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Dietary greenhouse gas emissions of meat-eaters, fish-eaters, vegetarians and vegans in the UK


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The production of animal-based foods is associated with higher greenhouse gas (GHG) emissions than plant-based foods. The objective of this study was to estimate the difference in dietary GHG emissions between self-selected meat-eaters, fish-eaters, vegetarians and vegans in the UK. Subjects were participants in the EPIC-Oxford cohort study. The diets of 2,041 vegans, 15,751 vegetarians, 8,123 fish-eaters and 29,589 meat-eaters aged 20-79 were assessed using a validated food frequency questionnaire. Comparable GHG emissions parameters were developed for the underlying food codes using a dataset of GHG emissions for 94 food commodities in the UK, with a weighting for the global warming potential of each component gas. The average GHG emissions associated with a standard 2,000 kcal diet were estimated for all subjects. ANOVA was used to estimate average dietary GHG emissions by diet group adjusted for sex and age. The age-and-sex-adjusted mean (95 % confidence interval) GHG emissions in kilograms of carbon dioxide equivalents per day (kgCO(2)e/day) were 7.19 (7.16, 7.22) for high meat-eaters ( > = 100 g/d), 5.63 (5.61, 5.65) for medium meat-eaters (50-99 g/d), 4.67 (4.65, 4.70) for low meat-eaters ( < 50 g/d), 3.91 (3.88, 3.94) for fish-eaters, 3.81 (3.79, 3.83) for vegetarians and 2.89 (2.83, 2.94) for vegans. In conclusion, dietary GHG emissions in self-selected meat-eaters are approximately twice as high as those in vegans. It is likely that reductions in meat consumption would lead to reductions in dietary GHG emissions.
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Dietary greenhouse gas emissions of meat-eaters,
fish-eaters, vegetarians and vegans in the UK
Peter Scarborough &Paul N. Appleby &Anja Mizdrak &
Adam D. M. Briggs &Ruth C. Travis &
Kathryn E. Bradbury &Timothy J. Key
Received: 24 October 2013 / Accepted: 31 May 2014
#The Author(s) 2014. This article is published with open access at
Abstract The production of animal-based foods is associated with higher greenhouse gas
(GHG) emissions than plant-based foods. The objective of this study was to estimate the
difference in dietary GHG emissions between self-selected meat-eaters, fish-eaters, vegetarians
and vegans in the UK. Subjects were participants in the EPIC-Oxford cohort study. The diets of
2,041 vegans, 15,751 vegetarians, 8,123 fish-eaters and 29,589 meat-eaters aged 2079 were
assessed using a validated food frequency questionnaire. Comparable GHG emissions param-
eters were developed for the underlying food codes using a dataset of GHG emissions for 94
food commodities in the UK, with a weighting for the global warming potential of each
component gas. The average GHG emissions associated with a standard 2,000 kcal diet were
estimated for all subjects. ANOVA was used to estimate average dietary GHG emissions by diet
group adjusted for sex and age. The age-and-sex-adjusted mean (95 % confidence interval)
GHG emissions in kilograms of carbon dioxide equivalents per day (kgCO
e/day) were 7.19
(7.16, 7.22) for high meat-eaters (>=100 g/d), 5.63 (5.61, 5.65) for medium meat-eaters (50-
99 g/d), 4.67 (4.65, 4.70) for low meat-eaters (< 50 g/d), 3.91 (3.88, 3.94) for fish-eaters, 3.81
(3.79, 3.83) for vegetarians and 2.89 (2.83, 2.94) for vegans. In conclusion, dietary GHG
emissions in self-selected meat-eaters are approximately twice as high as those in vegans. It is
likely that reductions in meat consumption would lead to reductions in dietary GHG emissions.
1 Introduction
Production, transport, storage, cooking and wastage of food are substantial contributors to green-
house gas (GHG) emissions (Committee on Climate Change 2010;Garnett2008;
Intergovernmental Panel on Climate Change 2007). These GHG emissions include carbon dioxide
(from fossil fuels used to power farm machinery and to transport, store and cook foods), methane
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DOI 10.1007/s10584-014-1169-1
P. Scarborough (*):A. Mizdrak :A. D. M. Briggs
British Heart Foundation Centre on Population Approaches for Non-Communicable Disease Prevention,
Nuffield Department of Population Health, University of Oxford,
Old Road Campus, Headington, Oxford OX3 7LF, UK
P. N. Appleby :R. C. Travis :K. E. Bradbury:T. J. Key
Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Old Road
Campus, Roosevelt Drive, Oxford OX3 7LF, UK
(from enteric fermentation in ruminant livestock) and nitrous oxide (released from tilled and
fertilised soils). Both methane and nitrous oxide are many times more potent GHGs than carbon
dioxide and the majority of GHG emissions related to food are produced at the agricultural stage
(Audsley et al. 2009;Garnett2011). When measured by consumption (that is, all GHG emissions
related to products consumed in the UK, regardless of where they were produced) food is
responsible for approximately one fifth of all GHG emissions attributable to the UK (Berners-Lee
et al. 2012;Garnett2008). There is considerable variation in the amount of GHG emissions related
to different food groups, with animal-based products generally having much greater emissions than
plant-based products per unit weight (Audsley et al. 2009; Carlsson-Kanyama and Gonzalez 2009;
Committee on Climate Change 2010; Gonzalez et al. 2011; Steinfeld et al. 2006). This is largely
because of the inefficiencies involved in growing cereal crops to be used as animal feed, and
methane produced in the digestive system of ruminants (Steinfeld et al. 2006). Although new
technologies and changes in farming practices provide some scope for reduction in GHG emissions,
substantial reductions can only be achieved through changes in consumption patterns and reduction
in food waste (Stehfest et al. 2009; Weidema et al. 2008). This paper is concerned with differences in
dietary GHG emissions between different diet groups within the UK and contributes to the debate
regarding what constitutes a healthy, sustainable diet. Specifically, we use comparable data on
actual diets of vegans, vegetarians, fish-eaters and meat-eaters to estimate the difference in dietary
GHG emissions attributable to these four diet groups. Previous estimates of dietary GHG emissions
for self-selected dietary groups have not compared meat consumers with those who abstain from
meat (Vieux et al. 2013; Masset et al. 2014). Other estimates of GHG emissions by diet groups have
been produced using modelled estimates of reduced meat diets (Baroni et al. 2007; Berners-Lee et al.
