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Eat JUST, Inc. Sustainability KPI Calculation Methodology

Authors: Seth Sheldona,b, Dahni-El Gilesa, and Udi Lazimya Reviewers: JD Capuanoc and Adam Freedgoodc

a Eat JUST, Inc., San Francisco, CAb Sheldon Data, LLC, Athens, OH c Third Partners, LLC, New York, NY

Overview

This document presents a method for calculating the impacts of Eat JUST, Inc. product ingredients for two important sustainability indicators: greenhouse gas emissions (GHG) and fresh water consumption. In fact, a major goal of this work is to demonstrate that the ingredients we use in our products have reduced GHG and water impacts when compared to those found in average alternative products. Specifically, we show that between January 1, 2017 and August 31, 2017, consumers have saved 275 (±72) million gallons of surface and groundwater and have avoided emitting 3,385 (±1,390) metric tons (tCO2e) of greenhouse gases by choosing Eat JUST, Inc. products over others, driven by differences in product formulations and ingredients. We also provide evidence for GHG and water savings associated with the ingredients in each of eight product suites (i.e. groupings) within our retail and foodservice offerings.

Our broader aim is to provide a solid and transparent first step to strengthening our sustainability efforts over time, with the assistance of experts and non-experts alike. Consequently, we wrote this document with several audiences in mind. In addition to providing a technical resource and point of dialogue within our own company, we seek to justify our approach to consumers, concerned individuals, and sustainability professionals. In this way, we can be confident that any resulting claims are both honest and defensible in all contexts.

Ultimately, we hope this work will encourage the trend of stronger sustainability analytics in the food industry by offering novel methods that enhance pre-competitive, collaborative efforts that lead to collective progress.

Key words: food sustainability, life cycle assessment, LCA, key performance indicators, GHG, carbon footprint, water footprint

Please cite this document as:

Sheldon, S., Giles, D., & Lazimy, U. (2017). Eat JUST, Inc. Sustainability KPI

Calculation Methodology. San Francisco, CA, USA.

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Contents

1. Introduction ............................................................................................................................ 4 1.1. Goals, Audience, and Scope ........................................................................................... 4

2. Core Methodology .................................................................................................................. 5 2.1. Functional Unit................................................................................................................. 6 2.2. Co-product Allocation ...................................................................................................... 7 2.3. System Boundaries ......................................................................................................... 7

3. Indicators ............................................................................................................................... 8

4. Ingredients ............................................................................................................................. 9

5. Product Definitions ................................................................................................................. 9 5.1. Market Share Weighting .................................................................................................10

6. Formulations .........................................................................................................................11 6.1. Model Uncertainty ...........................................................................................................12 6.2. Formulation Review ........................................................................................................13

7. Material Proxies ....................................................................................................................13

8. Impact Calculation.................................................................................................................15 8.1. Product-Level Savings ....................................................................................................16 8.2. Brand-Level Savings.......................................................................................................19 8.3. Product Suite-Level Savings ...........................................................................................21 8.4. Complex Ingredients .......................................................................................................21 8.5. Third Party Review .........................................................................................................21 8.6. Using the Insights ...........................................................................................................22

9. Performance Assessment .....................................................................................................22 9.1. Product-Level Results .....................................................................................................22 9.2. Brand-Level Results .......................................................................................................23 9.3. Product Suite-Level Results............................................................................................24

10. Discussion...........................................................................................................................27 10.1. Final Thoughts ..............................................................................................................30

About the Authors .....................................................................................................................32 Disclaimer .................................................................................................................................32 Acknowledgments .....................................................................................................................32 References ...............................................................................................................................33 Appendix ...................................................................................................................................36

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Figures

Figure 1. We follow a multistep, cyclical process to calculate our product-level ingredient impacts and improve our performance based on the results. ...................................................... 6

Figure 2. Simplified calculation hierarchy showing the relationship between individual ingredient impacts and aggregations of these impacts at various levels. ..................................... 7

Figure 3. Boundaries of the LCA applied to each ingredient and, by extension, to the aggregate, mass-weighted values calculated for product-level, product suite-level, and brand-level ingredient results. ..................................................................................................... 8

Figure 4. Results of the actual mass percentages in three of our products versus modeled results. ........................................................................................................................ 12

Figure 5. Example of a finalized competitor product formulation as predicted and reviewed using our process....................................................................................................... 15

Figure 6. Impact factor page for a calculated product-level ingredient combination material record in our database ................................................................................................. 18

Figure 7. Product-level comparisons of modeled water and GHG impacts for Eat JUST, Inc. product ingredients vs. market-share weighted product ingredient impacts (“Other Brands”) for 1 kg of unmixed, unpackaged product mass .......................................................... 22

Figure 8. Bar graphs showing total estimated water and GHG impacts for Eat JUST, Inc. product ingredients versus Other brands, corresponding to brand-level ingredient impact results. ........................................................................................................................... 23

Tables

Table 1. Water KPI results for Eat JUST, Inc. product suite-level

ingredientcombinations with supporting detail. .......................................................................................... 25

Table 2. Greenhouse gas emission results for Eat JUST, Inc. product suite-levelingredient combinations with supporting detail. ......................................................................... 26

Table 3. Working assumptions used for the present analysis, related limitations and proposed future work to address them. ..................................................................................... 28

Appendix Tables

Table A1. Challenges facing sustainability practitioners in the food industry ............................. 36

Table A2. List of websites used for finding public nutrition and ingredient information for products. ................................................................................................................................... 37

Table A3. Complete list of the materials used in our analysis and corresponding data sources used for environmental impact factors. ........................................................................ 38

Table A4. Complete list of water-related inventory items aggregated from the databases within SimaPro and imported into our database. ....................................................................... 40

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1. Introduction

In his 2015 piece, “Sustainability versus The System: An Operator’s Perspective,” the author draws on insight from the Global Footprint Network to provide a simple, but startling fact: We’re consuming natural resources and creating waste 50% faster than the planet can handle. Further, if all people were to consume resources at the same level as those living in the United States, that rate would exceed Earth’s assimilative capacity by 300%(Pucker, 2015).

The choices that we make as consumers have much broader impacts than many of us can imagine. And with few exceptions, nowhere do our choices have such outsized influence than with food. Fully 24% of the greenhouse gas (GHG) emissions generated between 1970-2010 (IPCC, 2014) and nearly 70% of the fresh water use annually (FAO, 2017)1 are the direct or indirect consequence of agriculture, i.e. the array of activities required to grow fodder for livestock and to grow crops that humans ingest or otherwise consume. The trends are alarming, and so it is not surprising that corporate reporting on sustainability has increased dramatically over the last decade.2 Likewise, the number of efforts aimed at creating new informational tools and indices to help individuals and organizations practice responsible consumption has also climbed.3 It is within this context that our company seeks to address sustainability challenges as part of our mission.4

1.1. Goals, Audience, and Scope

One goal of this document is to demonstrate that the ingredients we use in our products have lower GHG emissions and water use associated with them versus the leading alternatives. We make this claim and others using the comparative approach we describe in the following pages, and which we believe to be both fair and consistent.

But our broader aim is to provide a solid and transparent first step to bolstering our sustainability initiatives over time—with the assistance of experts and non-experts alike. The strength of these efforts will grow with the continued application of sustainability insights to guide things such as ingredient choices, packaging selection, and overall product development; with enhancements to the efficiency of our manufacturing operations and supply chains; and with reinforcement of the relationships we have with our suppliers and as we make new connections with farmers.

We have written this document with several audiences in mind. In addition to providing a technical resource and point of dialogue within our own company, we seek to justify our approach to concerned individuals and sustainability professionals. In this way, we can be confident that any resulting claims are both honest and defensible internally at Eat JUST, Inc. and in the eyes of consumers.

The scope of this document is limited to the climate and water impacts of our product ingredients and our present efforts to calculate them. And even within these modest bounds, the

1 Based on multiple years of data ranging from 1995-2015 by country. 2 The number of sustainability and corporate responsibility reports added annually to the GRI Sustainability Disclosure Database climbed from around 500 in 2005 to over 3,000 in 2015. As of July 2016, the total number of reports in GRI’s database was over 23,000 (Sandford, 2016). 3 In fact, there are so many sustainability measures and tools available now, that entire databases have been set up to navigate them (MacMillan Bankson, 2016) or synthesize them (UL Environment, 2017). 4 A comprehensive statement of Hampton Creek’s sustainability standards and stances is forthcoming, but we invite readers to review the United Nations’ 17 Sustainable Development Goals (UN, 2017) to understand how action on climate and water are vital.

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calculations include a number of practical difficulties. Foremost among those challenges are 1) the measurement uncertainty that every large food company faces when describing activities in its highly dynamic supply chains; 2) the sheer volume of information that can be considered relevant to the analysis; and 3) the simple fact that consumers have very diverse views on sustainability.5 Lastly, this document is not a statement of certification or accreditation for any particular sustainability standard, but we do think that it provides useful results and establishes a solid baseline for our future research in this area.

2. Core Methodology We use a quantitative comparative framework designed to accommodate tens of product formats (e.g. sizes), multiple product formulations, hundreds of ingredients, multiple suppliers and geographic origins, and a dynamic supply chain. Life cycle assessment (LCA) is a foundational building block in the framework, because it is performed on individual ingredients. The impacts of ingredients are the basis for results for higher-level aggregations, such as at the level of whole products and across all products. In this way, the approach weighs the necessity of technical accuracy with the need for timely insights that can be applied in a real-world business setting.6 The framework is actualized as a custom-built information technology platform7 that references and compiles information from multiple external data sets. The platform itself uses an open-source, Python-based web-framework called Django8 that sits atop a MySQL database. A key objective of the project is to keep all of our sustainability data current, consistent, and accessible. In order to compare the impacts of our individual product ingredients and combinations of those ingredients to leading alternatives as directly and equitably as possible, we follow a multistep process (Figure 1). Although successful attempts to merge LCA with existing business information systems have been made by others (Meinrenken, Sauerhaft, Garvan, & Lackner, 2014), and while many components of this approach will be familiar to the broader community of sustainability professionals, we assert that the combination of data interactions presented in this methodology is unique in the food technology space.

5 See Table A1 in the Appendix for a fuller list of the practical challenges we and others in the food technology industry face in performing these calculations. 6 Time and resource constraints are often major challenges for sustainability practitioners (Cooper & Fava, 2008; A. C. Hetherington, Borrion, Griffiths, & McManus, 2014). According to Reap et al. (2008), addressing this challenge “entails considering the right amount of breadth and depth in one’s boundary selection to inspire enough confidence in the interpretation of the LCA results” (Reap, Roman, Duncan, & Bras, 2008). The goal is to create a model that is both a reflection of reality (in fact) and a trustworthy enough representation to inspire confidence in decision makers. 7 Nicknamed “Condor” after the California condor, a critically endangered species and an international symbol of conservation. 8 https://www.djangoproject.com/

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Figure 1. We follow a multistep, cyclical process to calculate our product-level ingredient impacts and improve our performance based on the results.

