COMPARATIVE ENVIRONMENTAL LCA
OF THE IMPOSSIBLE BURGER WITHCONVENTIONAL GROUND BEEF BURGER

Prepared by:
Sofia Khan (Quantis) • Cristóbal Loyola (Quantis) • Jon Dettling (Quantis) • Joshua Hester(Quantis)Rebekah Moses (Impossible Foods)

For: Impossible Foods

Contact:
Sofia Khan

Cristóbal Loyola

Senior Sustainability Consultant

Sustainability Consultant

sofia.khan@quantis-intl.com

cristobal.loyola@quantis-intl.com

PROJECT INFORMATION
Project title

Comparative environmental LCA of the Impossible Burger® withconventional ground beef burger

Contracting organization

Impossible Foods

Liability statement

Information contained in this report has been compiled from
and/or computed from sources believed to be credible.
Application of the data is strictly at the discretion and the
responsibility of the reader. Quantis is not liable for any loss ordamage arising from the use of the information in thisdocument.

VersionFinal Report; 27 February 2019

Project team

Sofia Khan (sofia.khan@quantis-intl.com) – Project Manager
Jon Dettling (jon.dettling@quantis-intl.com) – Global Director ofServices & Innovation
Cristóbal Loyola (cristobal.loyola@quantis-intl.com) – Analyst
Joshua Hester (josh.hester@quantis-intl.com) – Analyst

Client contacts

Rebekah Moses (rebekah.moses@impossiblefoods.com) –Senior Manager, Impact Strategy

External reviewers

Gidon Eshel, Ph.D. (geshel@gmail.com) – Bard College
Greg Thoma, Ph.D. (gthoma@uark.edu) – University of Arkansas
Nathan Pelletier, Ph.D. (nathan.pelletier@ubc.ca) – University of
British Columbia

Associated files

This report is associated with the following electronic file:▪

Impact2002+ v2.28 Characterization Factors.

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

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Executive Summary
The global community is facing an imperative to feed 10 billion people by 2050, and an urgent
need to sustain a food secure future while also preserving and strengthening the naturalenvironment. Over the next several decades, world food demand is expected to increase by
50%, and demand for animal products by at least 70%, entailing risks for human resilience
due to the intensive resource use associated with animal-derived food (Alexandratos &Bruinsma, 2012).
The current agricultural system pushes many environmental thresholds past what can beconsidered sustainable or scalable. To preserve existing land and water resources, and to
implement strategies needed to keep global warming below a 1.5 °C rise as adopted by theinternational 2016 Paris Agreement, more sustainable consumer options are needed to meetthe growing demand for meat and dairy products without relying on the inefficiencies oftranslating plants through an animal and onto a plate. Alternatives to cattle products inparticular will be critical.
Impossible Foods has developed one such product: The Impossible Burger®, made directlyfrom plants. This analysis uses life cycle assessment (LCA) methodology to compare thepotential environmental impacts of Impossible Burger® 2.0 to those of a conventional,
industrial ground beef burger produced in the U.S. Comparison is possible due to the culinary
and nutritional equivalence of the Impossible Burger® with that of animal-derived ground
beef.
Animal farming is the largest resource user within agriculture. It requires between 1/3 to 1/2
of all ice-free land, about one third of fresh water, contributes close to a fifth of global GHGs,
and generates nutrient pollution creating enormous ‘dead zones’ in coastal ecosystems(Herrero, et al., 2016; Eshel, Martin, & Bowen, 2010; Reid, et al., 2008). Meat from ruminants
is particularly impactful, occupying about 2/3 of global agricultural land and generating about
half of agriculture’s total GHG emissions. Animals convert plants into meat and dairy, yet doso within the constraints of an animal metabolism, thus losing the vast majority of the protein
and calories contained in the plants consumed. This report focuses on the resource-sparing
potential of bypassing the animal entirely, and creating equivalent products directly fromplants.
This analysis finds that the Impossible Burger® requires 87% less water, 96% less land, andproduces 89% fewer GHG emissions than the animal version. Additionally, it contributes 92%

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

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less aquatic pollutants. These numbers reflect the latest Impossible Burger® recipe (2.0),
launched in 2019.
These unit-level, or ‘per burger’, LCAs are important to test product-based impact, yet must
be considered within the broader context of animal agriculture’s environmental impact andthe opportunity cost of relying on animals for meat and dairy. For example, 40% of thecontinental US land is used to produce beef, thus LCA results are most meaningful when
considered within the global or national consumption context (Eshel, Shepon, Makov, & Milo,2014).
We compare the cradle-to-gate potential impacts per kg of final product of the Impossible
Burger® and a U.S. ground beef burger. Nutritional desirability is outside our resourcefocused scope; further, the Impossible Burger® has a comparable nutritional content to thebeef burger. At the same time, the Impossible Burger® has a similar cooking time, shelf-life,and distribution system to the beef burger, hence these activities were excluded from thestudy.
Data from lifecycle inventory (LCI) databases (e.g., ecoinvent v3.3, allocation – recycledcontent SCLCI 2014, World Food Lifecycle Database v3.1) are used to calculate the potential
environmental impact of both products, focusing on four environmental impact indicators:
aquatic eutrophication potential, global warming potential, land occupation, and water
consumption. We have reported on other midpoint and endpoint indicators using IMPACT
2002+ method in the appendix, for broader understanding rather than as the focus of thestudy.
Table 1. Baseline results for a kg of Impossible Burger® and beef burger (IMPACT 2002+ v2.28).
Italic number in parenthesis represent 95% confidence intervals.

Impact Category

Unit

Aquatic eutrophication potential

g PO4-eq

Global warming potential

kg CO2-eq

Land occupation*

m2.yWater consumptionlImpossible Burger®
1.3

Beef Burger
15.1(2.3-9.7)(14.3-60.6)

3.5(3.1-4.0)

30.6(25.3-37.5)

2.5

62.0

(1.6-3.7)(37.0-102.5)

106.8(56.9-203.3)

Difference %850.1(617.9-1238.1)

-92%
-89%
-96%
-87%

* Land occupation is reported at an LCI level.

This assessment relies on the best available LCA-related information on food production andconforms with the ISO 14044 standard. For all studied impacts, the Impossible Burger® offers
substantial impact reductions ranging from 87% to 96% compared to U.S. beef burger. The
study results show that per kilogram of frozen, ready-to-ship burger patty:•

Aquatic eutrophication potential decreases from 15 g PO4-eq for beef burger to 1.3 g
PO4-eq for the Impossible Burger®. The reduction is due to the avoided manure

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

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•

•

•

emissions from raising beef cattle, avoided fertilizer emissions during feed production,
and a reduction in electricity consumption by avoiding slaughtering activities.
Global warming potential impacts reduced by 27.2 kgCO2-eq in favor of the Impossible
Burger®. The most significant reductions come from the avoided emissions associatedwith manure and enteric emissions resulting from raising beef cattle.
Land occupation reduced from 62.0 m2 per year for the beef burger to 2.5 m2 per year
for the Impossible Burger®. In the case of the Impossible Burger®, avoiding raisingbeef cattle not only reduces pasture land occupation for grazing, which represents
86% of beef burger land occupation; but also reduces agricultural land demand from
6.8 m2 per year to 2.4 m2 per year. This is because of the decreased agricultural
products demand for beef cattle feed.
Water consumption savings are estimated at 743 liters in favor of Impossible Burger®.
The largest water savings are a result of the avoided burden associated with irrigationin the cultivation of feed crops for beef cattle.

100%

75%

50%

25%

11%

8%

13%
4%

0%

Aquatic eutrophication
potential

Global warming potential
Impossible Burger

Land occupation

Water consumption

Beef Burger

Figure 1: Results comparison of Impossible Burger® and Beef Burger (Impact 2002+ v2.28).

For both products, the raw material production stage contributes the most to the total
environmental impacts studied across the four impact indicators. The results demonstratethat raw material production stage contribution to total impacts are >78% for aquatic
eutrophication, >60% for GHG emissions, >99% for land occupation and >79% of water
consumption for both beef burger and the Impossible Burger®. For the beef burger, cattle
raising and feed production are the primary contributing activities in raw material production.
For the Impossible Burger® the main contributing activities in the raw material production
stage were plant-based ingredient production and manufacturing. The Impossible Burger®
has a dramatically lower resource demand than that of beef. As a result, the proportion of

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

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total impact contributions contained within the production stages is higher than the
proportion of total impact contributions from the same stages within the cattle system.
The result shows that raising beef cattle for human consumption requires growing a muchlarger amount of upstream primary plant material than if humans directly consumed plants orplant-based products. Additionally, shifting to plant-based food options avoids the impacts
associated with raising beef cattle like manure emissions and enteric emissions, which
typically contribute to relatively high global warming potential and aquatic eutrophication
among other environmental impacts.
Sensitivity analysis was conducted for priority inputs where assumptions had to be made suchas the potato protein inputs, refrigeration life, and coconut irrigation etc. for Impossible
Burger®. The sensitivity analysis, explored in depth in Section 4.6, details that theseassumptions do not considerably affect total results and overall conclusions of the study. The
same conclusions were drawn from the uncertainty analysis, where no overlap between theimpact categories studied has occurred.
After considering uncertainty and alternative production scenarios, we find that the
Impossible Burger® offers clear and meaningful reductions over that of the conventional beef
burger in the four environmental impacts studied.

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Table of Contents
Executive Summary .................................................................................................................... 3
Table of Contents........................................................................................................................ 7
List of Tables ............................................................................................................................... 9
List of Figures ............................................................................................................................ 10
Acronyms .................................................................................................................................. 11
1.

Introduction ....................................................................................................................... 12

2.

Goal of the study ............................................................................................................... 13
Objectives ....................................................................................................................... 13
Intended audiences ........................................................................................................ 14

3.

Scope and system boundaries ........................................................................................... 14
General description of the products studied .................................................................. 14

3.1.1.

Impossible Burger® ...................................................................................................... 14

3.1.1.

Beef burger .................................................................................................................. 16
Comparative basis: Function and functional unit ........................................................... 20
System characterization and data sources ..................................................................... 20

3.3.1.

Impossible Burger® ...................................................................................................... 21

3.3.1.1.

Heme Production ...................................................................................................... 21

3.3.1.2.

Impossible Burger® production ................................................................................ 21

3.3.1.3.

Packaging .................................................................................................................. 23

3.3.2.

Beef burger .................................................................................................................. 23

3.3.2.1.

Animal feed production and animal-raising.............................................................. 23

3.3.2.2.

Beef burger production and packaging .................................................................... 27

Temporal and geographic boundaries ............................................................................ 27
Cut-off criteria ................................................................................................................ 27
4.

Assessment methodology .................................................................................................. 28
Allocation methodology.................................................................................................. 28

4.1.1.

Transport allocation ..................................................................................................... 29
Impact assessment ......................................................................................................... 30
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Calculation tool ............................................................................................................... 32
Contribution analysis ...................................................................................................... 32
Uncertainty analysis ........................................................................................................ 33
4.5.1.

Inventory data uncertainty analysis and data quality assessment .............................. 33

4.5.2.

Characterization model uncertainty analysis ............................................................... 35
Sensitivity analysis .......................................................................................................... 35
Critical review ................................................................................................................. 36

5.

Results ............................................................................................................................... 37
Impossible Burger® vs. beef burger comparison ............................................................ 37
Detailed Impossible Burger® results ............................................................................... 40

5.2.1.

Impossible Burger® ingredient analysis ....................................................................... 41
Sensitivity analysis .......................................................................................................... 42
Uncertainty analysis ........................................................................................................ 44
Inventory data quality assessment ................................................................................. 46
LCA applications and limits ............................................................................................. 46

6.

Conclusions ........................................................................................................................ 47

7.

References ......................................................................................................................... 49

8.

Appendices ........................................................................................................................ 55
Appendix A: Description of impact categories ................................................................ 55
Appendix C: Complete LCIA results ................................................................................. 56

8.2.1.

Detailed Impossible Burger® and beef burger results ................................................. 56
Appendix E: Review panel biographies ........................................................................... 61

8.3.1.

Gidon Eshel .................................................................................................................. 61

8.3.2.

Greg Thoma ................................................................................................................. 61

8.3.3.

Nathan Pelletier ........................................................................................................... 62

9.