2012; Macdiarmid et al. 2012;Saxeetal.2013; Vieux et al. 2012;Wilsonetal.2013) and may not
reflect true differences in consumption behaviour between dietary groups.
2.1 Subjects and study design
The analyses are based on data from participants in the EPIC-Oxford cohort, which consists of
65,000 participants generally aged 20 and over at recruitment between 1993 and 1999 (Davey
et al. 2003). Participants were resident in the UK and recruited through collaborating general
practitioners, by post via vegetarian and vegan societies, and by adverts in vegetarian and
health food magazines. Participants were also asked to recruit friends and relatives
(snowballing). A validated semi-quantitative food-frequency questionnaire (FFQ) that esti-
mates intake (frequency of consumption) of 130 different food items over the previous
12 months (Bingham et al. 1994; Bingham et al. 1995) was completed at recruitment by most
participants. For the analyses in this paper, we excluded individuals aged less than 20 years or
80 years and over at recruitment (n=532), those who did not complete the FFQ or whose
estimated daily energy intake from the FFQ was outside realistic values (men<3.3 MJ or>
16.7 MJ, women<2.1 MJ or>14.7 MJ) or for whom more than 20 % of relevant food
frequencies were unreported (n=9,304), and 71 participants with unknown diet group, leaving
a final analysis dataset of 12,666 males and 42,838 females.
2.2 Classification of diet groups
For these analyses, six dietary groups were identified: high meat-eaters (>= 100 g/d), medium
meat-eaters (50 to 99 g/d), low meat-eaters (>0 and< 50 g/d), fish-eaters, vegetarians and
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vegans. Initially, subjects were categorised into meat-eaters, fish-eaters, vegetarians and vegans
according to responses to the following yes/no questions:
&Do you eat any meat (including bacon, ham, poultry, game, meat pies, sausages)?
&Do you eat any fish?
&Do you eat any eggs?
&Do you eat any dairy products (including milk, cheese, butter, yoghurt)?
2.3 Calculation of dietary GHG emissions
Nutritional analyses of the 130 food-item FFQ were based on nutritional data for 289 food
codes taken from UK food composition tables, primarily the McCance & Widdowson series
(Roe et al. 2002). We estimated the GHG emissions associated with these 289 food codes,
measured as kgCO
e (that is, kg of GHG weighted by global warming potential over a 100 year
time frame, with carbon dioxide weighted as 1, methane weighted as 25 and nitrous oxide
weighted as 298) per 100 g of food. The method used was adapted from a previous investigation
of the health impact of applying a carbon tax to foods in the UK (Briggs et al. 2013). The source
document for GHG parameters was Audsley et al. (2009), which estimates comparable GHG
emissions for 94 food commodities consumed in the UK. These parameters incorporate the life
cycle of food commodities from the earliest stages of production to the retail distribution centre.
Different parameters are estimated for foods produced in the UK, the EU and outside the EU.
We produced single UK estimates of GHG emissions for each food commodity, weighted by
current import and export patterns using 2007 food balance sheet data from the FAO (Food and
Agriculture Organization. FAOSTAT. July 2013). The weighted estimates for the 94 food
commodities are presented in the appendix. The GHG emissions for the 289 food codes were
then constructed from these 94 parameters using two techniques which we describe below as
adjusting for densityand estimating GHG emissions for recipes.
Adjusting for density For cheese, fruit juices, dried fruit and soya milk, the weight of food that
is consumed is not equivalent to the weight of food that is produced. GHG emissions
parameters for these food categories were not available, so we applied adjustment factors to
their primary commodity to account for the change in weight between production and
consumption. Where possible these adjustment factors were taken from published life cycle
analyses, but when these were unavailable we used information from specific food products.
The adjustment factors that were applied are shown in Table 1.
Estimating GHG emissions for recipes For the remaining food codes a recipe was sought
which was used to split the weight of the food between food commodities. This split was then
used to construct a GHG emissions estimate using the GHG parameters of the original
commodities. The UK food composition table (Roe et al. 2002) which provides nutritional
data for the FFQ was used as the first source for identifying recipes. If no recipe was found
then a Google search was performed with the food code name as the search term and the top
website found with a recipe for the food was used. Where ingredients were identified that were
not comparable to a food commodity (e.g. flaky pastry), then a recipe was identified for the
ingredient using the same method. For each food code the weights of the ingredients were
added in the order of heaviest to lightest, and a 90 % threshold was used such that once a value
greater than 90 % was achieved the remaining ingredients were disregarded. This threshold
was applied to remove small ingredients with unknown GHG emissions from the calculations
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Tab le 1 Adjustment factors for cheese, fruit juices, dried fruit and soya milk
Food Related GHG emissions commodity kgCO2e per 100 g of commodity Adjustment
kgCO2e per 100 g of food Rationale
Cheese Milk 0.184 10.1 1.858 10.1 L of milk required for 1 kg
semi-hard cheese (Berlin 2002)
Orange juice Oranges 0.006 3.2 0.019 3.22 kg of oranges required for 1 kg
of juice (combining natural
and concentrated) (Beccali et al. 2009)
Apple juice Apples 0.072 1.4 0.099 Copella website states 3lbs of apples are
packed into every litre of apple juice
(Copella. Copella fruit juices.
our-juices/juices Accessed July 2013)
Soya milk Soy beans 0.201 0.1 0.025 Recipe website suggests 160 g soy beans
combined with 1.5-2 L of water
( Homemade soy milk.
make-soy-milk/Accessed September
Raisins Grapes 0.083 3.2 0.264 Comparison of water content in grapes
and raisins in UK food composition
tables (Roe et al. 2002)
kgCO2e, kilograms of carbon dioxide equivalents (carbon dioxide has weighting 1, methane has weighting 25, nitrous oxide has weighting 298)
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(e.g. a pinch of nutmeg). We did not account for the cooking process (either at the industrial
stage or at home) for any of the food codes.