The underlying numerical logic is “bottom-up” in the sense that the impacts by ingredient over the specified time period sum to the impacts of product- and brand-level combinations of those ingredients.9 This reduces errors associated with averaging across product categories or generalizing based only on one or two individual ingredients. It also ensures that aggregate impacts are appropriately weighted by mass and sales volumes10 after the LCA is performed for individual ingredients. Further, it allows us to identify ingredients of concern (e.g. where small modifications can create large impacts), whether or not they represent the greatest share of total ingredients purchased by mass.

2.1. Functional Unit

The key underlying functional unit within our analysis is 1 kg of each individual ingredient at the exit gate of the ingredient manufacturing facility. However, in this work we provide summary results and comparisons at various levels of aggregation of these ingredients (e.g. product-level, brand-level; See Figure 2).11 The Impact Calculation section provides greater detail on how the impact aggregations were performed.

9 Product suites (i.e. groupings) are a special case, because they generally include the latest formulations and ingredients of products and entirely new products which may not have been available in the past.

10 Product formulations (i.e. as ingredient mass balances) and sales volume figures are not provided in this document for reasons of confidentiality. However, this information was provided to the third party reviewers for the purposes of

validation.

11 As we state elsewhere, our working assumption is that the supply chain operations occurring from the exit gate of the ingredient manufacturers forward are 1) comparable for Eat JUST, Inc. products and competitors’ products, and 2) have modest impacts overall when compared to the sum of impacts occurring at preceding stages, and especially the agricultural stages. It could be argued that the functional unit of comparison at the product level is the product itself at the exit gate of the final product manufacturer or even farther downstream in the life cycle. However, lacking sufficient detail on the differences between our manufacturing operations and our competitors’ operations, as well as data on packaging choices and differences in transportation distances and modes, etc., we can only go so far as to claim that the functional unit is the ingredient and that all summary aggregations rely on the quality of the LCA performed for those.

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Figure 2. Simplified calculation hierarchy showing the relationship between individual ingredient impacts and aggregations of these impacts at various levels.

2.2. Co-product Allocation In dealing with multifunctional processes that result in the formation of co-products, (e.g. canola

meal from rapeseed fractionation), we follow an attributional, mass-based approach.12,13

2.3. System Boundaries The major boundaries of our model include the impacts of the key inputs to each of the cultivation stages of our ingredients through to the exit gates at each ingredient manufacturer before shipping to the final product manufacturing facility. Figure 3 provides a stylized representation of the system boundaries used. With food LCAs, the cultivation and ingredient processing stages are usually the most resource intensive (Roy et al., 2009). We assume that product packaging, material end-of-use choices, and transportation distances for competitive products are comparable and that differences are therefore marginal for the present analysis. Although these areas tend to be highly visible elements of the supply chain to consumers and ones where direct influence is often easiest for food manufacturers, the relative impacts of these stages is often modest when compared to the cultivation and processing stages of the ingredients themselves (A. Hetherington, 2014).14

12 Our logic here is that any underestimation in the total impacts that result from, for instance, canola oil—which would have a higher per mass economic value than canola meal, influencing the results—are offset by the fact that the logic is applied consistently to all ingredients and products regardless of whether they are Hampton Creek’s or a competitor’s. We have not yet explored the effect of other allocation methods (e.g. protein-based for dairy) on our results. Gerber, Vellinga, Opio, Henderson, & Steinfeld, 2010, provides several alternative allocation approaches related to meat and dairy products. 13 PRé Sustainability provides an excellent description of the differences and trade-offs between attributional and consequential approaches in its Introduction to LCA (Goedkoop, De Schryver, Oele, Durksz, & de Roest, 2016). 14 For instance, Hetherington (2014, p76, Figure 5.4-5) shows that when using a midpoint-based assessment for refined rapeseed oil, the cultivation stage accounts for 80% of climate change impacts. The author goes on to show that about 67% of the overall GHG footprint of rapeseed oil-based mayonnaise is due to the oil—even accounting for packaging choice (A. Hetherington, 2014, p100, Figure 6.3-3).

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Figure 3. Boundaries of the LCA applied to each ingredient and, by extension, to the aggregate, mass-weighted values calculated for product-level, product suite-level, and brand-level ingredient results. Inputs and outputs in bold and signified by an asterisk (*) are relevant to GHG and water indicators. Operations are modeled.

Note that the product-level assessments and entire brand-level assessments obey the same boundaries, but aggregate the total impacts by ingredient according to their representation in follow-on analyses according to mass.15 Data on transportation related to shipping prior to our boundary endpoint are generally included within the inventory stage and were not further modified or otherwise differentiated between specific brands or within Hampton Creek’s supply chain.

3. Indicators To fully understand the progression from real product ingredients to aggregated water and emissions results, we look at each of the steps involved in our calculation process, starting with the indicators. There are numerous key performance indicators (KPIs) related to sustainability that are publicly reported by businesses.16 This document focuses exclusively on two of the most commonly reported ones17 that we track and quantify: greenhouse gas emissions (GHGs) and surface and groundwater consumption.18

15 This means that the impacts associated with shipping to the final manufacturer, the final manufacturing process itself, and life cycle steps beyond manufacturing (e.g. shipping to retail locations, retail operations, food waste related to shelf-life and consumer behavior) are not yet included in our analysis. 16 By “reported,” we refer to the claims made on a company’s website or other media, on product packaging, in annual sustainability reports, or to centralized sustainability data and information repositories such as those run by the Global Reporting Initiative (GRI, 2017) and the Carbon Disclosure Project (CDP, 2017). 17 McKinsey & Company, 2010; The National Association for Environmental Management, 2011. 18 We stop short of making the normative claim that these two are the most important KPIs. Not only does the importance of an impact depend on its perceived magnitude, the actual harm that the impact causes depends on additional contextual information such as the vulnerability of the population or ecosystems being impacted (Birkmann, 2006). Perhaps more significantly, there are other categorical attributes of many of our products—such as being organic and/or vegan—that we do not address in this document.

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GHGs are chemicals that contribute to global climate change: CO2, CO, CH4, N2O, and fluorinated gases, among others. In global agriculture, the most significant contributors to climate change are land conversion (e.g. forested areas and marshes converted to cropland), enteric fermentation by cattle and other ruminants, manure applied to pastureland, and the production and use of synthetic fertilizers (IPCC, 2014). Except in the case of a few ingredients, the present model accounts for these emissions, measured in kilograms or metric tons of CO2-equivalent mass (kgCO2e, tCO2e).19,20

Fresh water can be divided into two major categories: surface water and groundwater. We calculate the total amount of these two resources consumed within the supply chains of each of our product ingredients, measured in terms of total volume (i.e. cubic meters or gallons). This indicator is synonymous with and described elsewhere as “blue water” (Hoekstra, Chapagain, Aldaya, & Mekonnen, 2012). Scarcity-weighting can be a worthwhile tactic to account for the relative differences in fresh water’s value depending on where it is used (Ridoutt & Pfister, 2013), but we have not yet applied a scarcity-based approach to our water accounting due to data and time constraints.

4. Ingredients

As part of our normal business practice, we track our ingredients, suppliers, and all relateddocumentation and certifications through food safety and quality assurance database software.This is a standard practice for food manufacturing companies where safety and quality are ofparamount importance.

This is valuable for our sustainability work, too, because it gives us deeper insight into the supply chain histories of our products’ ingredients; specific details about processing steps involved with each ingredient; and supplier data such as location and geographies of origin. With so many ingredient options available to our chefs, product designers, and manufacturers, it is essential to track each ingredient separately to minimize basic communication errors that might occur. For example, “white granulated sugar” may refer to either beet sugar or cane sugar, which each have different sustainability implications due to crop-specific cultivation factors like typical yield and location of origin.

5. Product Definitions

Food products such as mayo, cookie dough, salad dressing, and breakfast patties are soldunder more recognizable names and brands. A common way of classifying products is with aunique SKU, GTIN, or internal product code. Just as unique ingredient identifiers are importantfor distinguishing between otherwise identical ingredients, unique product identifiers areimportant for identifying products that have similar (or even identical) product names, butdifferent case sizes, packaging formats, unit weights, formulations, and market segments.

19 A handful of ingredients only include emissions related to direct and indirect fossil fuel combustion for agricultural practices and livestock enteric emissions. These included those ingredients that are based on models that were

custom built for Eat JUST, Inc. by a third party in 2016. Together, these ingredient models support impact estimation

for

approximately 10% of Eat JUST, Inc. brand-level ingredients and 15% of modeled competitor brand-level ingredients

by mass over the time period. See footnote 35 on page 14 for more information.

20 An even smaller percentage (by mass) of ingredients are placeholders in our model formulations, such that their related material GHG and water impact factors have a value of zero. This is true for 1.7% of Eat JUST, Inc. product ingredients and 0.4% of modeled competitor product ingredients by mass over the time period. Overall, we expect the

degree of any inaccuracies caused by these placeholder ingredients on the product- and brand-level ingredient

impacts to be small, but we do expect to address the limitation at a later date.

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Our analysis includes a total of 63 unique Eat JUST, Inc. products, representing more than 90% of our sales by case volume between 01/01/2017 and 08/31/2017. Each product is tracked with a unique code, which ensures that we know the total unit mass of unmixed, unpackaged ingredients as well as the number of units per case. Sales are further subdivided into two market segments: retail and foodservice.

For each of these products, we selected a set of at least 3 three market-leading competitive products which represent at least a plurality of the total market for that item in the United States.21 For products within the retail market segment, we identified the top competitors using actual sales data from IRI—a market research database that is well-known within the consumer packaged goods industry (IRI, 2017). We used the same sales data for market share weighting (described in a later section).

The market research database we use does not include information on the foodservice sector. However, such markets in the U.S. are generally less fragmented than retail markets, which means that only a handful of major brands lead sales in the food product categories we analyze. Therefore, we identified the competitive products for our foodservice offerings through an internal discussion. 22

Identifying specific competitive products ensures that we avoid comparing ourselves to “generic” products, which may not be representative of actual market offerings.

In sum, our analysis includes 126 competitive products bringing the total number of products for which we needed real or modeled formulations to 189 (i.e. 63 + 126). For reasons described in the next section, we also collected nutrition panel and ingredient information for each of the competitor products and added these data to their product records.

5.1. Market Share Weighting

The results of life cycle assessments can be highly sensitive to initial assumptions, especially where it concerns the selection of comparative baselines. Often sustainability claims that rely on LCA work will compare an organization’s ingredients or product to a generic alternative instead of specific, branded products or groups of product ingredients. There are many good reasons for doing this.23

We take a slightly different approach, because our goal is to measure the sustainability performance of our product ingredients against the ingredients that go into making the other products that our customers see on shelves or in catalogs. To do this, we needed to create an apples-to-apples comparison of our product ingredients versus the equivalent mass of a combination of competitor ingredients.

21 In some cases, the market for the product is so highly fragmented that even the top five market leading products represent less than half of the sales. In other cases, only two products make up the majority. So our choice of

representative products involves some level of subjectivity.

22 The sustainability and brand analytics teams consulted with the head of foodservice partnerships and their team to

list the major competitive brands for each Eat JUST, Inc. foodservice product, based on their experience.

23 The market may be highly fragmented as with beef (Impossible Foods, 2017). Or the product and its alternatives may only have one or two key ingredients that differentiate them from each other, with few other material differences

among brands (Henderson & Unnasch, 2017). Characterizing specific products can also be so time-intensive that

organizations opt to focus their resources on improving the accuracy of their analyses in other ways.

