national scan-level life cycle assessment for production

Center for Agricultural

and Rural Sustainability

• • •

Technical Report 3Q-2012-01

National Scan-level Life Cycle Assessment for Production of US Peanut Butter

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Table of Contents

EXECUTIVE SUMMARY ................................................................................................ 1

1 INTRODUCTION .................................................................................................... 5

2 LCA METHODOLOGY ............................................................................................. 7 2.1 GOAL AND SCOPE DEFINITION ................................................................................................. 8

2.1.1 Goal ............................................................................................................................................ 8 2.1.2 Project Scope and System Boundaries ...................................................................................... 8 2.1.3 Audience ..................................................................................................................................... 9 2.1.4 Functional Unit ........................................................................................................................... 9 2.1.5 Allocation ................................................................................................................................. 10 2.1.6 Life Cycle Impact Assessment ................................................................................................... 11 2.1.7 Cut-Off Criteria ......................................................................................................................... 12

2.2 LIFE CYCLE INVENTORIES ....................................................................................................... 12 2.2.1 Uncertainty ............................................................................................................................... 13 2.2.2 Software, Database and Model Validation .............................................................................. 13 2.2.3 Emission Factors ....................................................................................................................... 13 2.2.4 Industry Sector Model Development ........................................................................................ 15

3 SCAN LEVEL RESULTS AND ANALYSIS ................................................................... 33 3.1 RECIPE LIFE CYCLE IMPACT ASSESSMENT ................................................................................. 33

3.1.1 Highest Weighted Characterization Categories ....................................................................... 35 3.1.2 Lesser Characterization Categories .......................................................................................... 38

3.2 GREENHOUSE GAS AND CLIMATE CHANGE ............................................................................... 40 3.2.1 Cultivation Practice Comparison for Greenhouse Gases .......................................................... 43

3.3 UNCERTAINTY ANALYSIS ....................................................................................................... 44 3.4 SUMMARY OF SIMAPRO® MODELING ...................................................................................... 46

4 CONCLUSIONS .................................................................................................... 47 4.1 LCIA USING RECIPE MIDPOINT ............................................................................................. 47 4.2 LCIA USING IPCC 2007 GWP .............................................................................................. 48

4.2.1 Cradle to Grave ........................................................................................................................ 48 4.2.2 Peanut Production Scenarios .................................................................................................... 49

REFERENCES ............................................................................................................. 50

A. ADDITIONAL MODEL PARAMETERS FOR INDUSTRY SECTORS ............................... 53

B. ADDITIONAL RESULTS TABLES AND FIGURES ....................................................... 59

C. CRADLE TO ROASTER EMISSIONS ANALYSIS......................................................... 62

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List of Figures Figure ES-1*. Peanut butter industry climate change impacts, cradle to grave, kg CO2e per kg peanut butter

produced ......................................................................................................................................................... 2

Figure ES-2*. Total emissions for conventional and strip till cultivation practices in units of CO2 equivalents. Results determined using the Greenhouse Gas Protocol. ............................................................................... 3

Figure ES-3*. Monte Carlo uncertainty analysis results, 95% confidence interval, as compared with the initial SimaPro® model output and industry estimates for various packaged goods (EWG, 2011). ......................... 4

Figure 1-1. U.S. domestic use of peanuts (Pooley, 2005) .............................................................................................. 6

Figure 2-1. Stages of a life cycle assessment ................................................................................................................ 8

Figure 2-2. Overview of peanut butter production process .......................................................................................... 9

Figure 2-3. Process flow of national weighted average cultivation processes ............................................................ 18

Figure 2-4. Buying Point input/output flow diagram ................................................................................................... 19

Figure 2-5. Sheller input/output flow diagram ............................................................................................................ 22

Figure 2-6. Blancher input/output flow diagram ......................................................................................................... 25

Figure 2-7. Roaster input/output flow diagram .......................................................................................................... 25

Figure 2-8. Processing and packaging input/output flow diagram .............................................................................. 27

Figure 2-9. Retail input/output flow diagram .............................................................................................................. 29

Figure 3-1. ReCiPe midpoint impact analysis, cradle to grave ..................................................................................... 34

Figure 3-2. Recipe midpoint impact analysis, cradle to retail gate .............................................................................. 35

Figure 3-3. ReCiPe midpoint impact analysis for human toxicity characterization category, cradle to retail gate. .... 36

Figure 3-4. ReCiPe midpoint impact analysis for marine ecotoxicity characterization category, cradle to retail gate. ....................................................................................................................................................................... 37

Figure 3-5. ReCiPe midpoint impact analysis for freshwater ecotoxicity characterization category, cradle to retail gate. ............................................................................................................................................................... 38