For seven food codes (drinking chocolate, chocolate digestive biscuits, semi-sweet biscuits,
Coffeemate, Horlicks powder, mixed toffees, and soft drinks) the use of a recipe seemed
implausible given that these products are largely produced on an industrial scale. Here recipes
were estimated (using percentages in the ingredients list where available) by a single researcher
and agreed by the research team. A complete spreadsheet showing recipes and calculations for
all 289 food codes is available upon request.
2.4 Standardisation of diets to 2,000 kcal
GHG emissions for each individual were standardised to a 2,000 kcal daily diet (the level used for
guideline daily energy intake for adults in the UK) so that differences in estimated energy consump-
tion between diet groups do not affect the results. This was done for three reasons. Firstly, the FFQ
does not allow for differences in portion sizes of fruit, vegetables and cereals which may differ
between dietary groups and could exaggerate differences in energy intake between groups. Secondly,
to address under and over-reporting that is common to diet studies (Scagliusi et al. 2008). Thirdly, so
that the standardised results can be used to estimate the change in GHG emissions that would result
from changing between dietary groups without changing the energy intake of the diet, which may be
more relevant when considering the potential impact of changing diets on GHG emissions.
2.5 Statistical analysis
The arithmetic means and standard deviations of dietary GHG emissions for each diet group
were calculated. An ANOVA was conducted to estimate GHG emissions by diet group
adjusted for sex and age, categorised in 10 years age bands from 2029 to 7079. Statistical
significance was set at the 5 % level and all analyses were conducted using the Stata statistical
package (StataCorp 2011).
The analysis dataset included 2,041 vegans, 15,751 vegetarians, 8,123 fish-eaters and 29,589
meat-eaters. Baseline characteristics by meat consumption are shown in Table 2. Meat-eaters
tended to be older than fish-eaters, vegetarians and vegans. Assuming an ordered
categorisation (high meat medium meat low meatfish-eaters vegetariansvegans),
there were significant trends towards lower total fat, saturated fat and protein consumption and
higher carbohydrate, total sugar, fibre and fruit and vegetables consumption as animal-based
food consumption decreased.
Table 3shows differences in dietary GHG emissions by diet group. The highest dietary
GHG emissions were found in high meat-eating men and the lowest dietary GHG emissions
were found in vegan women. The mean observed values of dietary GHG emissions for meat-
eaters (results reported for women and then men) was 46 % and 51 % higher than for fish-
eaters, 50 % and 54 % higher than for vegetarians and 99 % and 102 % higher than for vegans.
The results of the ANOVA analysis showed highly statistically significant differences
(p<0.0001) in dietary GHG emissions between the six diet groups after adjustment
for age and sex, with progressively higher emissions for groups with greater intakes
of animal-based products.
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Tab le 2 Descriptive statistics
High meat
Medium meat
Low meat
Fish-eaters Vegetarians Vegans p for
N 29,589 8,286 11,971 9,332 8,123 15,751 2,041
Mean (SD) age at recruitment 49.1 (12.8) 49.7 (12.3) 49.8 (12.6) 47.5 (13.3) 41.8 (12.9) 38.6 (12.7) 37.3 (13.1) <0.0001
% female 76.9 72.1 77.8 80.0 82.2 76.9 63.4 0.13
Mean (SD) energy intake (kcal/d) 1,972 (535) 2,214 (528) 1,933 (500) 1,809 (508) 1,889 (526) 1,872 (528) 1,747 (554) <0.0001
Mean (SD) total fat (% energy) 31.6 (5.9) 33.2 (5.4) 31.4 (5.7) 30.5 (6.2) 30.7 (6.3) 30.5 (6.5) 28.0 (7.3) < 0.0001
Mean (SD) saturated fat (% energy) 11.5 (3.3) 12.4 (3.1) 11.5 (3.2) 10.9 (3.4) 10.6 (3.3) 10.6 (3.4) 6.5 (2.1) <0.0001
Mean (SD) protein (% energy) 17.0 (3.0) 18.5 (3.1) 17.1 (2.8) 15.6 (2.5) 14.7 (2.4) 13.6 (2.1) 13.3 (2.3) < 0.0001
Mean (SD) carbohydrate (% energy) 48.0 (6.2) 45.0 (5.7) 48.1 (5.7) 50.5 (6.3) 51.0 (6.5) 52.5 (6.6) 55.6 (7.8) < 0.0001
Mean (SD) total sugars (% energy) 24.3 (5.7) 22.5 (5.1) 24.4 (5.4) 25.8 (6.3) 25.1 (6.3) 25.4 (6.3) 24.7 (8.7) <0.0001
Mean (SD) dietary fibre (NSP; g/d) 18.7 (6.8) 18.6 (6.2) 18.3 (6.5) 19.4 (7.4) 21.3 (7.5) 21.6 (7.8) 26.1 (9.3) < 0.0001
Mean (SD) fruit and vegetables (80 g portions/d) 6.4 (3.5) 6.1 (3.2) 6.3 (3.2) 6.9 (4.0) 7.3 (3.9) 7.1 (3.9) 8.7 (5.6) <0.0001
SD, Standard deviation
High meat= 100 g/d meat consumption; Medium meat =5099 g/d; Low meat =<50 g/d. p values indicate significance of trend along ordered categorisation high meat medium
meatlow meat fish-eatersvegetariansvegans
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Tab le 3 Mean greenhouse gas emissions per 2,000 kcal by diet type and sex
Men (observed values) Women (observed values) Adjusted for age and sex
N Mean dietary GHG emissions
SD N Mean dietary GHG emissions
SD Mean dietary GHG emissions
95 % CIs
All meat-eaters 6,380 5.93 2.01 22,759 5.71 1.75
High meat-eaters (100 g/day) 2,310 7.26 2.11 5,976 7.17 1.94 7.19 (7.16, 7.22)
Medium meat-eaters (5099 g/day) 2,654 5.66 1.60 9,317 5.62 1.38 5.63 (5.61, 5.65)
Low meat-eaters ( <50 g/day) 1,866 4.67 1.35 7,466 4.67 1.05 4.67 (4.65, 4.70)
Fish-eaters 1,448 3.94 1.12 6,675 3.90 0.88 3.91 (3.88, 3.94)
Vegetarians 3,641 3.85 1.29 12,110 3.80 0.93 3.81 (3.79, 3.83)
Vegans 747 2.94 1.25 1,294 2.87 0.90 2.89 (2.83, 2.94)
SD, Standard deviation; CIs, Confidence intervals; N, Number of Participants
kgCO2e, kilograms of carbon dioxide equivalents
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We have shown that dietary GHG emissions associated with self-selected diets in the UK are
strongly associated with the amount of animal-based products in the diet. After adjustment for
sex and age, an average 2,000 kcal high meat diet had 2.5 times as many GHG emissions than
an average 2,000 kcal vegan diet. This is the first study to demonstrate these differences in real
self-selected diets of meat eaters and those who abstain from meat. There were also significant
trends towards lower saturated fat, higher fibre and higher fruit and vegetable intake (but a
higher intake of sugars) as the quantity of animal-based products in the diet decreases.