External panel review ........................................................................................................ 63
Panel statement of conformance with ISO 14044 .......................................................... 63

Are the methods used to carry out the LCA consistent with the international standards (ISO14040, 14044)? ........................................................................................................................ 63
Are the methods used to carry out the LCA scientifically and technically valid? ............................. 63
Are the data used appropriate and reasonable in relation to the goal of the study? ................... 64

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

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Do the interpretations reflect the limitations identified and the goal and scope of the study?..... 64
Is the study report transparent and consistent? ........................................................................ 64

List of Tables
Table 1. Baseline results for a kg of Impossible Burger® and beef burger (IMPACT 2002+
v2.28). Italic number in parenthesis represent 95% confidence intervals. ................................ 4
Table 2: 4 oz nutrition content for Impossible Burger® and beef burger with 80% lean meatand 20% fat. Sources: Impossible Foods Inc. (2018), USDA (2018) .......................................... 15
Table 3: Impossible Burger® main data sources, assumptions, and background dataset sources.................................................................................................................................................. 17
Table 4: Beef burger main data sources, assumptions, and background dataset sources ....... 18
Table 5: Electricity mix from PG&E. .......................................................................................... 22
Table 6: Impossible Burger® Packaging LCI ............................................................................... 23
Table 7: Feed inputs for cow-calf + backgrounding and feedlot operations to produce 3,123
ton of beef cattle. Sources: Rotz, Isenber, Stackhouse-Lawson & Pollak (2013) ...................... 24
Table 8: Water, feed transportation, fuel and energy consumption to produce 3,123 ton ofbeef cattle. Sources: Asem-Hiablie, Battagliese, Stackhouse-Lawson & Rotz (2018); Rotz,
Isenber, Stackhouse-Lawson & Pollak (2013) ........................................................................... 25
Table 9: Emissions factor for manure management and enteric emissions for beef cattle perunit of live-weight. All flows but phosphorus calculated using IPCC (2006) Tier 2 and WFLDB
v3.1 guideline, phosphorus per Rotz, Asem-Hiablie, Place & Thoma (2019) ............................ 25
Table 10: Beef cattle slaughtering LCI. Source: Bengoa, Rossi & Mouron (2017) ..................... 26
Table 11: Beef cattle slaughtering products and allocation factors. Source: Bengoa, Rossi &Mouron (2017) ......................................................................................................................... 26
Table 12: Pedigree matrix used data quality assessment, derived from Weidema & Wesnaes(1996) ....................................................................................................................................... 34
Table 13: Reviewer’s names, affiliations, and areas of expertise ............................................. 36
Table 14: LCIA results for priority categories for 1kg of Impossible Burger® and 1 kg of beef
burger. Italic numbers in parenthesis represent 95% confidence interval. (Impact 2002+
v2.28) ........................................................................................................................................ 38
Table 15: LCIA Priority results for 1 kg of Impossible Burger® per life cycle stage (Impact 2002+
v2.28). ....................................................................................................................................... 40
Table 16: Detailed endpoint results for 1 kg of Impossible Burger® ......................................... 56
Table 17: Detailed endpoint results for 1 kg of beef burger (Impact 2002+ v2.28). ................. 58
Table 18: Complete endpoint results for 1.0526 kg of Burger Production (Impact 2002+
v2.28). ....................................................................................................................................... 59

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Table 19: Detailed endpoint results for 1kg of burger packaging (Impact 2002+ v2.28). ......... 59
Table 20: Detailed endpoint results for 1kg of fresh beef meat (Impact 2002+ v2.28). ........... 60
Table 21: Detailed endpoint results for 1 kg of live beef cattle (Impact 2002+ v2.28). ............ 60

List of Figures
Figure 1: Results comparison of Impossible Burger® and Beef Burger (Impact 2002+ v2.28). ... 5
Figure 2: Manufacturing process of the Impossible Burger® under study. Material and energy
inputs, transportation of ingredients, as well as emissions to air, water, and soil, andgeneration of waste are considered throughout the supply chain........................................... 16
Figure 3: Process diagram of beef burger under study. Material and energy inputs,
transportation of ingredients, as well as emissions to air, water, and soil, and generation ofwaste are considered throughout the supply chain ................................................................. 19
Figure 4: Description of the four environmental impact indicators given primary focus in thisassessment (Impact 2002+ v2.28). ........................................................................................... 31
Figure 5: Impossible Burger® vs. beef burger life cycle impact assessment relative resultscomparison, with results normalized to beef burger impacts for each indicator (Impact 2002+
v2.28). ....................................................................................................................................... 39
Figure 6: Impossible Burger® life cycle impact assessment distribution among life cycle stages(Impact 2002+ v2.28) ................................................................................................................ 40
Figure 7: Impossible Burger® Ingredients Rank Order Impact Categories over total life cycle
impacts (Impact 2002+ v2.28) .................................................................................................. 41
Figure 8: Results comparison for Impossible Burger® sensitivity analysis scenarios. 100%
represents baseline results (Impact 2002+ v2.28). ................................................................... 42
Figure 10: Detailed endpoint results for 1 kg of Impossible Burger® (Impact 2002+ v2.28) .... 56
Figure 11: Midpoint results by life cycle stage for 1 kg of Impossible Burger® (as percentage ofeach midpoint’s total result) (Impact 2002+ v2.28).................................................................. 57
Figure 12: Detailed endpoint results for 1 kg of beef burger (Impact 2002+ v2.28). ............... 57
Figure 13: Detailed midpoint results for 1 kg of beef burger (Impact 2002+ v2.28). ............... 58

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Acronyms
FAO – Food and Agriculture Organization of the United Nations
GHG – Greenhouse Gas
IB – Impossible Burger®
IFSM – Integrated Farm System Model
IPCC – Intergovernmental Panel on Climate Change
ISO – International Organization for Standardization
LCA – Life Cycle Assessment
LCI – Life Cycle Inventory
LCIA – Life Cycle Impact Assessment
PG&E – Pacific Gas and Electric Company
SCLCI – Swiss Center for Life Cycle Inventories
TVP – Textured Vegetable Protein
US(A) – United States (of America)
USDA – United States Department of Agriculture
USMARC – US Meat Animal Research Center
WECC – Western Electricity Coordinating Council
WFLDB – World Food Lifecycle Database

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1. Introduction
The food and agriculture sector has become a focal area in the debate around strategies tosustainably feed a growing population, expected to reach nearly 10 billion by 2050. To meetthis demand, total food production will need to increase an estimated 50% to 70% compared
to 2009 levels, increasing annual cereal production to 3 billion tons and meat production to
470 million tons (Alexandratos & Bruinsma, 2012; FAO, 2009; Searchinger, et al., 2018). Total
demand for meat and dairy will increase even more dramatically. At the same time, global
agriculture and food production contributes more than 25% of all global greenhouse gas(GHG) emissions, inflicts significant negative impacts on fresh and marine water through useof agrochemicals, and uses nearly half of the ice-free land on the planet (Tilman & Clark,
2014; Reid, et al., 2008; Steinfeld, et al., 2006; Eshel, Martin, & Bowen, Land use and reactive
nitrogen discharge: Effects of dietary choices, 2010). This begs the question: how can thefood sector meet demand without significantly increasing its impact on the environment?
The question must be examined through the dual lens of consumption and production: what
will people eat, and how will the food be produced? Consumption behavior is driven by factorsof individual dietary preferences and purchasing power, quantities of food eaten, and bychanges in food utilization and waste. On the food production front, agricultural technology,
access to inputs and training, and adoption of best management practices can boost material
efficiency as food is brought from the farm to the dinner table. While efficiency in yield peracre and yield per input is important, there also exists a wide spectrum of agricultural
management practices that include application of agroecological theory and diversified
farming which can also contribute to environmental health (Searchinger, et al., 2018).
Plant-based transitions span both production and consumption in the toolkit of sustainably
meeting future food demand.
Dietary choices can lead to a meaningful reduction of overall environmental impacts. Recent
studies have shown that eliminating or reducing meat consumption can reduce GHG
emissions from 15% to 77%, water consumption up to 84%, and help lessen cropland demandby 540 million hectares by 2050 compared to a business-as-usual scenario (Dettling, Tu, Faist,
DelDuce, & Mandlebaum, 2016; Tilman & Clark, 2014; Heller, Willits-Smith, Meyer, Keoleian,& Rose, 2018; van de Kamp, Seves, & Temme, 2018; Goldstein, Moses, Sammons, & Birkved,
2017; Eshel, Martin, & Bowen, Land use and reactive nitrogen discharge: Effects of dietary
choices, 2010).
Market interventions will be necessary to reduce reliance on animal farming. Specifically,
products that can be readily exchanged for animal-based equivalents can provide a viableoption for meeting rapidly increasing animal product consumption in emerging economies,

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while supporting existing demand in current high consumption markets. In turn, once
production increases and an array of plant-based meat and dairy alternatives become bothavailable and desirable to consumers, current and future resource extraction and greenhousegas emissions resulting from animal agriculture can be mitigated and abated. Increased scaleof production is necessary to achieve the environmental goals associated with plant-based
diets, and in turn, to meet widely recognized international commitments to maintain climate
and biodiversity within safe planetary thresholds. Access to alternative plant-based options
for popular food products such as burgers, sausages, steaks, and other relatively low
efficiency meat categories will be particularly important (Searchinger, et al., 2018; Shepon,
Eshel, Noor, & Milo, 2016).
The average American eats approximately three burgers worth of ground beef per week,
equivalent to 50 billion burgers annual consumption (Center for Investigative Reporting,2012). Impossible Foods targets this existing ground beef market, leveraging innovation tooffer the Impossible Burger®, a plant-based ground beef alternative offering reduced
environmental impacts while delivering similar nutrition content, flavor, aroma, and“beefiness” to traditional beef (Impossible Foods Inc., 2018).
The goal of this study is to understand the environmental impacts associated with the
Impossible Burger®, and how those environmental metrics compare to those of aconventional U.S. beef burger. The results will provide credible, transparent information for
external communication purposes and for internal footprint quantification at Impossible
Foods, backed by a science-based life cycle assessment (LCA) approach.

2. Goal of the study
Objectives
The objective of this study is to support Impossible Foods in making strategic businessdecisions and external communications regarding the environmental benefits of the
Impossible Burger® in comparison with a conventional U.S. beef burger.
More specifically, the study seeks to fulfill the following specific objectives:
1. Understand whether the Impossible Burger® provides an environmental benefit
relative to conventional beef burger, and the magnitude of this benefit; the focus of
the environmental impact assessment includes primarily aquatic eutrophication
potential, global warming potential, land occupation, and water consumption.
2. Understand the main impact drivers for both burger products and articulate theimpact differences.

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3. Provide transparent and credible information to support Impossible Foods in making
strategic communications.

Intended audiences
This project report is intended to support Impossible Foods® in quantitating full life cycle
environmental impact of Impossible Burger® production, and in communicating thecomparative environmental performance of its Impossible Burger® against the environmental
performance of a traditional beef burger. Specific audiences may include the company’s
employees, business partners, customers, and the general public.
The International Standards Organization (ISO) standard 14044 on LCA includes a set of
specific requirements for LCAs whose intent is to report specific product-to-productcomparisons to a broad audience. It is the intent of this assessment to meet thoserequirements in cases where explicit statements are made comparing the environmental
impact of the products under study (International Standard Organization, 2006; International
Standard Organization, 2006).

3. Scope and system boundaries
This section includes the LCA methodology, a description of the products’ function, thesystem and its boundaries, and data sources including the requirements for data quality andanalysis.

General description of the products studied
3.1.1. Impossible Burger®
Impossible Foods’® main product is the Impossible Burger®, which falls into the category ofbeef substitutes for human consumption. These products are intended to be included inrecipes and meals as direct and equivalent substitutes for ground beef, such as in hamburger
patties, meatballs, taco filling, sauces, and other culinary applications. Plant-based proteins,fats, oils, and binders are the primary recipe components, as well as the innovative ingredient
known as heme, which gives the product its characteristic meat flavor, color, and behavior.
Heme is manufactured through a fermentation and isolation process. It is then shipped to the
Impossible Foods manufacturing facilities, where it is mixed and processed with other plantbased proteins and fats. This mixture is then molded, frozen, and packaged for sale. The
frozen packaged product is then modeled as distributed in 20-pound boxes across the U.S.

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The model use of 20-pound boxes was selected as a representation of multiple different packout configurations for patties and bricks.
The scope of the system studied includes all activities necessary to produce frozen packaged
Impossible Burger®, from “cradle to manufacturers gate.” Figure 2 further details the system
under study, including raw materials manufacturing, heme production, Impossible Burger®
production, and packaging.
Retail, distribution, use, and end-of-life stages are excluded from the study, as these do notdiffer significantly between the Impossible Burger® and a traditional beef burger, andtherefore bear similar environmental impacts. The Impossible Burger® has been designed tohave nutritional content similar to a beef burger. Moreover, Impossible Foods product has
similar cook-time and shelf-life. Finally, the environmental impacts of these stages have notshown to be a significant driver of the overall life cycle of such food products. (Impossible
Foods Inc., 2018; Heller & Keoleian, 2018; Dettling, Tu, Faist, DelDuce, & Mandlebaum, 2016;
Asem-Hiablie, Battagliese, Stackhouse-Lawson, & Rotz, 2018; USDA, 2018).
Table 2: 4 oz nutrition content for Impossible Burger® and beef burger with 80% lean meat and 20% fat.
Sources: Impossible Foods Inc. (2018), USDA (2018)

Impossible Burger® (4 oz.)

