Carbon footprint of dietary patterns in Ontario, Canada: A case study based on actual food consumption

Accepted Manuscript Carbon footprint of dietary patterns in Ontario, Canada: A case study based on actual food consumption Anastasia Veeramani, Goretty M. Dias, Sharon Kirkpatrick PII:

S0959-6526(17)31194-0

DOI:

10.1016/j.jclepro.2017.06.025

Reference:

JCLP 9775

To appear in:

Journal of Cleaner Production

Received Date: 29 December 2016 Revised Date:

22 April 2017

Accepted Date: 4 June 2017

Please cite this article as: Veeramani A, Dias GM, Kirkpatrick S, Carbon footprint of dietary patterns in Ontario, Canada: A case study based on actual food consumption, Journal of Cleaner Production (2017), doi: 10.1016/j.jclepro.2017.06.025. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Anastasia Veeramania, Goretty M. Diasa*, and Sharon Kirkpatrickb

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University of Waterloo, School of Environment, Enterprise & Development, b School of Public Health and Health Systems 200 University Ave West, Waterloo, ON N2L 3G1, Canada * Corresponding author. E-mail address: [email protected] (G. Dias) Abstract

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Carbon footprint of dietary patterns in Ontario, Canada: A case study based on actual food consumption

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Recent studies have established the important contribution of food consumption to

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climate change, but the environmental implications of Canadians’ dietary choices have not

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been studied in this regard. In this study, dietary intake data for 10,000 residents of

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Ontario, Canada were used to identify dietary patterns and estimate the Global Warming

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Potential (GWP) of food consumption and waste. Cluster analysis was used to identify

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seven dietary patterns (DP):

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excluding red meat, beef and pork. Calorie-adjusted food baskets were formulated based on

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the most commonly consumed food items for each DP. Life cycle assessment was used to

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estimate GWP for each basket from farm operations, processing, distribution, to household

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processes (cooking, storage, food waste). The findings suggest that Ontario residents prefer

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DPs rich in animal products (particularly beef) that have very high GWP. Further, reducing

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food waste could reduce GWP by up to 8%. Though methods differ across studies and

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comparisons must be made carefully, available estimates of diet-related GWP from the US

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and UK are consistently higher than values in this study. Efforts are needed to standardize

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methods to facilitate a more cohesive body of evidence on the relevance of dietary choices

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and food waste to climate change.

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vegan, vegetarian, pescatarian, omnivorous, and diets

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Keywords: carbon footprint; dietary pattern; food basket; LCA; beef; food waste

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1. Introduction A growing body of research suggests that modern society’s dietary choices have tremendous

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impacts on the environment, health, and food systems; thus dietary shifts have an immense

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potential to ameliorate these impacts (Hallström et al., 2015). At the same time, climate change

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and food security are increasingly at the top of many countries’ agendas (European Commission,

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n.d.; WSFS, 2009), and climate change increasingly affects food systems worldwide (FAO,

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2008). Estimates suggest that around 30% of anthropogenic climate change is linked to food

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consumption and food systems at large (Macrae et al., 2013). To inform strategies that reduce the

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impact of dietary patterns (DPs) on climate, it is critical to better understand the implications of

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predominant dietary choices of various populations and regions.

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Increasing population and rising incomes have more than doubled global food consumption

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over the past four decades (Harrison et al., 2002), but at a great cost to the environment (Weis,

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2013). With projected global population of 9 billion by 2050, there is a need for a sustainable

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food system. Development and expansion of agriculture, which are intrinsically linked to dietary

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choices, contribute to deforestation, degradation of land, biodiversity loss, extensive freshwater

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use and water pollution (Foley et al., 2011). In addition, current dietary choices place a huge

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burden on the health of populations worldwide (Lock et al., 2005) and prevention of nutrition-

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related non-communicable diseases (NCDs) through diet improvement is on UN’s global agenda

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(Beaglehole et al., 2011).

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Because the impacts of food systems and food choices on climate change vary geographically,

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evidence-based research conducted in different regions is needed to inform sound policy and

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action plans (Alber et al., 2003; Macrae et al., 2013; Notarnicola, 2015). Food consumption

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patterns and their effect on the environment have been studied extensively across Europe

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(Tukker et al., 2011), and specifically in Sweden (Sonesson et al., 2005), Denmark (Saxe et al.,

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2013), Mediterranean region (Baroni et al., 2007; Pairotti et al., 2015; Muñoz et al., 2010),

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France (Vieux et al., 2012), Germany (Meier & Christen, 2012a, 2012b), Switzerland (Jungbluth

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et al., 2000) and the Netherlands (Van Dooren et al., 2014), and increasingly in the UK

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(Macdiarmid, 2013), the USA (Weber & Matthews, 2008), India (Pathak et al., 2010), China

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(Chen et al., 2010), Australia (Hendrie et al., 2014) and New Zealand (Wilson et al., 2013).