Figure 3-6. IPCC 2007 GWP cradle to grave impact analysis, kg CO2e per kg peanut butter produced ...................... 41

Figure 3-7. Comparison of GHG emissions from various food types (EWG, 2011) ...................................................... 42

Figure 3-8. IPCC 2007 GWP impact analysis process flow contributors, cradle to retail gate ..................................... 43

Figure 3-9. IPCC 2007 GWP impact analysis for conventional and strip till cultivation practices in units of CO2e ..... 44

Figure 3-10. Probability distribution function for GHG emissions for peanut butter (kg Peanut Butter/kg CO2e). 95% confidence interval displayed on chart as red vertical lines. ........................................................................ 45

Figure 3-11. Monte Carlo uncertainty analysis results, 95% confidence interval, as compared with the initial SimaPro® model output and industry estimates for various packaged goods (EWG, 2011). ....................... 46

Figure C-1. kg CO2e emissions per kg roasted nuts produced. IPCC 2007 GWP method ........................................... 62

Figure C-2. Percentage of total CO2e emissions by life cycle stage ............................................................................. 63

Figure C-3. CO2e emissions by input. Life cycle inputs contributing less than 1% of the total emissions are not shown. ........................................................................................................................................................... 63

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List of Tables Table 2-1. National Average 16oz Peanut Butter Product (The Peanut Institute, 2010) ............................................ 10

Table 2-2. Peanut supply chain product loss approximation....................................................................................... 11

Table 2-3. Midpoint Categories in the ReCiPe impact assessment methodology (Thoma, et al., 2010) ..................... 12

Table 2-4. Diesel combustion emission factors ........................................................................................................... 14

Table 2-5. Gasoline combustion emission factors ....................................................................................................... 14

Table 2-6. Natural Gas combustion emission factors .................................................................................................. 15

Table 2-7. Liquid Propane combustion emission factors ............................................................................................. 15

Table 2-8. Weighted average for peanut cultivation in the US ................................................................................... 16

Table 2-9. Summary of Georgia peanut enterprise budget inputs and outputs by production scenario for one acre of cultivated land (UGA, 2011) ...................................................................................................................... 17

Table 2-10. Farm model input and output processes for nation average aggregate farm peanut production .......... 17

Table 2-11. Buying Point model input and output processes ...................................................................................... 19

Table 2-12. Sheller model input and output processes ............................................................................................... 21

Table 2-13. Sheller chemical and pesticide inputs....................................................................................................... 23

Table 2-14. Blancher model input/output parameters ............................................................................................... 24

Table 2-15. Roaster model input/output parameters ................................................................................................. 24

Table 2-16. Summary of all material inputs to a 16 oz jar of peanut butter .............................................................. 26

Table 2-17. Transportation diesel totals for materials that make up a 16 oz peanut butter product on a per ton basis. .............................................................................................................................................................. 27

Table 2-18. Retail model input/output parameters .................................................................................................... 29

Table 2-19. Retail waste flows ..................................................................................................................................... 30

Table 2-20. National average dishwasher use data (Thoma, et al., 2012)................................................................... 31

Table 2-21. National average peanut butter and consumer behavior data (Hogass, 2002) ....................................... 31

Table 2-22. Summary of consumer disposal assumptions .......................................................................................... 32

Table 3-1. IPCC 2007 GWP cradle to grave impact analysis, kg CO2e per kg of peanut butter produced ................... 41

Table 3-2. Greenhouse Gas Protocol results by impact category and cultivation method ......................................... 43

Table A-1. Farm chemical inputs ................................................................................................................................. 53

Table A-2. Farm chemical inputs, continued ............................................................................................................... 54

Table A-3. Blancher and roaster cleaner use per ton of peanuts produced ................................................................ 55

Table A-4. Blancher and roaster pesticide use per ton of peanuts produced ............................................................. 56

Table A-5. Processing ingredient inputs and diesel transport requirement for 1 ton of ingredients ......................... 57

Table A-6. Packaging material inputs and diesel transport requirements for 1 ton of materials ............................... 58

Table B-1. Fossil depletion process flow impacts for 1 kg peanut butter .................................................................... 59

Table B-2. Freshwater eutrophication process flow impacts for 1 kg peanut butter .................................................. 59

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Table B-3. Terrestrial Acidification process flow impacts for 1 kg of peanut butter ................................................... 60

Table B-4. Particulate matter formation process flow impacts for 1kg of peanut butter ........................................... 60

Table B-5. Photochemical oxidant formation process flow impacts for 1kg of peanut butter ................................... 61

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Executive Summary

The purpose of this study was to perform a scan-level life cycle assessment (LCA) and impact

analysis for peanut butter production in the U.S. Objectives for the LCA were: 1) a literature review of

the peanut industry, 2) a life cycle inventory (LCI) review, and 3) an assessment of a variety of impacts

including carbon. The literature review of the peanut industry is submitted as an attachment to this

report. This document provides a description of the life cycle inventory, assessment, and impact results.