Beef burger 20% fat (4oz.)

Calories

240

254

Total fat (g)1420Cholesterol (mg)

071

Protein (g)

19

17.17

Total Carbohydrate (g)90PDCAAS*

0.83

0.85

Nutrition Content* Protein Digestibility-Corrected Amino Acid Score: measure to evaluate protein quality based on humans’ aminoacids requirements and its digestibility

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Heme Production

Raw Material
Manufacturing• Organic chemicals• Inorganic chemicals• Plant fats
• Plant proteins• Plant carbohydrates

Fermentation

Separation andconcentration

Impossible Burger Production
Ingredient
MixingBurgerForming

Packaging

Figure 2: Manufacturing process of the Impossible Burger® under study. Material and energy inputs,
transportation of ingredients, as well as emissions to air, water, and soil, and generation of waste are consideredthroughout the supply chain

3.1.1. Beef burger
Cattle production generates the greatest economic value of any commodity in the U.S. foodsector, with an estimated valuation of $60 billion in 2016 (Field, 2017). Moreover, beef
burgers are among the most popular food products in the U.S., with an estimated annual
consumption of 50 billion burgers (Center for Investigative Reporting, 2012).
Beef production in the U.S. typically consists of a cow-calf operation on pasture, lasting six toeight months, followed by an optional phase of backgrounding with a high forage diet, andfinally 5- to 8-month on feedlot with a high concentrate diet (Field, 2017). Feedlot finishingdiets rely predominantly on corn, with some wheat or barley. Industrial byproducts likedistiller’s grains or gluten feed are also important in feedlot diets, representing 10-40% of thedry matter content depending on commodity prices and market conditions (Drouillard, 2018).
After the finishing phase, cattle are slaughtered, with dressing weights around 63% of live
weight (Asem-Hiablie, Battagliese, Stackhouse-Lawson, & Rotz, 2018).
.

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Table 3: Impossible Burger® main data sources, assumptions, and background dataset sources
Heme Production

Impossible Burger® Mix

Burger Manufacturing

Packaging

Data sources

Impossible Foods provided all primary
information.

Impossible Foods provided all primary
information.

Impossible Foods
provided necessary
information.

Impossible Foods
provided all necessary
information.

Assumptions

Chemicals ingredients representing lessthan 1% by mass are modeled bygeneric chemical organic or inorganic
dataset if exact match unavailable.

Processed food ingredients are represented by
the closest dataset available. Whenunavailable, they are modeled by the raw
material which is derived if they represent <1%
by mass.

Plant-based and beef
burger share similar
production and packaging
practices.

Plant-based and beef
burger share similar
production and packaging
practices.

Chemicals ingredients representing less than
1% by mass are modeled by generic chemicalorganic or inorganic dataset if exact match was
unavailable.

All refrigerants arerecharged every 8 years,
with a 0.1% leak at endof-life.

Table 4: Beef burger main data sources, assumptions, and background dataset sources
Feed Production

Cattle raising and fattening

Cattle slaughtering andprocessing

Burger Manufacturing

Packaging

Data sources

Rotz, Isenber, StackhouseLawson, & Pollak (2013

Asem-Hiablie, Battagliese,
Stackhouse-Lawson & Rotz,(2018); Rotz, Isenber,Stackhouse-Lawson & Pollak(2013); FAO (2018); (Rotz,
Asem-Hiablie, Place & Thoma(2019)

Bengoa, Rossi & Mouron(2017)

Impossible Foodburger manufacturing
process

Impossible Foodburger packaging

Assumptions

Processed food ingredients are
represented by the closest
dataset available. Whenunavailable, they are modeled by
the raw material which is derived
if they represent <1% by mass.

Impacts of supporting herd(cows, heifers, and bulls) are
assigned to cow-calf operations.

It’s assumed that there areno significant differences
in carcass yield andrevenue between theEuropean and the U.S.
cattle market.

Plant-based and beef
burger share similar
production andpackaging practices.

Plant-based andbeef burger sharesimilar production
and packaging
practices.

Manure and enteric emissions
are calculated using Tier 2 IPCC
(2006) and WFLDB v3.3 (2017)
guidelines.

No distinctions are madebetween different fresh
beef meat cuts.
An estimated 22% of beefcomes from dairy
operations.

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

18

There’s a 5% loss atmanufacturing.

Feed Production
•
•
•
•
•

PastureAlfalfa and Grass
GrainsSilageDistiller’s byproducts

Beef cattle raising and fattening
Cow-Calf Operations +Backgrounding(Pasture)

Finishing(Feedlots)

Dairy
Operations

Slaughtering

Packaging

Production

Figure 3: Process diagram of beef burger under study. Material and energy inputs, transportation ofingredients, as well as emissions to air, water, and soil, and generation of waste are considered throughout
the supply chain

A significant portion of beef consumed in the U.S. comes from the dairy sector. In 2002,
approximately 18% of beef consumed in the US was sourced from dairy cull and breeding
overhead, up to around 23% in 2016 (Boetel, 2017). Given that dairy cattle meat is asignificant portion of the total beef consumption, this input is considered in the scope of thestudy.
Ground beef is commonly made from either ground chuck, round, or sirloin, in proportions
reflecting the desired product fat content. In this study, we do not distinguish individual cuts,
as ground meat mass weighted-average pricing offers a close approximation of the mass
weighted-average revenue of beef cuts (Cattlemen's Beef Board and National Cattlemen's
Beef Association, 2014; Cattlemen’s Beef Board and National Cattlemen’s Beef Association,2018).
The scope of the studied system includes all the activities necessary to produce bonelessretail-ready ground beef, i.e. from “cradle to manufacturers gate”. Figure 3 depicts thestudied system, which includes feed production, cow-calf, backgrounding, and feedlot
operations, beef from dairy production, slaughtering, and packaging.

Comparative basis: Function and functional unit
Life cycle assessment relies on a “functional unit” as a reference for evaluating thecomponents within a single system, or among multiple systems, on a common basis. It istherefore critical that this parameter is clearly defined and measurable.
It is acknowledged that there is not a single clear and agreed-upon measurement on which to
set a functional basis for food consumption, due to the multiple functions that food serves (toprovide nutrition, alleviate hunger, support social interactions, and other psychologicalreasons), as well as the difficulty of quantifying how many of these needs are met.
The functional unit for this study is cradle-to-gate assessment of 1 kg of Impossible Burger®,which will be benchmarked against 1 kg of boneless retail-ready U.S. conventional groundbeef burger.
The purpose of this study is to compare the respective cradle-to-gate environmental impacts
across four priority environmental impact areas: aquatic eutrophication, climate change, land
occupation and water consumption. All infrastructure processes have been left out of thestudy, as they typically do not significantly contribute to the overall impact categories named.
Beyond nourishment, any other functions of the products are not considered here. For
example, taste, enjoyment, relief of psychological stress, providing a basis for social
interactions and others may all be reasons that people consume food in certain contexts. No
attempt is made here to compare these products based on these alternative functions.

System characterization and data sources
A reference flow refers to the quantity and type of material needed to fulfill a functional unit.
The following sections provide details on the data used to define these reference flows forthe products assessed in this study.
When modeling the two product systems under study, the ecoinvent v3.3, allocation –recycled content, unit database is prioritized as the default source for background data. In theabsence of a representative agricultural dataset, a dataset from the Quantis World Food Life
Cycle Database 3.1 processes is used in its place; whenever possible, appropriate country
inventories are selected. In the absence of country specific inventories, region specificinventories that have similar technology and climate conditions are used. When neither
country specific or region-specific inventories are available, global inventories are used.
Global inventories are typically average datasets of all the country specific datasets available
in the database for the specific product/process. This is assumed to be a reasonable
alternative in the absence of country or region-specific datasets (Swiss Center for Life Cycle

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

20

Inventories, 2016; Bengoa, Rossi, & Mouron, 2017; Durlinger, Koukouna, Broekema, vanPaassen, & Scholten, 2017).
Additionally, inputs representing a high proportion of mass or environmental impacts for
either product are adapted to relevant country conditions. This includes changing to country
or region specific electricity grid, updating crop yields, irrigation water and fertilizer inputs.
Each background process dataset is identified in Appendix B (section Error! Reference source n
ot found.) and changes to existing datasets are been described in subsections 3.3.1 and 3.3.2.
3.3.1. Impossible Burger®
Required inventory data for modeling the Impossible Burger® life cycle, including heme
production, burger ingredients, manufacturing, and packaging data are provided by
Impossible Foods with few exceptions. Primary packaging of a 20 lb. Impossible Burger® box
consists of separation paper leaves used to keep individual patties from sticking to one
another and plastic wrapping film. Packaging and burger production are collocated, obviatingtransportation emissions between these steps. Electricity and water consumption are fully
considered in the production process.
3.3.1.1.

Heme Production

Heme is produced through fermentation, in which a genetically modified yeast strain
expresses the naturally occurring leghemoglobin protein. Following fermentation, theleghemoglobin protein is isolated and concentrated from the fermentation media.
Two wastewater streams exit the process: one with a high suspended solids content which isdiverted to agricultural land application (high solid waste), the other has a lower suspended
solids concentration and is treated in municipal wastewater (low solid waste).
3.3.1.2.

Impossible Burger® production.

The product is comprised of a variety of plant-based materials and ingredients which are used
as flavor, fat, texture, or other purposes. The production entails protein and oils preparation,
burger forming, cooling, assembly and packaging. Electricity consumption in all processing
steps, including refrigeration, has been considered.
In this study, transportation of the eight highest-mass ingredients, representing 91% of thedry mass of the total product, was modeled in detail; the transport distance of the remaining
ingredients was assumed to be 786 km by truck, based on information provided by Impossible
Foods. Transportation by truck for distances up to 100 km was modeled using EURO3 3.5-7.5
metric ton truck, and above 100km using >32t EURO 4 truck.
One important component of the Impossible Burger® is the potato protein, for which
Impossible Foods’ supplier provided background life cycle information. The estimated impact

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21

incorporated into the baseline model was considered conservative estimate, as it assumes
that 100% of the potato’s environmental impacts are allocated to the potato protein (and noother co-products). A sensitivity analysis addressing this assumption is described in Section
4.6.
Coconut oil, another essential component of the Impossible Burger®, is sourced from thePhilippines. Based on personal communications with suppliers, Philippine coconut researchersand on data provided by the Philippine Coconut Authority, irrigation and irrigation
infrastructure is very rarely included in coconut production (Magat S. S., 2011; Quieta, 2012;
Magat S. ; Dy & Reyes, 2007). Therefore, the study assumes no water withdrawals for
irrigation management of this crop. Further, because specific oil processing data for coconut
oil does not exist in ecoinvent v3.3, oil processing was modeled as a simple average of palm
oil processing, soybean oil processing, and cottonseed oil datasets available, adapting
electricity sourcing to the Philippines.
In the California-based manufacturing plant, refrigerants are kept in circulation to maintainfreezer operation and refrigerated storage at an optimal temperature. These refrigerants are
estimated to be changed every 8 years based on conservative estimates by Impossible Foods.
The study, which takes place over the course of one production year, allocates the refrigerant
production, end of life processing, and 0.1% leakage at the end of life, evenly by mass across
8 years of production. Essentially applying 1/8 of the associated burdens to 1 year of
production. Water is used in the process, primarily for cleaning, and is treated as municipal
wastewater.
A small fraction of electricity is from unspecified origin, whose emission intensity wasconservatively modeled as California’s average mix. Transmission and voltage conversion
environmental impacts were incorporated using Ecoinvent’s Western Electricity Coordinating
Council (WECC) processes as an approximation (PG&E, 2016; PG&E, 2015; PG&E, 2017; PG&E,
2018).
Table 5: Electricity mix from PG&E.

PRODUCT

100 kWh of PG&E Electricity Production

Input

Quantity

Unit

Solar

11.5

kWh

Wind

7.75

kWh

Biomass and Waste

4.25

kWh

Hydroelectricity, Run-off

2kWhNatural Gas

21.5

kWh

Nuclear

23.75

kWh

Hydroelectricity, reservoir

11

kWh

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

22

Unspecified*

13.25

kWh

* PG&E cannot track the source of this amount of electricity.