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Using the same market research database we used earlier to identify the leading brands that directly compete with each of our products, we found the relative market share of each product in the retail category and scaled them so that the weights summed to 100%.24 For example, if Alpha Mayo makes up 35% of total market sales for that product category, Beta Mayo makes up 20% of the total, and Gamma Mayo makes up 5%, the weighting factors applied in follow-on, aggregated environmental impact calculations would be 35/(35 + 20 + 5) or 58.3% for Alpha Mayo, 33.3% for Beta, and 8.3% for Gamma.25 Because no specific foodservice sales data were available, leading foodservice products were assigned equal weights.

These weighting factors were used during the calculation of market share-representative product- and brand-level ingredient impacts, to highlight the likely GHG and water savings that result from choosing Eat JUST, Inc. products (containing certain ingredients) over average alternative products (containing different ingredients).26,27

6. Formulations

There are two types of formulations used in this analysis: our own proprietary Eat JUST, Inc. formulations and modeled competitor formulations. The steps we followed to ascertain these formulations were different.

The process of uploading product-specific formulations for our own Eat JUST, Inc. product ingredients was relatively straightforward. Occasionally, products share the same formulation, so formulations—like ingredients and products—are organized using unique identifiers. Formulation records that include specific ingredient information and the percentage by mass of each ingredient in the entire recipe were uploaded to our sustainability database.

It is understandable that most companies, including JUST, Inc., keep their proprietary mass-balance ingredient formulations (i.e. recipes) a secret. But it also presents a major challenge when the goal is to compare one’s products to competitors and when the results are highly dependent on ingredient formulations. Although it is possible to approximate competitor formulations using public nutrition and ingredient information by hand in a test kitchen, the approach is time consuming and not feasible for hundreds of products.

To overcome these obstacles, we designed a simple formulation predictor that takes publicly available nutrition and ingredient information for real products and combines it with standard nutrition data from the USDA Food Composition Database (USDA, 2017).28 Depending on the number of ingredients in the product and the desired precision, the algorithm will test millions of potential combinations. The main output is a “winning” viable formulation whose nutrition information most closely approximates the real product and whose ingredients appear in the

24 Based on sales data from the 52-week period ending May 14, 2017. 25 In theory, we could run the analysis without the need for scaling any market shares—at least for the retail segment—but only if we included the precise sales data for all of the competitive products sold, regardless of market share and significance to the final results. Scaling the market shares of a few major products to 100% greatly simplifies the analysis. 26 We note that the weighting method described here should not be confused with weighting factors that are sometimes used in life cycle impact assessments, which aggregate impacts across categories by normalizing them to comparable units such as disability-adjusted life years (DALYs) or to monetary units of externalized costs (e.g. US dollars). We have not attempted to normalize results across impact categories in this analysis. 27 Market share weights are symbolized by the Greek letter upsilon (υ) in the Impact Calculation section. 28 For the most part, these data were available directly on the competitors’ public websites, but when those were not available, others were used. See Table A2 in the Appendix for the complete list of references for product nutrition and ingredient listings.

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correct order.29 The model also generates a population of “runners-up” that are used for error estimation, with n >= 100.30 We measured the success of the model by testing it against known formulations for three unlike products. Root-mean-square error (RMSE) of the model versus the known mass percentage values was equal to 2.05%, which means that the percentages expressed for each ingredient as a fraction of the total mass of the product were higher or lower by 2.05% of the total product mass on average. For example, an ingredient with a predicted mass of 30% of the total product mass is expected to fall within the range of 27.95% – 32.05%.

Figure 4. Results of the actual mass percentages in three of our products versus modeled results. Error bars were developed in order to capture the true values. The specific product and ingredient details are not shown in order to preserve confidentiality.

6.1. Model Uncertainty We adjusted the formulation uncertainty algorithm to capture the true values, based on information contained within the population of non-winning, but still reasonable potential mass percentages. We calculate estimated error for each ingredient as

𝑦 =𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛

2+ 0.01 ,

where y is the estimated upper and lower error bound, xmax and xmin are the highest and lowest mass shares, respectively, for the specific ingredient to appear within the population of runners-up. Minimum upper and lower error is set to 1%. Letting x equal the mass percentage of the individual ingredient in the winning formulation, each ingredient has a different level of estimated error, so that we express the final value as x±y. In cases where the resulting value of x-y would imply that the actual mass percentage was less than zero (i.e. for ingredients with very small shares), the lower bound was set to a marginal, but non-negative value of 0.00000000001%. 29 Note that relatively low-mass food additives (e.g. known or expected mass share < 1%) are generally excluded from the analysis, which adds some uncertainty to the results found later. 30 The model begins with 0.1% of the total formulations attempted, and increases this threshold, if necessary, until at least 100 alternative formulations are available. Very often 0.1% represents more than 1,000 alternatives.

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Therefore, the uncertainty shown in our results (i.e. error, shown as upper and lower bounds) is solely the result of the uncertainty associated with estimating competitor formulations. However, we believe that the range of the upper and lower error bounds that result from our formulation uncertainty algorithm—as it is expressed in product-level and brand-level ingredient impact calculations—is wide enough to capture at least some of the error associated with other sources of uncertainty (e.g. LCA impact factors).31,32 Product-level ingredient impact error estimation and error for combinations beyond that are described in detail in the Impact Calculations section. 6.2. Formulation Review In practice, there are differences between the actual nutrition information of the real ingredients used by other leading brands and the USDA reference ingredients (USDA, 2017). There are also constraints on the granularity and specificity of the public nutrition and ingredient information available. To mitigate the effects of this additional uncertainty, we provided each of the computer simulated formulations to our culinary professionals to check their reasonableness and for potential mistakes. Example errors might include having too much vinegar in the model formulation or not enough fat, etc. We updated the model product formulations in our sustainability database according to these revisions, so that the changes would be reflected in the final product-level, product suite-level, and brand-level ingredient impact results. Figure 5 (p15) provides an example of how a product formulation appears in our database.

7. Material Proxies Life cycle assessments of more limited scope may use material and energy input and output information collected from key processes within their supply chain as part of what is called a life cycle inventory (LCI) analysis. The LCI stage of an LCA can be a very time-consuming and difficult step. An individual manufactured food product’s supply chain often contains thousands of processes, each with its own bill of material and energy inputs and outputs. To further complicate matters, these material transactions typically happen within a supply chain (as they do in most economic systems) without the collective knowledge of all actors. In other words, it is easiest to find out how much water one’s organization is using directly, harder to find out how

31 Our reasoning is that when we strictly apply the lower limit to each ingredient mass percentage in the competitor formula, the resulting total would be less than 100%, thereby underestimating the total mass of the model competitor product and therefore any resulting impacts. By the same token, if we strictly apply the upper limit for each ingredient, the total mass of the target product would be higher than we know is possible, which would, by definition, lead to an overestimation of the impacts. We expect to improve our approach in this area by updating the formulation prediction error algorithm so that it performs a calculation of impacts for each non-winning, but still reasonable ingredient mass percentage combination at the outset in order to identify upper and lower bounds for each KPI. 32 For example, a preliminary Monte Carlo analysis performed in SimaPro provides some guidance in this regard. Using a 90% confidence interval, we quantified the error associated with GHG factors (kgCO2e/kg material) for a few materials, including dehydrated whey and refined soybean oil, which were ±17.7% and ±23%, respectively. Further, uncertainty at the product level would be proportional to the relative contribution of each ingredient to the overall unmixed, unpackaged product mass.

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much direct suppliers are using, and even harder to find out the water use patterns of the suppliers’ suppliers, and so on.33

Thankfully, there are a number of software tools available that simplify the life cycle inventory stage by modeling complete product systems (e.g. the interaction of all material input-output processes that results in a finished good) for both finished products and ingredients. These software tools vary widely in terms of usability, compatibility, database comprehensiveness, and cost.34 For this analysis, we used SimaPro v8.4, which is supported by a variety of agriculture- and food-relevant databases including the following:

Agri-footprint version 3.0, March 2017.

Ecoinvent 3.3. Compiled October 2016.

European Life Cycle Database (ELCD) v3.2, November 2016.

DATASMART Life Cycle Inventory Package (formerly US-EI 2.2 library), May 2017.

U.S. Life Cycle Inventory (LCI) Database, September 2015.

For each ingredient in our database, including the name brand and supplier-specific ingredients that Eat JUST, Inc. uses as well as the model ingredients we identified for other leading brands, we selected a similar material within SimaPro. To ensure consistency, we selected only those that 1) followed mass-based allocation rules; 2) most closely resembled the target ingredient in terms of identity, functionality, and level of processing; 3) had complete system level data; and 4) were associated with a relevant geography (e.g. U.S. system process for soybean oil), where possible. See the Appendix for a complete list of the materials used in our analysis and corresponding data sources used for environmental impact factors (Table A3).

33 An excellent way of mitigating this tendency toward supply chain opacity is to work as directly as possible with key ingredient manufacturers. Not only do they enhance transparency, single origin sources of ingredients (e.g. coffee), encourage trust among all participants in the food value chain, all the way to the consumer and beyond. 34 Among them are SimaPro 8, GaBi 4, Umberto NXT LCA, Quantis Suite 2.0, TEAM 5.2, as well as more limited but freely available tools such as OpenLCA and Argonne’s GREET Model.

15

Figure 5. Example of a finalized competitor product formulation as predicted and reviewed using our process. Upper and lower error is shown to the right in the QUANT ERROR column. The ingredient name appears under the ING column. Corresponding proxy materials appear in the MAT column.

We assessed each resulting life cycle inventory using standard impact assessment methods to derive the final GHG and water impact factors associated with each ingredient.

8. Impact Calculation To estimate the GHG footprint values for each ingredient, we followed the ILCD 2011 Midpoint+ (v1.0.9, May 2016) assessment method (Hauschild et al., 2011), which is itself based on ISO 14040/44 guidelines (Goedkoop, De Schryver, Oele, Durksz, & de Roest, 2016). It provides climate change impacts as global warming potential in units of kgCO2e, and includes the array of GHG sources outlined previously in our description of the GHG indicator. For the surface and groundwater impact assessment, we followed the blue water assessment procedure outlined by the Water Footprint Network (WFN) (Hoekstra et al., 2012). We did this for several reasons. First, the ILCD 2011 Midpoint+ approach as applied using SimaPro provides scarcity-adjusted water impacts as part of its “Water resource depletion – Freshwater scarcity” impact category, which is not easily interpretable alongside non-scarcity adjusted water footprint factors available elsewhere. Second, the WFN provides straightforward rules that we are able to follow for aggregating forms of surface and groundwater consumption that appear within the inventories of our proxy materials. See Table A4 in the Appendix for the complete list of water-related inventory items aggregated from the databases within SimaPro to an overall water consumption number.

16

Third, we wanted our analysis to be backward-compatible with past assessments and custom product system analyses that were done for our product ingredients by third party consultants.35 Those assessments employed water footprint factors for animal and crop products provided by the WFN (M. M. Mekonnen & Hoekstra, 2010; Mesfin M. Mekonnen & Hoekstra, 2010).