Previous analyses of the same cohort have demonstrated lower BMI (Davey et al. 2003)and
fewer ischaemic heart disease events (Crowe et al. 2013) in diet groups with lower intakes of
animal products. Improved cardiovascular outcomes for vegetarian diets have been demon-
strated in meta-analyses of cohort studies conducted in western populations (Huang et al.
2012; Key et al. 1999). Although observational studies are susceptible to residual confounding,
non-systematic reviews of RCTs have demonstrated beneficial effects of plant-based diets on
lipids (Ferdowsian and Barnard 2009) and weight status (Berkow and Barnard 2006). This
suggests that advice to reduce the amount of meat and animal-based products in the diet would
be consistent with the definition of a healthy, sustainable diet, although reductions of meat at
the population level may pose nutritional challenges for key nutrients including iron and zinc
which should be monitored (Millward and Garnett 2010).
A recent representative survey in the UK using four day weighted food diaries, the National
Diet and Nutrition Survey 2008/10 (Bates et al. 2012), found that the average amount of meat
consumed in adults aged 1964 (including non-consumers) was 110 g/day, which suggests that
the majority of adults in the UK would be categorised as high meat consumersin our analysis.
Reducing the amount of animal-based products in the diet represents an achievable way for an
individual to reduce their carbon footprint. Assuming that the average daily energy intake in the
UK is 2,000 kcal, then moving from a high meat diet to a low meat diet would reduce an
individuals carbon footprint by 920kgCO
e every year, moving from a high meat diet to a
vegetarian diet would reduce the carbon footprint by 1,230kgCO
e/year, and moving from a high
meat diet to a vegan diet would reduce the carbon footprint by 1,560kgCO
e/year. For context, an
individual travelling on an economy return flight from London to New York has an addition to
their carbon footprint of 960kgCO
e (Carbon Footprint. Carbon footprint calculator. www. Accessed July 2013). A family running a 10 year old small
family car for 6,000miles has a carbon footprint of 2,440kgCO
e (Carbon Footprint. Carbon
footprint calculator. Accessed July 2013), roughly
equivalent to the annual carbon saving of two high meat eating adults moving to a vegetarian diet.
Two previous studies have estimated the difference in dietary GHG emissions of self-
selected dietary groups (Vieux et al. 2013;Massetetal.2014). Both of these studies were
based on the representative Individual and National Survey on Food consumption in France.
Vieux et al. (2013) showed that those who consumed a healthy diet, defined by low energy
density, high nutrient density and low consumption of saturated fat, sugar and sodium, had
higher dietary GHG emissions than those who consumed an unhealthy diet. Consumption of
ruminant meat and pork, poultry and eggs was similar in both healthy and unhealthy diet
groups. Masset et al. (2014) showed that diets with lower than average GHG emissions tended
to be less healthy, defined using a nutrient density index. These low GHG diets consisted of
approximately 20 % less meat, fish and eggs than the average diet. They also identified a
subset of the population who consumed a healthy and low GHG emissions diet which did not
cost more than an average diet and which was characterised by a higher content of plant-based
products. In contrast to the study reported here, the authors did not directly compare diet
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groups defined by levels of meat consumption. Other studies have estimated the difference in
dietary GHG emissions between diet groups, but have used modelled reduced meat dietary
scenarios for comparison. The modelling has been conducted using three methods: diets have
been constructed from scratch with reference to nutritionists (Baroni et al. 2007); observed
meat-eating diets have been modified with selected replacement foods(Berners-Lee et al.
2012;Saxeetal.2013;); or optimisation programmes have constructed a low-meat diet that
meets several pre-determined sustainability and nutritional criteria (Macdiarmid et al. 2012;
Vieux et al. 2012; Wilson et al. 2013). Most of these modelling studies suggested that reducing
animal-based products would reduce dietary GHG emissions (Baroni et al. 2007; Berners-Lee
et al. 2012; Macdiarmid et al. 2012;Saxeetal.2013), but one scenario considered by Vieux
et al. (2012) suggested that reduced meat diets may increase dietary GHG emissions. In this
scenario, meat was isocalorically replaced in the diet by fruit and vegetables - since the GHG
emissions per 100 kcal of food in their database were generally higher for fruit and vegetables
than for meat products, this resulted in an increase in GHG emissions. Saxe et al. (2013)found
that a vegetarian diet in Denmark would reduce dietary GHG emissions compared to the
average Danish diet by 27 %, compared to our estimate of 35 % reduction between meat eaters
and vegetarians. The difference may be due to the criterion in the Saxe et al. analysis that the
vegetarian diet should contain equal protein levels as the average Danish diet in the EPIC-
Oxford sample protein levels are significantly lower in vegetarians than in meat-eaters. By
comparing the actual diets of self-selected diet groups, our results are not limited by restrictive
criteria that may not be representative of true dietary behaviour. However, our data are based
on cross-sectional comparisons between dietary groups, and as such they do not tell us how
people would replace meat in the diet (although, the diet groups do at least represent diets that
are actually consumed in the population). This is an important limitation that should be
addressed by future longitudinal studies with repeated dietary measures.
The GHG emissions data used in this paper were developed specifically for this analysis.
Although the nutrient intakes estimated by the FFQ have been validated against food diaries and
some biomarkers (Bingham et al. 1994; Bingham et al. 1995), the GHG emissions have not. The
GHG estimates for food items used in this paper are subject to uncertainty that is not captured in
the confidence intervals shown in this paper for example, LCAs of animal-based foods have
shown considerable variation in GHG estimates, due to differences in methods used and in forms
of agricultural production (Nijdam et al. 2012; de Vries and de Boer 2010; Roy et al. 2009).
