–835 kg·CO₂·eq). When expressed relative to service volume, Team Three (EFL L1) exhibited the highest GHGE per cover per day (7.17 ± 0.14 kg·CO₂·eq; 95% CL: 7.15–7.19 kg·CO₂·eq) and per cover per service (3.59 ± 0.07 kg·CO₂·eq; 95% CL: 3.57–3.60 kg·CO₂·eq). Mean GHGE was higher for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs. Team One (EPL) (per day = 3.91 ± 0.07 kg·CO₂·eq; 95% CL: 3.90–3.92 kg·CO₂·eq; per service = 1.95 ± 0.03 kg·CO₂·eq; 95% CL: 1.95–1.96 kg·CO₂·eq) demonstrated higher GHGE than Team Two (EFL Champ) (per day = 2.79 ± 0.05 kg·CO₂·eq; 95% CL: 2.78–2.80 kg·CO₂·eq; per service = 1.39 ± 0.02 kg·CO₂·eq; 95% CL: 1.39–1.40 kg·CO₂·eq) and Team Four (WSL) (per day = 1.73 ± 0.04 kg·CO₂·eq; 95% CL: 1.73–1.74 kg·CO₂·eq; per service = 0.87 ± 0.02 kg·CO₂·eq; 95% CL: 0.86–0.87 kg·CO₂·eq). Team Two demonstrated higher per-cover GHGE than Team Four . 3.2.2 Contribution of food categories to total greenhouse gas emissions Table 3 presents the contribution of each food category to the overall GHGE for each football team, with detailed category-level results provided in Supplementary Material 1. Across all teams, meat was the largest contributor to total GHGE (~38%), primarily driven by beef procurement (see Supplementary Material 1). On average, dairy was the second largest contributor (~16%), followed by fruit (~10%), beverages (~9%), and eggs (~7%). Substantial variation was observed between teams in the relative contribution of food categories. For example, in Team Three (EFL L1) , beverages represented the second largest contributor to total GHGE (22%), primarily due to bottled water purchases (2,000 × 500 mL bottles). In contrast, in Team Two (EFL Champ) , fruit was the second largest contributor (16%), largely attributable to apple juice purchases (48 × 2 L cartons). 3.3 Water Footprint 3.3.1 Water footprint per team Table 2 presents the total and relative water footprint of the four football teams, with detailed procurement data provided in Supplementary Material 1. Team Two (EFL Champ) had the greatest total water footprint (3,225 ± 63 kL·eq; 95% CL: 3,216–3,234 kL·eq), followed by Team One (EPL) (2,760 ± 49 kL·eq; 95% CL: 2,752–2,767 kL·eq), Team Three (EFL L1) (2,284 ± 40 kL·eq; 95% CL: 2,277–2,290 kL·eq), and Team Four (WSL) (1,111 ± 25 kL·eq; 95% CL: 1,107–1,116 kL·eq). When expressed relative to service volume, Team Three (EFL L1) demonstrated the highest water footprint per cover per day (6.01 ± 0.11 kL·eq; 95% CL: 5.99–6.03 kL·eq) and per cover per service (3.00 ± 0.05 kL·eq; 95% CL: 2.99–3.01 kL·eq). Mean water footprint values were higher for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs. Team One (EPL) (per day = 4.60 ± 0.08 kL·eq; 95% CL: 4.59–4.61 kL·eq; per service = 2.30 ± 0.04 kL·eq; 95% CL: 2.29–2.31 kL·eq) also demonstrated higher water footprint per cover than Team Two (EFL Champ) (per day = 4.03 ± 0.08 kL·eq; 95% CL: 4.02–4.04 kL·eq; per service = 2.02 ± 0.04 kL·eq; 95% CL: 2.01–2.02 kL·eq)) and Team Four (WSL) ( per day = 2.32 ± 0.05 kL·eq; 95% CL: 2.31–2.32 kL·eq; per service = 1.16 ± 0.03 kL·eq; 95% CL: 1.15–1.16 kL·eq). Team Two demonstrated higher per-cover water footprint than Team Four . 3.3.2 Contribution of food categories to total water footprint Table 3 presents the contribution of each food category to the overall water footprint for each football team, with detailed results provided in Supplementary Material 1. Across all teams, meat was the largest contributor to total water footprint (~36%), primarily driven by beef procurement (see Supplementary Material 1). On average, vegetables represented the second largest contributor (~18%), followed by dairy (~12%), grains and starches (~8%), and oils (~6%). Table One. Energy and macronutrient provision across the food services of four professional football teams. Data are presented as mean ± standard deviation [95% confidence limits] Football Team Energy Carbohydrate Protein Fat Overall (kcal) Per cover per day (kcal) Per cover per service (kcal) Overall (g) Per cover per day (g) Per cover per service (g) Overall (g) Per cover per day (g) Per cover per service (g) Overall (g) Per cover per day (g) Per cover per service (g) Team One (EPL) 1,069,587 ± 14,601 [1,067,281 – 1,071,983] 1,783 ± 24 [1,779 – 1,787] 891 ± 12 [889 – 893] 101,747 ± 1,310 [101,540 – 101,954] 170 ± 2 [169 – 170] 85 ± 1 [85 – 85] 62,655 ± 1,202 [62,465 – 62,845] 104 ± 2 [104 – 105] 52 ± 1 [52 – 52] 45,823 ± 1,332 [45,613 – 46,033] 76 ± 2 [76 – 77] 38 ± 1 [38 – 38] Team Two (EFL Champ) 1,097,459 ± 10,261 [1,096,040 – 1,098,878] 1,373 ± 13 [1,370 – 1,374] 686 ± 6 [685 – 687] 121,088 ± 1,342 [120,902 – 121,274] 151 ± 2 [151 – 152] 77 ± 1 [76 – 76] 53,038 ± 925 [52,910 – 53,166] 66 ± 1 [66 – 67] 33 ± 1 [33 – 33] 44,534 ± 766 [44,428 – 44,640] 56 ± 1 [56 – 56] 28 ± 1 [28 – 28] Team Three (EFL L1) 863,256 ± 10,319 [861,626 – 864,886] 2,272 ± 27 [2,267 – 2,276] 1,136 ± 14 [1,134 – 1,138] 86,805 ± 1,102 [86,631 – 86,979] 228 ± 3 [228 – 229] 114 ± 1 [114 – 114] 52,383 ± 1,017 [52,222 – 52,544] 138 ± 3 [137 – 138] 69 ± 1 [69 – 69] 34,066 ± 665 [33,961 – 34,171] 90 ± 2 [89 – 90] 45 ± 1 [45 – 45] Team Four (WSL) 46,4203 ± 9,434 [462,494 – 465,912] 967 ± 20 [964 – 971] 484 ± 10 [482 – 485] 40,035 ± 699 [39,908 – 40,162] 83 ± 2 [83 – 84] 41 ± 1 [42 – 42] 23,594 ± 507 [23,502 – 23,686] 49 ± 1 [49 – 49] 25 ± 1 [25 – 25] 23,286 ± 958 [23,112 – 23,460] 49 ± 2 [48 – 49] 24 ± 1 [24 – 24] Table 2. Greenhouse gas emissions (GHGE) and water footprint of each football team presented as total values, per cover per day, and per cover per food service. Data are presented as mean ± standard deviation [95% confidence limits] Football Team GHGE Water Footprint Overall (kg×co 2 ×eq) Per Cover per Day (kg×co 2 ×eq) Per Cover per Service (kg×co 2 ×eq) Overall (kL×eq) Per Cover per Day (kL×eq) Per Cover Per Service (kL×eq) Team One (EPL) 2,345 ± 42 [2,338 – 2,352] 3.91 ± 0.07 [3.90 – 3.92] 1.95 ± 0.03 [1.95 – 1.96] 2,760 + 49 [2,752 – 2,767] 4.60 + 0.08 [4.59 – 4.61] 2.30 ± 0.04 [2.29 – 2.31] Team Two (EFL Champ) 2,231 ± 2 [2,225 – 2,237] 2.79 ± 0.05 [2.78 – 2.80] 1.39 ± 0.02 [1.39 – 1.40] 3,225 ± 63 [3,216 – 3,234] 4.03 ± 0.08 [4.02 – 4.04] 2.02 ± 0.04 [2.01 – 2.02] Team Three (EFL L1) 2,725 ± 54 [2,717 – 2,734] 7.17 ± 0.14 [7.15 – 7.19] 3.59 ± 0.07 [3.57 – 3.60] 2,284 ± 40 [2,277 – 2,290] 6.01 ± 0.11 [5.99 – 6.03] 3.00 ± 0.05 [2.99 – 3.01] Team Four (WSL) 832 ± 17 [829 – 835] 1.73 ± 0.04 [1.73 – 1.74] 0.87 ± 0.02 [0.86 – 0.87] 1,111 ± 25 [1,107 – 1,116] 2.32 ± 0.05 [2.31 – 2.32] 1.16 ± 0.03 [1.15 – 1.16] Table 3. Contribution of different food and beverage groups to the total greenhouse gas emissions (GHGE) and water footprint for each football team. Data are presented as value (%) Food & Beverage Type Team One (EPL) Team Two (EFL Champ) Team Three (EFL L1) Team Four (WSL) Overall GHGE (kg×co 2 ×eq) Water Footprint (kL×eq) GHGE (kg×co 2 ×eq) Water Footprint (kL×eq) GHGE (kg×co 2 ×eq) Water Footprint (kL×eq) GHGE (kg×co 2 ×eq) Water Footprint (kL×eq) GHGE (kg×co 2 ×eq) Water Footprint (kL×eq) Meat 897 (38%) 1019 (37%) 918 (41%) 1041 (32%) 997 (37%) 946 (41%) 323 (39%) 369 (33%) 3135 (39%) 3375 (36%) Fish 98 (4%) 85 (3%) 83 (4%) 61 (2%) 96 (4%) 64 (3%) 17 (2%) 15 (1%) 293 (3%) 224 (2%) Dairy 332 (14%) 281 (10%) 273 (12%) 227 (7%) 498 (18%) 418 (18%) 152 (18%) 127 (11%) 1255 (16%) 1053 (12%) Eggs 225 (10%) 236 (9%) 47 (2%) 50 (2%) 142 (5%) 149 (7%) 99 (12%) 104 (9%) 512 (7%) 538 (7%) Legumes 19 (1%) 20 (1%) 11 (1%) 6 (0%) 0 (0%) 0 (0%) 5 (1%) 4 (0%) 35 (1%) 30 (0%) Nuts 7 (0%) 27 (1%) 3 (0%) 12 (0%) 0 (0%) 1 (0%) 1 (0%) 3 (0%) 10 (0%) 42 (0) Grains & Starches 97 (4%) 119 (4%) 95 (4%) 591 18%) 70 (3%) 84 (4%) 50 (6%) 65 (6%) 312 (4%) 859 (8%) Plant Milks 2 (0%) 10 (0%) 4 (0%) 8 (0%) 20 (1%) 64 (3%) 1 (0%) 5 (0%) 27 (0%) 86 (1%) Oils 49 (2%) 216 (8%) 30 (1%) 154 (5%) 6 (0%) 43 (2%) 33 (4%) 144 (13%) 118 (2%) 558 (7%) Vegetables 156 (7%) 476 (17%) 252 (11%) 742 (23%) 103 (4%) 295 (13%) 56 (7%) 212 (19%) 567 (7%) 1725 (18%) Fruit 270 (12%) 234 (9%) 375 (17%) 167 (5%) 144 (5%) 158 (7%) 38 (5%) 56 (5%) 827 (10%) 616 (6%) Herbs & Spices 6 (0%) 3 (0%) 3 (0%) 4 (0%) 2 (0%) 4 (0%) 6 (1%) 3 (0%) 18 (0%) 14 (0%) Funghi 2 (0%) 3 (0%) 0 (0%) 1 (0%) 2 (0%) 5 (0%) 1 (0%) 1 (0%) 5 (0%) 10 (0%) Condiments 19 (1%) 21 (1%) 108 (5%) 106 (3%) 34 (1%) 12 (1%) 14 (2%) 5 (0%) 175 (2%) 144 (1%) Beverages 166 (7%) 9 (0%) 28 (1%) 57 (2%) 614 (23%) 41 (2%) 37 (5%) 0 (0%) 846 (9%) 107 (1%) 4.0 Discussion Food provision in professional football represents a substantial yet largely unrecognised contributor to environmental impact, making its sustainability a critical consideration as clubs strive to align elite performance nutrition with climate and resource goals. Therefore, this study aimed to (1) quantify the GHGEs and (2) water footprint of food services across four professional football clubs in England, and to (3) determine the contribution of individual food categories to each environmental metric. Large variations were observed across teams and leagues in both total and per-person values. Team Three (EFL L1) consistently demonstrated the highest relative energy, GHGE, and water footprint per cover, followed by Team One (EPL), Team Two (EFL Champ), and Team Four (WSL). Across all clubs, meat - particularly beef - was the dominant contributor to both GHGE and water footprint, followed by dairy and fruit for GHGE, and vegetables and dairy for water footprint. Collectively, these findings highlight the urgent need for football clubs to evolve their food service models - moving beyond traditional performance-focused menus toward sustainable practices that reduce emissions, conserve water, and set a global benchmark for sustainable elite sport nutrition. To the authors’ knowledge, this is the first study to quantify the environmental footprint of food service provision within professional football - or any professional sports organisation. Marked variation in GHGE was observed across the four teams with the EFL L1 club (Team Three) producing the highest emissions, followed by the EPL (Team One), EFL Championship (Team Two), and WSL (Team Four). These differences likely reflect variation in menu composition, size, service frequency, and overall covers, with higher energy and protein provision - particularly from red and processed meats - substantially contributing to total emissions. Notably, the third-tier men’s team demonstrated the greatest relative GHGE and water footprint despite operating with fewer overall resources than the EPL club. 53 This team’s extensive investment in performance nutrition staffing (full-time accredited practitioners, PhD researchers, and four full-time chefs) and comprehensive catering infrastructure likely contributed to its larger footprint. These findings highlight that environmental impact in professional football food services is shaped less by league status and more by the scale, sophistication, and investment of each club’s nutrition and catering provision - factors expected to amplify as resource allocation to performance food systems continues to expand across both men’s and women’s football. 39 Direct comparison with other institutional food services is limited by a lack of published data; however, estimates from German hospital and nursing home food services, calculated over a six-month period and standardised to a seven-day equivalent, indicate substantially greater total GHGE than those observed in all football clubs investigated in this study (Hospital 1 = 30,268 kg·CO₂·eq; Hospital 2 = 86,218 kg·CO₂·eq; Nursing Home 1 = 9,887 kg·CO₂·eq; Nursing Home 2 = 8,781 kg·CO₂·eq; Nursing Home 3 = 5,856 kg·CO₂·eq; men’s professional football: 2434 ± 259 kg·CO₂·eq; women’s professional football: 832 kg·CO₂·eq). 54 These institutions served markedly higher daily cover volumes (~ 323–2,700 covers per day; vs 141 ± 45 in professional male and female football), which likely accounts for much of the disparity in overall emissions. When expressed per person, German hospitals and nursing homes averaged 1.96 kg·CO₂·eq, lower than the EFL League One club but higher than the other three football teams. Collectively, these findings suggest that while professional football team performance catering services operate at a smaller scale, the per-meal emissions associated with fuelling elite players - driven by elevated energy, protein, and animal-source food requirements - can rival those of large institutional catering operations, highlighting the need for more sustainable menu design within elite sport. While no daily dietary GHGE target currently exists to ensure emissions remain within planetary boundaries, it is widely recognised that diets containing less meat produce lower GHGE. 27 Mean daily emissions are estimated at 2.16 kg·CO₂·eq·d⁻¹ for vegan diets, 3.33 kg·CO₂·eq·d⁻¹ for vegetarian diets, 3.81 kg·CO₂·eq·d⁻¹ for fish-eater diets, and 4.21 to 7.28 kg·CO₂·eq·d⁻¹ for low to high meat eater diets ( 100 g·d⁻¹ meat, respectively). The present data show that the WSL (Team Four) food service produced a GHGE per cover per day (1.73 kg·CO₂·eq) that sat below the mean value associated with a vegan diet, whereas the EFL Champ (Team Two) emissions were between those of vegan and vegetarian diets, the EPL (Team One) was comparable to a pescetarian diet, and the EFL L1 (Team Three) approximated a high meat-eater profile. All food services provided only breakfast and lunch, with players and staff sourcing additional meals and beverages independently. Consequently, while the club-based food services of the EPL, EFL Champ, and WSL teams align with diets typically associated with low meat consumption, total daily GHGE at the individual level are likely considerably higher, depending on off-site dietary choices. The comparatively low GHGE observed in the WSL (Team Four) likely reflects its insufficient daily energy (967 ± 19 kcal), carbohydrate (83 ± 1.46 g), protein (49 ± 1 g), and fat (48 ± 2 g) provision relative to current elite football nutrition guidelines and female player requirements (2,600-2,900 kcal.d -1 ). 39, 47,48,51 Increasing overall energy provision to meet performance requirements would therefore be expected to raise total GHGE, the magnitude of which would depend on the specific foods and beverages selected. The use of a life cycle assessment enabled detailed quantification of each food group’s contribution to GHGE, revealing that meat - predominantly beef - was the largest contributor across all teams (~ 38%) (see Supplementary Material 1). This aligns with previous findings from German hospital and nursing home food services, where meat accounted for 38% of total emissions, 54 and with global data identifying red meats such as beef (28.73 kg·CO₂·eq·kg⁻¹) and lamb (27.91 kg·CO₂·eq·kg⁻¹) as having the greatest warming potential. 26 Consistent with this, the EFL L1 (Team Three) had the highest GHGE and procured 24 kg of beef, while the WSL (Team Four) - with the lowest emissions - purchased only 10.5 kg (see Supplementary Material 1). Dairy products represented the second largest contributor (~ 16%), primarily from cow’s milk and cheese. In contrast, plant-based milks such as soy and almond generate substantially lower emissions, 65 suggesting an opportunity for substitution in professional food services. Across all four teams, 364 L of cow’s milk contributed 557 kg·CO₂·eq, compared with 22 L of plant-based milks, producing 21 kg·CO₂·eq (see Supplementary Material 1). When standardised per litre, cow’s milk generated approximately 1.6 times greater emissions than plant-based alternatives. These data emphasise that targeted ingredient substitutions - particularly reducing beef and dairy use - could meaningfully lower the environmental footprint of elite football food provision without necessarily compromising performance nutrition quality. The study also quantified the water footprint of each football team’s food service, revealing substantial variation across clubs. The EFL Champ (Team Two) demonstrated the highest overall water footprint (3,225 ± 63 kL·eq), while the EFL L1 (Team Three) had the greatest when expressed per cover per day (6.01 ± 0.11 kL·eq). Across all teams, meat - particularly beef - was the dominant contributor (~ 36%), followed by vegetables (~ 18%) and dairy (~ 12%), reflecting global trends in agricultural water use. These findings highlight that higher energy and protein provision, especially from animal sources, markedly increases water demand, underscoring the tension between fuelling performance and maintaining environmental responsibility. Practical mitigation strategies include shifting menus toward lower blue-water foods (e.g., legumes, oats, and plant-based milks) 28 and leveraging technological or agricultural innovations such as rainwater harvesting, 66 deficit or drip irrigation, 67,68 and organic farming systems that use less blue water. 69 For elite sport organisations, integrating these insights into procurement and catering policy offers a tangible route to reduce environmental impact while preserving nutritional quality and competitive standards. 4.1 Practical applications While elite footballers’ energy and macronutrient demands exceed those proposed in population-level sustainable diet models such as the EAT-Lancet reference diet, 41 the present findings highlight actionable opportunities to align performance nutrition with environmental responsibility. Across clubs, ruminant meat procurement (103.9 kg; 2425 kg·CO₂·eq) substantially outweighed legume purchases (50.4 kg; 100.6 kg·CO₂·eq), underscoring the disproportionate contribution of animal-derived protein to total GHGE. Team Three (EFL L1) provided the highest protein intake per cover per day (138 ± 3 g), exceeding the daily requirement for an 80 kg footballer (1.6 g·kg⁻¹ = 128 g) within just two meals. Even halving this provision would still meet per-meal targets of ~ 0.4 g·kg⁻¹ (~ 32 g) to optimise muscle protein synthesis, 70,71 lowering environmental impact without compromising adaptation. Given the comparable amino acid completeness achievable through complementary plant proteins (e.g., legumes and cereals) and enhanced digestibility through processing, 72,73 partial replacement of ruminant meats with plant or non-ruminant options could meaningfully reduce GHGE. Team Three’s food service also recorded a 22% GHGE contribution from beverages, predominantly due to 2000 bottles of 500 mL water (490 kg·CO₂·eq). Considering that the environmental impact of bottled water is 1,400–3,500 times greater than tap water, 74 installing filtered water systems would represent a simple, high-yield intervention to reduce emissions and associated health risks. 