3.3.1.3.

Packaging
Table 6: Impossible Burger® Packaging LCIPRODUCT

1 kg of Packaged IF Burger, at factory

Input

Quantity

Unit

Impossible Food Burger

1kg

Paper, patty paper

1.6g

Plastic Film

2.3

g

Corrugated cardboard

10gQuantity

Unit

0.4

g

Outputs
Plastic Film

3.3.2. Beef burger
3.3.2.1.

Animal feed production and animal-raising

Cattle raising is modeled based on Asem-Hiablie, Battagliese, Stackhouse-Lawson & Rotz
(2018) and Rotz, Isenber, Stackhouse-Lawson & Pollak (2013), both using data from the U.S.
Meat Animal Research Center (USMARC) in combination with an Integrated Farm System
Model (IFSM). This represents typical crop, feed, and animal management practices from
Nebraska, one of the largest beef feedlots producing state in the country (Field, 2017),
however it is important to note that this data does not represent the average US beef
production.
The model includes cow-calf operations lasting the 6 months on pasture, 3 months ofbackgrounding, and 7 months of feedlot finishing. The backgrounding diet was based on hay
and distiller’s grain, while the finishing diet was primarily grain-based. The herd being raisedat the facilities included 5,050 calves, 5,498 cows, 285 bulls, and 1180 replacement heifers in
the cow-calf operation; 3,724 cattle were finished in the feedlot with an average weight of
581 kg. In addition to finished cattle, it was assumed that 1100 cows and 80 bulls, according
to a 20% replacement rate, with average weights of 636 kg and 908 kg respectively, were also
sent to slaughter, for a total output of 3,213 ton of live weight (Asem-Hiablie, Battagliese,
Stackhouse-Lawson, & Rotz, 2018; Rotz, Isenber, Stackhouse-Lawson, & Pollak, 2013).
The environmental impacts associated with additional inputs necessary for supporting the herd, including feed,water, operations energy, as well as facility outputs including manure and enteric emissions, were assigned to thetotal cattle sent to slaughter. Fuel and electricity for pumped water and other cattle raising operations were

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23

included; electricity being modeled using a U.S. MRO grid dataset. An inbound transport distance of 100 km wasused for cattle feed, and the study assumes that this feed was produced entirely in the U.S. (Table 7 andTable 8) (Rotz, Isenber, Stackhouse-Lawson, & Pollak, 2013).
Feed production was modeled using background processes from Ecoinvent 3.3, allocation –recycled content, unit and the World Food Lifecycle Database (WFLDB) 3.1. Ecoinvent´s“Maize grain, feed {CA-QC}” was modified to more closely represent “Maize grain, feed {US}”,
based on a U.S. maize input, as well as a U.S. MRO electricity input. A grazing dataset from
WFLDB 3.1 representing average global conditions was adapted to U.S. conditions, with 15.49
ton/ha of fresh matter yield and 20 N kg/ha of additional nitrogen fertilizer based on Rotz,
Isenber, Stackhouse-Lawson, & Pollak (2013).

Table 9 reports emissions factors used per kg of live weight. 100% of manure produced by theentire herd is left on the pasture during cow-calf and backgrounding operations, while 95% of
manure produced on feedlots is spread on feed fields. Enteric emissions were calculated
based on dry mass intake by IPCC, 2006 Tier 2, using a 6.5% and 3% methane conversion
factor for grazing and feedlot animals respectively. Manure management emissions, including
methane and nitrogen, were calculated following IPCC, 2006 Tier 2 and WFLDB 3.1 guidelines.
Methane emissions were also calculated according to dry matter intake through volatile solid
emissions calculation. Ammonia, nitrogen oxides, and dinitrogen monoxide emissions frommanure management were also calculated, starting from a daily nitrogen emission factor of
0.31 kg per ton of live weight. Phosphorus manure emissions were estimated using emissions
factors for the whole operation from Rotz, Asem-Hiablie, Place & Thoma (2019). Nitrateemissions were outside the scope of the 4 priority impact categories and were not estimated(FAO, 2018; Asem-Hiablie, Battagliese, Stackhouse-Lawson, & Rotz, 2018; Dettling, Tu, Faist,
DelDuce, & Mandlebaum, 2016; IPCC, 2006; Pelletier, Pirog, & Rasmussen, 2010).
Finally, Soil Organic Carbon (SOC) changes, which might be relevant during grazing were notincluded in this study due to limited science and data availability to conduct such calculations.
SOC changes are highly contextual and variable, depending on grazing intensity and duration,
pasture management, grass type, precipitation and other climate conditions. A combinationof all these factors can lead to either an increase, decrease, or neutral effect on SOC(McSherry & Ritchie, 2013; Derner & Schuman, 2007; Hewins, et al., 2018).

Table 7: Feed inputs for cow-calf + backgrounding and feedlot operations to produce 3,123 tonof beef cattle. Sources: Rotz, Isenber, Stackhouse-Lawson & Pollak (2013)

Quantity (ton)FeedPastureCow-calf + Backgrounding

Feedlot

23,8080

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24

Alfalfa and grass hay

4,833

707

Corn silage

2,416

2,507

High moisture corn

104

2,718

Corn grain

56

1,596

Distillers grain

45

1,623

Table 8: Water, feed transportation, fuel and energy consumption to produce 3,123 ton of beef cattle. Sources:
Asem-Hiablie, Battagliese, Stackhouse-Lawson & Rotz (2018); Rotz, Isenber, Stackhouse-Lawson & Pollak (2013)

Input

Quantity

Unit

Electricity

1,070,441

kWh

Fuel

16,571,820

MJ

Natural gas

11,161,800

MJ

Water, for drinking

371,495

m3

Table 9: Emissions factor for manure management and enteric emissions for beef cattle per unitof live-weight. All flows but phosphorus calculated using IPCC (2006) Tier 2 and WFLDB v3.1 guideline,
phosphorus per Rotz, Asem-Hiablie, Place & Thoma (2019)

Quantity(g/kg LW)

Input
CH4, enteric fermentation

360

CH4, manure management

4.4

NH3-N, manure management

2.1

NO2, manure management

4

N2O, manure management

2.9

Phosphorus runoff, manure management

0.25

The allocation approach applied in this study is described in section 4.1. Economic allocation
was used in this study as this is one of the widely used allocation method for multi-output
agricultural systems and is one of the most viable allocation methods for animal co-products.
The demand for meat has been identified as the main driver to produce meat, and thus drive
related environmental impacts. Economic allocation was applied to assign impacts between
fresh meat and co-products, based on information from the European Fat Processors andRenderers Association – this is not adjusted to reflect the U.S. scenario, as it is assumed thatthe market and costs associated with beef and coproducts is similar between the U.S. andEuropean markets (Table 11). It is important to note that allocation decisions affect results, toinvestigate allocation and result dynamics, an APOS sensitivity analysis was conducted, as
COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

25

described in section 4.6. The study consistently uses the same allocation method across thetwo databases and the products under study.
Table 10: Beef cattle slaughtering LCI. Source: Bengoa, Rossi & Mouron (2017)

PRODUCT

1 kg of slaughtered beef cattle

Input

Quantity

Unit

Beef, cattle, for slaughter, at beef farm

780

g

Culled dairy cows

70gMale calves

150

g

Slaughtering activities

1kg

Cattle transportation

0.2

tkm

22% of beef was estimated to come from dairy operations: 7% from culled dairy cows and
15% from male calves. These environmental impacts are modeled using WFLDB datasets,
which follows the International Dairy Federation principles to allocate impacts between dairy(88%) and beef meat (12%), as well as the same guidelines used in this study to calculate
direct emissions, including manure management and enteric emissions1. Finally, 200 km is
assumed between the feedlot and the slaughterhouse. (Bengoa, Rossi, & Mouron, 2017;
Boetel, 2017; Rotz, Isenber, Stackhouse-Lawson, & Pollak, 2013; Geiger, 2016; USDA, 2018).

Table 11: Beef cattle slaughtering products and allocation factors. Source: Bengoa, Rossi & Mouron (2017)

Product

Quantity (g)Economic allocation factor (%)

Beef, fresh meat

460

93.2

Beef, food grade offal

30

0.61

Beef, food grade bones

80

0.7

Beef, food grade fat

70

1.42

Beef, food grade blood

30

0.32

Beef, cat. 3 slaughter by-products

30

0.01

Beef, hides and skins

70

3.78

Beef, cat. 1 and 2 materials and waste

230

0

1 Please visit https://quantis-intl.com/wp-content/uploads/2017/02/wfldb_methodologicalguidelines_v3.0.pdf

for detailed

calculation guidelines followed by WFLDB and this report

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26

3.3.2.2.

Beef burger production and packaging

Because burger production and packaging generally have a low contribution to the total
environmental footprint of ground beef, these processes are modeled using the same
assumptions as are used for the Impossible Burger® (Heller & Keoleian, 2018). A 5% meat loss
during these production steps is used according to Dettling et al, 2016.

Temporal and geographic boundaries
This assessment is intended to be representative of food production in the U.S. during theyear that the study is conducted (2018). Data and assumptions are intended to reflect current
equipment, processes, and market conditions. Data has been selected where possible to best
match these geographic and temporal conditions, although relevant geography data may notalways be available, most data being at least a year old and, in many cases, several years old.
Main databases and key reports used in this study are all from 2013 or later, which isconsidered to represent current conditions in the industry. Considerable efforts were madeto better represent current production conditions for high contribution processes in mass orenvironmental impacts terms. Production yields, current irrigation practices and electricity
sources were updated as a result of prioritizing applicable and representative inventories forthe study.
It should be noted that some processes within the system boundaries might in fact take place
anywhere in the world and over a much wider range of time than the current year. For
example, the processes associated with manufacturing food products consumed in the U.S.,
and their raw materials, take place both in the U.S. as well as in a wide variety of othercountries. The information to represent food production in this assessment has been selectedwith a preference for data representing U.S. production. To the extent that such data is notavailable in all cases, it is assumed that the use of data from other geographies, when needed,balances in part the actual sourcing of products from both within and outside the U.S.
Regarding the temporal boundaries, certain processes may generate emissions over a longer
period than the reference year. Regardless of such considerations, all data has been selected
to as closely represent conditions in 2018 as is practical.

Cut-off criteria
Processes may be excluded if their contributions to the total system’s environmental impact
are expected to be less than 1%. Materials that are less than 1% by mass are assumed to also
contribute less than 1% of the environmental impact, except in cases where there is a reason
to expect otherwise, such as with hazardous substances. Despite this criterion for allowing

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27

components to be excluded, all product components and production processes are included
when the necessary information is readily available, or a reasonable estimate can be made.
The following are just a few examples of items excluded from the study due to lack of reliabledata and expected contribution lower than the cut-off criteria: infrastructure processes, seals
and stickers on packaging or used in retail, shipping pallets and other ancillary services notdirectly linked to the product production system such as executive travel, legal, accountingetc.