Estimates of total food-related GHG emissions using measurements of food consumption (such as
the FFQ) are underestimates because they do not take account of food wastage. Throughout the
analyses presented here we have assumed that GHG emission related to food wastage is
reasonably similar across all food groups, but this may not be the case. Estimates of food wastage
in the UK suggest that wastage of fruit and vegetables is higher than for meat products (Quested
et al. 2013), which could reduce the difference in GHG emissions between the dietary groups.
Consumption estimates derived from FFQs are also prone to under-reporting (Becker and Welten
2001; Scagliusi et al. 2008). Similarly, we have not adjusted for differences in raw and cooked
weight of foods in these analyses. In most instances, such an adjustment results in smaller cooked
weights, as it incorporates discarding inedible material (e.g. bones, skin, cores etc.), which would
inflate the size of the dietary GHG estimates reported here.
The diets observed in the EPIC-Oxford cohort may not represent current consumption
patterns in the UK. Firstly, they are taken from the baseline data collection period which was
conducted in the 1990s. Secondly, a large proportion of the meat-eaters in the EPIC-Oxford
cohort consists of family or friends of vegetarians and vegans, who are likely to have diets
which differ from those of the general population in the UK. This difference can be assessed
by comparing the amount of meat consumed in meat-eaters recruited through the snowballing
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technique with those recruited directly by participating GPs. This shows that 24 % of meat-
eaters recruited via the snowballing technique were high meat consumers, compared to 41 %
of meat-eaters recruited directly. Restricting the data by removing participants directly recruit-
ed makes little difference to the results (results not shown). Thirdly, the cohort tended to
consume a healthier diet than the current UK population. For example, average consumption
of fruit and vegetables in the EPIC Oxford cohort is over 6 portions per day, even in the meat
eaters.ThemostrecentNationalDietandNutritionSurvey(Batesetal.2012) estimated that
average consumption in the UK was 4.1 portions per day.
Further work is needed to establish a defined healthy, sustainable diet, which should include the
recommendation to reduce the consumption of animal-based foods. This is already a recommenda-
tion put forward by the Health Council of the Netherlands. Guidelines for a healthy diet: the
ecological perspective. Health Council of the Netherlands: The Hague and The (2011) Although this
study has only considered the impact of food on GHG emissions, reducing animal-based food
consumption has also been proposed as a mechanism for achieving global food security given future
trends in yield and agricultural land use (Foley et al. 2011;Godfrayetal.2010; Ray et al. 2013)and
could also play a role in reducing water stress and biodiversity loss (Steinfeld et al. 2006). Although
the epidemiological evidence largely supports the health-promoting effects of a vegetarian diet, this
evidence is primarily drawn from observational studies and residual confounding cannot be ruled
out. Further research is required to determine the longitudinal health effects of reducing animal-
based products within the diet, preferably in randomised trials. This work should consider micro-
nutrient intakes in vulnerable groups (Macdiarmid 2013; Millward and Garnett 2010)aswellasrisk
factors for cardiovascular disease and cancer.
The current trend in the UK is towards increased meat consumption. The percentage of
vegetarians and vegans in UK adults has decreased from an estimated 5 % in 2000/01 to 2 % in
2008/10, although these estimates are based on relatively small samples (Bates et al. 2012;
Henderson et al. 2002). Over the same period FAOSTAT estimates that total meat supply in the
UK increased from 78.5 kg/person/year to 84.2 kg/person/year (Food and Agriculture Organization.
FAO STAT. Accessed July 2013), including an
increase in consumption of beef in 1961 (when FAOSTAT records began) total meat supply
was only 69.2 kg/person/year. It is necessary to find strategies and interventions that can turn this
trajectory around and support a population that is increasingly unfamiliar with a low meat diet.
Analysis of observed diets shows a positive relationship between dietary GHG emissions and
the amount of animal-based products in a standard 2,000 kcal diet. This work demonstrates
that reducing the intake of meat and other animal based products can make a valuable
contribution to climate change mitigation. Other work has demonstrated other environmental
and health benefits of a reduced meat diet. National governments that are considering an
update of dietary recommendations in order to define a healthy, sustainable dietmust
incorporate the recommendation to lower the consumption of animal-based products.
Acknowledgments Peter Scarborough is supported by a programme grant from the British Heart Foundation
(021/P&C/Core/2010/HPRG). Anja Mizdrak is supported by the Oxford Martin School. The EPIC-Oxford
Cohort study is supported by Cancer Research UK. Adam DM Briggs is supported by the National Institute
for Health Research.
Conflicts of interest Timothy J Key is a member of the Vegan Society. The authors have no other conflicts of
interest to declare.