75 Collectively, these findings emphasise that both menu composition and operational practices - from protein sourcing to packaging - represent key leverage points for reducing environmental impact in elite football nutrition. 4.2 Strengths, weaknesses, and future directions This study represents the first known application of life cycle assessment methodology to quantify the environmental footprint of food service provision in professional football. Using the Nutritics Foodprint Carbon Footprint Functionality, 59 a reference-standard life cycle assessment framework, 25 the study adopted a rigorous bottom-up approach analysing 289 individual foods and beverages across 52 categories and four professional teams spanning the EPL, EFL Champ, EFL L1, and WSL. The inclusion of both GHGE and water footprint metrics provides a comprehensive evaluation of environmental impact, offering an evidence-based foundation for future sustainability assessments in elite sport catering and performance nutrition contexts. However, several limitations warrant consideration. The absence of procurement data identifying organic versus conventionally farmed produce limited assessment of agricultural production effects on total emissions and water use. Organic produce has been shown to be 43% lower in GHGE per ‘land unit’ in comparison to conventionally farmed produce. 76 The GHGE and water footprint may also be dependent on the life cycle assessment used. For example, the water footprint of bottled water in the EFL L1 team was 1 kL·eq, equating to the quantity of water, not accounting for the water footprint of the plastic, typically polyethylene terephalate, and whether this is recycled or not. 77 Similarly, our data shows plant-based milks to have approximately half the water footprint than cow’s milk when standardised for weight (plant based milks = 0.31 kL·eq·kg vs 0.63 kL·eq·kg), despite contemporary research demonstrating comparable water footprints. 65 Similarly, reliance on database-derived life cycle data rather than supplier-specific information introduces potential variation related to sourcing practices, transport distance, and regional production systems. These complexities highlight a wider issue within life cycle methodology that the data is likely influenced by the quality of data, and the life cycle assessment database utilised. The study also assessed club food service provision over a defined seven-day period, rather than capturing full dietary intake, thereby underestimating the true GHGE and water footprint associated with players’ total consumption. Match day food service provision was not included (e.g. hotels, travel, stadia) or supplementation (e.g. energy gels, recovery powders) which may increase GHGE further. Moreover, variations in kitchen staffing, service frequency, and financial investment across clubs restrict generalisability to other professional or amateur sport settings. Future research should build upon these findings by conducting integrated dietary-level assessments that capture all meals consumed within and beyond the club environment. Incorporating direct procurement data, supplier-level sustainability credentials, and plate waste analysis will improve accuracy and ecological validity. Experimental interventions - such as menu redesigns substituting ruminant meats for plant-based or non-ruminant proteins, or chef and nutritionist education in sustainable menu engineering - could quantify the performance and environmental trade-offs of different food strategies. Furthermore, collaboration between sports nutritionists, environmental scientists, chefs, and club operations personnel is essential to embed sustainability principles into daily food service practice. Establishing benchmarks and reporting frameworks through governing bodies may ultimately enable football organisations to align nutritional excellence with planetary health objectives, advancing both performance and environmental leadership in elite sport. 5.0 Conclusion This study provides the first evidence that professional male and female football food services vary markedly in their environmental impact, with substantial differences in GHGEs and water footprints across teams. The findings reveal that ruminant meat and dairy were the dominant contributors to total emissions, while clubs with higher energy and protein provision - particularly from beef - produced the greatest GHGE and water footprint. These results highlight the potential for important environmental gains through practical menu-based interventions, such as reducing ruminant meat procurement, increasing plant-based protein inclusion, and replacing bottled water and dairy products with filtered systems and plant-based milks. Although football’s catering service operations appear smaller than those in healthcare and other institutional sectors, the sport’s global influence offers a unique opportunity to integrate sustainability into mainstream culture. Within the ‘Sport for Development’ agenda and the sustainability frameworks of FIFA, UEFA, and the EPL, 30–33 incorporating evidence-based sport nutrition strategies could align performance-focused catering with broader climate and resource goals, positioning football as a visible leader in sustainable sport nutrition. Abbreviations GHGE – Greenhouse gas emissions EPL – English Premier League EFL Champ – English Football League Championship EFL L1 – English Football League One WSL – Women’s Super League FIFA - Fédération Internationale de Football Association UEFA - Union of European Football Associations IPCC - Intergovernmental Panel on Climate Change CLs – Confidence Limits Declarations Ethics Approval and Consent to Participate Ethical approval for the study was obtained through Leeds Beckett University ethics committee (reference number: 139424). Consent for Publication All football clubs consented for their food procurement data to be published in this study Competing Interests The authors declare that they have no competing interests. Funding No funding was required for this study Author Contribution O Turner, N Costello, S Chantler, and N Mitchell designed the study. A Jenkinson, H Louis, C Oakley and S Parker provided food service procurement data for each respective football club which was collated by N Costello. O Turner analysed the data and drafted the manuscript. N Costello, S Chantler, and N Mitchell assisted with revising the manuscript. All authors read and approved the final manuscript. Data Availability All data which supports the conclusions made within the manuscript are provided in the main text. Supplementary materials such as the classifications and the categorisation of each individual food and beverage item are provided in Supplementary Material 1 for greater interpretation. References Allen MR, Dube OP, Solecki W, Aragón-Durand F, Cramer W, Humphreys S, et al. Framing and Context. In: Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D, Skea J, Shukla PR, et al., editors. Global Warming of 1.5°C. An IPCC Special Report. Cambridge (UK): Cambridge University Press; 2018. p. 49–92. doi:10.1017/9781009157940.003. Armstrong McKay DI, Staal A, Abrams JF, et al. Exceeding 1.5°C global warming could trigger multiple climate tipping points. Science. 2022;377(6611):eabn7950. doi:10.1126/science.abn7950. Intergovernmental Panel on Climate Change. IPCC [Internet]. Available from: https://www.ipcc.ch United Nations Framework Convention on Climate Change. The Paris Agreement [Internet]. Available from: https://unfccc.int/process-and-meetings/the-paris-agreement 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. doi:10.1038/s43016-021-00225-9. Lal R. Farming systems to return land for nature: It's all about soil health and re-carbonization of the terrestrial biosphere. Farming Syst. 2023;1(1):100002. doi:10.1016/j.farsys.2023.100002. Ivanovich CC, Sun T, Gordon DR, Ocko IB. Future warming from global food consumption. Nat Clim Chang. 2023;13(3):297–302. doi:10.1038/s41558-023-01605-8. 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. Ecol Soc. 2017;22(4). Available from: https://www.jstor.org/stable/26798991 Steffen W, Richardson K, Rockström J, Cornell SE, Fetzer I, Bennett EM, et al. Planetary boundaries: Guiding human development on a changing planet. Science. 2015;347(6223):1259855. doi:10.1126/science.1259855. United Nations. Global Issues: Population [Internet]. Available from: https://www.un.org/en/global-issues/population Springmann M, Clark M, Mason-D’Croz D, Wiebe K, Bodirsky BL, Lassaletta L, et al. Options for keeping the food system within environmental limits. Nature. 2018;562(7728):519–25. doi:10.1038/s41586-018-0594-0. Clark MA, Domingo NGG, Colgan K, Thakrar SK, Tilman D, Lynch J, et al. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science. 2020;370(6517):705–8. doi:10.1126/science.aba7357. Ringler C, Agbonlahor M, Barron J, Baye K, Meenakshi JV, Mekonnen DK, Uhlenbrook S. The role of water in transforming food systems. Glob Food Sec. 2022;33:100639. doi:10.1016/j.gfs.2022.100639. Ravinandrasana VP, Franzke CLE. The first emergence of unprecedented global water scarcity in the Anthropocene. Nat Commun. 2025;16:8281. doi:10.1038/s41467-025-63784-6. Rai SK, Kookana R, Drechsel P, Jamwal P, Vanderzalm J. Urbanisation and emerging economies: Issues and potential solutions for water and food security. Sci Total Environ. 2020;732:139057. Kummu M, Guillaume J, de Moel H, et al. The world’s road to water scarcity: shortage and stress in the 20th century and pathways towards sustainability. Sci Rep. 2016;6:38495. doi:10.1038/srep38495 Baggio G, Qadir M, Smakhtin V. Freshwater availability status across countries for human and ecosystem needs. Sci Total Environ. 2021;792:148230. doi:10.1016/j.scitotenv.2021.148230 Hestir EL, Brando VE, Bresciani M, Giardino C, Matta E, Villa P, Dekker AG. Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission. Remote Sens Environ. 2015;167:181–95. Available from: https://doi.org/10.1016/j.rse.2015.05.023 Khatiwala S, Tanhua T, Mikaloff Fletcher S, Gerber M, Doney SC, Graven HD, et al. Global ocean storage of anthropogenic carbon. Biogeosciences. 2013;10:2169–91. doi:10.5194/bg-10-2169-2013. UNICEF. Water scarcity [Internet]. Available from: https://www.unicef.org/wash/water-scarcity Richardson K, Steffen W, Lucht W, et al. Earth beyond six of nine planetary boundaries. Sci Adv. 2023;9:eadh2458. Porkka M, Virkki V, Wang-Erlandsson L, et al. Notable shifts beyond pre-industrial streamflow and soil moisture conditions transgress the planetary boundary for freshwater change. Nat Water. 2024;2:262–73. Ingrao C, Strippoli R, Lagioia G, Huisingh D. Water scarcity in agriculture: An overview of causes, impacts and approaches for reducing the risks. Heliyon. 2023;9(8):e18507. doi:10.1016/j.heliyon.2023.e18507. 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. Rebitzer G, Ekvall T, Frischknecht R, et al. Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications. Int J Life Cycle Assess. 2004;10(1):1–11. Clune S, Crossin E, Verghese K. Systematic review of greenhouse gas emissions for different fresh food categories. J Clean Prod. 2017;140:766–83. doi:10.1016/j.jclepro.2016.04.082. Scarborough P, Clark M, Cobiac L, et al. Vegans, vegetarians, fish-eaters and meat-eaters in the UK show discrepant environmental impacts. Nat Food. 2023;4:565–74. doi:10.1038/s43016-023-00795-w. Poore J, Nemecek T. Reducing food's environmental impacts through producers and consumers. Science. 2018;360(6392):987–92. doi:10.1126/science.aaq0216. Ramankutty N. Both technological innovations and cultural change are key to a sustainability transition. PLoS Biol. 2023;21(9):e3002298. doi:10.1371/journal.pbio.3002298. Peachey JW, Schulenkorf N, Spaaij R. Sport for Social Change: Bridging the Theory–Practice Divide. J Sport Manag. 2019;33(5):361–5. Fédération Internationale de Football Association. FIFA Climate Strategy [Internet]. Available from: https://digitalhub.fifa.com/m/a6e93d3f1e33b09/original/FIFA-Climate-Strategy.pdf Union of European Football Associations. UEFA Football Sustainability Strategy [Internet]. Available from: https://editorial.uefa.com/resources/0270-13f888ffa3e5-931c597968cb-1000/uefa_football_sustainability_strategy.pdf Premier League. Environmental Sustainability Strategy [Internet]. Available from: https://resources.premierleague.pulselive.com/premierleague/document/2025/03/14/76d43137-a03a-44b7-a339-4c719f31446f/Premier-League-Environmental-Sustainability-Strategy.pdf United Nations Framework Convention on Climate Change. Sports for Climate Action [Internet]. Available from: https://unfccc.int/climate-action/sectoral-engagement/sports-for-climate-action United Nations Framework Convention on Climate Change. Participants in the Sports for Climate Action Framework [Internet]. Available from: https://unfccc.int/climate-action/sectoral-engagement/sports-for-climate-action/participants-in-the-sports-for-climate-action-framework#Sports-for-Climate-Action-signatories Premier League. News: Premier League joins Sports for Climate Action [Internet]. Available from: https://www.premierleague.com/en/news/4266971 Meyer N, Reguant-Closa A. “Eat as if you could save the planet and win!” sustainability integration into nutrition for exercise and sport. Nutrients. 2017;9(4):412. doi:10.3390/nu9040412. Meyer NL, Reguant-Closa A, Nemecek T. Sustainable diets for athletes. Curr Nutr Rep. 2020;9(3):147–62. doi:10.1007/s13668-020-00318-0. Collins J, Maughan RJ, Gleeson M, Bilsborough J, Jeukendrup A, Morton JP, et al. UEFA expert group statement on nutrition in elite football: current evidence to inform practical recommendations and guide future research. Br J Sports Med. 2021;55(8):416. doi:10.1136/bjsports-2019-101961. Schulenkorf N, Sherry E, Rowe K. Sport for development: An integrated literature review. J Sport Manag. 2016;30(1):22–39. doi:10.1123/jsm.2014-0263. Rockström J, Willett W, Loken B, Springmann M, Lang T, Vermeulen S, et al. The EAT–Lancet Commission on healthy, sustainable, and just food systems. Lancet. 2025;406(10512):1625–1700. Ebine N, Rafamantanantsoa HH, Nayuki Y, et al. Measurement of total energy expenditure by the doubly labelled water method in professional soccer players. J Sports Sci. 2002;20(5):391–7. doi:10.1080/026404102317366645. Anderson L, Orme P, Naughton RJ, et al. Energy Intake and Expenditure of Professional Soccer Players of the English Premier League: Evidence of Carbohydrate Periodization. Int J Sport Nutr Exerc Metab. 2017;27(3):228–38. doi:10.1123/ijsnem.2016-0259. Anderson L, Close GL, Morgans R, et al. Assessment of Energy Expenditure of a Professional Goalkeeper From the English Premier League Using the Doubly Labeled Water Method. Int J Sports Physiol Perform. 2019;14(5):681–4. doi:10.1123/ijspp.2018-0520. Brinkmans NYJ, Iedema N, Plasqui G, Wouters L, Saris WHM, van Loon LJC, van Dijk JW. Energy expenditure and dietary intake in professional football players in the Dutch Premier League: Implications for nutritional counselling. J Sports Sci. 2019;37(24):2759–67. doi:10.1080/02640414.2019.1576256. Hannon MP, Parker LJF, Carney DJ, et al. Energy Requirements of Male Academy Soccer Players from the English Premier League. Med Sci Sports Exerc. 2021;53(1):200–10. doi:10.1249/MSS.0000000000002443. Morehen JC, Rosimus C, Cavanagh BP, et al. Energy Expenditure of Female International Standard Soccer Players: A Doubly Labeled Water Investigation. Med Sci Sports Exerc. 2022;54(5):769–79. doi:10.1249/MSS.0000000000002850. Dasa MS, Friborg O, Kristoffersen M, Pettersen G, Plasqui G, Sundgot-Borgen JK, et al. Energy expenditure, dietary intake and energy availability in female professional football players. BMJ Open Sport Exerc Med. 2023;9:e001553. doi:10.1136/bmjsem-2023-001553. Stables RG, Hannon MP, Jacob AD, Topping O, Costello NB, Boddy LM, et al. Daily energy requirements of male academy soccer players are greater than age-matched non-academy soccer players: A doubly labelled water investigation. J Sports Sci. 2023;41(12):1218–30. doi:10.1080/02640414.2023.2263707. Brinkmans N, Plasqui G, van Loon L, van Dijk JW. Energy expenditure and dietary intake in professional female football players in the Dutch Women's League: Implications for nutritional counselling. J Sports Sci. 2024;42(4):313–22. doi:10.1080/02640414.2024.2329850. McHaffie SJ, Langan-Evans C, Strauss JA, Areta JL, Rosimus C, Evans M, et al. Energy expenditure, intake and availability in female soccer players via doubly labelled water: Are we misrepresenting low energy availability? Exp Physiol. 2024 Aug 15. doi:10.1113/EP091589. Foo WL, Hambly C, Tester E, et al. Energy Expenditure of Male Soccer Players from an English Premier League Team Does Not Differ Between One-Game and Two-Game Per Week Microcycles. Med Sci Sports Exerc. 2025 Sep 2. doi:10.1249/MSS.0000000000003850. Arrieta-Aspilcueta AG, Bentley MRN, Backhouse SH, et al. The role of the chef in professional football: a survey of current practice in the English Premier and Football Leagues. Perform Nutr. 2025;1:3. doi:10.1186/s44410-025-00004-8. Pörtner LM, Schlenger L, Gabrysch S, Lambrecht NJ. Dietary quality and environmental footprint of health-care foodservice: a quantitative analysis using dietary indices and lifecycle assessment data. Lancet Planet Health. 