4. Assessment methodology
Allocation methodology
A common methodological decision point in LCA occurs when the system being studied
creates co-products. When systems are linked in this manner, the boundaries of the system
of interest must be widened to include using all co-products, or the impacts of producing thelinked product must be distributed—or allocated—across the systems. While there is no clear
scientific consensus regarding an optimal method for handling this in all cases, many possible
approaches have been developed, and each may have a greater level of appropriateness incertain circumstances (Reap, Roman, Duncan, & Bras, 2008).
ISO 14044 prioritizes methodologies related to multi-functionality processes, which candeliver more than one product, including allocation and system expansion. It is best to avoid
allocation through system subdivision or expansion whenever possible. If not, then one
should perform allocation using an underlying physical relationship. If allocation using aphysical relationship is not possible or does not makes sense, then one can use anotherrelationship, such as economic, or other characteristic.
In alignment with the cut-off allocation approach for addressing systems that donate orreceive material or energy source from an upstream or downstream system, applied in theforeground modeling described above, the ecoinvent life cycle inventory system model
chosen to apply in this study is that called cutoff by classification. This approach is explained
in detail on the ecoinvent website and in an ecoinvent v3 overview and methodology paper(Wernet, et al., 2016). In summary, the burdens of producing primary materials are always
assigned to the first user of those materials, and recyclable materials are burden- and creditfree to those users.
Ecoinvent 3.3 typically uses economic allocation for multi-product systems. WFLDB 3.1 alsouses economic allocation for multi-product systems, except when stated otherwise, as in thecase of dairy system where it follows International Dairy Federation allocation guidelines. The
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28

economic allocation principle was used for crop co-products, animal feed, and animal coproducts specifically and agricultural production systems in general, and has been widely used
for this products (Bengoa, Rossi, & Mouron, 2017; Dettling, Tu, Faist, DelDuce, &Mandlebaum, 2016; Rotz, Asem-Hiablie, Place, & Thoma, 2019; de Vries & de Boer, 2010;
Wiedemann, et al., 2015). Nguyen et al. (2012) and Nguyen et al. (2013) investigated theinfluence of the allocation rule for animal feed in carbon footprints of meat. Although for the
single feed components the allocation rule is very important, on the level of meat, theinfluence is relatively small. The study intends to consistently use the same allocation
classification for similar processes across the two databases used for this comparative study.
Furthermore, many of the processes in the ecoinvent database also provide multiple
functions, and allocation is required to provide inventory data per function. This study
consistently uses the allocation method used by ecoinvent v3.3 in its cutoff by classification
approach and the allocation used by WFLDB v 3.1 as previously described. Most products inthis category are allocated on a revenue basis. As described in section 3.3, economic
allocation is one of the widely used allocation method for multi-output agricultural systems
and is considered to be addressing the main driver for these production systems- revenue
and demand. The choice of allocation metric and factors can be highly influential to theresulting environmental indicator impact of each product. The allocation choices influence on
the study results was tested through a sensitivity analysis using “point of substitution”
allocation version of ecoinvent, further described in section 4.6. No significant differences(less than 0.1%) was observed in the study results as per the APOS results.
4.1.1. Transport allocation
Transport vehicles have both a weight and a volume capacity. These are important aspects toconsider when allocating the impacts of an entire transportation journey to one product.
Vehicles transporting products with a high density (high mass-per-volume ratio) will reach
their weight capacity before reaching their volume capacity. Vehicles transporting productswith a low density (low mass-per-volume ratio) will reach their volume capacity before
reaching their weight capacity. Therefore, the density of the product is critical for determining
whether to model transportation as volume-limited or weight-limited.
In this study, all transport is assumed to be weight-limited and the transportation of the cargowithin the vehicle is therefore allocated based on its weight.
Transportation by truck for distances up to 100 km was modeled using EURO3 3.5-7.5 tontruck, and above 100km using >32 ton EURO 4 truck. Refrigerated transport was modeledusing 7.5-16 ton EURO4 R134a refrigerated truck.

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29

Impact assessment
Impact assessment classifies and combines the flows of materials, energy, and emissions intoand out of each product system by the type of impact their use or release has on theenvironment. The method used here to evaluate the environmental impacts is the peerreviewed and internationally recognized life cycle impact assessment (LCIA) method IMPACT
2002+ vQ2.28 (Humbert, De Schryver, Margni, & Jolliet, 2012). This method assesses
seventeen different potential impact categories (midpoints)2 and then aggregates them into
endpoint categories.
The final report considers most heavily the four impact categories shown and described in
Figure 4. These are aquatic eutrophication potential, climate change, land occupation andwater consumption. Land occupation is mostly reported at an inventory level, unlessindicated otherwise, with complete LCIA results in section 8.2. These 4 environmental
indicators are international accepted and widely used for assessing livestock and agricultural
products environmental impacts. Most meat related environmental studies identify carbon
footprint, land occupation, aquatic eutrophication and water consumption as priority
hotspots (Asem-Hiablie, Battagliese, Stackhouse-Lawson, & Rotz, 2018; Heller & Keoleian,2018; McClelland, Arndta, Gordon, & Thoma, 2018; Tilman & Clark, 2014; Rotz, Isenber,Stackhouse-Lawson, & Pollak, 2013; Goldstein, Moses, Sammons, & Birkved, 2017).
It is the aim of this study to concentrate on the most relevant identified impact indicators forcomparison and discussions. The study doesn’t intend to omit other mid-point indicators,
which are available in the report Appendix C. However other indicators are not the focus of
this study and are added in the appendix for the sake of transparency. Appendix C (8.3)
includes the contribution of each midpoint indicator per Impact 2002+ v2.28 method, in
determining the overall result for all endpoint indicators for the products under study. Impact
2002+ V2.28 method is a scientifically reviewed, published and widely used environmental
impact assessment method that has characterization factors for over 1500 different LCI
results, which are included as supplementary material. The method is regularly updated anddiscusses assumptions, limitations and uncertainty in the website and the methodology
document. Overall the fate exposure and effect related uncertainty of the method is high forland occupation and low for aquatic eutrophication, global warming potential and water
consumption. The specific method related assumptions and limitations are as described
below:••

Aquatic Eutrophication: The eutrophication potential does not include nitrates.
Global Warming Potential: The timescale in which the GWP is assessed is considered avalid choice (100yr GWP). The impact will be significantly higher in a shorter

2 The Human Toxicity midpoint category is divided between carcinogenic and non-carcinogenic effects, hence a total of 17

midpoint indicators (Humbert et al. 2012).

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

30

•
•

timeframe and lower in a longer timeframe. While GWP has a low uncertainty ingeneral. The uncertainty for beef could be higher due to methane emissions.
Land Occupation: The method potentially overestimates the impact of grazing
compared to plowing
Water Consumption: The study does not consider local water stress.

Figure 4: Description of the four environmental impact indicators given primary focus in this assessment(Impact 2002+ v2.28).

A more detailed description of the impact categories than what is shown in Figure 4 isprovided in Appendix A (8.1).
No results normalization is conducted with the exception being those results presented on arelative basis (%) compared to the reference for each system. No endpoint or midpoint
category weighting is done; being presented individually and not as a single score, as there isno objective method by which to achieve this.
The LCA results represent an estimation of the potential environmental impacts that canoccur and do not represent a measurement of actual environmental impacts that haveoccurred. They are relative expressions, which are not intended to predict the final impact, or
whether standards or safety margins are exceeded. Additionally, these categories do notcover all the environmental impacts associated with human activities. For example, impacts
such as noise pollution, odor production, and electromagnetic field generation are notincluded in the present assessment, as the methodological developments regarding suchimpacts are not sufficient to allow for their consideration within the scope of this life cycle
assessment.

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

31

Calculation tool
SimaPro 8.4 software, developed by PRé Consultants (2017) is used to assist the LCA
modeling, link the reference flows with the life cycle inventory database, and compute thecomplete inventory of the systems. Results are calculated by combining foreground data(intermediate products and elementary flows) with generic datasets providing cradle-to-gatebackground elementary flows to create a complete inventory of the two systems. Microsoft
Excel is used to help with processing the results from the LCA.
Foreground data are defined as data/processes that are in the control of the study
commissioner; these include primary data available for Impossible Burger®. Background data
are defined as data/processes which are not under the control of the commissioner of thestudy, or indirect in nature due to the lack of specific information available, which in this caseinclude beef burger processes. Although ground beef foreground data was sourced from thepublished paper, which uses primary data, while this data was not in control of the study
commissioner, the referred study does gather and use primary data. Ecoinvent 3.3 is themost widely used, published and reviewed third party dataset available and is considered tothe best choice for background datasets. Similarly, WFLDB datasets are used for agricultural
datasets only when a suitable ecoinvent dataset was not available, a total of five instances.
Based on the documentation and available information, it is observed that ecoinvent 3.3 and
WFLDB 3.1 are consistent. Effort has been made to use the most specific country-level
datasets throughout the modeling choices made. Where specific country-specific datasets
were not available, datasets from similar technological countries/regions were used. Global
datasets (average of all available regional/ country datasets) were used when relevant
country specific or similar regional datasets were not available. Appendix B (8.2) details all thedatasets used for the study.

Contribution analysis
In addition to the comparative assessment, a contribution analysis is calculated to determine
the extent to which each modeled process contributes to the overall impact of the Impossible
Burger®. Lower quality data may be suitable in the case of a process whose contribution isminimal. Similarly, processes with a great influence on the study results are characterized byhigh-quality information. In this study, the contribution analysis is a simple observation of therelative importance of the different processes to the overall potential impact.

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32

Uncertainty analysis
Three types of uncertainty related to the LCA models are typically discussed and defined:
uncertainty in inventory data, uncertainty in the impact characterization models and choice
related uncertainty. With assessment of comparative results, it is important to note thedifference between the uncertainty in the impact of a given product and the uncertainty incomparing the impact between two products. It is possible for the uncertainty in the absolute
impact of two given products to each be relatively high and yet the uncertainty of how they
compare to be very low. The more similar two products are in terms of the processes andmaterials that comprise them, the more the factors that contribute to the uncertainty in theabsolute impact of each will cancel each other out when comparing them. Choice related
uncertainties are typically investigated using sensitivity analysis.
4.5.1. Inventory data uncertainty analysis and data quality assessment
An analysis of the uncertainty due to the variability of inventory data is performed. Data
sources are assessed based on time-related coverage, geographical coverage, technology
coverage, precision, completeness, representativeness, consistency, reproducibility, source
description and uncertainty of the information as prescribed in ISO 14044. The pedigree
matrix for rating inventory data appears below, and a complete discussion of this topic can befound in Frischknecht & Jungbluth (2007) (Table 12).

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33

Table 12: Pedigree matrix used data quality assessment, derived from Weidema & Wesnaes (1996)

Indicator
Score
Reliability

1

2

3

4

Verified data
based onmeasurements.

Verified data
partly basedonassumptions
or non-verified
data based on
measurements
Representativ
e data from>50% of thesites relevant
for the market
considered,over anadequate
period to evenout normal
fluctuations
Less than 6
years
difference tothe time
period of thedatasetAverage datafrom larger
area in which
the area understudy isincluded
Data fromprocesses andmaterials
under study
but from
different
enterprises

Non-verified
data partly
based onqualifiedestimates

Qualifiedestimate (e.g.,
by industrial
expert)

Non-qualified
estimate

Representati
ve data fromonly somesites (<<50%)
relevant forthe market
considered or>50% of sites
but from
shorter
periods
Less than 10
years
difference tothe time
period of thedatasetData fromarea withsimilarproduction
conditions

Representativ
e data fromonly one sites
relevant forthe market
considered orsome sites
but from
shorter
periods

Representativeness unknown orincomplete datafrom a smaller
number of sites
and from shorter
periods

Less than 15
years
difference tothe time
period of thedatasetData fromarea withslightly similar
production
conditions

Age of data
unknown or morethan 15 years ofdifference to thetime-period of thedatasetData fromunknown or
distinctly different
area

Data fromprocesses
and materials
under study
but from
different
technology

Data onrelatedprocesses ormaterials

Data on related
processes onlaboratory scaleor from different
technology

Completeness

Representative
data from allsites relevant to
the market
considered, overan adequate
period to evenout normal
fluctuations

Temporaldifferences

Less than 3 yearsof difference tothe time-periodof the dataset

Geographicaldifferences

Data from area
under study

Furthertechnologicaldifferences

Data fromenterprises,
processes andmaterials under
study

5

Data quality assessment results are included in Appendix D (Error! Reference source not found.), listing each indicator score for processes and flows.
Further, SimaPro 8.4 software (PRé 2017) includes a module for Monte Carlo simulation,
which allows assessment of the uncertainty and variability embedded in inventory data. Mostof the data here is drawn from the ecoinvent 3.3 database, which has a thoroughcharacterization of the uncertainty for most of the flows of energy and material within the life
cycle inventory data that it provides. Adapted ecoinvent and WFLDB datasets’ DQI wererecalculated in the context of the study application and its role in the model based on thepedigree matrix explained in Table 12.

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34

Monte Carlo analysis is used to understand the uncertainty within the product systems. 1,000
iterations for each product system was performed to understand the range of LCIA results, byrandomizing LCI data using geometric standard deviations reported in Appendix D for eachflow. A 1,000 iterations comparative Monte Carlo analysis was also performed.
4.5.2. Characterization model uncertainty analysis
In addition to the inventory data uncertainty described above, there are two types of
uncertainty related to the LCIA method. The first is about the characterization of the LCI
results into mid-point indicators, and the second is about the subsequent characterization of
those midpoint scores into end-point indicators. The uncertainty ranges associated withcharacterization factors at both levels vary from one mid-point or end-point indicator toanother. The accuracy of characterization factors depends on the ongoing research in the
many scientific fields behind life cycle impact modeling, as well as on the integration ofcurrent findings within operational LCIA methods.
There are presently no systematic methods available for quantifying or evaluating theinfluence of the uncertainty in these characterization models within the comparative
assessments made here. Without consideration of the uncertainty in LCIA characterization
factors, the uncertainty assessment results derived here should be seen as something like alower bound on the level of uncertainty in the systems and the uncertainty would be higher if
uncertainty in these characterization factors are also considered.