Climatic Change
Open Access This article is distributed under the terms of the Creative Commons Attribution License which
permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are
Table 4 Greenhouse gas emis-
sions for the 94 food commodities,
weighted for production in the UK,
imports from the EU, and imports
from outside the EU
Food category UK GHG emissions (kgCO2e/kg)
Alcoholic Beverages 3.3
Animal Fats 40.1
Apples 0.7
Aquatic Animals, Others 0.0
Aquatic Plants 0.0
Aquatic Products, Other 0.0
Bananas 1.4
Barley 3.8
Beans 0.8
Beer 3.8
Beverages, Alcoholic 3.5
Beverages, Fermented 2.4
Bovine Meat 68.8
Butter, Ghee 1.8
Cephalopods 5.4
Cereals - Excluding Beer 1.8
Cereals, Other 1.8
Citrus, Other 0.7
Cocoa Beans 3.4
Coconut Oil 2.1
Coconuts - Incl Copra 2.1
Coffee 10.1
Cream 2.4
Crustaceans 5.4
Dates 1.1
Demersal Fish 5.4
Eggs 4.9
Fats, Animals, Raw 35.6
Fish, Body Oil 0.0
Fish, Liver Oil 0.0
Fish, Seafood 5.4
Freshwater Fish 5.4
Fruits - Excluding Wine 0.9
Fruits, Other 0.9
Grapefruit 0.8
Grapes 0.8
Groundnut Oil 0.9
Groundnuts (Shelled Eq) 1.4
Honey 1.0
Lemons, Limes 0.6
Climatic Change
Tab le 4 (continued)
Food category UK GHG emissions (kgCO2e/kg)
Maize 0.7
Maize Germ Oil 0.4
Marine Fish, Other 5.4
Meat 35.9
Meat, Other 35.7
Milk - Excluding Butter 1.8
Molluscs, Other 0.0
Mutton & Goat Meat 64.2
Oats 1.0
Offals 35.9
Oilcrops 1.8
Oilcrops Oil, Other 2.4
Oilcrops, Other 2.3
Olive Oil 4.5
Olives 4.5
Onions 0.5
Oranges, Mandarines 0.6
Palm Oil 3.3
Palmkernel Oil 0.0
Peas 1.2
Pelagic Fish 5.4
Pepper 2.5
Pigmeat 7.9
Pimento 3.2
Pineapples 1.9
Plantains 1.6
Potatoes 0.4
Poultry Meat 5.4
Pulses 3.3
Pulses, Other 3.5
Rape and Mustard Oil 2.9
Rape and Mustardseed 2.9
Rice (Milled Equivalent) 3.9
Rye 1.0
Sesameseed 4.2
Sesameseed Oil 4.2
Soyabean Oil 1.8
Soyabeans 2.0
Spices 1.6
Spices, Other 1.6
Starchy Roots 0.4
Stimulants 0.0
Sugar & Sweeteners 0.1
Sugar (Raw Equivalent) 0.1
Sunflowerseed Oil 3.3
Sweeteners, Other 0.0
Climatic Change
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Climatic Change
... Sustainable diets have different environmental footprints and costs in low-and middle-income countries compared with highincome countries (24)(25)(26)(27). Studies to date show that diets with lower consumption of animal-source foods generally reduce the carbon footprint and land use (1,(28)(29)(30)(31). However, some diets high in fruit and vegetables have a relatively high water footprint (32). ...
... Some define MSD using a theoretical reference diet, whereas we identified MSD relative to the average diet in the study population. Most studies define MSD with only indicators of nutritional adequacy and one environmental footprint indicator (22,31,63,71), while we included diet quality, four environmental footprint indicators, and diet cost (13,20,57). The MSD characteristics identified were consistent with those found for other populations (31,64,71). ...
... Most studies define MSD with only indicators of nutritional adequacy and one environmental footprint indicator (22,31,63,71), while we included diet quality, four environmental footprint indicators, and diet cost (13,20,57). The MSD characteristics identified were consistent with those found for other populations (31,64,71). Characteristics of MSD in the Mexican population were closer to the EAT Lancet Commission Healthy Reference Diet (EAT-HRD) (28) than the average diet for added fats and sugars and whole grains (mainly in rural areas), while beef and pork intake was below/close to EAT-HRD. ...
Full-text available
Background: Little is known about the current intake of sustainable diets globally and specifically in middle-income countries, considering nutritional, environmental and economic factors. Objective: To assess and characterize the sustainability of Mexican diets and their association with sociodemographic factors. Design: Dietary data of 2,438 adults within the National Health and Nutrition Survey 2012 by integrating diet quality measured by the Healthy Eating Index (HEI-2015), diet cost, and four environmental indicators were analyzed: land use (LU), biodiversity loss (BDL), carbon footprint (CFP), and blue water footprint (BWFP). We defined healthier more sustainable diets (MSD) as those with HEI-2015 above the overall median, and diet cost and environmental indicators below the median. Logistic regression was used to evaluate the association of sociodemographic factors with MSD. Results: MSD were consumed by 10.2% of adults (4% of urban and 22% of rural), who had lower intake of animal-source foods, unhealthy foods (refined grains, added sugar and fats, mixed processed dishes and sweetened beverages), fruits, and vegetables, and higher intake of whole grains than non-MSD subjects. Characteristics of MSD vs. non-MSD (urban; rural) were: HEI-2015 (62.6 vs. 51.9; 66.8 vs. 57.6), diet-cost (1.9 vs. 2.8; 1.9 vs. 2.5 USD), LU (3.3 vs. 6.6; 3.2 vs. 5.9 m2), BDL (105 vs. 780; 87 vs. 586 species × 10−10), BWFP (244 vs. 403; 244 vs. 391 L), and CFP (1.6 vs. 4.4; 1.6 vs. 3.7 kg CO2eq). Adults from rural vs. urban (OR 2.7; 95% CI: 1.7, 4.1), or from the South (OR 2.1; 95% CI: 1.1, 3.9), Center (OR 2.3; 95% CI: 1.3, 4.4) vs. the North were more likely to consume MSD, while adults with high vs. low socioeconomic status were less likely (OR 0.17; 95% CI: 0.09, 0.3). Conclusions: The MSD is a realistic diet pattern mainly found in disadvantaged populations, but diet quality is still sub-optimal. Increased consumption of legumes, fruits, and vegetables, and a reduction in unhealthy foods, is required to improve nutritional quality of diets while ensuring their environmental sustainability.
... The type of foods and drinks offered at a festival also has a significant impact on the balance of emissions generated in the context of an event [58]. The required variety of dishes is reflected in the offered categories: vegan, vegetarian, regional, seasonal, etc. ...
... However, event attendees decide on their purchase and consumption. Studies show that a vegan diet can cut the emissions that arise in the production of food in half [58]. The choice of regional and seasonal products also has a positive impact on the climate balance due to the reduced transport and storage costs. ...
... In the catering sector, not using animal products has the greatest potential for avoiding emissions [58]. Many larger festivals, like the Melt! or the Feel Festival, fear a decline in sales if there is a complete switch to vegetarian dishes. ...
Full-text available
This research answers the question of which nudges can be created to subtly influence event attendees in such a way that they contribute to the implementation of a green event. Using the qualitative content analysis according to Mayring, three music festivals were analyzed in detail with regard to their measures for ecological sustainability. All available online resources—in particular, the websites of the music festivals, blog entries, online newspaper articles and, also, observations during several personal visits—were used as sources of the qualitative data. A key result was the development of a model for nudges that can generally be used and implemented for events. The goals, the nudges developed and the possible measures with regard to ecological sustainability are defined in the areas of transport, garbage, electricity, gastronomy, sanitation and compensation of emissions. The more music festival participants already behave in an ecologically sustainable manner, the more others will join them. This can be exploited by using social nudges that consciously inspire group social dynamics. In order to be able to use nudging sensibly, organizers have to internalize that they act as decision-makers, which entails a great ethical and moral responsibility. Even inconspicuous nudges can be very effective.