Lastly, these water footprint values are useful to us in cases where the proxy material that we selected are representative of cultivation and raw ingredient processing in a country or region (e.g. Global) that does not exactly match the region relevant to the ingredient of interest. The WFN factors are specific for ingredients at the subnational level. In cases where we could refine our impact factors by using WFN’s factors, we did so.

For both the GHG and water assessments, the functional unit was 1 kg of the ingredient material. These impact factors are stored in our database for each material. We followed a mass-based, attributional approach to handle co-products (i.e. rather than using system expansion in a consequential approach).

8.1. Product-Level Savings

For a Eat JUST, Inc. product-level ingredient combination H, having n ingredients, we calculate the total product-level ingredient GHG and water impacts as

𝑛

𝐻𝐺 = ∑ 𝑎𝑥𝑖𝛽𝑖

𝑖=1

𝑎𝑛𝑑

𝑛

𝐻𝑊 = ∑ 𝑎𝑥𝑖𝜂𝑖 ,

𝑖=1

where HG is the total impact in units of kgCO2e, HW is the total impact in units of cubic meters of fresh water, i signifies the target ingredient number, n is the total number of ingredients in the corresponding product formulation, a is the total mass of the product without packaging in kilograms, x is the mass share of the target ingredient as a percent of the total product, β and η are the ingredient-specific impact factors of the proxy material associated with the target ingredient in units of kgCO2e/kg target ingredient and m3 water/kg target ingredient, respectively.

Likewise, for a competitor product-level ingredient combination J, having n ingredients, we calculate the total product-level ingredient GHG and water impacts as

𝑛

𝐽𝐺 = ∑ 𝑎𝑥𝑖𝛽𝑖

𝑖=1

35 In 2016, Eat JUST, Inc. worked with Lux Research, a Boston-based emerging technology market research and consulting firm, to assess the impacts of a few of our products. The work produced a variety of custom-built ingredient

manufacturing and cultivation input-output unit and system process models, which we use in some cases. For

additional information on these customized unit process and product system models, related references, and

additional documentation, please email the request to wecare@eatjust.com.

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𝑎𝑛𝑑

𝑛

𝐽𝑊 = ∑ 𝑎𝑥𝑖𝜂𝑖 ,

𝑖=1

where JG is the total impact in units of kgCO2e, JW is the total impact in units of cubic meters of fresh water, i signifies the target ingredient number, n is the total number of ingredients in the model product formulation, a is the total mass of the corresponding Eat JUST, Inc. product without packaging in kilograms,36 x is the mass share of the target ingredient as a percent of the total product, β and η are the ingredient-specific impact factors of the proxy material associated with the target ingredient in units of kgCO2e/kg target ingredient and m3 water/kg target ingredient, respectively.

Since our current error estimation is based on uncertainty in ingredient formulations, we do not yet provide error bounds on the KPI calculations for our own products. For the modeled product-level ingredient combinations, we calculate the upper and lower limits of impacts for the same competitor product-level ingredient combination J as

𝐽𝐺𝑚𝑖𝑛 = ∑ 𝑎(𝑥𝑖−𝑦𝑖)𝛽𝑖

𝑛

𝑖=1

𝑎𝑛𝑑 𝐽𝐺𝑚𝑎𝑥 = ∑ 𝑎(𝑥𝑖+𝑦𝑖)𝛽𝑖

𝑛

𝑖=1

,

𝐽𝑊𝑚𝑖𝑛 = ∑ 𝑎(𝑥𝑖−𝑦𝑖)𝜂𝑖

𝑛

𝑖=1

𝑎𝑛𝑑 𝐽𝑊𝑚𝑎𝑥 = ∑ 𝑎(𝑥𝑖+𝑦𝑖)𝜂𝑖 ,

𝑛

𝑖=1

where 𝐽𝐺𝑚𝑖𝑛 and 𝐽𝐺

𝑚𝑎𝑥 represent the minimum and maximum estimated total product level GHG

impacts, respectively, and where 𝐽𝑊𝑚𝑖𝑛 and 𝐽𝑊

𝑚𝑎𝑥 represent the minimum and maximumestimated water impacts. As before, y corresponds to the highest and lowest mass shares for the target ingredient found during formulation prediction.

While the upper and lower bounds are stored as separate values in the database, as a practical

matter, because |𝐽𝐺 − 𝐽𝐺𝑚𝑖𝑛| and |𝐽𝐺 − 𝐽𝐺

𝑚𝑎𝑥| are not necessarily equivalent, when writing thefinal error in terms of ± some value or percentage, we use the higher of the two values to ensure that we have captured both bounds. We do the same when expressing water impacts, so this type of error can be expressed as

𝑒𝐺 = max (|𝐽𝐺 − 𝐽𝐺𝑚𝑖𝑛|, |𝐽𝐺 − 𝐽𝐺

𝑚𝑎𝑥|)

𝑎𝑛𝑑

𝑒𝑊 = max (|𝐽𝑊 − 𝐽𝑊𝑚𝑖𝑛|, |𝐽𝑊 − 𝐽𝑊

𝑚𝑎𝑥|) ,

36 We set the mass of the unmixed, unpackaged competitor product to equal the mass of the corresponding

unmixed, unpackaged, Eat JUST, Inc. product so that the comparisons are equitable.

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where eG and eW represent the upper and lower error used when characterizing the competitor ingredient impacts at the product level, using only the ± symbol followed by a single number.

After each calculation is completed, a new material record, including impact values, maximum value, and minimum value is added to the database (see Figure 6). This avoids the need to repeat the same calculation again in other contexts for that product-level ingredient combination (e.g. inclusion in automated visualizations or brand-wide statistics).

Figure 6. Impact factor page for a calculated product-level ingredient combination material record in our database. Brand name was removed.

For product-level ingredient impact comparisons, we employ the scaled market-share weights described previously and calculate the market share-weighted product-level ingredient impacts, for an average alternative product K as

𝐾𝐺 = ∑ 𝜐𝑖(𝐽𝐺)𝑖

𝑛

𝑖=1

𝑎𝑛𝑑

𝐾𝑊 = ∑ 𝜐𝑖(𝐽𝑊)𝑖

𝑛

𝑖=1

,

where n is the total number of competitor products used for comparison to a corresponding Eat JUST, Inc. product-level ingredient combination, i signifies the target number of the

target competitor product-level ingredient combination, υ is the scaled market share weight

for the

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corresponding target competitor product, and KG and KW are the total GHG and water impacts, respectively, for the market share-weighted competitor product-level ingredient combinations.

In the same fashion, we apply the market share weights to calculate the error, E, associated with the average alternative product-level ingredient impacts as

𝐸𝐺 = ∑ 𝜐𝑖(𝑒𝐺)𝑖

𝑛

𝑖=1

𝑎𝑛𝑑

𝐸𝑊 = ∑ 𝜐𝑖(𝑒𝑊)𝑖

𝑛

,

𝑖=1

where eG and eW represent the individual, unweighted competitor product-level ingredient GHG and water impacts, respectively, while EG and EW represent the upper and lower bound error associated with the market share-weighted competitor product-level ingredient impacts.

From here, product-level ingredient impact “savings”—in terms of avoidance of emissions and water consumption—can be calculated as

𝑆𝐺 ± 𝐸𝐺 = 𝐾𝐺 ± 𝐸𝐺 − 𝐻𝐺

𝑎𝑛𝑑

𝑆𝑊 ± 𝐸𝑊 = 𝐾𝑊 ± 𝐸𝑊 − 𝐻𝑊 ,

where SG and SW equal the avoided GHG impacts in units of kgCO2e and water impacts in units of m3 water, respectively.

8.2. Brand-Level Savings

The overall Eat JUST, Inc. ingredient GHG and water impacts, representative of over 90% of actual sales by case volume over the January 1, 2017 to August 31, 2017 time period, were calculated as

𝐻𝐺∗ = ∑ 𝜀𝑖𝛾𝑖(𝐻𝐺)𝑖

𝑛

𝑖=1

𝑎𝑛𝑑

𝐻𝑊∗ = ∑ 𝜀𝑖𝛾𝑖(𝐻𝑊)𝑖

𝑛

𝑖=1

,

𝐺 𝑊∗where 𝐻∗ and 𝐻 represent the overall brand-level ingredient GHG and water impacts for

Eat JUST, Inc. in units of kgCO2e and m3 water, respectively; n is equal to the total number ofunique products (i.e. SKUs) considered in the calculation, i signifies the target product number, ε is the number of units per case for the target product, 𝛾 is the total number of cases sold over

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𝐾𝐺∗ = ∑ 𝜀𝑖𝛾𝑖(𝐾𝐺)𝑖

the time period, and HG and HW are the product-level ingredient combination’s total GHG and water impacts, respectively, that correspond to the target Eat JUST, Inc. product.

Likewise, the overall competitive brand-level ingredient impacts (i.e. for the same quantity by mass of the market share-weighted alternative product-level ingredients over the same time period) were calculated as

𝑛

𝑖=1

𝑎𝑛𝑑

𝐾𝑊∗ = ∑ 𝜀𝑖𝛾𝑖(𝐾𝑊)𝑖

𝑛

𝑖=1

,

where 𝐾𝐺∗ and 𝐾𝑊

∗ represent the overall estimated brand-level ingredient GHG and water

impacts in units of kgCO2e and m3 water, respectively, and KG and KW are the market share-weighted competitor product-level ingredient combination’s total GHG and water impacts, respectively, of the target alternative product.

Error bounds for these summary values were calculated as

𝐾𝐺∗𝑚𝑖𝑛 = ∑ 𝜀𝑖𝛾𝑖((𝐾𝐺)𝑖 − (𝐸𝐺)𝑖)

𝑛

𝑖=1

𝑎𝑛𝑑 𝐾𝐺∗𝑚𝑎𝑥 = ∑ 𝜀𝑖𝛾𝑖((𝐾𝐺)𝑖 + (𝐸𝐺)𝑖)

𝑛

𝑖=1

,

𝐾𝑊∗𝑚𝑖𝑛 = ∑ 𝜀𝑖𝛾𝑖((𝐾𝑊)𝑖 − (𝐸𝑊)𝑖)

𝑛

𝑖=1

𝑎𝑛𝑑 𝐾𝑊∗𝑚𝑎𝑥 = ∑ 𝜀𝑖𝛾𝑖((𝐾𝑊)𝑖 + (𝐸𝑊)𝑖)

𝑛

𝑖=1

,

where 𝐾𝐺∗𝑚𝑖𝑛 and 𝐾𝐺

∗𝑚𝑎𝑥 represent the minimum and maximum estimated competitive brand-

level ingredient GHG impacts, respectively, and where 𝐾𝑊∗𝑚𝑖𝑛 and 𝐾𝑊

∗𝑚𝑎𝑥 represent the minimum and maximum estimated water impacts.

Brand-level ingredient impact calculation error, expressed as a single value, E*, is calculated as

𝐸𝐺∗ = max (|𝐾𝐺

∗ − 𝐾𝐺∗𝑚𝑖𝑛|, |𝐾𝐺

∗ − 𝐾𝐺∗𝑚𝑎𝑥|)

𝑎𝑛𝑑

𝐸𝑊∗ = max (|𝐾𝑊

∗ − 𝐾𝑊∗𝑚𝑖𝑛|, |𝐾𝑊

∗ − 𝐾𝑊∗𝑚𝑎𝑥|)

where 𝐸𝐺∗ and 𝐸𝑊

∗ represent the upper and lower error used when characterizing the brand-level market share weighted competitor ingredient impacts, using only the ± symbol followed by a single number.