Sensitivity analysis
The parameters, methodological choices, and assumptions used when modeling the systems
present a certain degree of uncertainty and variability. It is important to evaluate whether
these selections significantly influence the study’s conclusions and to what extent the findings
are dependent upon certain sets of conditions. Following the ISO 14044 standard, a series ofsensitivity analyses are used to study the influence of uncertainty and variability, thereby
evaluating the robustness and reliability of the results and conclusions.
As described in the previous section, uncertainty analysis allows for the identification of
influential factors and parameters that warrant further investigation through sensitivity
analyses. The following scenarios are identified as important based on its contribution to thetotal impacts and the data quality/uncertainty known:
1. Potato protein: An alternative scenario using this dataset was run and compared tothe baseline model. The results are then evaluated to identify Impossible Burger®’s
total impact and potato protein’s contribution to it.
2. Coconut oil: Oils have shown to contribute more than 10% of total water consumption
for plant-based burgers; the country of origin may have wide implications on water
COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

35

3.

4.

5.

6.

consumption in the production of coconut oil (Heller & Keoleian, 2018; Bengoa, Rossi,& Mouron, 2017). As the Philippines is identified as the country of origin for coconut
oil in this study, and regional variations could exist despite prevailing lack of irrigation,a sensitivity analysis exploring currently present in ecoinvent dataset is included.
Ground beef with and without meat from dairy: Beef coming from dairy tends to havea lower impact than beef cattle (Heller & Keoleian, 2018). To understand how
Impossible Burger® compares to conventional meat from beef cattle, a sensitivity
analysis excluding beef coming from dairy, as well as a scenario where all the meat isderived from dairy, are conducted. This scenario is performed using the same cattle
proportion as described in Table 10.
Refrigerants lifetime: 8 years is the base assumption for refrigerant lifetime,
established on conservative estimates provided by Impossible Foods. Nevertheless,refrigerants lifetime may vary. Therefore, a 6 years and 12 years scenario were run tosee their effect in results, predominantly on global warming potential.
Impossible Burger®’s Electricity Source: To explore how much Impossible Burger®’spotential environmental impacts may increase by using less clean electricity sources,
an alternative scenario using U.S. average electricity grid was explored.
Alternative LCIA method for land occupation: ReCiPe 2016 – Midpoint (Hierarchical)
was used to assess the range of land occupation impacts, as both methods, widely
recognized by the international LCA community, has significant different
characterization factors for pasture land occupation: ReCiPe has a characterization
factor of 0.55 m2 crop/yr/m2, while Impact2002+ has a factor of 1.037m2
org.arab./yr/m2.

An additional sensitivity analysis was run to test the influence of ecoinvent 3.3 allocation
method, substituting cut-off by classification to allocation at the point of substitution (APOS).
Results differed less than 0.1% for both products under study, and thus no further analysis
was done.

Critical review
Because the results of this study are intended to be used to support a comparative assertion
disclosed to the public, a critical review must be conducted by a panel of interested, relevant
parties, particularly an LCA expert and other stakeholders.
The peer review panel is intended to comprise of experts in beef environmental impacts
assessment, agricultural environmental impact assessment and ISO 14040/14044 standard
requirements. The selected critical review committee comprises the following members:
Table 13: Reviewer’s names, affiliations, and areas of expertiseName

Affiliation

Expertise

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

36

Gidon Eshel

Bard College

Geophysical processes, Numericalanalysis, Science based disclosures

Greg Thoma

University of Arkansas

Life Cycle Analysis, Food Systems

Nathan Pelletier

University of British Columbia

Life Cycle Analysis, Food Systems

Detailed resumes of each of the peer review panel member is described in appendix 8.3
In accordance with 14040 and 14044 ISO standards (2006a, b), the goal of the critical review
process is to check that the report follows the following stipulations:
•

•
•
•

The methods used by Quantis to carry out the LCA are as follows:o consistent with the 14044 International Standards, ando scientifically and technically valid;
The data used are appropriate and reasonable in relation to the goal of the study;
The interpretations of Quantis reflect the limitations identified and the goal of thestudy; andThe study report is transparent and consistent.

The critical review process will be carried out in two steps:
1. Final report submitted (Dec 2018);
2. Corrections and clarification of points raised by the reviewers in step 1 (January 2019).
The reviewers’ comments will be provided to Quantis as distinct review reports. The criticalreview reports and the responses to reviewers’ recommendations will be presented in the
appendix.

5. Results
The following section presents the study results. The first part focuses on the comparison of
the impacts of Impossible Burger® to the traditional beef burger. The second part presents a
more detailed analysis of the Impossible Burger® results.

Impossible Burger® vs. beef burger comparison
Results show that potential environmental impacts of producing the Impossible Burger® arelower than those of the traditional beef burger in the 4 priority impact categories. Most of the
impacts associated with the beef burger, and thus most of beef’s environmental liabilities

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37

relative to the Impossible Burger®, come from the cattle-raising stage that represents
between 41% and 93% of the potential environmental impacts of the beef burger.3
Table 14: LCIA results for priority categories for 1kg of Impossible Burger® and 1 kg of beef burger. Italic
numbers in parenthesis represent 95% confidence interval. (Impact 2002+ v2.28)

Impact Category

Unit

Aquatic eutrophication potential

g PO4-eq

Global warming potential

kg CO2-eq

Land occupation*

m2.yWater consumptionlImpossible
Burger®
1.28(2.3-9.7)

Beef Burger

Difference %15.1(14.3-60.6)

3.5

30.6

(3.1-4.0)(25.3-37.5)

2.5

62.0

(1.6-3.7)(37.0-102.5)

106.8

850.1

(56.9-203.3)(617.9-1238.1)

-92%
-89%
-96%
-87%

* Land occupation is at the inventory level.

The largest potential impact savings is seen in the land occupation indicator, where thetraditional beef burger occupies 62 m2 per year compared to the Impossible Burger®’s 2.5 m2
per year. For the beef burger, 90% of this impact comes from pasture needs for the beef
cattle raising, mainly in the grazing phase. Regarding land occupation related to agricultural
production, the beef burger occupies 6.4 m2 per year compared to 2.3 m2 per year for the
Impossible Burger®.
The second most significant impact savings offered by the Impossible Burger® over beef
burger is the aquatic eutrophication impact category, which is 92%. This difference stems
from eliminating phosphorus emissions from cattle operations, including emission frommanure, fertilizers use during feed production, and electricity consumption for slaughtering.

3 Complete results can be found in Annex B for all endpoint and midpoint categories.

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38

100%

75%

50%

25%

13%

11%

8%

4%

0%
Aquatic eutrophication
potential

Global warming potential
Impossible Burger

Land occupation

Water consumption

Beef Burger

Figure 5: Impossible Burger® vs. beef burger life cycle impact assessment relative results comparison,with results normalized to beef burger impacts for each indicator (Impact 2002+ v2.28).

Water consumption and global warming potential impacts are 87% and 89% lower
respectively in the Impossible Burger® than in the beef burger, with 743 fewer liters of water
consumed and 27.1 fewer kg CO2-eq. emitted per kilogram of beef. Lower water consumption
is driven by the fact that a large amount of water (82% of the total) is consumed in the
production of maize for cattle feed, which is not used in the Impossible Burger®.
Also, as the Impossible Burger® production does not include production of cattle, this leads toa reduction of 26.3 kg CO2-eq per kg of beef burger not produced. From this, roughly 79%
comes from emissions associated with manure management and enteric fermentation, 11%from feed production and transportation, while an extra 10% comes from cattle slaughtering.

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39

Detailed Impossible Burger® results
The two lifecycle stages that represent the most significant impacts for the Impossible
Burger® are production and manufacturing of the ingredient mix, with the packaging stage
adding negligible contributions to all impact categories (Figure 6).

100%
80%
60%

40%
20%
0%
Aquatic eutrophication

Global warming potential

1.- Impossible Burger Mix

Land occupation

2.- Production

Water consumption

3.- Packaging

Figure 6: Impossible Burger® life cycle impact assessment distribution among life cycle stages (Impact
2002+ v2.28)

Impacts generated at the production stage are mainly explained by electricity consumption,
along with production of nitrogen and carbon dioxide for refrigeration purposes. Impacts
associated with electricity consumption lead to 21% of total global warming potential
emissions, 14% of total water consumption impacts, and 15% of total aquatic eutrophication
impacts due to emissions associated with fossil fuel production and consumption,
contributing just around 0.1% to overall land occupation. Refrigerant emissions at the end-oflife add up to 0.19 kgCO2-eq, which is equivalent to 5% of total potential global warming
potential emissions, due to small leakages at end-of-life stage.
Table 15: LCIA Priority results for 1 kg of Impossible Burger® per life cycle stage (Impact 2002+ v2.28).

Impact Category
Aquatic eutrophication
potential
Global warming potential
Land occupation*
Water consumption

Unit

Total

1.Impossible
Burger® Mix

2.Production

3.Packaging

g PO4-eq

1.28

1.00

0.27

<0.1

kg CO2-eq
m2.y
l

3.50
2.5
106.82

2.09
2.46
84.49

1.38
0.01
22.13

0.02
<0.1
0.19

* Land occupation is at the inventory level.

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40

Much of the remaining impacts arise in the Impossible Burger® Mix production, with threeingredients—heme, fats, and texture agents—accounting for 42-82% of all impacts across the
Impossible Burger® life cycle.
5.2.1. Impossible Burger® ingredient analysis
Ingredient contributions to GHG emissions are led by leghemoglobin, followed by potato
protein and coconut oil. Potato protein is also the largest contributor to water use byingredient, followed by coconut oil, then heme. Coconut oil is the highest land-using
ingredient, followed by soy protein and sunflower oil.

Aquatic eutrophication
potential

Global warming
potential

Land
Occupation

Water
consumption

Potato protein
Coconut oil
LegH protein
Sunflower oil
Soy protein
Flavor mix

LegH protein
Potato protein
Coconut oil
Flavor mix
Sunflower oil
Soy protein

Coconut oil
Soy protein
Sunflower oil
Potato protein
LegH protein
Flavor mix

Potato protein
Coconut oil
LegH protein
Flavor mix
Soy protein
Sunflower oil

Figure 7: Impossible Burger® Ingredients Rank Order Impact Categories over total life cycle impacts(Impact 2002+ v2.28)

The production of potato protein leads to a total consumption of 62 liters of water,
equivalent to 54% of Impossible Burger® total, mainly due to the use of heavy irrigation in
potato production. At the same time, it occupies 0.38 m2 of land per year, equivalent to 14%
of land occupation impacts, which were primarily driven by land occupation during the crop
production. It’s also associated with 30% of the total aquatic eutrophication impacts, due tophosphorus leaching from fertilizers use, and 9% of global warming potential impacts, due tofertilizer use emissions. Given that this ingredient was modeled using conservative
assumptions, it’s possible that these high potential impacts are overestimated.
Sunflower oil and coconut oil significantly contribute to land occupation, leading to 0.89 and0.46 m2 of land occupied per year respectively, for crop production. The refining stage of
coconut oil also requires 8.5 liters of water.
Texturized vegetable protein from soybean meal contributes 24% of the land occupation
footprint in the Impossible Burger® total, also due to land occupation during crop production.

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41

Sensitivity analysis
Sensitivity analyses were conducted across impact categories to investigate alternativemodeling choices and methodologies. A summary figure is presented below.
Using an average U.S. electricity grid rather than PG&E grid would increase global warming
potential to 4.7 kgCO2-eq per kg and aquatic eutrophication potential by 39% of Impossible
Burger® driven by the relatively higher use of fossil fuel in the electricity grid across the rest of
the country.
An alternative, less conservative model, of potato protein produces significant changes foralmost all impact categories, and particularly for water consumption, which sees an absolute
reduction of 52 liters compared to the baseline assumptions. This model uses a wet milling
multi-output process, in which impacts are economically-allocated across potato protein andother co-products, rather than assigning 100% of the burden to potato protein. Differences inaquatic eutrophication potential, global warming potential, and land occupation can besimilarly attributed.
250%

200%

150%

100%

Baseline = 100%

50%

0%Average U.S. Alt. Potato
Elec.
ProteinCoconut
irrigation

6yr Ref.

12yr Ref.

Worst case Combination
a.a.
- 6yr.Ref.

Aquatic eutrophication potential

Global warming potential

Land occupation

Water consumption

Figure 8: Results comparison for Impossible Burger® sensitivity analysis scenarios. 100% represents
baseline results (Impact 2002+ v2.28).