The project scope included environmental effects at a national scale in order to put into context

the environmental impacts that can be attributed to the entire life cycle of a 1 lb jar of peanut butter in

the United States. A complete model of the peanut butter supply chain accounting for extractions from

nature through final disposal of packaging materials was constructed using life cycle inventory data from

survey results, scientific and industry literature, expert opinion, existing manufacturers’ LCAs, other

industry provided data sets, and U.S. based unit processes from the U.S. Life Cycle Inventory housed at

the National Renewable Energy Laboratory (NREL, 2012).

The primary impact categories were evaluated using the ReCiPe impact analysis model

(Goedkoop, Heijungs, Schryver, Struijs, & Van Zelm, 2008). ReCiPe identified toxicity impacts from

emissions from four main processes: natural gas production, disposal of municipal waste, disposal of

coal byproducts, and pesticide use. Natural gas production and use was associated with drying (buying

point) and roasting. Disposal of municipal waste was associated with blanching, roasting, and

processing. Disposal of coal byproducts was associated with processes on the farm. Pesticide impacts

were primarily associated with farm operations, however, pesticide use amounts reported at the sheller,

blancher, and roaster were in quantities large enough to be used in the model.

A scan level impact assessment was also performed for greenhouse gas emissions using the

Intergovernmental Panel on Climate Change’s (IPCC) 2007 Global Warming Potential (GWP) model

evaluated for impacts at the 100 year point (Frischknecht & Jungbluth, 2007). Greenhouse gas (GHG)

emissions were measured in kg CO2 equivalent (kg CO2e) per kg peanut butter produced. IPCC 2007

GWP showed the use and disposal phase had the highest GHG emission, followed by farm activities

(Figure ES-1). The consumer use and disposal GHG emissions were driven by the electricity required for

cleaning dishes (pumping and heating water). Because of the high consumer use impact and the peanut

industry’s lack of control over this phase the boundaries for the LCA were established from field to

peanut butter processor gate. Within these boundaries farm gate impact was roughly 27% of the total

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greenhouse gas emissions from the supply chain with the next highest contributor being the peanut

butter processor at 22%. The production of electricity by bituminous coal was the most significant

process contributor to greenhouse gases across the supply chain. This electricity is used to power all of

the processes from the buying point to retail with its most significant use at the peanut butter

processor. The complete cradle to grave life cycle impact assessment for peanut butter shows a GHG

impact of 2.64 kg CO2e.

Further analysis was performed using IPCC 2007 GWP comparing greenhouse gas emissions

among the four major peanut cultivation practices (Figure ES-2). Irrigated conventional till production

had the highest impacts, while non-irrigated strip till had the lowest. While irrigated varieties used less

herbicide and fungicide than their non-irrigated counterparts, electricity use for pumping water

accounted for a greater impact on GHG emissions.

Figure ES-1*. Peanut butter industry climate change impacts, cradle to grave, kg CO2e per kg peanut butter produced *Also Figure 3-6

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Finally, uncertainty analysis was performed using Monte Carlo analysis in order to determine the

statistical variability of GHG emissions from field to peanut butter factory gate. The variability of each

input variable was characterized, resulting in 95% confidence that the GHG emissions from one kg of

peanut butter was between 1.24 and 6.71 kg CO2e, with a mean of 2.91 and standard deviation of 1.61.

This is comparable to the initial SimaPro® model results of 2.64 kg CO2e and within range of other

estimates for emissions from consumer packaged goods (Figure ES-3).

Figure ES-2*. Total emissions for conventional and strip till cultivation practices in units of CO2 equivalents. Results determined using the Greenhouse Gas Protocol. *Also Figure 3-9

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Figure ES-3*. Monte Carlo uncertainty analysis results, 95% confidence interval, as compared with the initial SimaPro® model output and industry estimates for various packaged goods (EWG, 2011). *Also Figure 3-11

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1 Introduction

Corporate social responsibility, including sustainability, is one of the top priorities for executives

in the consumer goods industry, according to the 2010 “Top of Mind” survey sponsored by The

Consumer Goods Forum (The Consumer Goods Forum, 2010). Retailers, suppliers, manufacturers, and

distributors are seeking ways to improve their operations using the framework of sustainability.