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𝐺 𝐺 𝐺 𝐺∗

𝑊∗

𝑊∗

𝑊∗

𝑊∗

𝑊∗

𝐺 𝑊∗

Similar to product-level savings, brand-level ingredient impact savings—in terms of avoidance of emissions and water consumption across all product ingredients, weighted by total sales—can then be calculated as

𝑆 ∗ ± 𝐸 ∗ = 𝐾𝐺∗ ± 𝐸 ∗ − 𝐻

𝑎𝑛𝑑

𝑆 ± 𝐸 = 𝐾 ± 𝐸 − 𝐻 ,

where 𝑆∗ and 𝑆 equal the avoided GHG impacts in units of kgCO2e and water impacts in units

of m3 water, respectively. Upper and lower error bounds are defined and used in the same manner as the product-level error bounds.

8.3. Product Suite-Level Savings

Product suite-level ingredient impact calculations are a special case that measures the differences between ingredients in groups of current products (which may be new or have new formulations) and their nearest market competitors. In these instances, the set of products that belong to the suite (e.g. 14 oz. retail cookie dough, 1-gallon foodservice mayo) are chosen based on preexisting company sales and marketing conventions. The approach is identical to that of the brand-level calculation, except that n is limited to the set of unique products that were classified as part of the suite (i.e. SKUs) instead of including the much broader set of products that comprise company sales.

8.4. Complex Ingredients

In a few instances, certain ingredients were sufficiently complex to warrant their own product-level ingredient impact calculation (e.g. semisweet chocolate chips with dairy, Worcestershire sauce). For these, we applied the same logic as those above (i.e. formulation modeling, mass-weighted ingredient impact calculation), but then made the resulting material and impact factor records available for use as a standalone ingredient in other products. For example, chocolate chip cookie dough would have in its ingredient list standalone ingredient “semisweet chocolate chips,” which would itself reflect the aggregate impacts of a number of subingredients, including cocoa powder, cocoa butter, and beet sugar. This rule was applied for complex ingredients in both Eat JUST, Inc. and competitor products.

8.5. Third Party Review

While we have made every effort to ensure that the results of our analysis are accurate, all good scientific work involves critical review. With the primary aim of advancing our models so that we can be confident about our conclusions and ultimately improve our environmental performance, we submitted this work to the sustainability advisory firm Third Partners, LLC.37 The Third Partners validation statement can be found accompanying this document on the Eat JUST, Inc. website.

37 https://www.thirdpartners.com

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8.6. Using the Insights

After the results have been validated, we are confident to use them internally (e.g. as we seek new avenues for product development and innovation) and externally (e.g. to make public claims about our products).

9. Performance Assessment

The final stage of our multistep sustainability impact assessment process (Figure 1) is to assessthe performance of individual product-level ingredient combinations and ingredients in our entireproduct portfolio against the modeled alternatives that represent other brands. We are also ableto make claims going forward in various public contexts about how ingredients in specific groupsof items (i.e. product suites) compare to alternatives.

9.1. Product-Level Results

Figure 7. Product-level comparisons of modeled water and GHG impacts for Eat JUST, Inc. product ingredients vs. market-share weighted product ingredient impacts (“Other Brands”) for 1 kg of unmixed, unpackaged product mass. Error bars indicate the effects on impact estimates of uncertainty due to modeling competitor formulations. Each dashed line shows where impacts for the two comparable products would be equivalent.38

It is clear from Figure 7 that in the majority of cases, even accounting for the lower-bound error for competitor products, Eat JUST, Inc. product ingredients outperform other brands for the

38 Note that 12 of the 63 products that correspond to the ingredient aggregations shown are entirely new or have a new formulation associated with them. New products were not used when aggregating year-to-date impacts at the Brand-level ingredient aggregation.

23

KPIs specified. Note that ingredient combinations for new products (i.e. both products that are entirely new and products that have been reformulated) also appear in the graph.

9.2. Brand-Level Results

In aggregate, and accounting for the mass of each product ingredient in kilograms, the number of units (i.e. individual products) in each case, and our sales volumes over the January-August 2017 time period, the aggregate ingredient impact differences may be summarized in the following way:

Water

Total surface and groundwater use for Eat JUST, Inc. product ingredients this year equals 85 million gallons.

Total surface and ground water needed to make an equivalent quantity of food ingredients from other brands would equal 360±72 million gallons.

Based on ingredient differences, so far this year, consumers have saved 275 million gallons of fresh water by choosing Eat JUST, Inc. products over other brands (±72 million gallons or ±26%).

GHGs

Total GHG emissions for Eat JUST, Inc. product ingredients this year equal 10,087 metric tons (tCO2e).

Total GHG emissions associated with an equivalent quantity of food ingredients from other brands would equal 13,472±1,390 metric tons (tCO2e).

Based on ingredient differences, so far this year, consumers have avoided emitting 3,385 metric tons of greenhouse gases by choosing Eat JUST, Inc. products over other brands (±1,390 metric tons or ±41%).

Figure 8. Bar graphs showing total estimated water and GHG impacts for Eat JUST, Inc. product ingredients

versus Other Brands, corresponding to brand-level ingredient impact results.39

39 The brand-level analysis considers impacts associated with 51 Eat JUST, Inc. products (i.e. product-level ingredient aggregations), the 126 distinct competitor products and formulations (i.e. ingredient aggregations) that

underlie the

market share-weighted competitor product ingredient impacts, and 85 material proxies used for 252 real or generic

food ingredients, and 90.5% of our product sales by volume.

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9.3. Product Suite-Level Results It is important to be able to make assertions about the expected environmental performance of ingredients in products that are only just now available or will be available soon. The following product suite-level ingredient impact results include groups of products that have formulations that are new, and which therefore make up a special class of comparisons, because the groupings of these products together abide by marketing and other conventions. Tables 1 and 2 below provide water and GHG ingredient impact results by product suite, more precise numbers for the estimated savings of our products, information about what flavors and format sizes are included in the analysis, and additional notes on how equitable and accurate we think the estimates are. Only products with formulations that we have sold in the past appear in the brand-level ingredient impact calculations stated previously.

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Table 1. Water KPI results for Eat JUST, Inc. product suite-level ingredient combinations with supporting detail.

Water KPI Results (per case)

Savings (gallons)

Detail (Flavors included, format sizes)

Producing the ingredients in Eat JUST, Inc. retail mayo requires 94% (±15%) less surface and groundwater than those in leading brands.

87±14 Represents a simple average of newest formulations of five flavors (Original, Chipotle, Garlic, Sriracha, Truffle) and original formulation of Awesomesauce of shelf-stable, 12 oz bottles of our retail mayo products and their corresponding market share-weighted competitor products. Case of six.

Producing the ingredients in Eat JUST, Inc. retail dressing requires 88% (±33%) less surface and groundwater than those in leading brands.

86±32 Represents five key flavors (Caesar, Chipotle Ranch, Sweet Mustard, Thousand, and latest Ranch) of shelf-stable, 12 oz bottles of our retail dressing products, weighted by sales volumes 1/01/2017 - 08/31/2017 and their corresponding market share-weightedcompetitor products. Case of six.

Producing the ingredients in Eat JUST, Inc. retail cookie dough requires 36% (±23%) less surface and groundwater than those in leading brands.

41±26 Represents all flavors (Chocolate Chip, Birthday Cake, Peanut Butter Chocolate Chip) of 14 oz containers of our retail RTE40 cookie dough products, weighted by sales volumes (01/01/2017 - 08/31/2017 and their corresponding market share-weighted competitor products. Case of six.

Note: The comparison to competitor products is imperfect, because Eat JUST, Inc. products are RTE, while competitor products are not.

Producing the ingredients in Eat JUST, Inc. foodservice mayo requires 93% (±20%) less surface and groundwater than those in leading brands.

479±104 Represents a simple average of five newest flavor formulations (Original, Chipotle, Garlic, Sriracha, Truffle) and Original Light of shelf-stable, 1-gallon jars of our foodservice mayo products and their corresponding market share-weighted competitor products. Case of four.

Producing the ingredients in Eat JUST, Inc. foodservice dressing requires 90% (±37%) less surface and groundwater than those in leading brands.

431±177 Represents three flavors (Ranch, Ranch Light, Thousand) of shelf-stable, 1-gallon jars of our foodservice dressing products weighted by sales volumes 01/01/2017 - 08/31/2017 and their corresponding market share-weighted competitor products. Case of four.

Producing the ingredients in Eat JUST, Inc. foodservice cookies requires 22% (±35%) less surface and groundwater than those in leading brands.*

*Although this model has a high degree of uncertainty.

92±148 Represents eight flavors (Chocolate Chip, Sugar, Peanut Butter, Oatmeal Raisin, White Chocolate Macadamia Nut, Whole Grain Chocolate Chip, Whole Grain Double Chocolate Chip, Whole Grain Rainbow Chip) of our 1.5 oz foodservice frozen cookie products, weighted by sales volumes 01/01/2017 - 08/31/2017 and their corresponding market share-weighted competitor products. Case of 210.

Note: The difference is large, but also falls within the uncertainty bounds of the model. In other words, the difference between the water impacts of our foodservice cookie ingredients and those of competitors could be as much as 57% less, or in the worst case scenario, 13% higher.

Producing the ingredients in Eat JUST, Inc. foodservicecookie dough requires 44% (±27%) less surface and groundwater than those in leading brands.

53±33 Represents the Chocolate Chip flavor of our 5 lb foodservice container of RTE cookie dough and its corresponding market share-weighted competitor products. Case of one.

Note: The comparison to competitor products is imperfect, because Eat JUST, Inc. products are RTE, while competitor products are not.Producing the ingredients in

Eat JUST, Inc. foodservicescramble patty requires 79% (±18%) less surface and groundwater than those in leading brands.

404±93 Representing our 2 oz Original flavor egg-alternative frozen patty using the latest formulation and corresponding egg-based market share-weighted competitor products. Case of 120.

40 Ready-To-Eat

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Table 2. Greenhouse gas emission results for Eat JUST, Inc. product suite-level ingredient combinations

with supporting detail.

GHG KPI Results (per case)

Savings (kgCO2e)

Detail (Flavors included, format sizes)

The ingredients in Eat JUST, Inc. retail mayo have a 43% (±9%) lower carbon footprint than those in leading brands.

1.91±0.41 Represents a simple average of newest formulations of five flavors (Original, Chipotle, Garlic, Sriracha, Truffle) and original formulation of Awesomesauce of shelf-stable, 12 oz bottles of our retail mayo products and their corresponding market share-weighted competitor products. Case of six.

The ingredients in Eat JUST, Inc. retail dressing have a 33% (±24%) lower carbon footprint than those in leading brands.

1.24±0.9 Represents five key flavors (Caesar, Chipotle Ranch, Sweet Mustard, Thousand, and latest Ranch) of shelf-stable, 12 oz bottles of our retail dressing products, weighted by sales volumes 1/01/2017 - 08/31/2017 and their corresponding market share-weighted competitor products. Case of six.