Assuming coconut was irrigated 1.96 m3 ha-y-1, leads to more than doubling total water
consumption. Actual water consumption of irrigated coconut depends on yield, which was
not adjusted in this sensitivity analysis. If modeled more realistically, the expected added yield

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42

may offset the higher water consumption due to irrigation. Other impact categories are lesssensitive to this assumption, increasing by only 1-3%.
Changing refrigerant lifetimes to either 6 or 12 years does not significantly change results.
The GWP contribution from refrigerants remains approximately 5%. No other changes wereobserved due to modification in refrigerants lifetime (less than 0.1%).
In the case of all alternative scenarios occurring simultaneously, water consumption increases
by 63% to 174 liters, with higher consumption due to irrigated coconut oil offsetting potato
protein consumption reductions; global warming potential and aquatic eutrophication
potential also increases 58% and 39% respectively, due to increased use of energy andresources in general as a combination of switching the electricity source and amino acids andvitamin background dataset. On the other side, land occupation would be reduced by 12%
due reduced land occupation attributed to potato protein.
Regarding land occupation sensitivity analysis, ReCiPe 2006 Midpoint (H) delivers a total of
2.4 and 37.4 m2 crop eq./yr per kg of product for Impossible Burger® and beef burger
respectively, compared to 2.4 and 59.2 m 2 org. arab./yr given by Impact2002+ v2.28. While
this shows moderate changes to the animal-derived ground beef, the general differential of
impact between the two products remains similar. The beef burger still uses 2.7 times moreland with agricultural purposes (6.4 versus 2.4 for m 2 crop eq./yr per kg) than the Impossible
Burger®. Note that both methods calculate land occupation differently, have different units
and as such are not exactly comparable.
Since the conclusion of this sensitivity analysis does not change the results, it is notconsidered important to investigate and justify the implications of such methodologicaldifferences.
Alternative scenarios were also examined in the case of dairy herd allocations. Althoughsourcing beef entirely from the dairy herd is not industry practice, the scenario is analyzed
here. The results show the environmental impacts reduce for all the four priority indicators.
Land occupation has the biggest reduction, with 37% of the baseline occupation – a large
portion of dairy cows are modeled to be fed without grazing, reducing pasture land
occupation to 9.6 m2 per year, while increasing crop land occupation to 14.3 m2 per year as
more feed crop is needed. Global warming potential would be reduced to 24.7 kg CO 2-eq,
aquatic eutrophication to 7.7 g PO4-eq, and water consumption to 631 liters.
Given that milk is the main product in the dairy industry and generates the primary revenue,
beef products from dairy receive, a lower allocation of environmental impacts, between 10%35% depending on method chosen, compared to beef cattle, where main revenue driver isbeef meat production (Bengoa, Rossi, & Mouron, 2017; Thoma, et al., 2013). Land occupation
changes can be attributed to dietary differences between each type of cattle, where beef

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43

cattle spend more time on pastures instead of consuming feed mix. Diet is the main cause ofincreased pasture land occupation and the reduction in crop related land occupation.

Uncertainty analysis
Uncertainty assessments have been conducted for the two systems. The uncertainty
assessment considers the range of uncertainty in estimating the flows of material and energy
in the systems and the uncertainty in the emissions of pollutants or other impacts associatedwith each of these. It excludes the uncertainty associated with the characterization factors
used to transform the inventory results into impact indicator results. Figure 9 presents eachsystem’s individual uncertainty results based on Monte Carlo simulations for the Impossible
Burger® and beef burger, in terms of global warming potential, water consumption, land
occupation, and aquatic eutrophication potential.
Results show that there is no overlap between any of the analyzed midpoint categories,
despite significant variability in them. Further, comparative Monte Carlo simulation resulted
in 100% runs with beef burger impacts being higher than Impossible Burger®. For bothproducts, 97.5% whiskers extend further than 2.5%, meaning that there is higher probabilityof impacts being larger than current average.
Global warming potential demonstrates lower coefficient of variation (COV), calculated as thestandard deviation divided by the mean, for both products – COV being 6.7% for Impossible
Burger® and 10.3% for the Beef burger, as GHG emissions has a lower basic uncertainty thanother emissions estimated. Beef burger’s larger COV can be explained by the higher
uncertainty present in manure management and enteric fermentation calculated emissions.
Water consumption shows higher uncertainty, with a COV of 37.3% and 40%, which stems
from a general large uncertainty in water flows throughout the different backgroundprocesses used in the model.
Land occupation exhibits similar COV for both products, that is 21.3% for Impossible Burger®
and 29.2% for beef burger, with a long low-probability tail to larger impacts, and a shorter
higher-probability tail for smaller impacts. Variation in crop yields drives the distribution.

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44

Finally, aquatic eutrophication has the highest COV for both products -- 41.5% for the
Impossible Burger® and 35.3% for the beef burger, exhibiting a similar behavior to land

Figure 9: Statistics of MC based distribution estimates for beef burger (Beef) and Impossible Burger® (IB).
Mean impact is represented by the dark line bisecting the box. The boxes span ± 1 standard deviation
around mean (horizontal line at box vertical centers). The whiskers span the central 95% of the expecteddistribution, between the 2.5th and the 97.5th percentiles. Because these are empirical and apparently
skewed rather than theoretically normal distributions, these percentiles are not equidistant from therespective means.

occupation uncertainty, stemming from electricity consumption and crop production Pemissions uncertainty. Higher COV for Impossible Burger® can be explained as uncertainty onelectricity generation emissions tend to have a larger influence on results, when compared tothe small potential impact reported.

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45

Inventory data quality assessment
Inventory data quality assessment procedure and results are summarized in full. From thisassessment, it is evident that overall, data used for the analysis are acceptable.
Criteria for evaluation included data sources, data accuracy, technology coverage, timerelated coverage, data age, and geographical coverage. These criteria, for both systems undercomparison, are met with acceptable and consistent assessment.
The main data for which either further verification or improvement in quality would beparticularly useful to increase the robustness of the results are related to the following
processes:•••

Potato protein model to accurately represent manufacturing practices.
Manure emissions at grazing during cow-calf operations, due to large uncertainties inthe emissions factors and the influence it has over beef burger results.
Coconut oil processing is modeled using an average of three different oil refining
processes as previously described. Greater specificity would be of value, given thecontribution to water consumption overall.

LCA applications and limits
This LCA performed for Impossible Foods compares the production of the Impossible Burger®
against a traditional U.S. ground beef burger. Any conclusion described by this report must be
considered only within the context of the study and with considerations of the assumptions
made and study limitations.
Results of this exercise can be used to accomplish the following:••

Characterize the environmental profile of the Impossible Burger®, as well as its "hotspots" and key parameters driving its results;
Identify strengths and weaknesses of both products; and identify conditions for which
one alternative seems preferable to another.

There are limitations in the current study that should be reiterated and that might be madethe focus of future work. The study’s assumptions and limitations are listed below:••

The potato protein model is created under basic assumptions of protein content inpotatoes and GHG emissions from energy use disclosed from suppliers. A more
rigorous model that more closely represents reality would result in improved, andlikely lower, potential impacts for the Impossible Burger®;
The use of the USMARC production model may represent a best-case scenario in U.S.
beef production. Although being representative of U.S. production, it does notconstitute a national average, and other production systems in other parts of the
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46

•

•

•

•

country, like 100% pasture-raised beef may affect the degree of environmental
benefits the Impossible Burger® has over the beef burger.
Impossible Foods supplies its electricity from PG&E Company. Reverting back to autility supplier with a higher carbon footprint could increase potential environmental
impacts, as shown in the sensitivity analysis;
Use of dataset from Canadian agricultural practices and natural gas consumption
technology may change results, either positively or negatively, for both products,
although not the conclusions due to large difference in results;
Several ingredients in the Impossible Burger® mix are modeled generically as organic
or inorganic chemicals due to lack of more precise datasets, each represent 0.6% orless and altogether 3%. Results may change if more accurate models are madeavailable;
Soil Organic Carbon changes were not included in the study. Literature shows that
this might benefit or worsen beef burger global warming potential, dependent on
pasture management, grazing density, climate, and other conditions, thus exhibiting awide variation in results. SOC is not assessed in this study due to lack of reliable
science, data and method to build a usable reasonable impact assessment.

Finally, LCIA results are relative expressions and do not predict impacts on category
endpoints, the exceeding of thresholds, safety margins, or risks.

6. Conclusions
This assessment compares the Impossible Burger®, a plant-based alternative to beef, with a
traditional U.S. beef burger. Because of their similar function and nutritional values, these two
products are considered substitutable. The goal of the study is to understand whether the
Impossible Burger® has a lower environmental impact footprint compared to animal-derived
ground beef, and to what extent. In turn, this assessment estimates the degree to which
substitution of beef for plant-based meat might aid U.S. consumers who want to shift theirdiet toward less environmentally costly options. The following are the key findings from thiswork, focused on the assessments made here on both products.
When U.S. consumers choose to replace a kilogram of beef burger with a kilogram of
Impossible Burger®, they are reducing environmental impacts across every impact category
focused on in the study between 87%-96%.
Regarding global warming potential, replacing a kilogram of beef burger with the equivalentamount of Impossible Burger® will reduce 27.1 kg CO2-eq, mainly due to the lack of manureand enteric emissions in the plant-based burger’s lifecycle. Soil Organic Carbon changes
during beef cattle grazing were not included due it’s high variability and might increase or
reduce this benefit. A worst-case scenario for the Impossible Burger®, where it would be
COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

47

produced with a higher input of fossil fuels through electricity and highly impactful aminoacids manufacturing technologies, would reduce this benefit by 2 kg CO 2-eq per kg at most.
Further, a best-case scenario for the beef burger, where all meat comes from dairy cattle,
would further reduce the difference by 5.9kg of CO2-eq. Overall, the Impossible Burger®
would reduce a minimum, but meaningful, 19.2 kg of CO2-eq/kg.
Water consumption impacts for the Impossible Burger® are 87% lower than its beefcounterpart, largely due to a reduced demand for feed agricultural products and theirassociated irrigation water.
Land occupation for the Impossible Burger® is 96% lower than that for the beef burger, wherefor every kilogram of beef replaced, 66.5 m 2 per year for land can be saved from occupation.
Disaggregating this number, 4.2 m2 comes from agricultural use for the beef burger, and thus
its production requires 2.8 times more land for crop production than the Impossible Burger®.
Another 55.2 m2 comes from pasture used for grazing, where many arguments that no otheragricultural activity could be performed on them.
At the same time, aquatic eutrophication potential associated with the Impossible Burger® ismore than 10 times lower than the beef burger, as it avoids manure emissions and much of
the phosphorus emissions related to feed crops, and electricity use during cattle slaughtering.
For both products, production of raw inputs contributes >50% of environmental impacts:>50% GHG emissions, >78% of water consumption, >84% for aquatic eutrophication, and>99% for land occupation. Burger manufacturing impacts are only nontrivial for the
Impossible Burger®, whose overall impacts are much smaller than that of beef’s.
Put simply, raising beef cattle to feed humans requires far more resources in the upstream
cultivation of cattle feed than if humans use those crops directly to manufacture foodproducts. Additionally, shifting to plant-based options avoids environmental impacts
associated with cattle rearing, like land occupation for grazing, manure management, andenteric emissions. Also, even in the best-case scenario for beef, being entirely dairy-sourced,
and worst-case for the Impossible Burger®, as shown in the climate change impact category,
benefits are still substantial with plant-based burger impacts one order of magnitude lower.
In considering the results of this study, it should again be noted that nutritional content, an
important feature of food, has not been considered directly as both exhibit similar
characteristics. The intention here is to portray an environmental comparison as accurately
and clearly as possible, which can be used along with nutritional considerations, and otherconsiderations such as taste, cost and convenience, in helping U.S. consumers make theirfood choices.
In summary, the study has found that there are clear and unambiguous environmental
benefits to replacing beef with Impossible Burger®.

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48

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8. Appendices
Appendix A: Description of impact categories
Aquatic eutrophication potential
Aquatic eutrophication refers to the excessive addition of nutrients, primarily nitrogen andphosphorous, to aquatic ecosystems. In freshwater ecosystems, phosphorous is usually thedriving nutrient, while in marine and terrestrial ecosystems, nitrogen is usually the driving
nutrient. These nutrients often enter water bodies from agricultural runoff or fromwastewater treatment facilities. This excessive nutrification (“eutrophication” is also
sometimes referred to as “nutrification” or “over-nutrification”) encourages biomass growthsuch as algae blooms. When these blooms die off, their decomposition removes oxygen fromthe water body, resulting in hypoxic or even anaerobic conditions, contributing to fish kills
and decline or death of other aquatic life. Aquatic eutrophication is measured in kg of PO4(Phosphate) equivalents.
Global warming potential
Alterations in the statistical distribution of weather patterns of the planet over time that lastfor decades or longer; global warming potential is represented based on the International
Panel on Global warming potential’s 100-year weightings of the global warming potential ofvarious substances (IPCC 2013). Substances known to contribute to global warming areweighted based on an identified global warming potential expressed in grams of CO 2
equivalents. This indicator covers all greenhouse gas emissions.
Because the uptake and emission of CO2 from biological sources can often lead tomisinterpretations of results, it is not unusual to omit this biogenic CO 2 from consideration
when evaluating global warming potentials. Here, the recommendation of the PAS 2050
product Carbon Footprint guidance is followed in not considering either the uptake oremission of CO2 from biological systems and correcting biogenic emissions of other gases
accordingly by subtracting the equivalent value for CO2 based on the carbon content of thegas (BSI 2008).
Land occupation
Land occupation measures the potential impact on terrestrial ecosystems caused by direct
land use associated with a product, process or organization. It takes into account thecontribution of various types of land. It is measured in m2 organic arable land for one year.