2025;9(7):101274. doi:10.1016/j.lanplh.2025.05.004. Khanna M, Daddi T, Merlo F, et al. An Assessment on the Carbon Footprint of a Football Club—an Action Research from Theory to Practice. Circ Econ Sust. 2024;4:1587–612. doi:10.1007/s43615-024-00350-0. Pereira RPT, Filimonau V, Ribeiro GM. Score a goal for climate: Assessing the carbon footprint of travel patterns of the English Premier League clubs. J Clean Prod. 2019;227:167–77. Available from: https://doi.org/10.1016/j.jclepro.2019.04.138 Dosumu A, Colbeck I, Bragg R. Greenhouse gas emissions as a result of spectators travelling to football in England. Sci Rep. 2017;7:6986. doi:10.1038/s41598-017-06141-y. Nutritics. Homepage [Internet]. Available from: https://www.nutritics.com/en/ Nutritics. Foodprint Carbon Footprint Calculator [Internet]. Available from: https://www.nutritics.com/en/product/foodprint-carbon-footprint-calculator/ Scheelbeek P, Green R, Papier K, et al. Health impacts and environmental footprints of diets that meet the Eatwell Guide recommendations: analyses of multiple UK studies. BMJ Open. 2020;10(8):e037554. doi:10.1136/bmjopen-2020-037554. Agribalyse. Documentation [Internet]. Available from: https://doc.agribalyse.fr/documentation-en Petersson T, Secondi L, Magnani A, et al. A multilevel carbon and water footprint dataset of food commodities. Sci Data. 2021;8:127. doi:10.1038/s41597-021-00909-8. Hestia Earth [Internet]. Available from: https://www.hestia.earth Wall CJ, Pearce J. Greenhouse gas emissions of school lunches provided for children attending school nurseries: A cross-sectional study. J Hum Nutr Diet. 2024;37(5):1288–95. doi:10.1111/jhn.13345. Khanpit V, Viswanathan S, Hinrichsen O. Environmental impact of animal milk vs plant-based milk: Critical review. J Clean Prod. 2024;449:141703. doi:10.1016/j.jclepro.2024.141703. Rodrigues de Sá Silva AC, Bimbato AM, Balestieri JAP, Vilanova MRN. Exploring environmental, economic and social aspects of rainwater harvesting systems: A review. Sustain Cities Soc. 2022;76:103475. doi:10.1016/j.scs.2021.103475. Geerts S, Raes D. Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agric Water Manag. 2009;96:1275–84. Vishwakarma DK, Kumar R, Tomar AS, Kuriqi A. Eco-hydrological modeling of soil wetting pattern dimensions under drip irrigation systems. Heliyon. 2023;9(7):e18078. doi:10.1016/j.heliyon.2023.e18078. Wheeler SA, Bjornlund H, Zuo A. Watering the farm: Comparing organic and conventional irrigation water use in the Murray–Darling Basin, Australia. Ecol Econ. 2015;112:78–85. doi:10.1016/j.ecolecon.2015.02.019. Schoenfeld BJ, Aragon AA. How much protein can the body use in a single meal for muscle-building? Implications for daily protein distribution. J Int Soc Sports Nutr. 2018;15:10. doi:10.1186/s12970-018-0215-1. Macnaughton LS, Wardle SL, Witard OC, et al. The response of muscle protein synthesis following whole-body resistance exercise is greater following 40 g than 20 g of ingested whey protein. Physiol Rep. 2016;4(15):e12893. doi:10.14814/phy2.12893. Nichele S, Phillips SM, Boaventura BCB. Plant-based food patterns to stimulate muscle protein synthesis and support muscle mass in humans: a narrative review. Appl Physiol Nutr Metab. 2022;47(7):700–10. doi:10.1139/apnm-2021-0806. Sá A, Moreno Y, Carciofi B. Food processing for the improvement of plant proteins digestibility. Crit Rev Food Sci Nutr. 2020;60(20):3367–86. doi:10.1080/10408398.2019.1688249. Villanueva CM, Garfí M, Milà C, Olmos S, Ferrer I, Tonne C. Health and environmental impacts of drinking water choices in Barcelona, Spain: A modelling study. Sci Total Environ. 2021;795:148884. doi:10.1016/j.scitotenv.2021.148884. Raimann JG, Boaheng JM, Narh P, et al. Public health benefits of water purification using recycled hemodialyzers in developing countries. Sci Rep. 2020;10:11101. doi:10.1038/s41598-020-68408-1. Chiriacò MV, Castaldi S, Valentini R. Determining organic versus conventional food emissions to foster the transition to sustainable food systems and diets: Insights from a systematic review. J Clean Prod. 2022;380(Pt 2):134937. Available from: https://doi.org/10.1016/j.jclepro.2022.134937 Silva B, Costa I, Santana P, Zacarias ME, Machado B, Silva P, Carvalho S, Faria F, Basto-Silva C. Environmental performance of different water bottles with different compositions: A cradle to gate approach. Cleaner Prod Lett. 2024;6:100061. Available from: https://doi.org/10.1016/j.clpl.2024.100061 Additional Declarations No competing interests reported. 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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-8924973","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606440549,"identity":"20e026b8-d5a9-47dd-9275-ca20988bc113","order_by":0,"name":"Ollie Turner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYDACduaGAwwVDDJ8zCjCBgw8OLUwMwK1nGHgYWNmYGyAigEZBgZ4tTAwtgG1MKBoAVqDC/AzMzYe+DjPhoeNnfn4Y949d+TMZ/eYP64o+CPDL93A+OFjDoYWyWbGhoMzt6UBHcaW2Mzz7JmxzJ0zho1ngA6TnHOAWXLmNgwtBocZGw7zbjsM1MJj2Mxz4HDiDIkcw8YGoBaDGwlszLw4tPydA9LC/xGkpZ44LUAEsoURpCVBgpAWsF96joH9YjhzzoHDhjMk0gpnNhgY80jOSGzG5hd+9ubDH37U2Mjx8x9+8OHNgcPyEhLJGz42/JGz55dIPvjhI6YWvAAeUaNgFIyCUTAKSAQA3gJlBQ6KM34AAAAASUVORK5CYII=","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":true,"prefix":"","firstName":"Ollie","middleName":"","lastName":"Turner","suffix":""},{"id":606440550,"identity":"6dc2eb0f-e985-40e2-8ea1-68b4f3e530fc","order_by":1,"name":"Nigel Mitchell","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Nigel","middleName":"","lastName":"Mitchell","suffix":""},{"id":606440551,"identity":"c558343c-da13-4969-aa32-2ddcd099dece","order_by":2,"name":"Andrew Jenkinson","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Jenkinson","suffix":""},{"id":606440552,"identity":"808b73f2-2b2f-4ab2-b158-79e1f3689cfb","order_by":3,"name":"Hans Louis","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Hans","middleName":"","lastName":"Louis","suffix":""},{"id":606440553,"identity":"a2b28ffc-4902-42b3-8eab-6e016491af82","order_by":4,"name":"Colin Oakley","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Colin","middleName":"","lastName":"Oakley","suffix":""},{"id":606440554,"identity":"93332a1a-b5a5-46ab-8a40-7600da14ef85","order_by":5,"name":"Samantha Parker","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"","lastName":"Parker","suffix":""},{"id":606440555,"identity":"a7ca2804-7258-4b72-bb46-79f5d1d651ae","order_by":6,"name":"Sarah Chantler","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Chantler","suffix":""},{"id":606440556,"identity":"a2c4a264-abe4-4a1b-82c3-3ab2b504b5a3","order_by":7,"name":"Nessan Costello","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Nessan","middleName":"","lastName":"Costello","suffix":""}],"badges":[],"createdAt":"2026-02-20 10:58:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8924973/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8924973/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104835356,"identity":"d32134c9-f18d-4d83-830f-51ecacd4c9ca","added_by":"auto","created_at":"2026-03-17 17:44:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1952009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8924973/v1/9df2e336-e420-4c4f-883b-d43357fd7372.pdf"},{"id":104746944,"identity":"965b9bbc-faa9-4c37-8919-6254ef6e0923","added_by":"auto","created_at":"2026-03-16 18:02:04","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":272901,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8924973/v1/9e1d56e43f12ae2eadb05c2c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"What’s on the Menu? Quantifying the Greenhouse Gas Emissions and Water Footprint of the Food Provision within Elite Male \u0026 Female Football Teams","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eAnthropogenic greenhouse gas emissions (GHGE) are the primary drivers of contemporary climate change, with human-induced global warming estimated to have reached 1.1\u0026deg;C above pre-industrial levels in 2017.\u003csup\u003e1\u003c/sup\u003e Without substantial mitigation, current GHGE trajectories project that human-induced global warming will exceed 1.5\u0026deg;C above pre-industrial levels by the early 2030s, leading to severe environmental, economic, and health related consequences.\u003csup\u003e2\u003c/sup\u003e Recognising the urgency of this challenge, the United Nations has mobilised a global response through frameworks such as the Intergovernmental Panel on Climate Change (IPCC) and Paris Agreement,\u003csup\u003e3,4\u003c/sup\u003e which aim to stabilise human-induced global warming well below 2.0\u0026deg;C, and pursue efforts to limit human-induced global warming to below 1.5\u0026deg;C preindustrial levels by the end of the century.\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe global food system accounts for approximately 34% of anthropogenic GHGE,\u003csup\u003e5\u003c/sup\u003e with agricultural activities alone occupying nearly 40% of the Earth\u0026rsquo;s terrestrial surface.\u003csup\u003e6\u003c/sup\u003e Consequently, the global food system substantially contributes to climate change,\u003csup\u003e7\u003c/sup\u003e and is recognised as a major driver in the transgression of several environmental planetary boundaries,\u003csup\u003e8\u003c/sup\u003e thresholds established to maintain a stable Earth system and a safe operating space for humanity.\u003csup\u003e9\u003c/sup\u003e As the global population is projected to rise from 7.7\u0026nbsp;billion to between 9.4 and 10.2\u0026nbsp;billion by 2050,\u003csup\u003e10\u003c/sup\u003e the pressure on food systems and natural resources is expected to intensity.\u003csup\u003e11\u003c/sup\u003e Modelling studies suggest that even if all non-food related emissions were held at net zero from 2020 to 2100, global food system trends alone could result in global warming exceeding 1.5\u0026deg;C above pre-industrial levels by 2051 to 2063 and surpassing 2.0\u0026deg;C within the next 80 years.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn addition to its role in climate change, the global food system exerts substantial stress on freshwater resources.\u003csup\u003e13\u003c/sup\u003e human-induced global warming,\u003csup\u003e14\u003c/sup\u003e population growth,\u003csup\u003e10\u003c/sup\u003e and rapid urbanisation, projected to increase urban populations from 5.0\u0026nbsp;billion to 6.3\u0026nbsp;billion by 2050,\u003csup\u003e15\u003c/sup\u003e are compounding drivers of freshwater scarcity.\u003csup\u003e16\u003c/sup\u003e Freshwater supports essential ecosystem processes such as drinking water provision,\u003csup\u003e17\u003c/sup\u003e ecosystem functioning,\u003csup\u003e18\u003c/sup\u003e and carbon sequestration via aquatic systems.\u003csup\u003e19\u003c/sup\u003e Currently, an estimated four billion people experience severe water scarcity for at least one month annually,\u003csup\u003e20\u003c/sup\u003e with recent planetary boundary assessments indicating that both blue and green water boundaries have already been surpassed.\u003csup\u003e21,22\u003c/sup\u003e Agriculture remains a major contributor to this imbalance, with irrigation responsible for approximately 70% of global blue water use (freshwater found in rivers, lakes, and underground sources used for drinking, farming, and industry).\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe concept of a \u0026lsquo;sustainable diet\u0026rsquo; encompasses dietary patterns that promote optimal health, while minimising environmental impacts, thereby supporting food systems that operate within established environmental planetary boundaries.\u003csup\u003e24\u003c/sup\u003e Advances in environmental assessment methodologies, particularly the life-cycle assessment, have enabled the quantification of the GHGE and water footprint of various foods.\u003csup\u003e25\u003c/sup\u003e Evidence from life cycle assessment studies consistently demonstrate that red meats such as beef and lamb exhibited the greatest GHGE (beef\u0026thinsp;=\u0026thinsp;28.73 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;kg; lamb\u0026thinsp;=\u0026thinsp;27.91 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;kg), while poultry (4.12 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;kg), and pork (5.85 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;kg) produce substantially lower emissions.\u003csup\u003e26\u003c/sup\u003e Plant based foods such as legumes (0.66 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;kg), cereals (0.53 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;kg), fruits (0.50 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;kg), and vegetables (0.47 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;kg) demonstrate the lowest GHGE.\u003csup\u003e26\u003c/sup\u003e When standardised to a 2000 Kcal\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e diet, GHGE are markedly higher among high meat eater consumers (\u0026gt;\u0026thinsp;100 g\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e meat; 7.28 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), compared with medium (50\u0026ndash;100 g\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e meat; 5.34 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and low meat eaters (\u0026lt;\u0026thinsp;50 g\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e meat; 4.21 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Dietary patterns of pescatarians (3.81 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), vegetarian (3.33 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and vegan diets (2.16 kg\u0026sdot;CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) reduce GHGE further.\u003csup\u003e27\u003c/sup\u003e Water use within food systems is complex: red meat, dairy products, nuts, and grains are typically associated with high blue water usage, in comparison to legumes, oats, and plant-based milks.\u003csup\u003e28\u003c/sup\u003e Collectively, these findings underscore environmental benefits of transitioning towards a \u0026lsquo;plant forward\u0026rsquo; dietary model.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA cultural shift towards more sustainable practices is urgently required across all sectors, including sport.\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFootball has increasingly positioned itself as a platform for sustainably-driven initiatives under the broader \u0026lsquo;Sport for Development\u0026rsquo; agenda.\u003csup\u003e30\u003c/sup\u003e Major governing bodies such as the Federation Internationale de Football Association (FIFA), the Union of Football Associations (UEFA), and the English Premier League (EPL) have all implemented sustainability strategies aimed at reducing GHGE.\u003csup\u003e31\u0026ndash;33\u003c/sup\u003e In alignment with the United Nations \u0026lsquo;Sports for Climate Change Action\u0026rsquo; Framework, these organisations have committed to reducing GHGE by 50% by 2030, and achieving net zero emissions by 2040.\u003csup\u003e34,35\u003c/sup\u003e The EPL further reinforced this objective through its \u0026lsquo;Environmental Sustainability Commitment\u0026rsquo; (2024), establishing a minimum standard of actions across all member clubs.\u003csup\u003e36\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite these advances, food-related emissions remain disappointedly absent from most football sustainability frameworks.\u003csup\u003e31\u0026ndash;33\u003c/sup\u003e For example, FIFA\u0026rsquo;s reported sustainability actions have primarily focused on food waste reductions during events,\u003csup\u003e31\u003c/sup\u003e while UEFA\u0026rsquo;s initiatives target improved access to \u0026lsquo;healthy\u0026rsquo; food at events and waste minimisation (e.g. food waste and single use items),\u003csup\u003e32\u003c/sup\u003e and the EPL\u0026rsquo;s \u0026lsquo;Primary Stars\u0026rsquo; initiative emphasises sustainability education for children.\u003csup\u003e33\u003c/sup\u003e The relative neglect of wider sustainable nutrition practices likely reflects the novelty of this concept within elite sport.\u003csup\u003e37,38\u003c/sup\u003e Indeed, the UEFA Expert Group Statement on Nutrition in Elite Football acknowledged that \u0026ldquo;\u003cem\u003efurther work is required to understand the interplay between sports nutrition and sustainability and how principles can be incorporated within best practice nutrition recommendations\u0026rdquo;.\u003c/em\u003e\u003csup\u003e39\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eElite football occupies a unique position to drive societal change due to its global influence, visibility, and cultural reach.\u003csup\u003e40\u003c/sup\u003e Players and clubs can create momentum through their sporting performances and media profile, utilising this as a strategic vehicle to communicate, implement, and drive nonsporting development goals such as sustainability.\u003csup\u003e40\u003c/sup\u003e This presents an important opportunity to promote awareness and behavioural change regarding sustainable diets through football. However, current dietary sustainable frameworks such as the \u003cem\u003ePlanetary Health Diet\u003c/em\u003e proposed by the EAT-Lancet Commission are designed for the general population,\u003csup\u003e41\u003c/sup\u003e and may not meet the health and performance demands of elite footballers.\u003csup\u003e42\u0026ndash;52\u003c/sup\u003e The \u003cem\u003ePlanetary Health Diet\u003c/em\u003e assumes an average energy intake of approximately 2400 Kcal\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, increasing to 3008 Kcal\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 2451 Kcal\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for \u0026lsquo;very active\u0026rsquo; adult men and women respectively.\u003csup\u003e41\u003c/sup\u003e These estimates remain well below the average energy requirements of elite adolescent and senior male (~\u0026thinsp;3,250-3,600 Kcal\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and female (2,600\u0026ndash;2900 Kcal\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) footballers.\u003csup\u003e42\u0026ndash;52\u003c/sup\u003e Moreover, football-specific nutritional guidelines recommend individualised \u0026ldquo;nutrition periodisation\u0026rdquo;, with carbohydrate intakes adjusted between 3\u0026ndash;8 g\u0026sdot;kg\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e depending on training intensity, and protein intakes of 1.6\u0026ndash;2.2 g\u0026sdot;kg\u0026sdot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to optimise adaptation and recovery.\u003csup\u003e39\u003c/sup\u003e Consequently, the elevated energy and protein requirements of footballers may predispose them to diets with higher associated GHGE and water footprints.