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Canada is one of the largest global producers and exporters of agricultural products (Grant et

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al., 2011), but there is limited research on the environmental repercussions of food consumption 2

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in Canada. Kissinger (2013) estimated the ecological footprint of Canadian food consumption,

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but did not differentiate among DPs across the country. There is also a growing body of research

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on the environmental footprinting of single agricultural products (e.g. beef (Beauchemin et al.,

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2010; Dias et al., 2014), fish (Ayer & Tyedmers, 2009), apples (Keyes et al., 2015), tomato (Dias

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et al., 2017), dairy products (McGeough et al., 2012; O'Brien et al., 2012) among others).

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However, single product analysis provides very little insight into the overall impact associated

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with DPs.

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The aim of this study was to estimate climate change implications of DPs in the province of

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Ontario (ON), the most populated province in Canada, representing 40% of the Canadian

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population, with 14 million residents comprising diverse ethnic and religious groups (MOF,

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2015). Within ON’s Action Plan on Climate Change, the provincial government seeks to

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establish local healthy and sustainable food systems at regional levels ("The Sustainable Food

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Systems project," n.d.) and to reduce greenhouse gas (GHG) emissions while supporting

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economic goals (Ontario Government, 2007).

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We used food intake data gathered from a 2004 survey of 10,723 ON residents to identify DPs and quantified the carbon footprint of each DP using a Life Cycle Assessment (LCA)

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approach. Thus, this research provides insights to guide evidence-based approaches to food and

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climate change policy in Canada and globally, as well as a foundation for further work related to

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the impact of dietary choices in Canada.

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2. Methods and data collection

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The study drew upon dietary intake data for ON residents from the 2004 Canadian

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Community Health Survey 2.2 (CCHS) that is the most recent available data with comprehensive

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dietary intake statistics for a representative Canadian sample. The intake data were analyzed to

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formulate FBs representing the key DPs and LCA was used to estimate the carbon footprint

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associated with the production and consumption of the food items in each of the food baskets

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(FB).

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2.1 DPs in Ontario

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The 2004 CCHS data included 24-hour dietary recalls for ON residents, providing actual

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food consumption, which was used to identify prominent DPs. Although existing published data

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provide details on national food consumption statistics in Canada and particularly Ontario 3

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(Statistics Canada, 2002a, 2002b, 2010), they lack the required level of detail to identify

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individual DPs for this study. The use of CCHS data facilitated a bottom-up approach and

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allowed us to identify prominent DPs and use realistic consumption data as a reference point (as

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in Hoolohan et al., 2013;Vieux et al., 2013) as opposed to production or disappearance data,

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national availability statistics, food balance sheets, or national agricultural production inventories

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often used in food-related LCA studies (Jungbluth et al., 2000; Tukker et al., 2011).

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For this study, data for 10,723 of 11,100 respondents were utilized after excluding breastfed

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babies and children consuming baby foods, which were considered to have a negligible effect on

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forming DPs. The foods and beverages reported in the survey had been grouped and assigned 1

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of 24 Canadian Nutrient File (CNF) codes by Health Canada. These groups were then reviewed

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and updated to distinguish between different types of meat and account for animal products in

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foods and beverages (Table 1). For example, the food group ‘Fats and oils’ was segregated into

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animal fats and plant-based oils, while ‘Meat’ group was segregated into products containing

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beef, pork, chicken or other white meat, and other mixed products. Peas and beans were

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considered proteins and assigned to the ‘Meat and alternatives’ group as part of a “Legume’ food

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group (Supplement Part 1). This helped create a better distinction between animal- and plant-

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based foods in the composition of various types of DPs, and evaluate the contribution of specific

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foods (beef, poultry, fish, etc.) to GWP of DPs.

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Cluster analysis, along with diet-related literature, was used to determine the most common

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types of DPs across various populations (e.g. Eshel & Martin, 2006; Goodland, 1997; Meier &

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Christen, 2012a). Omnivorous, vegan and lacto-ovo vegetarian diets were identified as

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prominent DPs in a number of studies (e.g. Baroni et al., 2007; Goodland, 1997; Meier &

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Christen, 2012a; Risku-Norja et al., 2009; van Dooren et al., 2014), as well as pescatarianism,

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which incorporates consumption of fish but no other meat (Eshel & Martin, 2006). The

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omnivorous DPs were differentiated based on the ranking and types of animal product

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consumed, i.e. based on poultry, beef, pork or mixed meat, based on the environmental and

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bioethical food chain ranking by Goodland (1997), which suggests that environmental impact of

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plant and animal species, and thus corresponding DPs, increases along the food chain. In ON,

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certain types of meat might be avoided for health reasons or cultural/religious background, given

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the ethnic diversity of ON population. For example, some may abstain from pork (e.g. Jewish,

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Muslim) or beef (e.g. Hindu) (Statistics Canada, 2001). Overall, there were 7 different DPs

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considered in the analysis based on prior research.