Consumers continue to ask for green products and services and express an interest in understanding the

sustainability of the products they purchase as well. There is an increasing willingness to make

purchasing decisions based on what they know about the environmental impacts of a product. Thus,

the peanut industry and the American Peanut Council identified a need to conduct a cradle-to-grave life

cycle assessment (LCA) for U.S. peanut products focused on quantifying embodied energy, emissions to

air, and the consumption of water and other natural resources. There was also the additional need to

assess the impacts of these inventory flows on climate change (global warming potential), resource

depletion, and human and ecosystem health.

Life cycle assessment (LCA) is a tool to account for complete interactions and combined effects

in a product chain. LCAs can provide quantitative, confirmable, and manageable models to evaluate

production processes, analyze options for innovation, and improve understanding of the complexity in

systems. LCAs have been used as a tool to identify “hot spots” in the production chain that may

introduce opportunities for simultaneously lowering environmental impacts and improving efficiency

and profitability (Hogass, 2002). This LCA will allow the peanut industry to position its products in the

market place in terms of their sustainable attributes and respond proactively to consumer concerns. It

will provide a milestone that will guide the peanut industry to engage more sustainable practices and

reduce environmental impacts, while validating those reductions through science-based product LCA.

Peanut production in the United States resulted in 3.96 billion pounds of product during the

2010 growing season, corresponding to a 7% increase in production from the previous year. Peanuts

produced in the U.S. are mostly used in food and confection products. Value-added peanut products

have been developed which have a number of applications including bakery, confectionery and the

general consumer market. Among these are: peanut butter, snack peanuts, peanut candy, roasted in-

shell, peanut oil/meal, and seed/residual as shown in Figure 1-1.

The largest area of consumption of peanuts in the U.S. is from peanut butter, which consumes

around 32% of the peanuts produced in the U.S. each year (Figure 1-1) (Pooley, 2005). As the

manufacture of peanut products expands, energy requirements along the process chain will increase.

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Food production processes like those involved in the manufacture of peanuts represent one of the

largest industrial sectors in the world, resulting in significant amounts of energy use and greenhouse gas

(GHG) emissions. Though emissions and other production outputs have been shown to impact

environmental quality at multiple scales, quantifying the environmental impacts of specific

manufacturing processes can be difficult (Roy, et al., 2009).

The goal of this project was to quantify a variety of impacts from U.S. peanut butter using Life

Cycle Assessment. Specific objectives for the LCA included: 1) Reviewing existing literature related to the

sustainability of peanut products; 2) Defining the detailed data requirements for the project; 3)

Gathering and analyzing data from provided and available sources - including literature, economic

census, USDA, and peanut plant surveys; and 4) Computing the life cycle inventories and evaluating

environmental impact metrics using linked unit process models in SimaPro®. The overarching goal of

this work will be to equip peanut industry stakeholders with timely, science-based environmental

performance data to inform decision-making and drive innovative new products, process and services.

Figure 1-1. U.S. domestic use of peanuts (Pooley, 2005)

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2 LCA Methodology

LCA is a tool to evaluate the environmental impacts of a product or process throughout the

entire life cycle. For agricultural products, LCA begins with production of fertilizers, then crop

cultivation, processing, use, and disposal of wastes associated with its end-use. This includes identifying

and quantifying energy and materials used, wastes released to the environment, calculating

environmental impact, interpreting results, and identifying improvement opportunities.

This LCA has been structured following ISO 14040:2006, and ISO 14044:2006 standards which

provide an internationally accepted method of conducting LCAs, while leaving significant degrees of

flexibility in methodology to customize individual projects to their desired application and outcomes.

Broadly, an LCA consists of four stages (Figure 2-1):

1) Define the goal and scope – including appropriate metrics (e.g. greenhouse gas

emissions, water consumption, hazardous materials generated, and/or quantity of

waste)

2) Conduct life cycle inventories (collection of data that identifies the system inputs and

outputs and discharges to the environment)

3) Perform impact assessment

4) Analyze and interpret the results

The goal and scope definition phase is a planning process that involves defining and describing

the product, process or activity, establishing the aims and context in which the LCA is to be performed,

and identifying the life cycle stages and environmental impact categories to be reviewed for the

assessment. The depth and breadth of LCA can differ considerably depending on the goal of the LCA.

The life cycle inventory (LCI) analysis is the second phase of the LCA. The LCI is an inventory of

input/output material and energy flows in the system of interest. The analysis involves identifying and

quantifying energy, water, materials and environmental releases (e.g. air emissions, solid wastes,

wastewater discharge) during each stage of the life cycle.