\u003c/p\u003e \u003cp\u003eElite football clubs operate continuous, large-scale food service operations throughout both the competitive and off-season periods, typically providing at least two daily meals, such as pre- and post-training provisions, and employing multiple performance chefs to support the footballer\u0026rsquo;s nutrition.\u003csup\u003e53\u003c/sup\u003e Although previous research has assessed GHGE and water footprints within institutional catering contexts, such as healthcare settings,\u003csup\u003e54\u003c/sup\u003e and explored emissions linked with football club operations,\u003csup\u003e55\u003c/sup\u003e travel,\u003csup\u003e56\u003c/sup\u003e and fan activites,\u003csup\u003e57\u003c/sup\u003e no study to date has quantified the environmental impacts associated with food service provision in elite male or female football. This absence of data limits the development of integrated, evidence-based strategies capable of aligning nutritional performance goals with environmental sustainability objectives. Calculating the GHGE and water footprint of the food services of elite football teams will therefore help inform football clubs on the sustainability of their food service and can implement appropriate adjustments if required, whilst also putting sustainable nutrition practices to the forefront of major stakeholders\u0026rsquo; attention, such as governing bodies and organisations, football clubs, and football players.\u003c/p\u003e \u003cp\u003eAccordingly, the present study aimed to quantify both the GHGE and water footprint associated with food service provision in four professional football clubs in the United Kingdom, representing the EPL, the Women\u0026rsquo;s Super League, the English Football League Championship, and English Football League One, as well as determining the contribution of individual food categories to the overall environmental impact within each club.\u003c/p\u003e"},{"header":"2.0 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThis cross-sectional investigation included four professional football clubs based in England, representing the English Premier League (Team One), English Football League Championship (EFL Champ; Team Two), English Football League One (EFL L1; Team Three), and Women\u0026rsquo;s Super League (WSL; Team Four).\u003c/p\u003e \u003cp\u003eFood service procurement data were collected for a typical in-season weekly microcycle, inclusive of at least one competitive match, during the final two months of the 2024\u0026ndash;2025 competitive season. Data acquisition was facilitated through the professional networks of the authorship team and was conducted with the full cooperation of participating clubs.\u003c/p\u003e \u003cp\u003eTeam One (EPL) provided 150 daily covers across four training days (600 total covers, due to two match-days and one rest day); Team Two (EFL Champ) had 200 daily covers across four training days (800 total covers, due to one match day and two rest days); Team Three (EFL L1) had 135 daily covers across four training days (540 total covers, due to one match day and two rest days); and Team Four (WSL) had 120 daily covers over four training days (480 total covers, due to two match days and one rest day). Each football team provided two meal services per day (pre-training meal: breakfast; and post-training meal: lunch). Ethical approval for the study was obtained through Leeds Beckett University ethics committee (reference number: 139424).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Procedures\u003c/h2\u003e \u003cp\u003eUsing the food service procurement data obtained from each club, the GHGE (expressed as kilogram [kg] CO\u003csub\u003e2\u003c/sub\u003e equivalents per kilogram of food) and water footprint (kL\u0026sdot;eq) were quantified. Data were classified into food categories following the framework of Poore \u0026amp; Nemecek (2018),\u003csup\u003e28\u003c/sup\u003e which outlines 42 distinct food groups (e.g. beef [beef herd], groundnuts, potatoes, etc.). Some food and beverage items could be directly mapped into existing categories (e.g. olive oil into \u003cem\u003eolive oil\u003c/em\u003e), whereas others could not (e.g. mushrooms). Consequently, ten additional food categories were created: \u003cem\u003eother milks; other oils; other vegetables; other fruits; herbs; fungi; spices; condiments; confectionary and beverages\u003c/em\u003e, resulting in 52 total categories. Details of these classifications and the categorisation of each individual food and beverage item are provided in Supplementary Material 1. Notably, fruit juices such as apple juice or orange juice were classified under \u003cem\u003efruits\u003c/em\u003e rather than \u003cem\u003ebeverages\u003c/em\u003e due to Poore \u0026amp; Nemecek (2018) having categories for apples and citrus, whereas beverages was an additional category created by the authorship.\u003c/p\u003e \u003cp\u003eEach item was entered into Nutritics nutrition analysis software (Nutritics Version 6.14, Nutritics Ltd, Ireland) by the lead author (OT).\u003csup\u003e58\u003c/sup\u003e Individual datasets were created for each team using the Nutritics \u003cem\u003eMeal Plan\u003c/em\u003e function, with all food and beverage items being entered as raw weights unless otherwise specified. Missing or unclear information (e.g. portion sizes, or item specification) was clarified with the club head chef and lead sport nutritionist to ensure accuracy and completeness.\u003c/p\u003e \u003cp\u003eThe GHGE and water footprint for each food and beverage item was derived using the Nutritics \u003cem\u003eFoodprint Carbon Footprint Functionality\u003c/em\u003e (Nutritics Version 6, Nutritics Ltd, Ireland),\u003csup\u003e59\u003c/sup\u003e which estimates environmental impact values based on peer reviewed published life cycle assessment research across five databases.\u003csup\u003e26,60\u0026ndash;63\u003c/sup\u003e The system prioritises primary life cycle assessment data where available, locking it as the definitive value, while secondary data are extended to incorporate outbound transportation and packaging stages. Each individual ingredient\u0026rsquo;s name, description, and country of origin were inputted to further improve accuracy where possible. Each assigned GHGE and water footprint value was manually verified to ensure the most appropriate match between the item and corresponding life cycle assessment dataset. This methodology has previously been utilised in the literature to quantify the GHGE of school lunches for children attending school nurseries.\u003csup\u003e64\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn total, 289 individual foods and beverages (Team One, EPL\u0026thinsp;=\u0026thinsp;154 individual foods or beverages; Team Two, EFL Champ\u0026thinsp;=\u0026thinsp;201 individual foods or beverages; Team Three, EFL L1\u0026thinsp;=\u0026thinsp;154 individual foods or beverages; Team Four, WSL\u0026thinsp;=\u0026thinsp;117 individual foods or beverages). Following verification, the data were exported from Nutritics into\u003c/p\u003e \u003cp\u003eMicrosoft Excel (Excel Version 16.99, Microsoft, Redmond, Washington, USA) for data cleaning and preparation prior to analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 External Funding\u003c/h2\u003e \u003cp\u003eNo funding was received to support the design, data collection, analysis, interpretation, or writing of this study. The research was conducted independently by the authors without financial support from an organisation or sponsor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, with 95% confidence limits (CLs). Given the small sample size (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;four), no formal inferential statistical analyses were conducted. Instead, a descriptive analytical approach was employed to enable meaningful interpretation of the environmental impact data while avoiding inappropriate statistical inference. Comparisons between teams and food groups were made by examining whether mean values fell outside the respective 95% CLs of other teams or food groups. A mean value was interpreted as \u003cem\u003ehigher\u003c/em\u003e or \u003cem\u003elower\u003c/em\u003e when it lay outside another team\u0026rsquo;s or food groups 95% CLs, and \u003cem\u003ecomparable\u003c/em\u003e when within these bounds. This approach was selected to preserve the integrity of interpretation. Descriptive comparison using 95% CLs allows for transparent presentation of potential differences while appropriately reflecting the exploratory and observational nature of the study. All analyses were conducted using Microsoft Excel (Excel Version 16.99, Microsoft, Redmond, Washington, USA).\u003c/p\u003e \u003cp\u003eTo calculate GHGE per cover per day, the overall GHGE was divided by the number of days food service was provided for, by the number of covers per day:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\frac{\\frac{GHGE\\:of\\:food}{Number\\:of\\:days\\:of\\:food\\:service}}{Number\\:of\\:covers}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEquation 1. Calculation of GHGE per cover per day.\u003c/p\u003e \u003cp\u003eTo calculate GHGE per cover per food service, this was further divided by the number of food services per day:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\frac{\\left(\\frac{\\frac{GHGE\\:of\\:food}{Number\\:of\\:days\\:of\\:food\\:service\\:}}{Number\\:of\\:cover}\\right)}{Number\\:of\\:food\\:services\\:per\\:day}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEquation 2. Calculation of GHGE per cover per service.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003ch3\u003e\u003cstrong\u003e3.1 Energy \u0026amp; Macronutrient Breakdown\u003c/strong\u003e\u003c/h3\u003e\n\u003ch4\u003e\u003cstrong\u003e3.1.1 Energy provision\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eTable 1 summarises the total and relative energy and macronutrient provision across the four football team\u0026rsquo;s food services. Team Two (EFL Champ) provided the greatest total energy (1,097,459 \u0026plusmn; 10,261 kcal; 95% CL: 1,096,040\u0026ndash;1,098,878 kcal]), followed by Team One (EPL) (1,069,587 \u0026plusmn; 14,601 kcal; 95% CL: 1,067,281\u0026ndash;1,071,983 kcal), Team Three (EFL L1) (863,256 \u0026plusmn; 10,319 kcal; 95% CL: 861,626\u0026ndash;864,886 kcal) and Team Four (WSL) (464,203 \u0026plusmn; 9,4334 kcal; 95% CL: 462,494\u0026ndash;465,912 kcal).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen expressed relative to service volume, \u003cstrong\u003eTeam Three (EFL League One)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edemonstrated the greatest energy provision per cover per day (2,272 \u0026plusmn; 27 kcal; 95% CL: 2,267\u0026ndash;2,276 kcal) and per cover per service (1,136 \u0026plusmn; 14 kcal; 95% CL: 1,134\u0026ndash;1,138 kcal). Mean energy provision was \u003cem\u003ehigher\u003c/em\u003e for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs. \u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e (per day = 1,783 \u0026plusmn; 24 kcal; 95% CL: 1,779\u0026ndash;1,787 kcal; per service = 891 \u0026plusmn; 12 kcal; 95% CL: 889\u0026ndash;893 kcal) also exhibited \u003cem\u003ehigher\u003c/em\u003e per-cover energy provision than \u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e (per day = 1,373 \u0026plusmn; 13 kcal; 95% CL: 1,370\u0026ndash;1,374 kcal; per service = 686 \u0026plusmn; 6 kcal; 95% CL: 685\u0026ndash;687 kcal) and \u003cstrong\u003eTeam Four (WSL)\u0026nbsp;\u003c/strong\u003e(per day = 967 \u0026plusmn; 20 kcal; 95% CL: 964\u0026ndash;971 kcal; per service = 484 \u0026plusmn; 10 kcal; 95% CL: 482\u0026ndash;485 kcal). \u003cstrong\u003eTeam Two\u003c/strong\u003e exhibited \u003cem\u003ehigher\u003c/em\u003e per-cover energy provision than \u003cstrong\u003eTeam Four\u003c/strong\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.1.2 Carbohydrate provision\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e provided the greatest total carbohydrate quantity (121,088 \u0026plusmn; 1,342 g; 95% CL: 120,902\u0026ndash;121,274 g), followed by\u0026nbsp;\u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e (101,747 \u0026plusmn; 1,310 g; 95% CL: 101,540\u0026ndash;101,954 g),\u0026nbsp;\u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e (86,805 \u0026plusmn; 1,102 g; 95% CL: 86,631\u0026ndash;86,979 g), and\u0026nbsp;\u003cstrong\u003eTeam Four (WSL)\u003c/strong\u003e (40,035 \u0026plusmn; 699 g; 95% CL: 39,908\u0026ndash;40,162 g).\u003c/p\u003e\n\u003cp\u003eWhen expressed relative to service volume,\u0026nbsp;\u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e demonstrated the highest carbohydrate provision per cover per day (228 \u0026plusmn; 3 g; 95% CL: 228\u0026ndash;229 g) and per cover per service (114 \u0026plusmn; 2 g; 95% CL: 114\u0026ndash;115 g). Mean carbohydrate provision was\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs.\u0026nbsp;\u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e (per day = 170 \u0026plusmn; 2 g; 95% CL: 169\u0026ndash;170 g; per service = 85 \u0026plusmn; 1 g; 95% CL: 85\u0026ndash;85 g) exhibited\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e per-cover carbohydrate provision than\u0026nbsp;\u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e (per day = 151 \u0026plusmn; 2 g; 95% CL: 151\u0026ndash;152 g; per service = 77 \u0026plusmn; 1 g; 95% CL: 76\u0026ndash;76 g) and\u0026nbsp;\u003cstrong\u003eTeam Four (WSL)\u003c/strong\u003e (per day = 83 \u0026plusmn; 2 g; 95% CL: 83\u0026ndash;84 g; per service = 41 \u0026plusmn; 1 g; 95% CL: 42\u0026ndash;42 g).\u0026nbsp;\u003cstrong\u003eTeam Two\u003c/strong\u003e exhibited\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e per-cover carbohydrate provision than\u0026nbsp;\u003cstrong\u003eTeam Four\u003c/strong\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.1.3 Protein provision\u0026nbsp;\u003c/strong\u003e\u003c/h4\u003e\n\u003ch4\u003e\u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e provided the greatest total protein quantity (62,655 \u0026plusmn; 1,202 g; 95% CL: 62,465\u0026ndash;62,846 g), followed by \u003cstrong\u003eTeam Two (EFL Championship)\u003c/strong\u003e (53,038 \u0026plusmn; 925 g; 95% CL: 52,910\u0026ndash;53,166 g), \u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e (52,383 \u0026plusmn; 1,017 g; 95% CL: 52,222\u0026ndash;52,544 g), and \u003cstrong\u003eTeam Four (WSL)\u003c/strong\u003e (23,594 \u0026plusmn; 507 g; 95% CL: 23,502\u0026ndash;23,686 g).\u003c/h4\u003e\n\u003cp\u003eWhen expressed relative to service volume,\u0026nbsp;\u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e demonstrated the highest protein provision per cover per day (138 \u0026plusmn; 3 g; 95% CL: 137\u0026ndash;138 g) and per cover per service (69 \u0026plusmn; 1 g; 95% CL: 69\u0026ndash;69 g). Descriptively, mean protein provision was\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs.\u0026nbsp;\u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e (per day = 104 \u0026plusmn; 2 g; 95% CL: 104\u0026ndash;105 g; per service = 52 \u0026plusmn; 1 g; 95% CL: 52\u0026ndash;52 g) exhibited\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e per-cover protein provision than\u0026nbsp;\u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e (per day = 66 \u0026plusmn; 1 g; 95% CL: 66\u0026ndash;67 g; per service = 33 \u0026plusmn; 1 g; 95% CL: 33\u0026ndash;33 g) and\u0026nbsp;\u003cstrong\u003eTeam Four (WSL)\u0026nbsp;\u003c/strong\u003e(per day = 49 \u0026plusmn; 1 g; 95% CL: 49\u0026ndash;49 g; per service = 25 \u0026plusmn; 1 g; 95% CL: 25\u0026ndash;25 g).\u0026nbsp;\u003cstrong\u003eTeam Two\u003c/strong\u003e exhibited\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e per-cover protein provision than\u0026nbsp;\u003cstrong\u003eTeam Four\u003c/strong\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.1.4 Fat provision\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e provided the greatest total fat quantity (45,823 \u0026plusmn; 1,332 g; 95% CL: 45,613\u0026ndash;46,033 g), followed by\u0026nbsp;\u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e (44,534 \u0026plusmn; 766 g; 95% CL: 44,428\u0026ndash;44,640 g),\u0026nbsp;\u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e (34,066 \u0026plusmn; 665 g; 95% CL: 33,961\u0026ndash;34,171 g), and\u0026nbsp;\u003cstrong\u003eTeam Four (WSL)\u003c/strong\u003e (23,286 \u0026plusmn; 958 g; 95% CL: 23,112\u0026ndash;23,460 g).\u003c/p\u003e\n\u003cp\u003eWhen expressed relative to service volume,\u0026nbsp;\u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e demonstrated the highest fat provision per cover per day (90 \u0026plusmn; 2 g; 95% CL: 89\u0026ndash;90 g) and per cover per service (45 \u0026plusmn; 1 g; 95% CL: 45\u0026ndash;45 g). Descriptively, mean fat provision was\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs.\u0026nbsp;\u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e (per day = 77 \u0026plusmn; 2 g; 95% CL: 76\u0026ndash;77 g; per service = 38 \u0026plusmn; 1 g; 95% CL: 38\u0026ndash;38 g) exhibited\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e per-cover fat provision than\u0026nbsp;\u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e (per day = 56 \u0026plusmn; 1 g; 95% CL: 56\u0026ndash;56 g; per service = 28 \u0026plusmn; 1 g; 95% CL: 28\u0026ndash;28 g) and\u0026nbsp;\u003cstrong\u003eTeam Four (WSL)\u0026nbsp;\u003c/strong\u003e(per day = 49 \u0026plusmn; 2 g; 95% CL: 48\u0026ndash;49 g; per service = 24 \u0026plusmn; 1 g; 95% CL: 24\u0026ndash;24 g).\u0026nbsp;\u003cstrong\u003eTeam Two\u003c/strong\u003e exhibited\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e per-cover fat provision than\u0026nbsp;\u003cstrong\u003eTeam Four\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.2 Greenhouse Gas Emissions (GHGE)\u003c/strong\u003e\u003c/h3\u003e\n\u003ch4\u003e\u003cstrong\u003e3.2.1 Greenhouse gas emissions per team\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eTable 2 presents the GHGE of the four football teams, with detailed procurement and GHGE breakdowns provided in Supplementary Material 1.