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Table 1. Food groups used for grouping all food products consumed by ON population into DPs (based on modified CNF groups). Food groups

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Dairy & eggs

Poultry

Fruit

Grains

Spices & herbs

Beef

Fruit juice

Plant-based fats & oils

Pork

Vegetables

Animal fat: pork

Game meat

Vegetable juices

Animal fat: beef

Fish & seafood

Nuts & seeds

Sweets

Animal fat: poultry

Unspecified meat

Beverages

Sweets (with dairy/egg/gelatin)

Animal fat: game meat

Unspecified mixed dishes

Snacks

Breakfast cereal

Animal fat: fish

Legumes

Snacks (with dairy/egg)

Breakfast cereal (with dairy)

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Pasta

Baked products

Baked products (with dairy/egg)

Analysis was conducted using SAS, version 9.4 (SAS Institute Inc., Cary, NC). Cluster

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analysis was used to identify respondents exhibiting one of the DPs based on their reported one-

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day food consumption. Survey respondents with similar consumption of food groups in Table 1

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were clustered into the following DPs:

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1. ‘Vegan’ – excludes animal products (dairy, eggs, fish, meat);

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2. ‘Vegetarian’ – excludes meat and fish products, but includes dairy and eggs;

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3. ‘Pescatarian’ – excludes meat in favor of fish, with optional intake of dairy and eggs;

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4. ‘No Red Meat’ – excludes red meat, but includes other animal products;

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5. ‘No Pork’ –excludes pork products, but includes other animal products;

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6. ‘No Beef’ – excludes beef products, but includes other animal products;

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7. ‘Omnivorous’ – no apparent exclusions or dietary restrictions.

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A list of specific food groups, commonly consumed food items within each DP,

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corresponding preparation methods and the average consumed amounts (Supplement Table 1)

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were used to form daily FBs, representing each DP. However, only food items for which life

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cycle data were available were included in the final FBs that were further extrapolated to annual

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FBs by multiplying consumed daily amounts by 365 days (Supplement Tables 3-9). 2.2 Carbon footprinting of dietary choices

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This study used a life cycle approach according to the ISO14040/14044 (2006) standards.

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The modeling was performed in SimaPro v. 8.0.2 software and analyzed using the IPCC 2007

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GWP 100a V1.02, the Global Warming Potential 100–year method. Only GWP was considered

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due to the lack of Canadian data for other impact categories.

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2.2.1 Functional unit (FU)

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The function of a diet is to supply nutrition. The functional unit is 837,436 kcal, based on

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delivery of age and gender-weighted calorific equivalent of each FB (Health Canada, n.d.) over a

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1 year period (i.e. each FB was intentionally balanced based on recommended daily calorie

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intake for an average person from the sample population (51% women of average age of 38

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years; 49% of men aged 36 years on average) with a low activity level).

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2.4 System boundaries

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The LCA of all foods and beverages in each FB encompassed raw material extraction,

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processing, farm-based activities, transportation to processing facilities and retail, processing

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(where applicable), production of packaging and household processes, including transportation

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of food, storage, cooking and dishwashing (Fig. 1). Production of capital goods (farm machinery,

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buildings, cooking equipment, etc.), storage at retail, port and distribution centers and waste

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management were not included in the analysis due to data gaps or negligible contributions to

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GWP (e.g., Berlin & Sund, 2010; Muñoz et al., 2010). The production of various food products

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was modeled based on statistics of average production and imports over the past five years

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(Industry Canada, 2014; Kissinger, 2012).

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Fig. 1. LCA system boundaries. Stages in bold are included in the analysis; stages in grey are excluded. T = transportation between life cycle stages. 6

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2.5Life cycle data

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In addition to type and amount of food consumed by ON residents, extrapolated to an annual

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basis as described by FBs, other key data included estimates of the distance traveled for grocery

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shopping, packaging used for transporting and storing food items, and electricity consumed for

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processing and cooking. Emissions were based on literature values and LCA databases within

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SimaPro. Data for these aspects were derived from existing LCA studies and published life cycle

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inventories (LCI) of food items, import and production statistics in ON, and country-specific

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data for production practices, packaging and transportation. Overall, 74 profiles of food items

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were created and used in the analysis of the FBs. Additional profiles were created for various

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types of packaging, processes, and electricity mix.