The life cycle impact assessment phase (LCIA) is the third phase of the LCA. This step quantifies

the human and ecological impacts of material consumption and environmental emissions identified

during the inventory analysis. Interdependencies often exist between impact categories, and poor

decisions can be made when only a single impact metric is used as the basis of the assessment. For this

study, eutrophication, acidification, ozone depletion, land use, and other impact categories were

analyzed using Impact 2002 and ReCiPe, two LCIA models integrated into SimaPro® 7.0, an ISO 1404x

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compliant platform for performing LCA. Life cycle interpretation is the final phase of the LCA procedure,

in which the results are summarized and discussed. Its goal is to identify the most significant

environmental impacts and the associated life cycle stage, and highlight opportunities for potential

change or innovation.

2.1 Goal and Scope Definition

2.1.1 Goal

The primary goal of this study was to identify environmental impacts including greenhouse gas

(GHG) emissions associated with the production, consumption, and disposal of peanut butter in the

United States. The scope of this work does not include declarations to the consuming public.

2.1.2 Project Scope and System Boundaries

This life cycle assessment is a cradle-to-grave global impact assessment and carbon footprint

(global warming potential) analysis of U.S. peanut products. The data collected was primarily from the

year 2010. The system boundaries begin with peanut crop production, and end with the disposal of the

empty package/container by the consumer including recycling (where applicable), incineration, or

landfilling (Figure 2-2). Also included will be the full analyses of the upstream impacts of materials and

Figure 2-1. Stages of a life cycle assessment

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energy consumed for each of these processes. Collection of detailed capital infrastructure data and the

corresponding GHG emissions are not included in this project.

2.1.3 Audience

For this life cycle assessment, the primary audience is food industry managers (farmers, peanut

processors, packaging companies, and retail) who may use the results to identify opportunities to

reduce GHG emissions and/or the consumption of natural resources. Consumers represent a second

potential audience for the study results. The LCA supports the peanut industry’s ability to work

proactively with retailers to educate consumers about agricultural and food sustainability issues.

2.1.4 Functional Unit

The functional unit of this study is defined as one pound of peanut butter as consumed by U.S.

consumers. For convenience, the analysis conducted in this study is for the 16oz peanut butter product

category and does not take into consideration processing and packaging material for additional product

sizes. Literature and survey data was used to define the components that make up a 16oz jar of peanut

butter. Using this data we created a national average 16oz peanut butter product that is an aggregate

representation of many different peanut butter brands (Table 2-1). All SimaPro® model inputs are for

the functional unit of 16oz, however, in the impact analysis we run the model for 1 kg of peanut butter.

Figure 2-2. Overview of peanut butter production process

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This is accomplished in order to be able to compare the peanut butter life cycle with that of other

consumer packaged goods using a standard unit of comparison (1 kg).

2.1.5 Allocation

Where allocation of inputs is required, the allocation procedures follow the ISO 14044 allocation

hierarchy. There are three stages in the supply chain where allocation occurs: alternative products

produced during shelling, retail energy use, and a third stage for consumer travel and storage allocated

to peanut butter. We used an economic allocation model for the shelling processes, allocating a

percentage of the energy footprint based on the economic value of the functional unit produced as

compared to the other products. For retail and consumer use we followed the methods of the US Dairy

LCA study (Thoma, et al., 2012). Retail shelf space was allocated to determine the appropriate retail

energy demands. For consumer use, an allocation model was used that allocates a portion of families

grocery expenditures and grocery travel for the purchase of peanut butter.

Losses in the supply chain can be broken down into peanut byproducts and product losses.

Peanut byproducts account for the majority of loss and include gravel, dirt, and debris, peanut hulls, and

peanut skins. Losses attributed to byproducts do not have a significant economic value to warrant any

of the environmental burden of production. Product loss or waste in the supply chain include, poor

quality peanuts, processing and consumer waste. Total loss of mass in the supply chain for byproducts

Table 2-1. National Average 16oz Peanut Butter Product (The Peanut Institute, 2010)

mass (oz) mass (g)

16oz Peanut Butter

Peanuts 14.40 408.23

Sugar 1.17 33.17

Salt 0.20 5.67

Oil (Stabilizer) 0.24 6.80

Total 16.00 454.00

16oz Packaging

Lid/Seal1 0.39 11.1

Jar2 0.99 28.00

Label3 0.05 1.30

Total 1.43 40.40 1 Polypropylene lid with aluminum seal

2 PET Jar

3 Paper Label

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and product loss combined are reported to represent an approximate 40% reduction of mass from

peanuts produced on farm to peanut paste consumed. This number is significantly lower at 19% if we

only include product loss. The majority of product loss in the supply chain occurs at the consumer level,

where we assumed that 20% of the peanut butter purchased is wasted (USDA-ERS, 2011). The burden

of product loss during manufacturing occurs between roasting and retail and was assumed to be a 3%

reduction of mass. Total losses (byproduct and product loss) used in the peanut butter model are

displayed in Table 2-2. These losses in the supply chain affect the reference flows of upstream

processes. In order to get the required flow of 14.4oz of peanut paste into the consumer use phase,

23.95oz of peanuts have to be produced on farm (Table 2-2).