... We measure indirect behaviours as participants' level of Charitable Donations in stage 17 This variable was constructed as follows: we identify the primary food type and ingredient of each dish on our menu using the McCance and Widdowson's CoFID user guide (England, 2021). Each food item is assigned a carbon score (in kgCO2e) using the UK Greenhouse gas emissions scale developed by Scarborough and colleagues (Scarborough et al., 2014); for details, see here 18 The GHGe variable has discrete jumps due to measurement of carbon intensiveness of each food item. To account for these value breaks, we further discretise the GHGe outcome into an ordinal variable. ...
... We find that the convergence to this modal food category is further exacerbated by our treatments 25 (see Figure 1). This has implications for our average treatment effects, as we discuss later, since a simple shift from a ruminant-based food item to a poultry-or fish-based food item can reduce emissions by ten times or more (Scarborough et al., 2014;Poore and Nemecek, 2018). In terms of charitable donations, we find donations are distributed with three clear peaks: participants are likely to donate nothing, half their endowments or mostly everything. ...
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Nudges have been increasingly deployed to deliver climate policies in the last decade. Recent evidence shows nudges are hard to scale–up. So can we use nudges more effectively, or should we rely on other tools of behaviour change? We argue that reflective strategies can enhance nudges by encouraging agency and ownership in citizens. We test this by systematically comparing nudges to reflective interventions like thinks, boosts, and nudge+ over orders of low-carbon meals using an online experiment with 3,074 participants in the United Kingdom. We find all behavioural interventions increase intentions for climate-friendly diets, but encouraging reflection prior to nudging (“nudge+”) strengthens these treatment effects. There is no evidence of negative behavioural spillovers as measured by participants’ donations to pro-social charities. There is potential for reflective policies in promoting climate citizenship.
Produced from proliferating cells in bioreactors with a controlled culture medium, “cultured meat” has been presented by its supporters, who are mainly private actors (start-ups), as a sustainable solution to meet the growing demand for animal proteins without weaknesses of animal husbandry in terms of environmental impact, animal welfare or even health. The aim of this chapter is to take stock of current knowledge on the potential benefits and pitfalls of this novel product. Since robust scientific arguments are lacking on these aspects, there is no consensus on the health and nutritional qualities of “cultured meat” for human consumption and on its potential low environmental impact. In addition, many issues related to the market, legislation, ethics and consumer perception remain to be addressed. The way in which this new product is regarded appears to be influenced by many factors related mainly to its price, as well as to the perception of safety, sensory traits but also environmental and nutritional issues. Therefore, research by universities and public research institutes indicates that “cultured meat” production does not present any major advantages in economic, nutritional, sensory, environmental, ethical or social terms compared to conventional meat. Thus, a more balanced diet by diversifying our sources of plant and animal proteins, consuming other meat substitutes, and reducing food losses and waste appear to be more effective short-term solutions to the urgent need of producing enough food for the growing human population (while reducing environmental degradation and animal suffering).
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The environmental impacts of the UK's domestic sector must be lowered if they are to meet UK government greenhouse gas emissions (GHGs) reduction targets. However, government initiatives to lower domestic GHGs have had little success, and progress has been too slow. Given this lack of top-down impetus, it is worth investigating alternative housing solutions. Previous research has shown that shared living - in which residents share spaces, resources, and social time - tends to have lower environmental impacts than the average household. However, this issue has not yet been explored within the UK. There is also research which shows that social networks can be effective in encouraging practice transitions and maintenance. This has not yet been thoroughly investigated within the context of shared living and environmental sustainability. This research aimed to explore the practices and infrastructures which enable pro-environmental outcomes within shared living. This aim was achieved through in-depth research in six shared living case studies. The research mainly adopted an ethnographic approach, complemented by quantitative measurement of GHGs. This research shows that the shared living case studies have significantly lower GHGs than the average UK household. This builds upon previous quantitative environmental evaluations of shared living. In studying practices, infrastructures and social networks within shared living, this research identifies four types of sharing that are significant to pro-environmental outcomes: shared ideals, shared governance, shared materials and spaces, and shared endeavour. For each type of sharing, the findings describe and analyse how processes of negotiation enable and constrain pro-environmental practices and outcomes. By exploring these processes, this research generates new knowledge on how and why shared living can produce lower-than-average domestic environmental impacts. Thus, the research demonstrates the potential and the mechanisms by which shared living may offer environmentally sustainable housing solutions for the UK.
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Livestock production systems are associated with climate change, land degradation, and animal welfare concerns, while overconsumption of animal-sourced foods is a major driver of human disease. Thus, shifting towards plant-rich diets is expected to deliver benefits for human health, the environment, and animal welfare. Nevertheless, diets high in animal products are flourishing, especially in high-income countries. Here, we take a novel inter-disciplinary approach to evaluating sustainability of diets by assessing five common plant-rich diets (Mediterranean, flexitarian/semi-vegetarian, vegan, vegetarian, and pescatarian) on two metrics. First, we established each diet's environmental, human health, and animal welfare impacts, using quantitative data sourced from a review of the literature, including life cycle assessments. Second, we evaluated the human factor by surveying current consumer dietary preferences (i.e., which diet participants had followed over the past week) and the likelihood of adopting each plant-rich diet in the future, among a sample of Australian adults (n = 253). Combining the results from the review and the survey in a Behaviour Prioritization Matrix (BPM), the Mediterranean diet was shown to have the greatest projected positive impact, followed by the vegetarian diet. This study is the first to combine assessments of sustainable diets on the three dimensions of environment, human health, and animal welfare with probability of adoption. Our findings highlight the necessity of assessing plant-rich diets through a holistic lens when identifying target diets to promote, in order to support sustainable food systems in high-income countries.