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Food waste data included estimates of the amount of avoidable (spoilage, reject, losses) food

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waste during processing and retail operations based on various sources (Statistics Canada, 2002a,

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2002b; Trolle et al., 2014; WRAP, 2008), as well as avoidable and unavoidable (seeds and

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peelings) waste at the household level (USDA, 2014; Urrutia Schroeder, 2014 Supplement Table

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10). Due to the scope of the study, waste management scenarios were not included.

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The food group ‘water’ was excluded from the analysis as it was assumed to have little to no

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effect on GWP. The ‘game meat’ was also excluded due to lack of data on emissions and

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relatively negligible amounts in the diets. Detailed assumptions for each life cycle stage are

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provided in the Table 1 (Supplement).

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3. Results

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3.1 Ontario DPs and Carbon Footprints

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The two most prominent types of DPs were ‘Omnivorous’ (30%) and ‘No Pork’ (27%)

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(Fig.2). Around 17% of the sample population did not eat beef and 16% did not eat any type of

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red meat on the day for which food consumption was reported. The ‘Pescatarian’, ‘Vegetarian’,

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and ‘Vegan’ DPs were represented by 3.5, 7, and 0.4% of the population, respectively.

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Fig. 2. Distribution of dietary preferences among the ON population.

Based on calorie-adjusted FBs, the ‘No Pork’ DP had the highest GWP (3,160 kg CO2-eq.),

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followed by the ‘Omnivorous’ DP, with 30% lower GWP (2,282 kg CO2-eq.) (Fig. 3). The GWP

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associated with the ‘No Red Meat’ (1,234 kg CO2-eq.) and ‘No Beef’ (1,290 kg CO2-eq.) DPs

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was approximately 60% lower than that of the ‘No Pork’ DP. The impact of the ‘Pescatarian’ DP

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was almost half that of the ‘No Pork’ DP (1,431 kg CO2-eq.), while the ‘Vegan’ and

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‘Vegetarian’ DPs had the lowest GWP (955 and 1,015 kg CO2-eq. respectively).

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Fig. 3. GWP of ON FBs on a farm-to-fork basis. The FBs are formed based on the actual oneday food consumption.

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The key contributors to GWP were meat, dairy and eggs. Meat, particularly beef, made the

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biggest contribution to the GWP of the two most popular DPs (‘No Pork’ and ‘Omnivorous’) at

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68% and 48%, respectively (Fig. 4 & 5). The high impact arises from both a high volume of beef

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consumption and the emissions associated with production (e.g., enteric methane, manure,

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cultivation of feed, inefficient conversion of raw weight to cooked meat) (WCRF, 2007). 8

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Fig. 4. Contribution analysis showing foods with highest GWP in the ‘No Pork’ DP.

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Fig. 5. Contribution analysis showing foods with highest GWP in the ‘Omnivorous’ DP.

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In the ‘Omnivorous’ and ‘No beef’ DP there were substantial contributions from butter (5-

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7%) and eggs (over 5%). Smaller GWP contributions arose from chicken (1.6%), pork (3.4%),

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and mixed meat (3.2%). Other high-impact foods included fish (salmon and tuna), similar to the

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findings of European studies (Baroni et al., 2007; Tukker et al., 2011). Vegetables accounted for

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around 7.5% of the total impact. Greenhouse-grown vegetables, particularly lettuce, had a

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significant impact (4%), consistent with results of other studies ((Carlsson-Kanyama et al., 2003;

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Virtanen et al., 2011). This is partly due to the amount of waste associated with lettuce, as

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discussed below.

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The impact on GWP was directly proportional to the share of these products in each DP.

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Thus, the ‘Vegetarian’ DP, which contained the highest share of dairy and egg products (21%),

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demonstrated the highest GWP in this food category (53%). The ‘No Pork’ DP with its high 9

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share of beef products (6%) and the disproportionately high GWP of beef products demonstrated

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the largest impact in the meat category (Fig. 4). Generally, protein-dense foods of animal origin (beef, salmon, tuna, sausage, pork, and

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cheese) had a higher GWP than plant-based protein sources (legumes and nuts). These findings

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were expected due to similar trends in other studies suggesting that the GWP of plant-based

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protein sources is significantly lower (Davis et al., 2010; González et al., 2011).