The ingredients in Eat JUST, Inc. retail cookie dough have about the same carbon impact as those in other leading brands.

-0.26±0.46 Represents all flavors (Chocolate Chip, Birthday Cake, Peanut Butter Chocolate Chip) of 14 oz containers of our retail RTE cookie dough products, weighted by sales volumes (01/01/2017 - 08/31/2017 and their corresponding market share-weighted competitor products. Case of six.

Note: The difference is small and falls within the uncertainty bounds of the model (i.e. we can’t yet tell whether it is higher or lower). Also note that the comparison to competitor products is imperfect, because Eat JUST, Inc. products are RTE, while competitor products are not.

The ingredients in Eat JUST, Inc. foodservice mayo have a 42% (±12%) lower carbon footprint than those in leading brands.

12.38±3.54 Represents a simple average of five newest flavor formulations (Original, Chipotle, Garlic, Sriracha, Truffle) and Original Light of shelf-stable, 1-gallon jars of our foodservice mayo products and their corresponding market share-weighted competitor products. Case of four.

The ingredients in Eat JUST, Inc. foodservice dressing have a 45% (±25%) lower carbon footprint than those in leading brands.

8.68±4.87 Represents three flavors (Ranch, Ranch Light, Thousand) of shelf-stable, 1-gallon jars of our foodservice dressing products weighted by sales volumes 01/01/2017 - 08/31/2017 and their corresponding market share-weighted competitor products. Case of four.

The ingredients in Eat JUST, Inc. foodservice cookies have a 24% (±35%) lower carbon footprint than those in leading brands.*

*Although this model has a high degree of uncertainty.

1.45±2.14 Represents 8 flavors (Chocolate Chip, Sugar, Peanut Butter, Oatmeal Raisin, White Chocolate Macadamia Nut, Whole Grain Chocolate Chip, Whole Grain Double Chocolate Chip, Whole Grain Rainbow Chip) of our 1.5 oz foodservice frozen cookie products, weighted by sales volumes 01/01/2017 - 08/31/2017 and their corresponding market share-weighted competitor products. Case of 210.

Note: The difference is large, but also falls within the uncertainty bounds of the model.

The ingredients in Eat JUST, Inc. foodservice cookie dough have about the same carbon impact as those in other leading brands.

-0.01±0.60 Represents the Chocolate Chip flavor of our 5 lb foodservice container of RTE cookie dough and its corresponding market share-weighted competitor products. Case of one.

Note: The difference is extremely small and falls within the uncertainty bounds of the model. Also note that the comparison to competitor products is imperfect, because Eat JUST, Inc. products are RTE, while competitor products are not.

The ingredients in Eat JUST, Inc. foodservice scramble patty has a 51% (±23%) lower carbon footprint than those in leading brands.

2.98±1.37 Representing our 2 oz Original flavor egg-alternative frozen patty using the latest formulation and corresponding egg-based market share-weighted competitor products. Case of 120.

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Once the performance assessment is completed, we are free to start the process again, beginning with the prioritization of methodological improvements, selection of new and worthwhile sustainability indicators, and refinement of our overarching sustainability goals.

10. Discussion

The approach we have described is only a part of the foundation for much broader sustainability efforts at JUST, Inc., but it is one that we expect will strengthen and grow with time. In fact, regularly reporting on our GHG emissions and water impacts isn’t enough—for us or anyone else. So we’ve designed this initial framework to scale by integrating it with a database that can support much more information, gathered from a variety of different sources including operational data from supply chain participants.41

And as we have shown, our approach for calculating sustainability KPIs is a slight departure from other sustainability reporting and life cycle assessment efforts in a few ways:

We report on ingredient-level environmental impacts for over 90% of our products(based on 2017 sales volumes), with a goal of 100% by early 2018. We do this to ensurethat the results we share for individual products are precisely consistent with broadercompany- and brand-wide results.

We compare our products to modeled results against real “on the shelf” products asbrand market share-representative products, instead of generic products.

We wanted to provide ourselves with the option of reporting our sustainability progressat a very fast pace, with a focus on new media (e.g. at least quarterly in multiplecontexts), instead of simply publishing annual reports or solitary product life cycleassessments.

By focusing on how the process can be streamlined to provide us with new insights on a recurring basis, we needed to conduct this analysis with a specific set of assumptions. And like all sustainability work, the results and any corresponding claims come with a specific set of limitations, which we outline in Table 3. The overarching assumption is that, in spite of the limitations, our approach gives us directionally accurate results as we work on proposed solutions.

41 Presently, the information from supply chain participants that we do include is focused on providing greater ingredient specificity, like source crop (e.g. beet or cane for sugar), organic status, country or region of origin, and water content. This allows us to select the most appropriate model material from the standard LCA databases or to customize our own system processes. With time, and as part of our broader sustainability initiative, we expect to work even more directly with our suppliers and customers to integrate the most precise data into our models.

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Table 3. Working assumptions used for the present analysis, related limitations, and proposed future work to address them.

Specific Limitation Response and Proposed Solution

There may be uncertainty of the impact factors found in the SimaPro LCA process database, which add to the overall uncertainty (i.e. upper and lower error bounds) of the results.

We agree and expect to incorporate any available, quantifiable uncertainty into the material proxy records in our technology platform, based on a specific confidence interval (e.g. 95%). We will incorporate that error estimation into all follow-on calculations.

The ILCD 2011 impact assessment method may be an expedient and reasonable choice, but there are at least seven other methods available in SimaPro alone.

While not all of the impact assessment methods provide the specific set of KPIs and in the familiar units that we desire, the ones that do can be used to find results for our products as part of a larger sensitivity analysis, which we expect to conduct in the future.

In some cases, there is no perfectly matching material for an ingredient in the LCA databases, while in other cases there may be multiple options available. This implies that the true impact factors will be different in some way than the factors used in the analysis.

We attempted to identify appropriate material matches by reviewing the specific descriptions of all available options first and by prioritizing ones which came from the most up-to-date databases and especially those which focus on food and agriculture. Often the ingredient model exactly matched the real ingredient (e.g. US-sourced refined soybean oil). In some instances, we needed to select an appropriate proxy based on our best professional judgment. For example, we may know that US-sourced cayenne pepper paste is an ingredient, but there may only be a model and resulting impact factors available for Globally-sourced green peppers. In that case, we would use the green pepper model. However, it would be useful to test these assumptions as part of a future sensitivity analysis.

No insights are provided on the relative differences among process stages (e.g. cultivation, processing, manufacturing) or the specific material inventory drivers (e.g. fuel use, fertilizer use, land conversion) for the water or GHG impacts, so it is unclear where the impact “hot spots” may be.

At present, we are able to identify the ingredients that are key impact drivers at the product level and at the brand level. But because not all of the impact factors we currently use are based on linked unit-level processes (i.e. some are “system” level, which means that the inputs and outputs are aggregated into a single comprehensive process), our hot spot analysis capabilities are restricted. A major future step for our work is to provide a discrete accounting of the material and energy inputs and outputs of all major supply chain process nodes.

Ingredients that have a very small mass in the overall product (e.g. lecithin, stabilizers, flavorings) may have outsized environmental impact implications, especially when they are highly refined.

This is a limitation in nearly all food-related LCAs, and there are very few studies that look at the impacts of highly refined ingredients. But two things will make our results more accurate in this regard. First, we will draw on Hampton Creek’s ingredient manufacturing knowledge base to create our own customized impact factor assessments for these items. Second, we will refine the formulation prediction algorithm to reduce the upper and lower error bounds of the expected mass of each ingredient.

The life cycle assessment does not incorporate specific operational data of supply chain participants.

This is a key challenge for us and others. There are major logistical and confidentiality hurdles to getting operational data on manufacturing of all products and all unit process nodes within each product system. Longer term, node-level supply chain accounting will allow us to incorporate the impacts of more specific cultivation practices, transportation choices, and packaging scenarios. As a stopgap measure, we use important ingredient-specific information from separate food safety and quality assurance database software so that we can find or develop the best impact factors already available.

The analysis excludes the final manufacturing step and end of material life stages, including disposal.

As previously mentioned, these stages do contribute to the overall environmental impacts of products, but we have prioritized source ingredient differentiation over other aspects of the model (Hetherington, 2014). To make this explicit throughout the document, we refer to “product-level ingredient impacts” instead of “product impacts” to emphasize the fact that the underlying LCA focuses on ingredient impacts first, and that we then quantify the impacts of combinations of those ingredients. To close this methodological gap, though, we expect to expand the bounds of our model to include final manufacturing data, packaging choices, use and disposal stages in future analyses.

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There is not a perfect match between the geographies of Eat JUST, Inc. products and the material proxies selected to represent them.

In most cases, specific agricultural cultivation and industrial manufacturing input-output data are only available and published for a representative operation or group of operations and for a limited number of geographies. For instance, it is much more common to find LCA data for operations that occur in Europe (where life cycle impact assessment is the norm) than for operations occurring elsewhere. In these instances, we selected the material processes that we believed to be the most appropriate proxy, based on our best professional judgment. For water, if the manufacturing data were specific to another country, we would modify the water impact factor so that it matched the correct geography according to the WFN databases (M. M. Mekonnen & Hoekstra, 2010; Mesfin M. Mekonnen & Hoekstra, 2010). These are detailed in Table A3.

To further address this limitation of the model, we will continue to seek out real operational data and/or data that are most relevant to the supply chain geographies we model.

The specific geographies of competitor supply chains are unknown.

In general, we use global impact factors to model the ingredients of competitors, except in cases where it is clear that the source origin is more obvious (e.g. soybean oil from the US). We hope to verify our product assumptions with other brands in the future.

Local water scarcity is not accounted for in the surface and groundwater impact numbers.

As we add greater geographic specificity to our models, we expect to add adjustments for water scarcity.

Some material proxies for ingredients rely on past customized analysis instead of those available in the standard LCA databases.

For two ingredients (egg and cocoa), we decided to keep the customized process models that we already had available in lieu of using ones that were accessible in the purchased LCA databases. The only egg process model in the LCA databases available to us is from operations occurring in the Netherlands, which we expect are materially different than operations occurring in the U.S. Our existing egg process model was built for us using several more recent, U.S.-centric sources (Eshel, Shepon, Makov, & Milo, 2014; NC Egg Association, 2016; Xin et al., 2011). Eggs make up 6.7% of the total mass for the competitor product ingredients modeled, whereas 8.1% of the total water impacts and only 1.7% of the total GHG impacts for the competitor ingredients modeled are due to the use of eggs and egg-based ingredients.

For cocoa, while there were a few process models available for different countries, we have not yet been able to reconcile the differences between them and our existing, custom-built model, especially with regard to GHG impacts, which appear to be 10-100x higher than expected. Until we can identify the specific source of the discrepancy, and because the same cocoa process impact factors are applied to our products as other brands, we will continue to use the custom system process which references the literature (Ntiamoah & Afrane, 2008). Cocoa and cacao-based ingredients constitute about 2.5% of the total mass of Eat JUST, Inc. product ingredients, and about 2.2% by mass of the competitor product ingredients. The similarity of masses implies that the GHG and water impacts are essentially equal.