\u0026nbsp;\u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e demonstrated the greatest total GHGE (2,725 \u0026plusmn; 54 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 2,717\u0026ndash;2,734 kg\u0026middot;CO₂\u0026middot;eq), followed by\u0026nbsp;\u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e (2,345 \u0026plusmn; 42 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 2,338\u0026ndash;2,352 kg\u0026middot;CO₂\u0026middot;eq),\u0026nbsp;\u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e (2,231 \u0026plusmn; 2 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 2,225\u0026ndash;2,237 kg\u0026middot;CO₂\u0026middot;eq), and\u0026nbsp;\u003cstrong\u003eTeam Four (WSL)\u003c/strong\u003e (832 \u0026plusmn; 18 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 829\u0026ndash;835 kg\u0026middot;CO₂\u0026middot;eq).\u003c/p\u003e\n\u003cp\u003eWhen expressed relative to service volume,\u0026nbsp;\u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e exhibited the highest GHGE per cover per day (7.17 \u0026plusmn; 0.14 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 7.15\u0026ndash;7.19 kg\u0026middot;CO₂\u0026middot;eq) and per cover per service (3.59 \u0026plusmn; 0.07 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 3.57\u0026ndash;3.60 kg\u0026middot;CO₂\u0026middot;eq). Mean GHGE was\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs.\u0026nbsp;\u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e (per day = 3.91 \u0026plusmn; 0.07 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 3.90\u0026ndash;3.92 kg\u0026middot;CO₂\u0026middot;eq; per service = 1.95 \u0026plusmn; 0.03 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 1.95\u0026ndash;1.96 kg\u0026middot;CO₂\u0026middot;eq) demonstrated\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e GHGE than\u0026nbsp;\u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e (per day = 2.79 \u0026plusmn; 0.05 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 2.78\u0026ndash;2.80 kg\u0026middot;CO₂\u0026middot;eq; per service = 1.39 \u0026plusmn; 0.02 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 1.39\u0026ndash;1.40 kg\u0026middot;CO₂\u0026middot;eq) and\u0026nbsp;\u003cstrong\u003eTeam Four (WSL)\u0026nbsp;\u003c/strong\u003e(per day = 1.73 \u0026plusmn; 0.04 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 1.73\u0026ndash;1.74 kg\u0026middot;CO₂\u0026middot;eq; per service = 0.87 \u0026plusmn; 0.02 kg\u0026middot;CO₂\u0026middot;eq; 95% CL: 0.86\u0026ndash;0.87 kg\u0026middot;CO₂\u0026middot;eq).\u0026nbsp;\u003cstrong\u003eTeam Two\u003c/strong\u003e demonstrated\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e per-cover GHGE than\u0026nbsp;\u003cstrong\u003eTeam Four\u003c/strong\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.2.2 Contribution of food categories to total greenhouse gas emissions\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eTable 3 presents the contribution of each food category to the overall GHGE for each football team, with detailed category-level results provided in Supplementary Material 1. Across all teams,\u0026nbsp;\u003cstrong\u003emeat\u003c/strong\u003e was the largest contributor to total GHGE (~38%), primarily driven by\u0026nbsp;\u003cstrong\u003ebeef\u003c/strong\u003e procurement (see Supplementary Material 1). On average,\u0026nbsp;\u003cstrong\u003edairy\u003c/strong\u003e was the second largest contributor (~16%), followed by\u0026nbsp;\u003cstrong\u003efruit\u003c/strong\u003e (~10%),\u0026nbsp;\u003cstrong\u003ebeverages\u003c/strong\u003e (~9%), and\u0026nbsp;\u003cstrong\u003eeggs\u003c/strong\u003e (~7%).\u003c/p\u003e\n\u003cp\u003eSubstantial variation was observed between teams in the relative contribution of food categories. For example, in\u0026nbsp;\u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e,\u0026nbsp;\u003cstrong\u003ebeverages\u003c/strong\u003e represented the second largest contributor to total GHGE (22%), primarily due to bottled water purchases (2,000 \u0026times; 500 mL bottles). In contrast, in\u0026nbsp;\u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e,\u0026nbsp;\u003cstrong\u003efruit\u003c/strong\u003e was the second largest contributor (16%), largely attributable to apple juice purchases (48 \u0026times; 2 L cartons).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.3 Water Footprint\u003c/strong\u003e\u003c/h3\u003e\n\u003ch4\u003e\u003cstrong\u003e3.3.1 Water footprint per team\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eTable 2 presents the total and relative water footprint of the four football teams, with detailed procurement data provided in Supplementary Material 1.\u0026nbsp;\u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e had the greatest total water footprint (3,225 \u0026plusmn; 63 kL\u0026middot;eq; 95% CL: 3,216\u0026ndash;3,234 kL\u0026middot;eq), followed by\u0026nbsp;\u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e (2,760 \u0026plusmn; 49 kL\u0026middot;eq; 95% CL: 2,752\u0026ndash;2,767 kL\u0026middot;eq),\u0026nbsp;\u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e (2,284 \u0026plusmn; 40 kL\u0026middot;eq; 95% CL: 2,277\u0026ndash;2,290 kL\u0026middot;eq), and\u0026nbsp;\u003cstrong\u003eTeam Four (WSL)\u003c/strong\u003e (1,111 \u0026plusmn; 25 kL\u0026middot;eq; 95% CL: 1,107\u0026ndash;1,116 kL\u0026middot;eq).\u003c/p\u003e\n\u003cp\u003eWhen expressed relative to service volume,\u0026nbsp;\u003cstrong\u003eTeam Three (EFL L1)\u003c/strong\u003e demonstrated the highest water footprint per cover per day (6.01 \u0026plusmn; 0.11 kL\u0026middot;eq; 95% CL: 5.99\u0026ndash;6.03 kL\u0026middot;eq) and per cover per service (3.00 \u0026plusmn; 0.05 kL\u0026middot;eq; 95% CL: 2.99\u0026ndash;3.01 kL\u0026middot;eq). Mean water footprint values were\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e for Team Three compared with all other teams, with mean values falling outside their respective 95% CLs.\u0026nbsp;\u003cstrong\u003eTeam One (EPL)\u003c/strong\u003e (per day = 4.60 \u0026plusmn; 0.08 kL\u0026middot;eq; 95% CL: 4.59\u0026ndash;4.61 kL\u0026middot;eq; per service = 2.30 \u0026plusmn; 0.04 kL\u0026middot;eq; 95% CL: 2.29\u0026ndash;2.31 kL\u0026middot;eq) also demonstrated\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e water footprint per cover than\u0026nbsp;\u003cstrong\u003eTeam Two (EFL Champ)\u003c/strong\u003e (per day = 4.03 \u0026plusmn; 0.08 kL\u0026middot;eq; 95% CL: 4.02\u0026ndash;4.04 kL\u0026middot;eq; per service = 2.02 \u0026plusmn; 0.04 kL\u0026middot;eq; 95% CL: 2.01\u0026ndash;2.02 kL\u0026middot;eq)) and\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTeam Four (WSL)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003eper day = 2.32 \u0026plusmn; 0.05 kL\u0026middot;eq; 95% CL: 2.31\u0026ndash;2.32 kL\u0026middot;eq; per service = 1.16 \u0026plusmn; 0.03 kL\u0026middot;eq; 95% CL: 1.15\u0026ndash;1.16 kL\u0026middot;eq).\u0026nbsp;\u003cstrong\u003eTeam Two\u003c/strong\u003e demonstrated\u0026nbsp;\u003cem\u003ehigher\u003c/em\u003e per-cover water footprint than\u0026nbsp;\u003cstrong\u003eTeam Four\u003c/strong\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.3.2 Contribution of food categories to total water footprint\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eTable 3 presents the contribution of each food category to the overall water footprint for each football team, with detailed results provided in Supplementary Material 1. Across all teams, \u003cstrong\u003emeat\u003c/strong\u003e was the largest contributor to total water footprint (~36%), primarily driven by \u003cstrong\u003ebeef\u003c/strong\u003e procurement (see Supplementary Material 1). On average, \u003cstrong\u003evegetables\u003c/strong\u003e represented the second largest contributor (~18%), followed by \u003cstrong\u003edairy\u003c/strong\u003e (~12%), \u003cstrong\u003egrains and starches\u003c/strong\u003e (~8%), and \u003cstrong\u003eoils\u003c/strong\u003e (~6%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable One.\u003c/strong\u003e Energy and macronutrient provision across the food services of four professional football teams. Data are presented as mean \u0026plusmn; standard deviation [95% confidence limits]\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1027\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFootball Team\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Energy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 291px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Carbohydrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kcal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer cover per day\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kcal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer cover per service\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kcal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.4892%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.7554%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer cover per day\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer cover per service\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer cover per day\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer cover per service\u0026nbsp;\u003c/strong\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer cover per day\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer cover per service\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam One\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(EPL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1,069,587 \u0026plusmn; 14,601 [1,067,281 \u0026ndash; 1,071,983]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1,783 \u0026plusmn; 24 [1,779 \u0026ndash; 1,787]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e891 \u0026plusmn; 12 [889 \u0026ndash; 893]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.4892%;\"\u003e\n \u003cp\u003e101,747 \u0026plusmn; 1,310 [101,540 \u0026ndash; 101,954]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.7554%;\"\u003e\n \u003cp\u003e170 \u0026plusmn; 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[169 \u0026ndash; 170]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e85 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[85 \u0026ndash; 85]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e62,655 \u0026plusmn; 1,202 [62,465 \u0026ndash; 62,845]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e104 \u0026plusmn; 2 [104 \u0026ndash; 105]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e52 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[52 \u0026ndash; 52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e45,823 \u0026plusmn; 1,332 [45,613 \u0026ndash; 46,033]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e76 \u0026plusmn; 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[76 \u0026ndash; 77]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e38 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[38 \u0026ndash; 38]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam Two\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(EFL Champ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1,097,459 \u0026plusmn; 10,261 [1,096,040 \u0026ndash; 1,098,878]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1,373 \u0026plusmn; 13\u003c/p\u003e\n \u003cp\u003e[1,370 \u0026ndash; 1,374]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e686 \u0026plusmn; 6\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[685 \u0026ndash; 687]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.4892%;\"\u003e\n \u003cp\u003e121,088 \u0026plusmn; 1,342 [120,902 \u0026ndash; 121,274]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.7554%;\"\u003e\n \u003cp\u003e151 \u0026plusmn; 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[151 \u0026ndash; 152]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e77 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[76 \u0026ndash; 76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e53,038 \u0026plusmn; 925\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[52,910 \u0026ndash; 53,166]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e66 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[66 \u0026ndash; 67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e33 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[33 \u0026ndash; 33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e44,534 \u0026plusmn; 766 [44,428 \u0026ndash; 44,640]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e56 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[56 \u0026ndash; 56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e28 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[28 \u0026ndash; 28]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam Three\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(EFL L1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e863,256 \u0026plusmn; 10,319 [861,626 \u0026ndash; 864,886]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2,272 \u0026plusmn; 27 [2,267 \u0026ndash; 2,276]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1,136 \u0026plusmn; 14 [1,134 \u0026ndash; 1,138]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.4892%;\"\u003e\n \u003cp\u003e86,805 \u0026plusmn; 1,102 [86,631 \u0026ndash; 86,979]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.7554%;\"\u003e\n \u003cp\u003e228 \u0026plusmn; 3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[228 \u0026ndash; 229]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e114 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[114 \u0026ndash; 114]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e52,383 \u0026plusmn; 1,017 [52,222 \u0026ndash; 52,544]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e138 \u0026plusmn; 3 [137 \u0026ndash; 138]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e69 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[69 \u0026ndash; 69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e34,066 \u0026plusmn; 665 [33,961 \u0026ndash; 34,171]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e90 \u0026plusmn; 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[89 \u0026ndash; 90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e45 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[45 \u0026ndash; 45]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam Four\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(WSL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e46,4203 \u0026plusmn; 9,434 [462,494 \u0026ndash; 465,912]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e967 \u0026plusmn; 20 [964 \u0026ndash; 971]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e484 \u0026plusmn; 10 [482 \u0026ndash; 485]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.4892%;\"\u003e\n \u003cp\u003e40,035 \u0026plusmn; 699 [39,908 \u0026ndash; 40,162]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.7554%;\"\u003e\n \u003cp\u003e83 \u0026plusmn; 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[83 \u0026ndash; 84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e41 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[42 \u0026ndash; 42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e23,594 \u0026plusmn; 507\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[23,502 \u0026ndash; 23,686]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e49 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[49 \u0026ndash; 49]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e25 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[25 \u0026ndash; 25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e23,286 \u0026plusmn; 958 [23,112 \u0026ndash; 23,460]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e49 \u0026plusmn; 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[48 \u0026ndash; 49]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e24 \u0026plusmn; 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[24 \u0026ndash; 24]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Greenhouse gas emissions (GHGE) and water footprint of each football team presented as total values, per cover per day, and per cover per food service. Data are presented as mean \u0026plusmn; standard deviation [95% confidence limits]\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1025\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFootball Team\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHGE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater Footprint\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg\u0026times;co\u003csub\u003e2\u003c/sub\u003e\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3957%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer Cover\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eper Day\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg\u0026times;co\u003csub\u003e2\u003c/sub\u003e\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3957%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer Cover\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eper Service\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg\u0026times;co\u003csub\u003e2\u003c/sub\u003e\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kL\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer Cover\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eper Day\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kL\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer Cover\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePer Service\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kL\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam One\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(EPL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2,345 \u0026plusmn; 42\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[2,338 \u0026ndash; 2,352]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3957%;\"\u003e\n \u003cp\u003e3.91 \u0026plusmn; 0.07\u003c/p\u003e\n \u003cp\u003e[3.90 \u0026ndash; 3.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3957%;\"\u003e\n \u003cp\u003e1.95 \u0026plusmn; 0.03\u003c/p\u003e\n \u003cp\u003e[1.95 \u0026ndash; 1.