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The food groups with the lowest GWP were pasta, snacks, cereal, sweets, fats and oils, seeds

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and nuts. This contrasts to other studies that demonstrated that ‘non-core’ foods such as sweets,

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snacks, fats and oils have the second largest contribution to the overall GWP (Hendrie et al.,

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2014). However, the present findings can be largely explained by a relatively lower share of

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these food groups, which accounted for less than 6% of total consumption, by mass

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(Supplement Table 3-9).

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Avoidable household food waste contributed from 9.5 to 15% of the GWP in the various

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DPs. Food waste along the supply chain and in households contributed to the overall impact due

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to increased resource intensity and emissions associated with the unnecessary production. As an

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example, based on waste amounts in Canada, 1.62 kilograms of lettuce must be produced for

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every kilogram of lettuce consumed.

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3.2 Sensitivity analysis

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Sensitivity analysis was performed on both the FU (the FBs) and the source of beef, as the key contributor to the GWP of key DPs.

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3.2.1 Sensitivity analysis: FU

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The sensitivity analysis tested the robustness of the produced results based on the current FU

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by introducing an alternative FU. Protein-adjusted FU, as opposed to the calorie-adjusted FU,

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refers to a FB representing a particular DP and delivering an annual supply of recommended

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amount of protein. The optimal protein intake for the ON sample is 50.9 grams daily, or 18,581

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grams annually (Health Canada, n.d.). Thus, the function of FBs created under the protein-

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adjusted FU used for sensitivity analysis was to supply protein, assuming 18,581 grams (18.6 kg)

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of protein to one person throughout one year. The amount of protein required reflects a low

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activity level and corresponds to age and gender-related requirements of the population sample,

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and would vary for different weight categories and activity levels (Health Canada, n.d.).

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Total GWP of protein-adjusted FBs decreased by up to 50% relative to the calorie-adjusted

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FBs, because protein content was reduced towards recommended levels; however, the trends

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were similar to the comparison of DPs based on equalized calorie intake (Table 2), with the

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exception of the ‘No Red Meat’ FB, which had the lowest GWP. The ‘No Pork’ FB had the

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highest GWP among all seven DPs regardless of the choice of the FU.

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Table 2. Sensitivity analysis based on choice of FU (calorie- versus protein-adjusted):

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Percent deviation in GWP based on protein-adjusted FU relative to calorie-adjusted (baseline)

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FU. GWP calorie-adjusted FU

Deviation

protein-adjusted FU

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(%)

(kg CO2-eg. / person / year) 955

Vegetarian

1,053

Pescatarian

1,431

No Red Meat

No Pork

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715

- 32

813

- 43

1,234

644

- 48

1,290

751

- 42

3,160

1,575

- 50

1,158

- 49

2,282

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Omnivorous

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No Beef

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Vegan

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The ‘Vegan’ FB had the third largest GWP on a protein basis, largely due to the originally

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low level of protein content and a substantial increase in the amount of food needed to balance

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the protein levels with other baskets.

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Overall, the GWP of all the DPs changed significantly based on various FUs. This trend is

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widely observed in LCA studies on individual food products (e.g. Kendall & Brodt, 2014), but

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has not been previously tested in diet-related research.

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3.2.2 Sensitivity analysis: beef sources 11

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Beef was one of the most important contributors to GWP in the meat-based FBs. The

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sensitivity analysis tested the key assumptions that the beef was supplied from Alberta by

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introducing an alternative location in the Northern Great Plains states, USA (Lupo et al., 2013;

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Pelletier et al., 2010). We also introduced an alternative management practice (Dias et al., 2014)

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(Table 3).

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Table 3. Sensitivity analysis testing assumptions with regard to beef production practices and origin. Results are reported as percentage change from the baseline GWP. No Pork Omnivorous Reference

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3,160

2,282

This study

-1%

Dias et al., 2014

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Baseline (kg CO2-eq. / person / year)

-1%

Supply from the US – 1

+26%

+18%

Lupo et al., 2013

Supply from the US - 2

+13%

+9%

Pelletier et al., 2010

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Management practice (extended bale grazing)

Regardless of the origins of meat, and farming practices, the GWP increased (by 9 to 26%),

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with only a minor reduction (-1%) based on a change in management practices, suggesting that

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the most effective way to reduce the climate impacts of animal-based DPs is to reduce beef

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consumption.

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3.3 Scenario analysis: Reduction in food waste

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Avoidable household food waste contributed from 9.5 to 15% of diets’ GWP. Thus, waste

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reduction provides an opportunity to reduce GWP at a household level. The scenario analysis

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(Table 4) revealed that a 20%-reduction in avoidable food waste would result in a 3% decrease

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in GWP of DPs. By halving avoidable household food waste, the impact reduction would range

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from 5%-8% of GWP.