We report evidence from a field experiment (N=561) on how different reasons for reducing the consumption of red meat (health, climate and animal welfare) impact intentions to change behavior, the consumption of red meat and the enjoyment of meals. Surprisingly, the three concepts are not aligned. On average, two treatments affect intentions to reduce meat consumption, only one affects behavior, while all affect enjoyment of meals containing red meat. This contributes to the emerging discussion of the welfare effects of nudging. We find that behavioral changes are driven by our female participants eating in company. This confirms the importance of the social environment both in explaining gender differences and the channels by which nudges affect behavior.
Given the complexity of the connections between food and climate change, this chapter describes the links between greenhouse gas emissions, extreme weather events and food production. It also specifically analyses the impact of industrial fishing on the ability of oceans to store carbon. Vulnerability of food production to climate change and extreme weather events, and the vulnerability of people to changes in food production are further examined. The overall message in the current planetary emergency is that a transition to plant-based options is a good strategy for responding to the climate change crisis.
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Studies on theoretical diets are not sufficient to implement sustainable diets in practice because of unknown cultural acceptability. In contrast, self-selected diets can be considered culturally acceptable. The objective was to identify the most sustainable diets consumed by people in everyday life. The diet-related greenhouse gas emissions (GHGE) for self-selected diets of 1918 adults participating in the cross-sectional French national dietary survey Individual and National Survey on Food Consumption (INCA2) were estimated. "Lower-Carbon," "Higher-Quality," and "More Sustainable" diets were defined as having GHGE lower than the overall median value, a probability of adequate nutrition intake (PANDiet) score (a measure of the overall nutritional adequacy of a diet) higher than the overall median value, and a combination of both criteria, respectively. Diet cost, as a proxy for affordability, and energy density were also assessed. More Sustainable diets were consumed by 23% of men and 20% of women, and their GHGE values were 19% and 17% lower than the population average (mean) value, respectively. In comparison with the average value, Lower-Carbon diets achieved a 20% GHGE reduction and lower cost, but they were not sustainable because they had a lower PANDiet score. Higher-Quality diets were not sustainable because of their above-average GHGE and cost. More Sustainable diets had an above-average PANDiet score and a below-average energy density, cost, GHGE, and energy content; the energy share of plant-based products was increased by 20% and 15% compared with the average for men and women, respectively. A strength of this study was that most of the dimensions for "sustainable diets" were considered, ie, not only nutritional quality and GHGE but also affordability and cultural acceptability. A reduction in diet-related GHGE by 20% while maintaining high nutritional quality seems realistic. This goal could be achieved at no extra cost by reducing energy intake and energy density and tincreasing the share of plant-based products.
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This report first presents a systematic overview of the life cycle of meat and dairy products and their environmental impacts, covering the full food chain. It goes on to provide a comprehensive analysis of the improvement options that allow reducing the environmental impacts throughout the life cycle. Finally, the report assesses the different options regarding their feasibility as well as their potential environmental and socioeconomic benefits and costs. The report focuses on improvement options in three main areas: • Household improvements, mainly to reduce food losses (wastage) and to reduce car use for shopping; • Agricultural improvements, mainly to reduce water and air emissions (in particular nitrate, ammonia and methane) and land requirements; • Power savings in farming, food industry, retail, catering, and for household appliances. The study presents the consequences that the adoption of these options might have on a broad range of different environmental issues, including global warming, eutrophication, respiratory health impacts, etc. It shows that when all environmental improvement potentials are taken together, the aggregated environmental impacts (external costs) of meat and dairy products may be reduced by about 20 %. The study has also quantified the economic costs and benefits of implementing the different options.
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To model the impact on chronic disease of a tax on UK food and drink that internalises the wider costs to society of greenhouse gas (GHG) emissions and to estimate the potential revenue. An econometric and comparative risk assessment modelling study. The UK. The UK adult population. Two tax scenarios are modelled: (A) a tax of £2.72/tonne carbon dioxide equivalents (tCO2e)/100 g product applied to all food and drink groups with above average GHG emissions. (B) As with scenario (A) but food groups with emissions below average are subsidised to create a tax neutral scenario. Primary outcomes are change in UK population mortality from chronic diseases following the implementation of each taxation strategy, the change in the UK GHG emissions and the predicted revenue. Secondary outcomes are the changes to the micronutrient composition of the UK diet. Scenario (A) results in 7770 (95% credible intervals 7150 to 8390) deaths averted and a reduction in GHG emissions of 18 683 (14 665to 22 889) ktCO2e/year. Estimated annual revenue is £2.02 (£1.98 to £2.06) billion. Scenario (B) results in 2685 (1966 to 3402) extra deaths and a reduction in GHG emissions of 15 228 (11 245to 19 492) ktCO2e/year. Incorporating the societal cost of GHG into the price of foods could save 7770 lives in the UK each year, reduce food-related GHG emissions and generate substantial tax revenue. The revenue neutral scenario (B) demonstrates that sustainability and health goals are not always aligned. Future work should focus on investigating the health impact by population subgroup and on designing fiscal strategies to promote both sustainable and healthy diets.
This article was submitted without an abstract, please refer to the full-text PDF file.
The potential greenhouse gas (GHG) emissions from the production of food for three different diets are compared using consequential Life Cycle Assessment. Diet 1 is an Average Danish Diet (ADD); diet 2 is based on the Nordic Nutritional Recommendations (NNR), whilst diet 3 is a New Nordic Diet (NND) developed by the OPUS project. The NND contains locally produced Nordic foods where more than 75 % is organically produced. NNR and NND include less meat and more fruit and vegetables than the ADD. All diets were adjusted to contain a similar energy and protein content. The GHG emissions from the provision of NNR and NND were lower than for ADD, 8 % and 7 % respectively. If GHG emissions from transport (locally produced versus imported food) are also taken into account, the difference in GHG emissions between NND and ADD increases to 12 %. If the production method (organic versus conventional) is taken into account so that the ADD contains the actual ratio of organically produced food (6.6 %) and the NND contains 80 %, the GHG emissions for the NND are only 6 % less than for the ADD. When the NND was optimised to be more climate friendly, the global warming potential of the NND was 27 % lower than it was for the ADD. This was achieved by including less beef, and only including organic produce if the GHG emissions are lower than for the conventional version, or by substituting all meat with legumes, dairy products and eggs.