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55

Water consumption
Sum of all volumes of fresh water used in the life cycle of the product, except for water usedin turbines (for hydropower production), less the amount of water returned to the freshwater
systems. This includes the volume of water taken from freshwater reservoirs (lakes, rivers,
aquifers, etc.) that is evaporated during industrial or agricultural processes, embedded in
products or otherwise consumed. Drinking water, irrigation water and water for and inindustrialized processes (including cooling water) are all considered. Use of seawater is notconsidered. Neither is the use of rainwater, which has not yet reached a lake, river or aquifer.

Appendix C: Complete LCIA results
8.2.1. Detailed Impossible and beef burger results
100%
75%
50%
25%
0%
Human health

Ecosystem quality

1.- Impossible Burger Mix

2.- Production

Resources
3.- Packaging

Figure 10: Detailed endpoint results for 1 kg of Impossible Burger® (Impact 2002+ v2.28)

Damage category
Human health10-6 * DALY

Table 16: Detailed endpoint results for 1 kg of Impossible Burger®
1.- Impossible Burger®
Total
2.- Burger Production
3.- Packaging
Mix
4.77
2.72
2.04
0.01

Ecosystem quality
Resources

PDF.m2.y
MJ

4.83
57.06

Unit

4.61
27.69

0.20
28.97

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

0.02
0.40

56

100%
75%
50%
25%

0%

1.- Impossible Burger Mix

2.- Production

3.- Packaging

Figure 11: Midpoint results by life cycle stage for 1 kg of Impossible Burger® (as percentage of each midpoint’s
total result) (Impact 2002+ v2.28).

100%

75%

50%

25%

0%
Human health

Ecosystem quality

1.- Beef Meat Production

2.- Processing

Resources
3.- Packaging

Figure 12: Detailed endpoint results for 1 kg of beef burger (Impact 2002+ v2.28).

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

57

Table 17: Detailed endpoint results for 1 kg of beef burger (Impact 2002+ v2.28).

Damage category

Unit

Total

Human health
Ecosystem quality
Resources10-6 * DALY
PDF.m2.y
MJ

28.5
72.5
146.9

1.- Beef Meat

2.- Production

1.- Beef
Meat
26.4
72.3
117.5

2.- Burger
Production
2.0
0.2
29.0

3.Packaging
0.0
0.0
0.4

100%

75%

50%

25%

0%

3.- Packaging

Figure 13: Detailed midpoint results for 1 kg of beef burger (Impact 2002+ v2.28).

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

58

Table 18: Complete endpoint results for 1.0526 kg of Burger Production (Impact 2002+ v2.28).

Damage category

Human health

Ecosystem quality

Resources

DALY

PDF.m2.yMJ

2.0E-06
1.7E-06
9.3E-08
2.4E-07
4.6E-08

1.97E-01
5.88E-02
1.93E-02
4.46E-02
1.96E-03

28.97
20.92
2.96
1.50
3.53

1.0E-09
3.3E-13
1.3E-09
4.0E-09

1.13E-04
6.82E-10
2.47E-04
7.23E-02

0.01
0.00
0.04
0.02

Unit
Total
Electricity
NitrogenCarbon dioxide production
Gas
Refrigerant Production
Refrigerant Emissions
Water
Wastewater

Table 19: Detailed endpoint results for 1kg of burger packaging (Impact 2002+ v2.28).

Damage category
Unit
Total
Paper
Packaging film, low density polyethylene
Corrugated board box
Waste polyethylene

Human health

Ecosystem quality

Resources

DALY

PDF.m2.y

MJ

1.42E-08
2.36E-09
4.35E-09
7.54E-09

1.82E-02
2.32E-03
4.11E-04
1.55E-02

3.98E-01
4.48E-02
2.10E-01
1.43E-01

2.54E-13

3.27E-08

1.56E-06

COMPARATIVE LCA OF IMPOSSIBLE BURGER WITH CONVENTIONAL BEEF BURGER

59

Damage category

Unit

Total

Human health
Ecosystem quality
Resources

DALY
PDF.m2.yMJ

2.6E-05
72.3
117.5

Impact Category

Unit

Human health
Ecosystem quality
Resources

DALY
PDF.m2.yMJTotal
9.8E-06
40.4
32.5

CattleEmissions
2.1E-06
0.4
0.0

Grazing4
3.8E-06
30.2
4.2

Table 20: Detailed endpoint results for 1kg of fresh beef meat (Impact 2002+ v2.28).
Beef cattle, for slaughter
Slaughtering
Transport
Culled dairy cow
Dairy calves
1.6E-05
67.1
54.1

Alfalfa-grasssilage4.6E-07
2.4
2.3

6.6E-06
0.2
44.9

6.1E-080.0
0.9

5.4E-072.4
2.9

2.9E-06
2.5
14.7

Table 21: Detailed endpoint results for 1 kg of live beef cattle (Impact 2002+ v2.28).
Maize
Maize grain,
Distiller's Dried
Electricity andFeedsilagefeed
Grainsfuels
Transport
6.5E-07
6.1E-07
3.6E-07
1.6E-08
5.6E-08
3.9
0.6
0.3
0.0
0.0
2.2
5.0
2.9
0.2
1.3

4 Grazing includes manure emissions during cow-calf operations. It does not include enteric emissions, which are under “Cattle emissions”

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60

Appendix E: Review panel biographies
8.3.1. Gidon Eshel
Gidon Eshel, Ph.D., is a research professor of environmental physics at Bard College and runs
the website environmentalCalculations.com. He is best known for his work quantifying thegeophysical consequences of agriculture and diet. Most recently, he has compared various
livestock in terms of land and water use, fertilizer-based water pollution, and greenhouse gas
emissions per unit product and compared the global-warming consequences of different
beef-production strategies (including grass- versus trough-fed beef). His widely variedscientific interests also include the development of algebraic tools for simultaneous
optimization of health and environmental outcomes through dietary choices, climate physics,
and measures of time scale–specific ecosystem stability.
Eshel studied physics and earth sciences at the Technion and the University of Haifa, in Israel,
before getting an MA, an MPhil, and a PhD at Columbia University in mathematicalgeophysics. Before his post at Bard, he was a NOAA Climate & Global Change Postdoctoral
Fellow hosted by Harvard, a staff scientist at the Woods Hole Oceanographic Institute, and afaculty member of the Department of the Geophysical Sciences at the University of Chicago.
Adapted from Radcliffe Institute for Advanced Study. Harvard University (2017)
8.3.2. Greg Thoma
Greg Thoma, Ph.D., has been on the faculty at the University of Arkansas since receiving his
Ph.D. in Chemical Engineering in 1994 from Louisiana State University, and is a Registered
Professional Engineer in the state of Arkansas. He has held the Ray C. Adam Chair in Chemical
Engineering and is currently the Bates Teaching Professor in Chemical Engineering. He also
served as director for research and is currently senior advisor to The Sustainability
Consortium, which focuses on measuring and improving the sustainability of consumer goods,
including food.
His research focuses on the application of chemical engineering principles to find solutions toenvironmental problems. He is currently lead investigator for several life cycle initiatives in
the food and agriculture sector including studies on fluid milk, cheese, milk delivery systems,
and U.S. swine production. Dr. Thoma also consults on other LCA work focusing on rice,
cotton, corn, and sweet corn. Recently he became the scientific lead for the UNFAO
Partnership on the Environmental Benchmarking of Livestock Supply Chains technical advisorygroup for poultry which is working to create guidance in the application of LCA for assessmentof sustainable poultry and egg production. He is currently serving on the steering committee

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for the Swiss National Science Foundation’s National Research Program titled, “Healthy
Nutrition and Sustainable Food Production”.
Adapted from College of Engineering. University of Arkansas (2019)
8.3.3. Nathan Pelletier
Nathan Pelletier, Ph.D., is an Assistant Professor, jointly appointed in the Faculties of Arts andSciences (Biology) and Management at the University of British Columbia - Okanagan. He
currently holds the Endowed Chair in Bio-economy Sustainability Management / Egg Industry
Chair in Sustainability and NSERC/Egg Farmers of Canada Industrial Research Chair in
Sustainability.
His work is broadly situated in the fields of ecological economics and industrial ecology,
emergent research areas focused on understanding and managing the sustainability
dimensions of economic activity.
Areas of research interest are theory and practical application of ecological economic
instruments in bio-economy (food, feed, and biomass) sustainability measurement,
management and communication initiatives. He has contributed to the development ofmethodological frameworks for evaluation and management of the scale, resource efficiency,
and social dimensions of sustainability - in particular, life cycle-based product andorganization-level accountancy tools for supply chain sustainability management.
Adapted from Food Systems PRISM Lab (2019)

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9. External panel review
An external panel review has been performed for the present study, based on the guidelines
in the ISO 14044 standard for assessments intending to support public disclosure ofcomparative statements. This external review was chaired by Nathan Pelletier, PhD, from
University of British Columbia, Greg Thoma, PhD, from University of Arkansas, and Gidon
Eshel, Ph.D., from Bard College. Below is the final statement issued by the panel.

Panel statement of conformance with ISO 14044
The review panel has concluded that the study is, in the majority of instances, in compliancewith the ISO 14040 and 14044 standards for LCA studies used to support comparative
assertions to be disclosed to the public. There are no major outstanding methodological or
technical issues upon completion of this review, and the general findings of the review panel
are summarized below. Summary statements from individual reviewers describing
outstanding minor issues are provided at the end of this report. More detailed comments on
the study methodology and technical assumptions, including the panel’s responses, can befound in the attached review summary.
Are the methods used to carry out the LCA consistent with the international standards (ISO 14040,
14044)?
The review panel finds that the study is largely consistent with the ISO LCA standards, and inparticular, the reporting requirements under Section 5.3 for studies used to support
comparative assertions. The methodology is clearly described, and most modeling assumptions
are documented and adequately explained.
Sensitivity analyses were conducted to verify key assumptions and few of the sensitivity
analyses showed results that varied significantly from the primary results, generally supporting
the study conclusions. A detailed data quality assessment was also conducted, and the study
conclusions were supported by uncertainty analysis using Monte Carlo simulations in the
SimaPro software program.
Are the methods used to carry out the LCA scientifically and technically valid?
The review panel finds that the methods used are scientifically and technically valid. The
IMPACT 2002+ V2.28 impact assessment methods were used, which consist of a suite of aninternationally-accepted environmental impact assessment methods spanning a variety ofresource and emissions-related impact categories. The environmental indicators reported are

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relevant to the production systems under study, including a mix of selected mid-point andend-point indicators. The limitations of the impact assessment methods are described, andthe chosen impact assessment methods were tested by also generating results with the
ReCiPe method for comparison. The technical accuracy of the system descriptions,assumptions, and modeling were verified by the panel and found to be acceptably
representative of the production systems under study.
Are the data used appropriate and reasonable in relation to the goal of the study?
The review panel finds that the data used are appropriate with respect to the study
objectives. Primary data were used to characterize the Impossible Foods burger andrepresent the best available data to characterize its production. Published data drawn largely
from two sources (Ecoinvent and the WFLDB) were generally used to characterize the beef
production system (including “upstream” production systems such as provision of feed inputs
for beef production. The sources of data and the rationale for the data used has been
documented in the report to the extent allowed under confidentiality agreements. The
review panel reviewed these data and supporting assumptions and is confident that these
represent appropriate data to achieve the study objectives.
The report provides a great deal of discussion around data quality, and in instances where data
quality was less than desirable, sensitivity analysis has been conducted to show that there is no
impact on the overall study results.
Do the interpretations reflect the limitations identified and the goal and scope of the study?
The review panel finds that the interpretation of the results reflects the limitations identified
and the sensitivity analyses and uncertainty analysis provided support the conclusions.
Is the study report transparent and consistent?
The review panel finds that the study report is transparent and consistent. A high-level of detailis provided in the description of the product systems, key assumptions, and data used.

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