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Table 4. Scenario analysis: reducing avoidable food waste at a household level by 20% and 50% and quantifying a potential GWP reduction. Percentage change from the baseline GWP. No No No Vegan Vegetarian Pescatarian Red Omnivorous Beef Pork Meat

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20% waste

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reduction

-3.6%

-2.7%

-2.7%

-3%

-3.2%

-3%

-3%

-6.4%

-4.9%

-5.5%

-6.1%

-6.2%

-7.8%

-7.2%

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50% waste reduction

4. Discussion

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This study is the first to determine the GWP of DPs in a Canadian context, and revealed some

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differences in consumption and impacts compared to other studies.

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4.1 Benchmarking against other countries

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Environmental impacts of food products are influenced by land topography, wind regimes,

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sun exposure, soil type, proximity to water, and climate (Notarnicola, 2015). Local agricultural

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practices, assortment of available foods, and traditional diets also differ from region to region.

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Thus, we compare the results of this study to those from other regions to understand implications

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for climate and identify key factors affecting the analysis and contributing to GWP.

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Tables 5-6 illustrate comparisons among carbon footprints of vegan, vegetarian, and

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omnivorous DPs across the world. The GWP of an ‘Omnivorous’ DP in Australia is twice that of

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ON, while the USA and UK DPs have marginally higher GWP. The GWP of an average

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European DP (EU27) is 39% lower (Tukker et al., 2011); however, this value comprises only

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the GHG emissions associated with the production of food, and may exceed the Canadian

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estimate on a farm-to-fork basis. The most striking difference is in the GWP of ‘Omnivorous’

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DPs in India which were 6 times lower than ON. Although the analysis was similarly conducted

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on a farm-to-fork basis, the Indian FBs were formulated and modeled to represent healthier

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balanced diets as opposed to actual food intake.

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The GWP of DPs across various countries may differ due to variations in traditional diets,

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food preferences, and consumed amounts within similar DPs (Meier & Christen, 2012a), choices

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of commonly consumed foods, FB composition, food availability,

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statistics, local production practices, and technologies. For example, the average Danish diet had

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a similar amount of high impact products as the ON “Omnivorous” DP, such as dairy, egg, meat,

307

and grain products and included a similar share of food waste, but a slightly lower GWP was

308

estimated (Trolle et al., 2014, Table 6). This may be due to different production practices and 13

production and import

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technologies, or preference towards milk rather than eggs or cheese, or preference for pork rather

310

than beef (DAFC, 2016). Differences may also stem from methodological definitions, different

311

LCA databases or national energy mixes, variations in system boundaries, scope and processes

312

included in the farm-to-fork analysis, or key assumptions (Vieux et al., 2013).

313

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Table 5. Comparison of GWP related to ‘Omnivorous’ DPs in various countries GWP

Country

Reference

(kg CO2-eq. / person / year)

USA

2,780

UK

from 2,701

Hendrie et al., 2014

SC

5,293

Kim & Neff, 2009

Berners-Lee et al., 2012

M AN U

Australia

Hoolohan et al., 2013

Ontario (Canada)

2,282

This study

Spain

2,100

Muñoz et al., 2010

Germany

2,050

Meier & Christen, 2012a

Denmark

1,820

Trolle et al., 2014

TE D

up to 3,212

Finland

from 1,692

Risku-Norja et al., 2009

up to 2,811

Virtanen et al., 2011

1,522

Vieux et al., 2012

1,390

Tukker et al., 2011

Netherlands

1,285

van Dooren et al., 2014

India*

351

Pathak et al., 2010

France

AC C

EP

EU (27)

314

* DP that was modeled based on dietary guidelines of India as opposed to actual food intake.

315

The ON ‘Vegan’ and ‘Vegetarian’ DP had the lowest GWP among the developed countries

316

(Table 7). GWP for the UK was twice that of ON, despite considering fewer life cycle stages (no

317

cooking or household storage). The difference may be due to a higher intake of dairy, grain

318

products, legumes, vegetables, nuts and seeds, and 80% more fruit in British vegetarian DPs

14

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319

(Berners-Lee et al., 2012). The GWP of the ‘Vegetarian’ and ‘Vegan’ DPs in the USA are about

320

1.5 times higher than that of ON, even though the US study did not account for food waste. Table 6. Comparison of GWP related to ‘Vegetarian’ DPs in various countries GWP

Reference

(kg CO2-eq. / person / year) Vegan

UK

2,113

1,876

USA

1,850

1,530

Germany

1,560

960

Ontario (Canada)

1,053

India*

239

Finland

N/A

Berners-Lee et al., 2012

M AN U

Vegetarian

RI PT

Country

SC

321

Kim & Neff, 2009

Meier & Christen, 2012a

955

This study

N/A

Pathak et al., 2010

879

Risku-Norja et al., 2009

* DP formulated based on dietary guidelines of India as opposed to actual food intake.