Going forward, we will also continue to seek out the most accurate process data for these and all other ingredients.

The results are not representative of all of the product sales (i.e. 9.5% of the sales data are not included).

Based on a high level review of the missing products versus the respective competitive counterparts, we think that any environmental gains or losses will be modest for the remaining sales, since most of the competitor products are also free of animal products and use fairly similar ingredients. However, for the sake of rigor, we expect to include these in our analysis by early next year.

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The analysis is limited to only two KPIs and doesn’t appear to consider other food sustainability issues that are relevant to consumers.

We plan to consider many additional KPIs in the future, including indicators such as raw fossil fuel energy consumption, nutrient runoff, usable local rainfall, and soil organic carbon depletion.42 Overall, the scope of this methodology document is restricted by design. Other issues that may be of concern to consumers, such as organic vs. conventional ingredients, GMO vs. non-GMO crops, vegan certification, animal welfare, farm tillage practices, product packaging, food waste, recycling, etc., will be addressed in a sustainability stances and standards document which is still in production.

Selection of leading brands may be difficult for others to replicate.

This depends on the type of researcher. Large food industry participants who wish to conduct a similar analysis will likely have access to retail market sales software of the same type that we have access to (i.e. IRI). Others, such as academic researchers, may want to make simplifying assumptions about the relative importance of high profile brands in constructing any market share-weighted results.

The method for distinguishing between beet and cane sugar for competitor products is unclear.

Unless otherwise stated on the competitor product ingredient list or elsewhere on the packaging, we assumed that the sugar used in competitor products was beet-based, due to differences in price. In the future, we will reach out to the brand representatives to confirm these assumptions. For our own products, the source of the sugar depends on the sales channel (i.e. retail vs. foodservice).

Sales volumes of products may not be representative of total impacts.

While we expect total comparative impacts to be well represented by the current approach, for future iterations of our sustainability analyses, we expect to incorporate information on product waste, unsold products, and donated products.

The final results are based on a fairly modest number of material proxies (i.e. 85) for which there are LCA impact factors.

There are undoubtedly tens if not hundreds of thousands of different commercially available refined food ingredients available for food manufacturers in the U.S. alone. There are far fewer process models available to LCA researchers (for reasons outlined previously). Interestingly, our model shows that combinations of ingredients alone can have major implications for the overall sustainability profiles of food products. Nonetheless, over time we expect to refine our models for each ingredient so that the customized unit processes we use when evaluating their impacts are specific to each of their supply chain histories. What this means numerically is that we would have 252 (or more) material models for each of the 252 (or more) real or generic ingredients that are used by us and by others.

10.1. Final Thoughts By now it has become a truism in food sustainability that, all other things being equal, removing animal ingredients from a product will lower its overall environmental footprint. But one point we hope to convey with this analysis is that all other things are not equal. The details can make a huge difference in the final accounting. In short, sustainability is not simple. For us, it is true that a major driver of sustainability has been the replacement of animal-derived ingredients with vegetable-based alternatives. It’s also true that less obvious choices – for instance, what oils we use (rainfed canola) or which type of sugar (cane for retail products) – can have huge benefits. But there may also be trade-offs. By focusing solely on a small number of indicators, we and many other organizations run the risk of de facto prioritization of some things (e.g. GHGs, water

42 The raw fossil fuel energy consumption assessment will follow the methodology outlined by Moomaw et al., 2011. Nutrient runoff and soil organic carbon depletion are major environmental concerns (Erisman et al., 2015; Lal, 2004), but not generally discussed in consumer contexts. We’re using the term “usable local rainfall,” which in other contexts is described as “green water” (Hoekstra, Chapagain, Aldaya, & Mekonnen, 2012).

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use) over others (e.g. nutrient runoff, soil erosion, biodiversity), to say nothing of the vast array of promising social sustainability metrics that have been developed in recent years.

So as we continue to work on our sustainability analytics, we will broaden our assessment by including a greater number and variety of indicators, even ones that are still unfamiliar to consumers. Likewise, we will work with others to streamline the ways that sustainability data are communicated so that we can more quickly identify the areas of our food system that need upgrading.

Ultimately, though, food sustainability isn’t simply about new metrics and greater accountability: it’s about building trust from the farm to the dinner table, so that we can all do more while using less. And there’s no time to waste.

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About the Authors

Seth Sheldon is the principal consultant at Sheldon Data, LLC.43 He works closely with Eat JUST, Inc. to measure and improve the company’s sustainability performance.

Dahni-El Giles serves as Senior Corporate Counsel and Udi Lazimy serves as the Global Plant Sourcing Lead at Eat JUST, Inc.. Both are employees of the company and are passionate about total sustainability.

Disclaimer

This report was prepared by Sheldon Data, LLC, in collaboration with Eat JUST, Inc.. Neither Sheldon Data nor Eat JUST, Inc. make any warranty as to the comprehensiveness, accuracy,

reliability, or usefulness of the results contained in this report beyond that which has been specified in the report itself, and neither Sheldon Data nor Eat JUST, Inc. assumes any liability with regard to any harm resulting from the use of information contained within this report by third parties.

Acknowledgments

First, we gladly acknowledge Third Partners, LLC, for their excellent work in reviewing this report. We are sincerely grateful for their efforts in validating the methodology, and we look forward to improving it based on their thoughtful recommendations.

We would also like to recognize the many individuals at Eat JUST, Inc. who helped to make this report possible. To the team members in product development, ingredient sourcing, culinary, information technology, and beyond: Thank you!

43 http://www.sheldondata.com

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Appendix

44 Carbon Disclosure Project, Global Reporting Initiative, Natural Capital Protocol, and Good Agricultural Practice, respectively

Table A1. Challenges facing sustainability practitioners in the food industry

Food companies often have hundreds of products, each with tens of ingredients, multiple formulations, and packaging formats that change with time.

Ingredients can have multiple names, suppliers, supplier locations, and source plants.

Supply chains are generally opaque and dynamic, and supplier questionnaires are often very time consuming and cumbersome.

Competitor product formulations and ingredient histories are not public.

There are few turn-key methods linking standard LCA software with common programming languages (e.g. Python, R).

Databases of process models are incomplete, especially regarding unusual agricultural commodities (e.g. adzuki beans, kernza wheatgrass).

High-level sustainability claims are plagued by uncertainty throughout (e.g. error in process models, error in formulation prediction).

There is a mismatch between the typical pace needed for internal analytics (order of weeks or months), supplier reporting (quarterly, annually), detailed scientific review (months), and public statements (daily to annually).

There is basic disagreement within the scientific community about impact assessment methods (e.g. ILCD 2011 vs. ReCiPe 2016), LCA approaches (e.g. attributional vs. consequential), and the relative significance of specific indicators.

There are multiple reporting and certification frameworks (e.g. CDP, GRI, NCP, GAP),44 each with varying degrees of standardization, comprehensiveness, rigor, and adoption.

There are countless potential environmental and social indicators that a company may consider when assessing the sustainability of its products.

Multiple stakeholders have diverse views and highly variable knowledge bases.

Individuals’ preexisting intuitions about sustainability and health are difficult to overcome (e.g. terms such as “non-GMO,” “free range,” and “natural”).

The complexity of the underlying information makes it prone to oversimplification or misrepresentation in consumer contexts.

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Table A2. List of websites used for finding public nutrition and ingredient information for products.

1fsschools.com indonesiamediagrocery.com smartlabel.breyers.com

amazon.com irenesbakery.com smartlabel.countrycrock.com

annies.com jet.com smartlabel.hellmanns.com

cannatas.com jjsnackfoodservice.com snackworks.com

cmbc.com kingarthurflour.com stonewallkitchen.com

costcobusinessdelivery.com kraftheinz-foodservice.com sunnyfresh.com

dilussodeli.com mms.com sweetlorens.com

efooddepot.com mortonsalt.com target.com

foodprofile.com myfitnesspal.com unileverfoodsolutions.com

foodservice.davidscookies.com ndb.nal.usda.gov vitacost.com

foodservicedirect.com nowfoods.com vitaminusa.com

foodstoragemanagement.com nutritionix.com walmart.com

fooducate.com opensky.com webstaurantstore.com

goodfoodinc.com shopwell.com world.openfoodfacts.com

harrisburgstore.com sifu.unileversolutions.com

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Table A3. Complete list of the materials used in our analysis and corresponding data sources used for environmental impact factors. Materials from the LCA databases that are marked with an asterisk* were modified using WFN water footprint values.

Material proxy Impact factor data source

50-50 palm canola oil blend, based on SimaProAgri-footprint version 3.0, March 2017; Ecoinvent 3.3. Compiled October 2016

50-50 palm soybean oil blend, based on SimaPro Ecoinvent 3.3. Compiled October 2016

50-50 soybean canola oil blend, based on SimaProAgri-footprint version 3.0, March 2017; Ecoinvent 3.3. Compiled October 2016

50-50 soybean cottonseed oil blend, based onSimaPro

Ecoinvent 3.3. Compiled October 2016

79 prcnt palm oil margarine, based on refined GLO palm oil

Ecoinvent 3.3. Compiled October 2016

additional ingredients Eat JUST, Inc. custom factor (2016 Lux Research)Apple GLO production Alloc Def S Ecoinvent 3.3. Compiled October 2016 baking soda Eat JUST, Inc. custom factor (2016 Lux Research)black pepper Eat JUST, Inc. custom factor (2016 Lux Research)Cassava root dried, from tapioca processing, at plant TH Mass

Agri-footprint version 3.0, March 2017

Cheese, Cheddar, at plant, Average US U DATASMART Life Cycle Inventory Package (formerly US-EI 2.2 library), May 2017

Chocolate Chips Real Cocoa Butter, dairy Eat JUST, Inc. formulation prediction modelCitric acid RNA production - Alloc Def S Ecoinvent 3.3. Compiled October 2016 cocoa powder Eat JUST, Inc. custom factor (2016 Lux Research)Coconut copra meal, from crushing, at plant ID Mass

Agri-footprint version 3.0, March 2017

Cottonseed oil, refined US - cottonseed oil refinery operation - Alloc Def S

Ecoinvent 3.3. Compiled October 2016

Cream, at processing facility US US-EI S DATASMART Life Cycle Inventory Package (formerly US-EI 2.2 library), May 2017

Crude peanut oil, from crushing at plant US Mass Agri-footprint version 3.0, March 2017 Cucumber GLO production - Alloc Def S Ecoinvent 3.3. Compiled October 2016 dijon mustard Eat JUST, Inc. custom factor (2016 Lux Research)

Dry Whey, at plant, Average US U DATASMART Life Cycle Inventory Package (formerly US-EI 2.2 library), May 2017