2.1.6 Life Cycle Impact Assessment

For the our model analysis we used two impact assessment methodologies: ReCiPe midpoint

impact assessment and IPCC 2007 GWP. ReCiPe midpoint is a problem-oriented approach and uses

environmental themes such as climate change, terrestrial ecotoxicity, and ozone depletion to

communicate impacts (Heijungs, Goedkoop, Strujis, Effting, Sevenster, & Huppes, 2010) (Table 2-3).

Midpoint impact assessment uses equivalent values of reference chemicals such as CO2 equivalent or P

Table 2-2. Peanut supply chain product loss approximation

% Starting

Weight (oz)

Supply Chain Product Loss

Farm1 2% 23.95

Buying Points2 2% 23.48

Sheller3 22% 23.02

Blancher4 6% 18.87

Roaster5 2% 17.80

Processor6 2% 17.45

Retail7 1% 17.28

Consumer8 20% 14.40 1 (USDA, 2011)

2 Assumed loss due to peanut grading and debris

3 Survey reported loss including hulls, debris, and waste

4 Survey reported skin loss

5 Survey reported quality nut loss

6 Survey reported product loss

7 Assumed expiration and product damage loss

8 (Thoma, et al., 2010)

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equivalent to measure impact. The IPCC 2007 GWP impact assessment methodology is a way of

reporting GHG emissions from peanut butter production on a kg CO2e basis. The main greenhouse gases

emitted by humans are: Carbon Dioxide, Methane, Nitrous Oxide, and Fluorinated Gases (EPA, 2012).

2.1.7 Cut-Off Criteria

In determining whether to include specific inputs, a one percent cut off threshold for the impact

analysis of any input was adopted; however, small flows were not omitted when data was readily

available. The one percent cutoff threshold was applied when running the impact analysis model. This

means that process flows contributing to less than one percent of the total impact for a specific impact

category were excluded from analysis. Emissions resulting from capital goods were also excluded from

the analysis.

2.2 Life Cycle Inventories

The Life Cycle Inventory stage of an LCA requires gathering input and output data for each

element of the system and for each process and product option. The major system elements are the

peanut butter industry sectors: farm, buying point, sheller, blancher, roaster, processor, distributor,

retail, and consumer. Each system is composed of smaller unit processes which correspond to their

primary inputs and outputs.

Literature was the informational foundation for the construction of the peanut butter life cycle

model. In addition to literature, multiple peanut industry experts were consulted throughout the study.

Data was required for each input and output and was sourced from survey results, scientific and

industry literature, expert opinion, existing manufacturers’ LCAs, other industry provided data sets, and

Table 2-3. Midpoint Categories in the ReCiPe impact assessment methodology (Thoma, et al., 2010)

Midpoint Categories Unit of measure Midpoint Category Unit of measure climate change kg CO2 eq freshwater ecotoxicity kg 1,4-DB eq ozone depletion kg CFC-11 eq

1 marine ecotoxicity kg 1,4-DB eq

terrestrial acidification kg SO2 eq ionizing radiation kg U235 eq freshwater eutrophication kg P eq agricultural land occupation m

2a (occupation of 1 yr)

marine eutrophication kg N eq urban land occupation m2a (occupation of 1 yr)

human toxicity kg 1,4-DB eq2 natural land transformation m

2

photochemical oxidant formation kg NMVOC3 water depletion m

3

particulate matter formation kg PM10 eq4 mineral resource depletion kg Fe eq

terrestrial ecotoxicity kg 1,4-DB eq fossil fuel depletion kg oil eq 1 chlorofluorocarbon-11 equivalent 2 1,4-dichlorobenzene 3 Non-Methane Volatile Organic Compounds 4 Particulate matter smaller than 10 micrometers

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U.S. based unit processes from the U.S. Life Cycle Inventory housed at the National Renewable Energy

Laboratory (NREL, 2012). Unit processes were constructed within SimaPro® and linked to EcoInvent

upstream processes, which created a picture of the footprint for the entire system. Information and

data from two previous GHG emissions studies were also used for several processes within the peanut

butter model (Thoma, et al., 2012) (Thoma, et al., 2010).