323

A few new DPs emerged from the present study. The ‘Pescatarian’, ‘No Red Meat’, ‘No

324

Beef’ and ‘No Pork’ DPs are variations of the meat-based DPs. These DPs are well represented

325

in ON but may not have prototypes in other countries. Thus, the comparison of these results was

326

not possible. Although this analysis provides insights into variations of GWP for similar DPs

327

across the regions, given the differences in cultural aspects and methodological approaches in

328

calculating the estimates, the comparison is made cautiously. Standardization of methods for

329

LCA dietary studies is required to ensure comparability of results and effective exchange of

330

knowledge between regions.

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331

4.2 Food choices as environmental hotspot

332

Beef was found to be the key contributor to GWP in ON diets, similar to findings of other

333

diet-related studies (Baroni et al., 2007; Carlsson-Kanyama & González, 2009; Hendrie et al.,

334

2014; Muñoz et al., 2010; Saxe et al., 2013). Apart from contributing to climate change,

335

livestock also competes with other food sources for resources, water and land, and can contribute

336

to acidification and eutrophication (de Vries & de Boer, 2010; Pimentel & Pimentel, 2003).

337

Over the past fifteen years, the beef industry in Canada has expanded and is expected to grow 15

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further (FCC, 2015), as one of the industry’s key strategic goals is to enhance demand for beef

339

(FCC, 2015). Consequently, the national inventory of GHG emissions associated with beef

340

production has increased by over 40% and is likely to rise if the trend continues (Beauchemin et

341

al., 2010), despite lower GWP intensity per kg of beef due to production efficiencies. Therefore,

342

reducing the production of beef, as a whole, is likely going to create the most benefits in

343

reducing GWP and other impacts.

RI PT

338

A widely proposed strategy to minimize the climate impact of the livestock sector is to

345

reduce consumption of high-impact beef and substitute it with protein alternatives that exhibit a

346

lower GWP, such as poultry, pork or legumes (BCFN, 2014; de Vries & de Boer, 2010). Protein-

347

rich legumes widely grown in Canada present a viable way to reduce GWP. Over the past 25

348

years, Canadian production of pulses has increased more than five-fold and reached a third

349

(35%) of the global market share (Pulse Canada, n.d.). However, reducing the leading cause of

350

diet-related emissions in ON may be challenging. Even though per capita beef consumption has

351

declined over the years, the total domestic demand for beef is strong and growing (FCC, 2015).

352

The decline in per capita consumption may be due to increasing meat prices, a growing variety

353

of competing protein sources, increasing preference of other DPs, and increasing cultural

354

diversity. However, growing population is promoting the overall demand. Canadian consumers

355

express their preference of beef over other meat types through willingness to pay higher prices

356

for the product (FCC, 2015). The results of the present study corroborate this preference with

357

over 60% of the ON population leading a dietary lifestyle heavily dependent on beef.

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M AN U

SC

344

Preference for beef is likely to be influenced by socioeconomic status, a northern climate,

359

preference for beef texture and taste, and culture and traditions (Richardson et al., 1993).

360

Consumers enjoy eating meat and there is a strong perception, especially among the male

361

population, that humans were meant to eat meat (Lea & Worsley, 2003). However, consumers

362

may be more willing to reduce meat consumption due to health consideration as opposed to

363

environmental considerations (Joyce et al., 2012), and the increasing adoption of vegetarian and

364

vegan DPs has been shown to be largely related to health and ethical motives (Fox & Ward,

365

2008). Health concerns may become a major influence on DPs given the strong link between

366

processed and red meat consumption and cancer (WHO, 2015). This may provide an indirect

367

opportunity to address environmental concerns of meat.

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EP

358

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368

Other opportunities for reducing GWP of DPs include minimizing consumption of other

369

high-impact foods such as cheese, eggs, and foods likely to be transported by air or grown in a

370

greenhouse; however, this may be challenging due to the nature of Ontario’s agri-food sector and

371

the limitations inherent in terms of food production in a colder climate (Dias et al., 2017). A top priority is to minimize waste associated with high-impact foods such as meat, dairy,

373

eggs, resource-intensive products such as imported or greenhouse-grown fruit and vegetables as

374

well as foods with a large share of avoidable waste (Supplement Table 11). Since over 50% of

375

total food waste along the supply chain originates in Canadian households (Gooch et al., 2010),

376

outreach and education programs (MacRae et al., 2016) are needed to convey the environmental,

377

social and economic repercussions of food waste. Food waste along the supply chain accounts

378

for around 2% of the Canadian GDP (Gooch et al., 2010), therefore food waste reduction not

379

only reduces GHG emissions, but is a cost-saving strategy that does not directly affect eating

380

habits.