EDTA, ethylenediaminetetraacetic acid, at plant US US-EI U

DATASMART Life Cycle Inventory Package (formerly US-EI 2.2 library), May 2017

egg yolk dried [USA] Eat JUST, Inc. custom factor (2016 Lux Research)egg yolks [USA] Eat JUST, Inc. custom factor (2016 Lux Research)eggs [USA] Eat JUST, Inc. custom factor (2016 Lux Research)extra virgin olive oil Eat JUST, Inc. custom factor (2016 Lux Research)fava bean protein isolate (90%) Eat JUST, Inc. custom factor (2016 Lux Research)fruit and vegetable juice color Eat JUST, Inc. custom factor (2016 Lux Research)garlic fresh Eat JUST, Inc. custom factor (2017)Grape GLO production Alloc Def S Ecoinvent 3.3. Compiled October 2016 Green bell pepper GLO production Alloc Def S Ecoinvent 3.3. Compiled October 2016 Groundnut meal, from crushing, at plant US Mass Agri-footprint version 3.0, March 2017 hemp seeds Eat JUST, Inc. custom factor (2016 Lux Research)high fructose corn syrup HFCS Eat JUST, Inc. custom factor (2017)honey Eat JUST, Inc. custom factor (2016 Lux Research)invert sugar Eat JUST, Inc. custom factor (2016 Lux Research)Landed fish, from fishery, at plant PE Mass Agri-footprint version 3.0, March 2017 Lemon MX lemon production Alloc Def S Ecoinvent 3.3. Compiled October 2016 Maize starch, from wet milling (starch drying), at plant US Mass

Agri-footprint version 3.0, March 2017

milk chocolate bar Eat JUST, Inc. formulation prediction modelMilk Chocolate, candy pieces Eat JUST, Inc. formulation prediction modelmustard seed dry Eat JUST, Inc. custom factor (2016 Lux Research)natural flavors Eat JUST, Inc. custom factor (2016 Lux Research)natural vanilla JUST, Inc. custom factor (2016 Lux Research)

NonFat Dry Milk, at processing facility US US-EI S DATASMART Life Cycle Inventory Package (formerly US-EI 2.2 library), May 2017

Oat grain CA-QC oat grain, feed production - Alloc Def

Ecoinvent 3.3. Compiled October 2016

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olive Eat JUST, Inc. custom factor (2016 Lux Research)onions Eat JUST, Inc. custom factor (2017)Palm oil, refined GLO palm oil refinery operation Alloc Def

Ecoinvent 3.3. Compiled October 2016

Pea, protein-isolate, at plant RER Mass Agri-footprint version 3.0, March 2017 Peach CN peach production - Alloc Def Ecoinvent 3.3. Compiled October 2016 pepper bell chili Capricum EatJUST, Inc. custom factor (2017)Pineapple GLO production Alloc Def S Ecoinvent 3.3. Compiled October 2016 Real Milk Chocolate Chips Eat JUST, Inc. formulation prediction modelRefined coconut oil, at plant ID Mass Agri-footprint version 3.0, March 2017 *Refined rapeseed oil (AU water), from crushing(solvent), at plant US Mass

Agri-footprint version 3.0, March 2017

*Refined rapeseed oil (CA water), from crushing(solvent), at plant US Mass

Agri-footprint version 3.0, March 2017

Refined rapeseed oil, from crushing (solvent), at plant US Mass

Agri-footprint version 3.0, March 2017

Refined sunflower oil, from crushing (pressing) at plant UA Mass

Agri-footprint version 3.0, March 2017

salt Eat JUST, Inc. custom factor (2016 Lux Research)Semi-Sweet Chocolate Chips Eat JUST, Inc. formulation prediction modelsemisweet chocolate dairy-free Eat JUST, Inc. custom factor (2017)sodium benzoate Eat JUST, Inc. custom factor (2017)Sorghum, seed, at farm US Mass Agri-footprint version 3.0, March 2017 *Soybean lecithin (US water), from crushing(solvent) at plant DE Mass

Agri-footprint version 3.0, March 2017

Soybean oil, refined US soybean oil refinery operation Alloc Def S

Ecoinvent 3.3. Compiled October 2016

Strawberry US strawberry production, open field, macro tunnel Alloc Def S

Ecoinvent 3.3. Compiled October 2016

*Sugar (US water), from sugar beet, from sugarproduction, at plant FR Mass

Agri-footprint version 3.0, March 2017

*Sugar beet molasses (US water), from sugarproduction, at plant FR Mass

Agri-footprint version 3.0, March 2017

Sugar cane molasses, from sugar production, at plant US Mass

Agri-footprint version 3.0, March 2017

Sugar, from sugar cane, from sugar production, at plant US Mass

Agri-footprint version 3.0, March 2017

Tap water ROW tap water production, conventional treatment Alloc Def S

Ecoinvent 3.3. Compiled October 2016

Tomato Ketchup Eat JUST, Inc. formulation prediction modelTomato, fresh grade MX tomato production fresh grade open field Alloc Def S

Ecoinvent 3.3. Compiled October 2016

Vanilla RoW vanilla production Alloc Def S Ecoinvent 3.3. Compiled October 2016 Vegan Worcestershire Sauce Eat JUST, Inc. formulation prediction model*Wheat flour (US water), from dry milling, at plantDE Mass

Agri-footprint version 3.0, March 2017

White Chips, palm, dairy Eat JUST, Inc. formulation prediction modelWhite Chocolate Chips (1) Eat JUST, Inc. formulation prediction modelWhite Chocolate Chips (2) Eat JUST, Inc. formulation prediction modelwhite distilled vinegar, 100 grain, from 98 prcnt acetic acid model

Eat JUST, Inc. custom factor (2017)

white distilled vinegar, 50 grain, from 98 prcnt acetic acid model

Eat JUST, Inc. custom factor (2017)

White rice, from dry milling, at plant CN Mass Agri-footprint version 3.0, March 2017

Whole milk, at processing facility US US-EI U DATASMART Life Cycle Inventory Package (formerly US-EI 2.2 library), May 2017

xanthan gum Eat JUST, Inc. custom factor (2017)

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Table A4. Complete list of water-related inventory items aggregated from the databases within SimaPro and imported into our database.

Water, fresh Water, river, RER Water, unspecified natural origin, RU Water, lake Water, river, RLA Water, unspecified natural origin, SD Water, lake, AT Water, river, RNA Water, unspecified natural origin, SE Water, lake, BE Water, river, RO Water, unspecified natural origin, SK Water, lake, BG Water, river, RoW Water, unspecified natural origin, TH Water, lake, CA Water, river, RU Water, unspecified natural origin, TR Water, lake, CH Water, river, SE Water, unspecified natural origin, TW Water, lake, CN Water, river, SK Water, unspecified natural origin, UA

Water, lake, CZ Water, river, TN Water, unspecified natural origin, UN-OCEANIA

Water, lake, DE Water, river, TR Water, unspecified natural origin, US Water, lake, DK Water, river, TW Water, unspecified natural origin, VN Water, lake, ES Water, river, TZ Water, unspecified natural origin, WEU Water, lake, Europe without Switzerland

Water, river, US Water, unspecified natural origin/kg

Water, lake, FI Water, river, WEU Water, unspecified natural origin/m3 Water, lake, FR Water, river, ZA Water, well, in ground Water, lake, GB Water, unspecified natural origin, AR Water, well, in ground, AT Water, lake, GLO Water, unspecified natural origin, AT Water, well, in ground, AU Water, lake, HU Water, unspecified natural origin, AU Water, well, in ground, BE Water, lake, IT Water, unspecified natural origin, BE Water, well, in ground, BG Water, lake, JP Water, unspecified natural origin, BG Water, well, in ground, BR Water, lake, KR Water, unspecified natural origin, BR Water, well, in ground, CA Water, lake, LU Water, unspecified natural origin, CA Water, well, in ground, CH Water, lake, NL Water, unspecified natural origin, CH Water, well, in ground, CN Water, lake, NO Water, unspecified natural origin, CL Water, well, in ground, CZ Water, lake, PL Water, unspecified natural origin, CN Water, well, in ground, DE Water, lake, PT Water, unspecified natural origin, CO Water, well, in ground, DK Water, lake, RER Water, unspecified natural origin, CZ Water, well, in ground, ES

Water, lake, RNA Water, unspecified natural origin, DE Water, well, in ground, Europe without Switzerland

Water, lake, RoW Water, unspecified natural origin, DK Water, well, in ground, FI Water, lake, RU Water, unspecified natural origin, EE Water, well, in ground, FR Water, lake, SE Water, unspecified natural origin, ES Water, well, in ground, GB

Water, lake, SK Water, unspecified natural origin, Europe without Switzerland

Water, well, in ground, GLO

Water, lake, TR Water, unspecified natural origin, FI Water, well, in ground, HU Water, lake, TW Water, unspecified natural origin, FR Water, well, in ground, ID Water, lake, US Water, unspecified natural origin, GB Water, well, in ground, IN Water, river Water, unspecified natural origin, GLO Water, well, in ground, IS Water, river, AT Water, unspecified natural origin, HN Water, well, in ground, IT Water, river, AU Water, unspecified natural origin, HU Water, well, in ground, JP

Water, river, BE Water, unspecified natural origin, IAI Area, Africa

Water, well, in ground, KR

Water, river, BG Water, unspecified natural origin, IAI Area, Asia, without China and GCC

Water, well, in ground, LU

Water, river, BR Water, unspecified natural origin, IAI Area, EU27 & EFTA

Water, well, in ground, MA

Water, river, CA Water, unspecified natural origin, IAI Area, Gulf Cooperation Council

Water, well, in ground, MX

Water, river, CH Water, unspecified natural origin, IAI Area, North America, without Quebec

Water, well, in ground, MY

Water, river, CN Water, unspecified natural origin, IAI Area, Russia & RER w/o EU27 & EFTA

Water, well, in ground, NL

Water, river, CZ Water, unspecified natural origin, IAI Area, South America

Water, well, in ground, NO

Water, river, DE Water, unspecified natural origin, ID Water, well, in ground, NORDEL Water, river, DK Water, unspecified natural origin, IN Water, well, in ground, PE Water, river, ES Water, unspecified natural origin, IT Water, well, in ground, PG Water, river, Europe without Switzerland

Water, unspecified natural origin, JP Water, well, in ground, PH

Water, river, FI Water, unspecified natural origin, KR Water, well, in ground, PL Water, river, FR Water, unspecified natural origin, LT Water, well, in ground, PT Water, river, GB Water, unspecified natural origin, LU Water, well, in ground, RER Water, river, GLO Water, unspecified natural origin, MX Water, well, in ground, RLA Water, river, HU Water, unspecified natural origin, MY Water, well, in ground, RNA

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Water, river, IN Water, unspecified natural origin, NL Water, well, in ground, RoW Water, river, IT Water, unspecified natural origin, NO Water, well, in ground, RU Water, river, JP Water, unspecified natural origin, PG Water, well, in ground, SE Water, river, KR Water, unspecified natural origin, PH Water, well, in ground, SK Water, river, LU Water, unspecified natural origin, PK Water, well, in ground, TH Water, river, MY Water, unspecified natural origin, PL Water, well, in ground, TN Water, river, NL Water, unspecified natural origin, PT Water, well, in ground, TR Water, river, NO Water, unspecified natural origin, RAF Water, well, in ground, TW Water, river, PE Water, unspecified natural origin, RER Water, well, in ground, US Water, river, PH Water, unspecified natural origin, RME Water, well, in ground, WEU Water, river, PL Water, unspecified natural origin, RNA Water, well, in ground, ZA Water, river, PT Water, unspecified natural origin, RO Water, river, RAS Water, unspecified natural origin, RoW