2.2.1 Uncertainty

Monte Carlo simulation was used to quantify and characterize uncertainty from ranges of input

data. This process was rules-based and incorporated probability distributions for variable data that

reflected the knowledge and process uncertainty associated with the variable. Monte Carlo analysis is

indispensable for establishing defensible metrics for evaluation, providing a quantified measure of what

is known and what is unknown, as well as the inherent variability of a process.

2.2.2 Software, Database and Model Validation

SimaPro® software system for life cycle analysis, developed by Netherlands based Pre

Consulting, was used to create the framework for this LCA model. Background processes used to

develop the entire reference flow for a major process, such as injection molding for PET jars, are from

the EcoInvent and U.S. Life Cycle Inventory databases. All data, formulas, and assumptions within the

model have been reviewed by the author and at least one additional team member.

2.2.3 Emission Factors

2.2.3.1 Electricity

The scale of this analysis and the limited location information that was provided required that

we use a national average for electricity production. However, there are three electricity production

regions in the U.S.: Eastern Interconnection, Western Interconnection, and the Electric Reliability

Council of Texas (ERCOT) Interconnection (Deru & Torcellini, 2007). The use of the three main

interconnections as the basis for calculating environmental burdens from electricity production are

better than national, state, or even utility-level emission factors because there is virtually no electricity

energy transfer between the interconnect grids, so at this level there is some certainty about the actual

fuel mix used to provide electricity. However, due to the low resolution of data and the need for

confidentiality of sources, the national average mix of primary fuels was used to characterize power

plant emissions for all processes within this LCA.

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2.2.3.2 Transportation and Fuels Combustion

Emissions from transportation were calculated using EcoInvent unit processes for specific

vehicles. When unit processes for vehicles were not available, EPA emission factors were combined

with fuel production processes to quantify transportation emissions on a ton-mile basis. Diesel (Table

2-4) and petrol (Table 2-5) emissions were calculated using emissions factors published by the U.S.

Environmental Protection Agency (EPA, 2008). Natural Gas (Table 2-6) and liquid propane (Table 2-7)

were also similarly modeled using emissions factors published by the U.S. Environmental Protection

Agency (EPA, 1995). When a fuel is combusted, it is an oxidation process where the original fuel

molecule is split and combines with oxygen from the air to produce water, carbon dioxide, and heat.

This is how 1 kg of diesel can produce a greater amount of carbon dioxide.

Table 2-4. Diesel combustion emission factors

Unit Process or Emission Name Model Units

Model Amount Units Amount

Inputs from Technosphere

Diesel, at regional storage/CH WITH US ELECTRICITY U kg1 1 gal 1

Emmissions to Air

Carbon dioxide, fossil kg2 3.17188 kg 10.15

Methane, fossil kg2 4.50E-04 kg 1.44E-03

Nitrous oxide kg2 8.13E-05 kg 2.60E-04

Outputs to Technosphere

Diesel, manufacture and combustion/US U kg 1 gal 1 1 Converted using density of diesel fuel at 20°C

2 (EPA, 2008)

Table 2-5. Gasoline combustion emission factors




Petrol, unleaded, at regional storage/CH WITH US ELECTRICITY U kg1 1 gal 1

Emmissions to Air




Outputs to Technosphere

Gasoline, manufacture and combustion/US U kg 1 gal 1 1 Converted using density of petrol fuel at 20°C

2 (EPA, 2008)

15

2.2.4 Industry Sector Model Development

2.2.4.1 Farm

Input data for each peanut production practice was obtained from enterprise budgets prepared

by the University of Georgia Extension Agricultural and Applied Economics Department for the different

cultivation scenarios (UGA, 2011). The enterprise budgets provided information on the recommended

chemical, fuel, and other input amounts for one acre of cultivated land, along with the anticipated

yields. For this LCA, we analyzed the farm footprint based on an aggregate farm that is a weighted

average of each production practices inputs and outputs (Table 2-8). An industry estimate was provided

by the Agricultural Research Service of the USDA that roughly 80% of peanut farmers use conventional

tillage practices while 20% use strip tillage. Further, a U.S. peanut crop irrigation percentage of 35% was

also given (Lamb, 2012). The inputs and yields described in the Georgia crop budgets for all practices

Table 2-6. Natural Gas combustion emission factors




Natural gas, at evaporation plant/JP WITH US ELECTRICITY U kg1 1 m

3 1

Emmissions to Air




Outputs to Technosphere Natural Gas, manufacture and combustion/US U kg 1 m

3 1

1 Converted using density of Natural Gas at standard temperature and pressure

2 (EPA, 1995)

Table 2-7. Liquid Propane combustion emission factors




Propane/ butane, at refinery/RER WITH US ELECTRICITY U kg1 1 m

3 1

Emmissions to Air




Outputs to Technosphere Propane, liquid, manufacture and combustion/US U kg 1 m

3 1

1 Converted using density of Liquid Propane at standard temperature and pressure

2 (EPA, 1995)

16

are summarized in Table 2-9. Figure 2-3 shows the input flows for the aggregate farm that was

incorporated into the model.