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2,760 + 49\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[2,752 \u0026ndash; 2,767]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 133px;\"\u003e\n \u003cp\u003e4.60 + 0.08 [4.59 \u0026ndash; 4.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2.30 \u0026plusmn; 0.04\u003c/p\u003e\n \u003cp\u003e[2.29 \u0026ndash; 2.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam Two\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(EFL Champ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2,231 \u0026plusmn; 2\u003c/p\u003e\n \u003cp\u003e[2,225 \u0026ndash; 2,237]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3957%;\"\u003e\n \u003cp\u003e2.79 \u0026plusmn; 0.05\u003c/p\u003e\n \u003cp\u003e[2.78 \u0026ndash; 2.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3957%;\"\u003e\n \u003cp\u003e1.39 \u0026plusmn; 0.02\u003c/p\u003e\n \u003cp\u003e[1.39 \u0026ndash; 1.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e3,225 \u0026plusmn; 63\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[3,216 \u0026ndash; 3,234]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 133px;\"\u003e\n \u003cp\u003e4.03 \u0026plusmn; 0.08 [4.02 \u0026ndash; 4.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2.02 \u0026plusmn; 0.04\u003c/p\u003e\n \u003cp\u003e[2.01 \u0026ndash; 2.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam Three\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(EFL L1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2,725 \u0026plusmn; 54\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[2,717 \u0026ndash; 2,734]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3957%;\"\u003e\n \u003cp\u003e7.17 \u0026plusmn; 0.14\u003c/p\u003e\n \u003cp\u003e[7.15 \u0026ndash; 7.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3957%;\"\u003e\n \u003cp\u003e3.59 \u0026plusmn; 0.07 [3.57 \u0026ndash; 3.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2,284 \u0026plusmn; 40\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[2,277 \u0026ndash; 2,290]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 133px;\"\u003e\n \u003cp\u003e6.01 \u0026plusmn; 0.11 [5.99 \u0026ndash; 6.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e3.00 \u0026plusmn; 0.05\u003c/p\u003e\n \u003cp\u003e[2.99 \u0026ndash; 3.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam Four\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(WSL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e832 \u0026plusmn; 17\u003c/p\u003e\n \u003cp\u003e[829 \u0026ndash; 835]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3957%;\"\u003e\n \u003cp\u003e1.73 \u0026plusmn; 0.04\u003c/p\u003e\n \u003cp\u003e[1.73 \u0026ndash; 1.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3957%;\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 0.02 [0.86 \u0026ndash; 0.87]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e1,111 \u0026plusmn; 25\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1,107 \u0026ndash; 1,116]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2.32 \u0026plusmn; 0.05 [2.31 \u0026ndash; 2.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e1.16 \u0026plusmn; 0.03\u003c/p\u003e\n \u003cp\u003e[1.15 \u0026ndash; 1.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Contribution of different food and beverage groups to the total greenhouse gas emissions (GHGE) and water footprint for each football team. Data are presented as value (%)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1021\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFood \u0026amp; Beverage Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam One\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(EPL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam Two\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(EFL Champ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam Three\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(EFL L1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeam Four\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(WSL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHGE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg\u0026times;co\u003csub\u003e2\u003c/sub\u003e\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater Footprint\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kL\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHGE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg\u0026times;co\u003csub\u003e2\u003c/sub\u003e\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater Footprint\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kL\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHGE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg\u0026times;co\u003csub\u003e2\u003c/sub\u003e\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater Footprint\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kL\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHGE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg\u0026times;co\u003csub\u003e2\u003c/sub\u003e\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater Footprint\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kL\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHGE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg\u0026times;co\u003csub\u003e2\u003c/sub\u003e\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater Footprint\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kL\u0026times;eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e897\u003c/p\u003e\n \u003cp\u003e(38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1019\u003c/p\u003e\n \u003cp\u003e(37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e918\u003c/p\u003e\n \u003cp\u003e(41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1041\u003c/p\u003e\n \u003cp\u003e(32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e997\u003c/p\u003e\n \u003cp\u003e(37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e946\u003c/p\u003e\n \u003cp\u003e(41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003cp\u003e(39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003cp\u003e(33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e3135\u003c/p\u003e\n \u003cp\u003e(39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e3375\u003c/p\u003e\n \u003cp\u003e(36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFish\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003cp\u003e(3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003cp\u003e(3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003cp\u003e(3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDairy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003cp\u003e(14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003cp\u003e(10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003cp\u003e(12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003cp\u003e(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e498\u003c/p\u003e\n \u003cp\u003e(18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e418\u003c/p\u003e\n \u003cp\u003e(18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003cp\u003e(18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003cp\u003e(11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1255\u003c/p\u003e\n \u003cp\u003e(16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1053\u003c/p\u003e\n \u003cp\u003e(12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEggs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003cp\u003e(10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003cp\u003e(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003cp\u003e(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003cp\u003e(12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003cp\u003e(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e512\u003c/p\u003e\n \u003cp\u003e(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e538\u003c/p\u003e\n \u003cp\u003e(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLegumes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNuts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrains \u0026amp; Starches\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e591\u003c/p\u003e\n \u003cp\u003e18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003cp\u003e(3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003cp\u003e(6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003cp\u003e(6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e859\u003c/p\u003e\n \u003cp\u003e(8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlant Milks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003cp\u003e(3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOils\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003cp\u003e(8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003cp\u003e(13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003cp\u003e(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVegetables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003cp\u003e(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003cp\u003e(17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003cp\u003e(11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e742\u003c/p\u003e\n \u003cp\u003e(23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003cp\u003e(13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003cp\u003e(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003cp\u003e(19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e567\u003c/p\u003e\n \u003cp\u003e(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1725\u003c/p\u003e\n \u003cp\u003e(18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFruit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003cp\u003e(12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003cp\u003e(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003cp\u003e(17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003cp\u003e(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e827\u003c/p\u003e\n \u003cp\u003e(10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003cp\u003e(6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHerbs \u0026amp; Spices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunghi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCondiments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003cp\u003e(3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeverages\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003cp\u003e(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e614\u003c/p\u003e\n \u003cp\u003e(23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e846\u003c/p\u003e\n \u003cp\u003e(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eFood provision in professional football represents a substantial yet largely unrecognised contributor to environmental impact, making its sustainability a critical consideration as clubs strive to align elite performance nutrition with climate and resource goals. Therefore, this study aimed to (1) quantify the GHGEs and (2) water footprint of food services across four professional football clubs in England, and to (3) determine the contribution of individual food categories to each environmental metric. Large variations were observed across teams and leagues in both total and per-person values. Team Three (EFL L1) consistently demonstrated the highest relative energy, GHGE, and water footprint per cover, followed by Team One (EPL), Team Two (EFL Champ), and Team Four (WSL). Across all clubs, meat - particularly beef - was the dominant contributor to both GHGE and water footprint, followed by dairy and fruit for GHGE, and vegetables and dairy for water footprint. Collectively, these findings highlight the urgent need for football clubs to evolve their food service models - moving beyond traditional performance-focused menus toward sustainable practices that reduce emissions, conserve water, and set a global benchmark for sustainable elite sport nutrition.\u003c/p\u003e \u003cp\u003eTo the authors\u0026rsquo; knowledge, this is the first study to quantify the environmental footprint of food service provision within professional football - or any professional sports organisation. Marked variation in GHGE was observed across the four teams with the EFL L1 club (Team Three) producing the highest emissions, followed by the EPL (Team One), EFL Championship (Team Two), and WSL (Team Four). These differences likely reflect variation in menu composition, size, service frequency, and overall covers, with higher energy and protein provision - particularly from red and processed meats - substantially contributing to total emissions. Notably, the third-tier men\u0026rsquo;s team demonstrated the greatest relative GHGE and water footprint despite operating with fewer overall resources than the EPL club.\u003csup\u003e53\u003c/sup\u003e This team\u0026rsquo;s extensive investment in performance nutrition staffing (full-time accredited practitioners, PhD researchers, and four full-time chefs) and comprehensive catering infrastructure likely contributed to its larger footprint. These findings highlight that environmental impact in professional football food services is shaped less by league status and more by the scale, sophistication, and investment of each club\u0026rsquo;s nutrition and catering provision - factors expected to amplify as resource allocation to performance food systems continues to expand across both men\u0026rsquo;s and women\u0026rsquo;s football.\u003csup\u003e39\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDirect comparison with other institutional food services is limited by a lack of published data; however, estimates from German hospital and nursing home food services, calculated over a six-month period and standardised to a seven-day equivalent, indicate substantially greater total GHGE than those observed in all football clubs investigated in this study (Hospital 1\u0026thinsp;=\u0026thinsp;30,268 kg\u0026middot;CO₂\u0026middot;eq; Hospital 2\u0026thinsp;=\u0026thinsp;86,218 kg\u0026middot;CO₂\u0026middot;eq; Nursing Home 1\u0026thinsp;=\u0026thinsp;9,887 kg\u0026middot;CO₂\u0026middot;eq; Nursing Home 2\u0026thinsp;=\u0026thinsp;8,781 kg\u0026middot;CO₂\u0026middot;eq; Nursing Home 3\u0026thinsp;=\u0026thinsp;5,856 kg\u0026middot;CO₂\u0026middot;eq; men\u0026rsquo;s professional football: 2434\u0026thinsp;\u0026plusmn;\u0026thinsp;259 kg\u0026middot;CO₂\u0026middot;eq; women\u0026rsquo;s professional football: 832 kg\u0026middot;CO₂\u0026middot;eq).\u003csup\u003e54\u003c/sup\u003e These institutions served markedly higher daily cover volumes (~\u0026thinsp;323\u0026ndash;2,700 covers per day; vs 141\u0026thinsp;\u0026plusmn;\u0026thinsp;45 in professional male and female football), which likely accounts for much of the disparity in overall emissions. When expressed per person, German hospitals and nursing homes averaged 1.96 kg\u0026middot;CO₂\u0026middot;eq, lower than the EFL League One club but higher than the other three football teams. Collectively, these findings suggest that while professional football team performance catering services operate at a smaller scale, the per-meal emissions associated with fuelling elite players - driven by elevated energy, protein, and animal-source food requirements - can rival those of large institutional catering operations, highlighting the need for more sustainable menu design within elite sport.\u003c/p\u003e \u003cp\u003eWhile no daily dietary GHGE target currently exists to ensure emissions remain within planetary boundaries, it is widely recognised that diets containing less meat produce lower GHGE.\u003csup\u003e27\u003c/sup\u003e Mean daily emissions are estimated at 2.16 kg\u0026middot;CO₂\u0026middot;eq\u0026middot;d⁻\u0026sup1; for vegan diets, 3.33 kg\u0026middot;CO₂\u0026middot;eq\u0026middot;d⁻\u0026sup1; for vegetarian diets, 3.81 kg\u0026middot;CO₂\u0026middot;eq\u0026middot;d⁻\u0026sup1; for fish-eater diets, and 4.21 to 7.28 kg\u0026middot;CO₂\u0026middot;eq\u0026middot;d⁻\u0026sup1; for low to high meat eater diets (\u0026lt;\u0026thinsp;50 to \u0026gt;\u0026thinsp;100 g\u0026middot;d⁻\u0026sup1; meat, respectively). The present data show that the WSL (Team Four) food service produced a GHGE per cover per day (1.73 kg\u0026middot;CO₂\u0026middot;eq) that sat below the mean value associated with a vegan diet, whereas the EFL Champ (Team Two) emissions were between those of vegan and vegetarian diets, the EPL (Team One) was comparable to a pescetarian diet, and the EFL L1 (Team Three) approximated a high meat-eater profile. All food services provided only breakfast and lunch, with players and staff sourcing additional meals and beverages independently. Consequently, while the club-based food services of the EPL, EFL Champ, and WSL teams align with diets typically associated with low meat consumption, total daily GHGE at the individual level are likely considerably higher, depending on off-site dietary choices. The comparatively low GHGE observed in the WSL (Team Four) likely reflects its insufficient daily energy (967\u0026thinsp;\u0026plusmn;\u0026thinsp;19 kcal), carbohydrate (83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46 g), protein (49\u0026thinsp;\u0026plusmn;\u0026thinsp;1 g), and fat (48\u0026thinsp;\u0026plusmn;\u0026thinsp;2 g) provision relative to current elite football nutrition guidelines and female player requirements (2,600-2,900 kcal.d\u003csup\u003e-1\u003c/sup\u003e).\u003csup\u003e39, 47,48,51\u003c/sup\u003e Increasing overall energy provision to meet performance requirements would therefore be expected to raise total GHGE, the magnitude of which would depend on the specific foods and beverages selected.\u003c/p\u003e \u003cp\u003eThe use of a life cycle assessment enabled detailed quantification of each food group\u0026rsquo;s contribution to GHGE, revealing that meat - predominantly beef - was the largest contributor across all teams (~\u0026thinsp;38%) (see Supplementary Material 1). This aligns with previous findings from German hospital and nursing home food services, where meat accounted for 38% of total emissions,\u003csup\u003e54\u003c/sup\u003e and with global data identifying red meats such as beef (28.73 kg\u0026middot;CO₂\u0026middot;eq\u0026middot;kg⁻\u0026sup1;) and lamb (27.91 kg\u0026middot;CO₂\u0026middot;eq\u0026middot;kg⁻\u0026sup1;) as having the greatest warming potential.