SC

M AN U

381

RI PT

372

4.3 Limitations

This was an exploratory study using reported food intake from 2004 data, the most recently

383

available comprehensive intake data for a representative sample of Ontarians. It is possible that

384

DPs may have shifted over time in response to new diet trends, fluctuations in food costs, and

385

changes in socioeconomic status after the financial crisis of 2008. Additionally, DPs were based

386

on reported food consumption on a single day, but most of the respondents indicated that the

387

reported food consumption was typical.

EP

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382

We also did not account for seasonality; however, the collection of dietary recalls was spread

389

throughout the year to address this. In addition, we were unable to conduct analyses for foods for

390

which limited LCA data was available, which affected the composition of FBs. Although some

391

international LCI databases are available, they often lack transparency, consistency, and

392

completeness and need regular updates (Peano et al., 2014). Moreover they may not be

393

representative of local agricultural and production practices and related emissions. According to

394

Emhart et al. (2014), the lack of consistent and reliable LCIs is the key obstacle to using the LCA

395

results in food-related policy making. Despite limitations in data availability, trends among

396

different DPs observed in this analysis are consistent with other studies.

AC C

388

17

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Finally, there is a consensus that a comprehensive understanding of food consumption and

398

DPs requires additional impact categories to capture the overall long-term environmental

399

implications of dietary choices (e.g. Heller et al., 2013). Using only GWP provides a limited

400

perspective of the complexity of the environmental implications and trade-offs associated with

401

food production and DPs.

RI PT

397

5. Conclusions

403

This study used 2004 actual food intake typical for the ON population to provide insights

404

into how Canadian DPs affect the climate compared to those DPs found in other parts of the

405

world, and showed the important contribution of Ontario DPs and associated food waste on

406

climate change. Particularly, GWP reductions can be achieved by minimizing consumption of

407

beef and dairy products, as well as minimizing food wastage at the household level.

M AN U

SC

402

408

This study assessed the carbon footprint of three DPs that have not been previously

409

considered in diet-related LCAs (‘No Red Meat’, ‘No Pork’, ‘No Beef’). The differentiation of

410

various omnivorous DPs provides a stronger understanding of overall impacts, hotspots and

411

potential improvements of meat-based DPs.

The present study is a foundation for diet-related LCA research in Canada, providing a

413

benchmark for further studies. Dietary intake data for the Canadian and Ontario populations were

414

again collected in the 2015 CCHS, allowing updated analyses and examination of trends over

415

time. The analysis should be expanded to include a wide range of impact categories (e.g. water

416

use, eutrophication, toxicity), but due to the lack of Canadian life cycle data, there is a strong

417

need for a detailed Canadian-specific database for foods produced and consumed in Canada.

EP

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412

Finally, these results corroborate the importance and significant contribution of our dietary

419

choices to climate change and provide valuable insights that will help inform policies promoting

420

healthy diets with lower environmental impact.

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421

Acknowledgments

422

We thank Steven B. Young and Simron Singh (School of Environment, Enterprise and

423

Development, University of Waterloo) for their valuable insights and suggestions. We also thank

424

Pat Newcombe-Welch (South Western Ontario Research Data Centre, Waterloo, ON, Canada)

425

for her assistance in statistical analysis.

18

ACCEPTED MANUSCRIPT

This research was supported by funds to the Canadian Research Data Centre Network

427

(CRDCN) from the Social Sciences and Humanities Research Council (SSHRC), the Canadian

428

Institute for Health Research (CIHR), the Canadian Foundation for Innovation (CFI), and

429

Statistics Canada. Sharon Kirkpatrick is funded by a Capacity Development Award from the

430

Canadian Cancer Society Research Institute (702855). The research was also partly funded by

431

the Bob Harding and Lois Claxton Humanities and Social Sciences Endowment Fund. Although

432

the research and analysis are based on data from Statistics Canada, the opinions expressed do not

433

represent the views of Statistics Canada.

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First exploratory study of the impact of Canadian dietary patterns on climate change Seven dietary patterns are identified and evaluated based on actual single-day food intake from a survey of 10,723 Ontario residents The study is based on life cycle assessment (LCA) Consumption of beef, a key hotspot of GWP, is considerably high in Ontario Minimizing household consumption of beef, eggs & cheese as well as food waste has a potential to reduce GWP of Ontario diets

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