\u003csup\u003e26\u003c/sup\u003e Consistent with this, the EFL L1 (Team Three) had the highest GHGE and procured 24 kg of beef, while the WSL (Team Four) - with the lowest emissions - purchased only 10.5 kg (see Supplementary Material 1). Dairy products represented the second largest contributor (~\u0026thinsp;16%), primarily from cow\u0026rsquo;s milk and cheese. In contrast, plant-based milks such as soy and almond generate substantially lower emissions,\u003csup\u003e65\u003c/sup\u003e suggesting an opportunity for substitution in professional food services. Across all four teams, 364 L of cow\u0026rsquo;s milk contributed 557 kg\u0026middot;CO₂\u0026middot;eq, compared with 22 L of plant-based milks, producing 21 kg\u0026middot;CO₂\u0026middot;eq (see Supplementary Material 1). When standardised per litre, cow\u0026rsquo;s milk generated approximately 1.6 times greater emissions than plant-based alternatives. These data emphasise that targeted ingredient substitutions - particularly reducing beef and dairy use - could meaningfully lower the environmental footprint of elite football food provision without necessarily compromising performance nutrition quality.\u003c/p\u003e \u003cp\u003eThe study also quantified the water footprint of each football team\u0026rsquo;s food service, revealing substantial variation across clubs. The EFL Champ (Team Two) demonstrated the highest overall water footprint (3,225\u0026thinsp;\u0026plusmn;\u0026thinsp;63 kL\u0026middot;eq), while the EFL L1 (Team Three) had the greatest when expressed per cover per day (6.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 kL\u0026middot;eq). Across all teams, meat - particularly beef - was the dominant contributor (~\u0026thinsp;36%), followed by vegetables (~\u0026thinsp;18%) and dairy (~\u0026thinsp;12%), reflecting global trends in agricultural water use. These findings highlight that higher energy and protein provision, especially from animal sources, markedly increases water demand, underscoring the tension between fuelling performance and maintaining environmental responsibility. Practical mitigation strategies include shifting menus toward lower blue-water foods (e.g., legumes, oats, and plant-based milks)\u003csup\u003e28\u003c/sup\u003e and leveraging technological or agricultural innovations such as rainwater harvesting,\u003csup\u003e66\u003c/sup\u003e deficit or drip irrigation,\u003csup\u003e67,68\u003c/sup\u003e and organic farming systems that use less blue water.\u003csup\u003e69\u003c/sup\u003e For elite sport organisations, integrating these insights into procurement and catering policy offers a tangible route to reduce environmental impact while preserving nutritional quality and competitive standards.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Practical applications\u003c/h2\u003e \u003cp\u003eWhile elite footballers\u0026rsquo; energy and macronutrient demands exceed those proposed in population-level sustainable diet models such as the EAT-Lancet reference diet,\u003csup\u003e41\u003c/sup\u003e the present findings highlight actionable opportunities to align performance nutrition with environmental responsibility. Across clubs, ruminant meat procurement (103.9 kg; 2425 kg\u0026middot;CO₂\u0026middot;eq) substantially outweighed legume purchases (50.4 kg; 100.6 kg\u0026middot;CO₂\u0026middot;eq), underscoring the disproportionate contribution of animal-derived protein to total GHGE. Team Three (EFL L1) provided the highest protein intake per cover per day (138\u0026thinsp;\u0026plusmn;\u0026thinsp;3 g), exceeding the daily requirement for an 80 kg footballer (1.6 g\u0026middot;kg⁻\u0026sup1; = 128 g) within just two meals. Even halving this provision would still meet per-meal targets of ~\u0026thinsp;0.4 g\u0026middot;kg⁻\u0026sup1; (~\u0026thinsp;32 g) to optimise muscle protein synthesis,\u003csup\u003e70,71\u003c/sup\u003e lowering environmental impact without compromising adaptation. Given the comparable amino acid completeness achievable through complementary plant proteins (e.g., legumes and cereals) and enhanced digestibility through processing,\u003csup\u003e72,73\u003c/sup\u003e partial replacement of ruminant meats with plant or non-ruminant options could meaningfully reduce GHGE. Team Three\u0026rsquo;s food service also recorded a 22% GHGE contribution from beverages, predominantly due to 2000 bottles of 500 mL water (490 kg\u0026middot;CO₂\u0026middot;eq). Considering that the environmental impact of bottled water is 1,400\u0026ndash;3,500 times greater than tap water,\u003csup\u003e74\u003c/sup\u003e installing filtered water systems would represent a simple, high-yield intervention to reduce emissions and associated health risks.\u003csup\u003e75\u003c/sup\u003e Collectively, these findings emphasise that both menu composition and operational practices - from protein sourcing to packaging - represent key leverage points for reducing environmental impact in elite football nutrition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Strengths, weaknesses, and future directions\u003c/h2\u003e \u003cp\u003eThis study represents the first known application of life cycle assessment methodology to quantify the environmental footprint of food service provision in professional football. Using the Nutritics Foodprint Carbon Footprint Functionality,\u003csup\u003e59\u003c/sup\u003e a reference-standard life cycle assessment framework,\u003csup\u003e25\u003c/sup\u003e the study adopted a rigorous bottom-up approach analysing 289 individual foods and beverages across 52 categories and four professional teams spanning the EPL, EFL Champ, EFL L1, and WSL. The inclusion of both GHGE and water footprint metrics provides a comprehensive evaluation of environmental impact, offering an evidence-based foundation for future sustainability assessments in elite sport catering and performance nutrition contexts. However, several limitations warrant consideration. The absence of procurement data identifying organic versus conventionally farmed produce limited assessment of agricultural production effects on total emissions and water use. Organic produce has been shown to be 43% lower in GHGE per \u0026lsquo;land unit\u0026rsquo; in comparison to conventionally farmed produce.\u003csup\u003e76\u003c/sup\u003e The GHGE and water footprint may also be dependent on the life cycle assessment used. For example, the water footprint of bottled water in the EFL L1 team was 1 kL\u0026middot;eq, equating to the quantity of water, not accounting for the water footprint of the plastic, typically polyethylene terephalate, and whether this is recycled or not.\u003csup\u003e77\u003c/sup\u003e Similarly, our data shows plant-based milks to have approximately half the water footprint than cow\u0026rsquo;s milk when standardised for weight (plant based milks\u0026thinsp;=\u0026thinsp;0.31 kL\u0026middot;eq\u0026middot;kg vs 0.63 kL\u0026middot;eq\u0026middot;kg), despite contemporary research demonstrating comparable water footprints.\u003csup\u003e65\u003c/sup\u003e Similarly, reliance on database-derived life cycle data rather than supplier-specific information introduces potential variation related to sourcing practices, transport distance, and regional production systems. These complexities highlight a wider issue within life cycle methodology that the data is likely influenced by the quality of data, and the life cycle assessment database utilised.\u003c/p\u003e \u003cp\u003eThe study also assessed club food service provision over a defined seven-day period, rather than capturing full dietary intake, thereby underestimating the true GHGE and water footprint associated with players\u0026rsquo; total consumption. Match day food service provision was not included (e.g. hotels, travel, stadia) or supplementation (e.g. energy gels, recovery powders) which may increase GHGE further. Moreover, variations in kitchen staffing, service frequency, and financial investment across clubs restrict generalisability to other professional or amateur sport settings.\u003c/p\u003e \u003cp\u003eFuture research should build upon these findings by conducting integrated dietary-level assessments that capture all meals consumed within and beyond the club environment. Incorporating direct procurement data, supplier-level sustainability credentials, and plate waste analysis will improve accuracy and ecological validity. Experimental interventions - such as menu redesigns substituting ruminant meats for plant-based or non-ruminant proteins, or chef and nutritionist education in sustainable menu engineering - could quantify the performance and environmental trade-offs of different food strategies. Furthermore, collaboration between sports nutritionists, environmental scientists, chefs, and club operations personnel is essential to embed sustainability principles into daily food service practice. Establishing benchmarks and reporting frameworks through governing bodies may ultimately enable football organisations to align nutritional excellence with planetary health objectives, advancing both performance and environmental leadership in elite sport.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThis study provides the first evidence that professional male and female football food services vary markedly in their environmental impact, with substantial differences in GHGEs and water footprints across teams. The findings reveal that ruminant meat and dairy were the dominant contributors to total emissions, while clubs with higher energy and protein provision - particularly from beef - produced the greatest GHGE and water footprint. These results highlight the potential for important environmental gains through practical menu-based interventions, such as reducing ruminant meat procurement, increasing plant-based protein inclusion, and replacing bottled water and dairy products with filtered systems and plant-based milks. Although football\u0026rsquo;s catering service operations appear smaller than those in healthcare and other institutional sectors, the sport\u0026rsquo;s global influence offers a unique opportunity to integrate sustainability into mainstream culture. Within the \u0026lsquo;Sport for Development\u0026rsquo; agenda and the sustainability frameworks of FIFA, UEFA, and the EPL,\u003csup\u003e30\u0026ndash;33\u003c/sup\u003e incorporating evidence-based sport nutrition strategies could align performance-focused catering with broader climate and resource goals, positioning football as a visible leader in sustainable sport nutrition.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGHGE \u0026ndash; Greenhouse gas emissions\u003c/p\u003e\n\u003cp\u003eEPL \u0026ndash; English Premier League\u003c/p\u003e\n\u003cp\u003eEFL Champ \u0026ndash; English Football League Championship\u003c/p\u003e\n\u003cp\u003eEFL L1 \u0026ndash; English Football League One\u003c/p\u003e\n\u003cp\u003eWSL \u0026ndash; Women\u0026rsquo;s Super League\u003c/p\u003e\n\u003cp\u003eFIFA -\u0026nbsp;\u003cstrong\u003eF\u0026eacute;d\u0026eacute;ration Internationale de Football Association\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUEFA -\u0026nbsp;\u003cstrong\u003eUnion of European Football Associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIPCC - Intergovernmental Panel on Climate Change\u003c/p\u003e\n\u003cp\u003eCLs \u0026ndash; Confidence Limits\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eEthical approval for the study was obtained through Leeds Beckett University ethics committee (reference number: 139424).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eAll football clubs consented for their food procurement data to be published in this study\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was required for this study\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eO Turner, N Costello, S Chantler, and N Mitchell designed the study. A Jenkinson, H Louis, C Oakley and S Parker provided food service procurement data for each respective football club which was collated by N Costello. O Turner analysed the data and drafted the manuscript. N Costello, S Chantler, and N Mitchell assisted with revising the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data which supports the conclusions made within the manuscript are provided in the main text. Supplementary materials such as the classifications and the categorisation of each individual food and beverage item are provided in Supplementary Material 1 for greater interpretation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen MR, Dube OP, Solecki W, Arag\u0026oacute;n-Durand F, Cramer W, Humphreys S, et al. Framing and Context. In: Masson-Delmotte V, Zhai P, P\u0026ouml;rtner H-O, Roberts D, Skea J, Shukla PR, et al., editors. Global Warming of 1.5\u0026deg;C. An IPCC Special Report. Cambridge (UK): Cambridge University Press; 2018. p. 49\u0026ndash;92. doi:10.1017/9781009157940.003.\u003c/li\u003e\n\u003cli\u003eArmstrong McKay DI, Staal A, Abrams JF, et al. Exceeding 1.5\u0026deg;C global warming could trigger multiple climate tipping points. Science. 2022;377(6611):eabn7950. doi:10.1126/science.abn7950.\u003c/li\u003e\n\u003cli\u003eIntergovernmental Panel on Climate Change. IPCC [Internet]. Available from: https://www.ipcc.ch\u003c/li\u003e\n\u003cli\u003eUnited Nations Framework Convention on Climate Change. The Paris Agreement [Internet]. Available from: https://unfccc.int/process-and-meetings/the-paris-agreement\u003c/li\u003e\n\u003cli\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. doi:10.1038/s43016-021-00225-9.\u003c/li\u003e\n\u003cli\u003eLal R. Farming systems to return land for nature: It\u0026apos;s all about soil health and re-carbonization of the terrestrial biosphere. Farming Syst. 2023;1(1):100002. doi:10.1016/j.farsys.2023.100002.\u003c/li\u003e\n\u003cli\u003eIvanovich CC, Sun T, Gordon DR, Ocko IB. Future warming from global food consumption. Nat Clim Chang. 2023;13(3):297\u0026ndash;302. doi:10.1038/s41558-023-01605-8.\u003c/li\u003e\n\u003cli\u003eCampbell 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. Ecol Soc. 2017;22(4). Available from: https://www.jstor.org/stable/26798991\u003c/li\u003e\n\u003cli\u003eSteffen W, Richardson K, Rockstr\u0026ouml;m J, Cornell SE, Fetzer I, Bennett EM, et al. Planetary boundaries: Guiding human development on a changing planet. Science. 2015;347(6223):1259855. doi:10.1126/science.1259855.\u003c/li\u003e\n\u003cli\u003eUnited Nations. Global Issues: Population [Internet]. Available from: https://www.un.org/en/global-issues/population\u003c/li\u003e\n\u003cli\u003eSpringmann M, Clark M, Mason-D\u0026rsquo;Croz D, Wiebe K, Bodirsky BL, Lassaletta L, et al. Options for keeping the food system within environmental limits. Nature. 2018;562(7728):519\u0026ndash;25. doi:10.1038/s41586-018-0594-0.\u003c/li\u003e\n\u003cli\u003eClark MA, Domingo NGG, Colgan K, Thakrar SK, Tilman D, Lynch J, et al. Global food system emissions could preclude achieving the 1.5\u0026deg; and 2\u0026deg;C climate change targets. Science. 2020;370(6517):705\u0026ndash;8. doi:10.1126/science.aba7357.\u003c/li\u003e\n\u003cli\u003eRingler C, Agbonlahor M, Barron J, Baye K, Meenakshi JV, Mekonnen DK, Uhlenbrook S. The role of water in transforming food systems. Glob Food Sec. 2022;33:100639. doi:10.1016/j.gfs.2022.100639.\u003c/li\u003e\n\u003cli\u003eRavinandrasana VP, Franzke CLE. The first emergence of unprecedented global water scarcity in the Anthropocene. Nat Commun. 2025;16:8281. doi:10.1038/s41467-025-63784-6.\u003c/li\u003e\n\u003cli\u003eRai SK, Kookana R, Drechsel P, Jamwal P, Vanderzalm J. Urbanisation and emerging economies: Issues and potential solutions for water and food security. Sci Total Environ. 2020;732:139057.\u003c/li\u003e\n\u003cli\u003eKummu M, Guillaume J, de Moel H, et al. The world\u0026rsquo;s road to water scarcity: shortage and stress in the 20th century and pathways towards sustainability. Sci Rep. 2016;6:38495. doi:10.1038/srep38495\u003c/li\u003e\n\u003cli\u003eBaggio G, Qadir M, Smakhtin V. Freshwater availability status across countries for human and ecosystem needs. Sci Total Environ. 2021;792:148230. doi:10.1016/j.scitotenv.2021.148230\u003c/li\u003e\n\u003cli\u003eHestir EL, Brando VE, Bresciani M, Giardino C, Matta E, Villa P, Dekker AG. Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission. Remote Sens Environ. 2015;167:181\u0026ndash;95. Available from: https://doi.org/10.1016/j.rse.2015.05.023\u003c/li\u003e\n\u003cli\u003eKhatiwala S, Tanhua T, Mikaloff Fletcher S, Gerber M, Doney SC, Graven HD, et al. Global ocean storage of anthropogenic carbon. Biogeosciences. 2013;10:2169\u0026ndash;91. doi:10.5194/bg-10-2169-2013.\u003c/li\u003e\n\u003cli\u003eUNICEF. Water scarcity [Internet]. Available from: https://www.unicef.org/wash/water-scarcity\u003c/li\u003e\n\u003cli\u003eRichardson K, Steffen W, Lucht W, et al. Earth beyond six of nine planetary boundaries. Sci Adv. 2023;9:eadh2458.\u003c/li\u003e\n\u003cli\u003ePorkka M, Virkki V, Wang-Erlandsson L, et al. Notable shifts beyond pre-industrial streamflow and soil moisture conditions transgress the planetary boundary for freshwater change. Nat Water. 2024;2:262\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eIngrao C, Strippoli R, Lagioia G, Huisingh D. Water scarcity in agriculture: An overview of causes, impacts and approaches for reducing the risks. Heliyon. 2023;9(8):e18507. doi:10.1016/j.heliyon.2023.e18507.\u003c/li\u003e\n\u003cli\u003eWillett W, Rockstr\u0026ouml;m J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Food in the Anthropocene: the EAT\u0026ndash;Lancet Commission on healthy diets from sustainable food systems. Lancet. 2019;393(10170):447\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eRebitzer G, Ekvall T, Frischknecht R, et al. Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications. Int J Life Cycle Assess. 2004;10(1):1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eClune S, Crossin E, Verghese K. Systematic review of greenhouse gas emissions for different fresh food categories. J Clean Prod. 2017;140:766\u0026ndash;83. doi:10.1016/j.jclepro.2016.04.082.\u003c/li\u003e\n\u003cli\u003eScarborough P, Clark M, Cobiac L, et al. Vegans, vegetarians, fish-eaters and meat-eaters in the UK show discrepant environmental impacts. Nat Food. 2023;4:565\u0026ndash;74. doi:10.1038/s43016-023-00795-w.\u003c/li\u003e\n\u003cli\u003ePoore J, Nemecek T. Reducing food\u0026apos;s environmental impacts through producers and consumers. Science. 2018;360(6392):987\u0026ndash;92. doi:10.1126/science.aaq0216.\u003c/li\u003e\n\u003cli\u003eRamankutty N. Both technological innovations and cultural change are key to a sustainability transition. PLoS Biol. 2023;21(9):e3002298. doi:10.1371/journal.pbio.3002298.\u003c/li\u003e\n\u003cli\u003ePeachey JW, Schulenkorf N, Spaaij R. Sport for Social Change: Bridging the Theory\u0026ndash;Practice Divide. J Sport Manag. 2019;33(5):361\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eF\u0026eacute;d\u0026eacute;ration Internationale de Football Association. FIFA Climate Strategy [Internet]. Available from: https://digitalhub.fifa.com/m/a6e93d3f1e33b09/original/FIFA-Climate-Strategy.pdf\u003c/li\u003e\n\u003cli\u003eUnion of European Football Associations. UEFA Football Sustainability Strategy [Internet]. Available from: https://editorial.uefa.com/resources/0270-13f888ffa3e5-931c597968cb-1000/uefa_football_sustainability_strategy.pdf\u003c/li\u003e\n\u003cli\u003ePremier League. Environmental Sustainability Strategy [Internet]. Available from: https://resources.premierleague.pulselive.com/premierleague/document/2025/03/14/76d43137-a03a-44b7-a339-4c719f31446f/Premier-League-Environmental-Sustainability-Strategy.pdf\u003c/li\u003e\n\u003cli\u003eUnited Nations Framework Convention on Climate Change. Sports for Climate Action [Internet]. Available from: https://unfccc.int/climate-action/sectoral-engagement/sports-for-climate-action\u003c/li\u003e\n\u003cli\u003eUnited Nations Framework Convention on Climate Change. Participants in the Sports for Climate Action Framework [Internet]. Available from: https://unfccc.int/climate-action/sectoral-engagement/sports-for-climate-action/participants-in-the-sports-for-climate-action-framework#Sports-for-Climate-Action-signatories\u003c/li\u003e\n\u003cli\u003ePremier League. News: Premier League joins Sports for Climate Action [Internet]. Available from: https://www.premierleague.com/en/news/4266971\u003c/li\u003e\n\u003cli\u003eMeyer N, Reguant-Closa A. \u0026ldquo;Eat as if you could save the planet and win!\u0026rdquo; sustainability integration into nutrition for exercise and sport. Nutrients. 2017;9(4):412. doi:10.3390/nu9040412.\u003c/li\u003e\n\u003cli\u003eMeyer NL, Reguant-Closa A, Nemecek T. Sustainable diets for athletes. Curr Nutr Rep. 2020;9(3):147\u0026ndash;62. doi:10.1007/s13668-020-00318-0.\u003c/li\u003e\n\u003cli\u003eCollins J, Maughan RJ, Gleeson M, Bilsborough J, Jeukendrup A, Morton JP, et al. UEFA expert group statement on nutrition in elite football: current evidence to inform practical recommendations and guide future research. Br J Sports Med. 2021;55(8):416. doi:10.1136/bjsports-2019-101961.\u003c/li\u003e\n\u003cli\u003eSchulenkorf N, Sherry E, Rowe K. Sport for development: An integrated literature review. J Sport Manag. 2016;30(1):22\u0026ndash;39. doi:10.1123/jsm.2014-0263.\u003c/li\u003e\n\u003cli\u003eRockstr\u0026ouml;m J, Willett W, Loken B, Springmann M, Lang T, Vermeulen S, et al. The EAT\u0026ndash;Lancet Commission on healthy, sustainable, and just food systems. Lancet. 2025;406(10512):1625\u0026ndash;1700.\u003c/li\u003e\n\u003cli\u003eEbine N, Rafamantanantsoa HH, Nayuki Y, et al. Measurement of total energy expenditure by the doubly labelled water method in professional soccer players. J Sports Sci. 2002;20(5):391\u0026ndash;7. doi:10.1080/026404102317366645.\u003c/li\u003e\n\u003cli\u003eAnderson L, Orme P, Naughton RJ, et al. Energy Intake and Expenditure of Professional Soccer Players of the English Premier League: Evidence of Carbohydrate Periodization. Int J Sport Nutr Exerc Metab. 2017;27(3):228\u0026ndash;38. doi:10.1123/ijsnem.2016-0259.\u003c/li\u003e\n\u003cli\u003eAnderson L, Close GL, Morgans R, et al. Assessment of Energy Expenditure of a Professional Goalkeeper From the English Premier League Using the Doubly Labeled Water Method. Int J Sports Physiol Perform. 2019;14(5):681\u0026ndash;4. doi:10.1123/ijspp.2018-0520.\u003c/li\u003e\n\u003cli\u003eBrinkmans NYJ, Iedema N, Plasqui G, Wouters L, Saris WHM, van Loon LJC, van Dijk JW. Energy expenditure and dietary intake in professional football players in the Dutch Premier League: Implications for nutritional counselling. J Sports Sci. 2019;37(24):2759\u0026ndash;67. doi:10.1080/02640414.2019.1576256.\u003c/li\u003e\n\u003cli\u003eHannon MP, Parker LJF, Carney DJ, et al. Energy Requirements of Male Academy Soccer Players from the English Premier League. Med Sci Sports Exerc. 2021;53(1):200\u0026ndash;10. doi:10.1249/MSS.0000000000002443.\u003c/li\u003e\n\u003cli\u003eMorehen JC, Rosimus C, Cavanagh BP, et al. Energy Expenditure of Female International Standard Soccer Players: A Doubly Labeled Water Investigation. Med Sci Sports Exerc. 2022;54(5):769\u0026ndash;79. doi:10.1249/MSS.0000000000002850.\u003c/li\u003e\n\u003cli\u003eDasa MS, Friborg O, Kristoffersen M, Pettersen G, Plasqui G, Sundgot-Borgen JK, et al. Energy expenditure, dietary intake and energy availability in female professional football players. BMJ Open Sport Exerc Med. 2023;9:e001553. doi:10.1136/bmjsem-2023-001553.\u003c/li\u003e\n\u003cli\u003eStables RG, Hannon MP, Jacob AD, Topping O, Costello NB, Boddy LM, et al. Daily energy requirements of male academy soccer players are greater than age-matched non-academy soccer players: A doubly labelled water investigation. J Sports Sci. 2023;41(12):1218\u0026ndash;30. doi:10.1080/02640414.2023.2263707.\u003c/li\u003e\n\u003cli\u003eBrinkmans N, Plasqui G, van Loon L, van Dijk JW. Energy expenditure and dietary intake in professional female football players in the Dutch Women\u0026apos;s League: Implications for nutritional counselling. J Sports Sci. 2024;42(4):313\u0026ndash;22. doi:10.1080/02640414.2024.2329850.\u003c/li\u003e\n\u003cli\u003eMcHaffie SJ, Langan-Evans C, Strauss JA, Areta JL, Rosimus C, Evans M, et al. Energy expenditure, intake and availability in female soccer players via doubly labelled water: Are we misrepresenting low energy availability? Exp Physiol. 2024 Aug 15. doi:10.1113/EP091589.\u003c/li\u003e\n\u003cli\u003eFoo WL, Hambly C, Tester E, et al. Energy Expenditure of Male Soccer Players from an English Premier League Team Does Not Differ Between One-Game and Two-Game Per Week Microcycles. Med Sci Sports Exerc. 2025 Sep 2. doi:10.1249/MSS.0000000000003850.\u003c/li\u003e\n\u003cli\u003eArrieta-Aspilcueta AG, Bentley MRN, Backhouse SH, et al. The role of the chef in professional football: a survey of current practice in the English Premier and Football Leagues. Perform Nutr. 2025;1:3. doi:10.1186/s44410-025-00004-8.\u003c/li\u003e\n\u003cli\u003eP\u0026ouml;rtner LM, Schlenger L, Gabrysch S, Lambrecht NJ. Dietary quality and environmental footprint of health-care foodservice: a quantitative analysis using dietary indices and lifecycle assessment data. Lancet Planet Health. 2025;9(7):101274. doi:10.1016/j.lanplh.2025.05.004.\u003c/li\u003e\n\u003cli\u003eKhanna M, Daddi T, Merlo F, et al. An Assessment on the Carbon Footprint of a Football Club\u0026mdash;an Action Research from Theory to Practice. Circ Econ Sust. 2024;4:1587\u0026ndash;612. doi:10.1007/s43615-024-00350-0.\u003c/li\u003e\n\u003cli\u003ePereira RPT, Filimonau V, Ribeiro GM. Score a goal for climate: Assessing the carbon footprint of travel patterns of the English Premier League clubs. J Clean Prod. 2019;227:167\u0026ndash;77. Available from: https://doi.org/10.1016/j.jclepro.2019.04.138\u003c/li\u003e\n\u003cli\u003eDosumu A, Colbeck I, Bragg R. Greenhouse gas emissions as a result of spectators travelling to football in England. Sci Rep. 2017;7:6986. doi:10.1038/s41598-017-06141-y.\u003c/li\u003e\n\u003cli\u003eNutritics. Homepage [Internet]. Available from: https://www.nutritics.com/en/\u003c/li\u003e\n\u003cli\u003eNutritics. Foodprint Carbon Footprint Calculator [Internet]. Available from: https://www.nutritics.com/en/product/foodprint-carbon-footprint-calculator/\u003c/li\u003e\n\u003cli\u003eScheelbeek P, Green R, Papier K, et al. Health impacts and environmental footprints of diets that meet the Eatwell Guide recommendations: analyses of multiple UK studies. BMJ Open. 2020;10(8):e037554. doi:10.1136/bmjopen-2020-037554.\u003c/li\u003e\n\u003cli\u003eAgribalyse. Documentation [Internet]. Available from: https://doc.agribalyse.fr/documentation-en\u003c/li\u003e\n\u003cli\u003ePetersson T, Secondi L, Magnani A, et al. A multilevel carbon and water footprint dataset of food commodities. Sci Data. 2021;8:127. doi:10.1038/s41597-021-00909-8.\u003c/li\u003e\n\u003cli\u003eHestia Earth [Internet]. Available from: https://www.hestia.earth\u003c/li\u003e\n\u003cli\u003eWall CJ, Pearce J. Greenhouse gas emissions of school lunches provided for children attending school nurseries: A cross-sectional study. J Hum Nutr Diet. 2024;37(5):1288\u0026ndash;95. doi:10.1111/jhn.13345.\u003c/li\u003e\n\u003cli\u003eKhanpit V, Viswanathan S, Hinrichsen O. Environmental impact of animal milk vs plant-based milk: Critical review. J Clean Prod. 2024;449:141703. doi:10.1016/j.jclepro.2024.141703.\u003c/li\u003e\n\u003cli\u003eRodrigues de S\u0026aacute; Silva AC, Bimbato AM, Balestieri JAP, Vilanova MRN. Exploring environmental, economic and social aspects of rainwater harvesting systems: A review. Sustain Cities Soc. 2022;76:103475. doi:10.1016/j.scs.2021.103475.\u003c/li\u003e\n\u003cli\u003eGeerts S, Raes D. Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agric Water Manag. 2009;96:1275\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eVishwakarma DK, Kumar R, Tomar AS, Kuriqi A. Eco-hydrological modeling of soil wetting pattern dimensions under drip irrigation systems. Heliyon. 2023;9(7):e18078. doi:10.1016/j.heliyon.2023.e18078.\u003c/li\u003e\n\u003cli\u003eWheeler SA, Bjornlund H, Zuo A. Watering the farm: Comparing organic and conventional irrigation water use in the Murray\u0026ndash;Darling Basin, Australia. Ecol Econ. 2015;112:78\u0026ndash;85. doi:10.1016/j.ecolecon.2015.02.019.\u003c/li\u003e\n\u003cli\u003eSchoenfeld BJ, Aragon AA. How much protein can the body use in a single meal for muscle-building? Implications for daily protein distribution. J Int Soc Sports Nutr. 2018;15:10. doi:10.1186/s12970-018-0215-1.\u003c/li\u003e\n\u003cli\u003eMacnaughton LS, Wardle SL, Witard OC, et al. The response of muscle protein synthesis following whole-body resistance exercise is greater following 40 g than 20 g of ingested whey protein. Physiol Rep. 2016;4(15):e12893. doi:10.14814/phy2.12893.\u003c/li\u003e\n\u003cli\u003eNichele S, Phillips SM, Boaventura BCB. Plant-based food patterns to stimulate muscle protein synthesis and support muscle mass in humans: a narrative review. Appl Physiol Nutr Metab. 2022;47(7):700\u0026ndash;10. doi:10.1139/apnm-2021-0806.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute; A, Moreno Y, Carciofi B. Food processing for the improvement of plant proteins digestibility. Crit Rev Food Sci Nutr. 2020;60(20):3367\u0026ndash;86. doi:10.1080/10408398.2019.1688249.\u003c/li\u003e\n\u003cli\u003eVillanueva CM, Garf\u0026iacute; M, Mil\u0026agrave; C, Olmos S, Ferrer I, Tonne C. Health and environmental impacts of drinking water choices in Barcelona, Spain: A modelling study. Sci Total Environ. 2021;795:148884. doi:10.1016/j.scitotenv.2021.148884.\u003c/li\u003e\n\u003cli\u003eRaimann JG, Boaheng JM, Narh P, et al. Public health benefits of water purification using recycled hemodialyzers in developing countries. Sci Rep. 2020;10:11101. doi:10.1038/s41598-020-68408-1.\u003c/li\u003e\n\u003cli\u003eChiriac\u0026ograve; MV, Castaldi S, Valentini R. Determining organic versus conventional food emissions to foster the transition to sustainable food systems and diets: Insights from a systematic review. J Clean Prod. 2022;380(Pt 2):134937. Available from: https://doi.org/10.1016/j.jclepro.2022.134937\u003c/li\u003e\n\u003cli\u003eSilva B, Costa I, Santana P, Zacarias ME, Machado B, Silva P, Carvalho S, Faria F, Basto-Silva C. Environmental performance of different water bottles with different compositions: A cradle to gate approach. Cleaner Prod Lett. 2024;6:100061. Available from: https://doi.org/10.1016/j.clpl.2024.100061\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"performance-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Performance Nutrition](https://performancenutrition.biomedcentral.com/)","snPcode":"44410","submissionUrl":"https://submission.springernature.com/new-submission/44410/3","title":"Performance Nutrition","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sustainability, nutrition, sport, football, soccer","lastPublishedDoi":"10.21203/rs.3.rs-8924973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8924973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe global food system contributes approximately 34% of anthropogenic greenhouse gas emissions (GHGE) and exerts substantial pressure on freshwater resources, yet the environmental impact of food provision within professional sport remains unexplored. Addressing this evidence gap, this study quantified the environmental footprint of food service provision across four professional football clubs in England, representing the English Premier League (EPL), English Football League Championship (EFL Champ), English Football League One (EFL L1), and Women\u0026rsquo;s Super League (WSL). The primary aims were to determine total and relative GHGE, water footprints, and the contribution of individual food categories to each environmental metric.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA seven-day cross-sectional design was employed using reference-standard life cycle assessment methodology via the Nutritics \u003cem\u003eFoodprint Carbon Footprint Functionality\u003c/em\u003e. A bottom-up analytical approach assessed 289 individual food and beverage items across 52 categories. Data were analysed descriptively, with comparisons made using 95% confidence limits to preserve interpretive transparency.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eMarked inter-team variation was observed in relative nutritional provision and environmental impact. The EFL L1 team demonstrated the highest energy (2,272\u0026thinsp;\u0026plusmn;\u0026thinsp;27 kcal\u0026middot;d⁻\u0026sup1;), carbohydrate (228\u0026thinsp;\u0026plusmn;\u0026thinsp;3 g\u0026middot;d⁻\u0026sup1;), protein (138\u0026thinsp;\u0026plusmn;\u0026thinsp;3 g\u0026middot;d⁻\u0026sup1;), and fat (90\u0026thinsp;\u0026plusmn;\u0026thinsp;2 g\u0026middot;d⁻\u0026sup1;) provision per cover per day, as well as the GHGEs (7.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 kg\u0026middot;CO₂\u0026middot;eq) and water footprint (6.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 kL\u0026middot;eq). The EPL, EFL Champ, and WSL teams followed in descending order for both energy and environmental measures. Across all clubs, meat - particularly beef - was the largest contributor to total GHGE (~\u0026thinsp;38%) and water footprint (~\u0026thinsp;36%), with dairy (~\u0026thinsp;16%) and vegetables (~\u0026thinsp;18%) representing notable secondary contributors. Beverages, primarily bottled water, accounted for 22% of total GHGE in the EFL L1 team, underscoring how both menu composition and operational practices influence environmental outcomes.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThis study provides the first evidence that professional football food services differ markedly in their environmental impact, with higher energy and protein provision - particularly from ruminant meat and dairy - driving greater GHGEs and water use. Practical strategies such as moderating overall protein provision, particularly from red meat procurement, while increasing plant-based protein inclusion, and replacing bottled water and dairy with filtered and plant-based alternatives could substantially reduce emissions without compromising performance nutrition. Embedding these actions within FIFA, UEFA, and EPL sustainability frameworks would align football with global climate goals and position the sport as a leader in advancing sustainable nutrition practices.\u003c/p\u003e","manuscriptTitle":"What’s on the Menu? Quantifying the Greenhouse Gas Emissions and Water Footprint of the Food Provision within Elite Male \u0026amp; Female Football Teams","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 18:01:58","doi":"10.21203/rs.3.rs-8924973/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-17T08:22:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T23:26:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3752554620780092889553772185015603014","date":"2026-03-14T01:13:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-13T06:56:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T01:29:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-23T01:28:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Performance Nutrition","date":"2026-02-20T10:33:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"performance-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Performance Nutrition](https://performancenutrition.biomedcentral.com/)","snPcode":"44410","submissionUrl":"https://submission.springernature.com/new-submission/44410/3","title":"Performance Nutrition","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c69c97f-a054-4d0a-bbc8-941bfeb32c34","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T08:43:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 18:01:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8924973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8924973","identity":"rs-8924973","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}