Each farm input was matched with an EcoInvent unit process for the production of that input.

Unit processes were selected based on the similarity of the end product with the input defined by the

enterprise budget. A list of the farm inputs and unit processes used in this aggregate model can be

found in Table 2-10. Due to a lack of unit process data for inoculants, this input was excluded from the

analysis. Seed requirements were also excluded. Different pesticides were listed for the varying

production practices requiring us to use a generic EcoInvent pesticide. The production of the inert

compounds in each pesticide was excluded and only the total mass of the active ingredients were

considered. Electricity use for irrigation was estimated using the estimated electricity cost from the

enterprise budgets along with the average cost of one kWh of electricity in the U.S. (BLS, 2011). The

total mass of inputs was assumed to travel 25 miles from a regional storehouse to the farm.

Table 2-8. Weighted average for peanut cultivation in the US

Production Practice Model Weight

Conventional, Irrigated 28%

Conventional, Non-irrigated 52%

Strip, Irrigated 7%

Strip, Non-irrigated 13%

17

Table 2-9. Summary of Georgia peanut enterprise budget inputs and outputs by production scenario for one acre of cultivated land (UGA, 2011)

Units

Conventional Strip Till

Non-Irrigated

Irrigated Non-

Irrigated Irrigated

Input

Seed lb 130 130 130 130

Inoculant lb 5 5 5 5

Cover Crop Seed bu - - 1.5 1.5

Fertilizer lb 0.5 0.5 0.5 0.5

Herbicide lb 4 1.1 5 2.7

Insecticide lb 14.2 14.2 14.2 14.2

Fungicide lb 7.2 5.7 7.2 5.7

Irrigation Electricity kWh - 265.2 - 212.1

Preharvest Fuel Use gal 9.2 9.2 5.2 5.2

Harvest Fuel Use gal 12 12 12 12

Output

Peanuts ton 1.4 2 1.4 2

Table 2-10. Farm model input and output processes for nation average aggregate farm peanut production

Units Amount Model Units

Model Amount SimaPro® Process Name

Input

Rye seed Bushel 0.3 g 2.18 Rye seed IP, at regional storehouse

Boron lb 0.5 g 0.07 Borax, anhydrous, powder, at plant Lime/Gypsum lb 1000 g 130.05 Lime, from carbonation, at regional

storehouse Pesticide lb 24.1 g 3.13 Pesticide, unspecified, at regional

storehouse

Preharvest Fuel Use gallon 8.4 g 7.58 Preharvest machinery fuel usage

Harvest Fuel Use gallon 12 g 10.84 Harvest machinery fuel usage

Electricity kWh 89.1 Wh 25.55 Electricity, low voltage, at grid

Output Farm Grade Peanuts ton 1.60 kg 0.41 Peanuts, at farm, conventional till,

non-irrigated/US U

18

2.2.4.2 Buying Points

Data for the Buying Points section of the model was derived mainly from returned survey data

for the year 2010. Survey questions centered mainly on fuel use, electricity use, and tons of peanuts

handled. The Buying Points model uses the national average Farm Grade Peanuts whose inputs were

incorporated from University of Georgia Agricultural Extension Services peanut crop budgets (UGA,

2011). Thirty eight buying point surveys were returned and all data was aggregated on a per ton basis.

All of the inputs to the buying point can be seen in Table 2-11. A flow chart diagraming the buying point

processes are shown in Figure 2-4.

Figure 2-3. Process flow of national weighted average cultivation processes

19

Diesel use was calculated by using the reported tonnage handled, the total distance traveled

round trip from the farm to the buying points, and the percentage of semis versus wagons used. The

percentage of semis and wagons was used to determine the average single trip total load capacity for a

particular Buying Point. The reported tonnage handled was then used to determine the number of trips

Table 2-11. Buying Point model input and output processes



Input Farm Grade Peanuts

a ton 1.03 kg 0.42 Peanuts, at farm, conventional

till, non-irrigated/US U Electricity

b KWh/ton 15.04 Wh 6.77 Electricity, low voltage, at grid,

US NREL/US Diesel gal/ton 0.44 g 0.65 Diesel, manufacture and

combustion/US U Natural Gas ft

3/ton 76.03 g 0.86 Natural Gas, manufacture and

combustion/US U Liquid Propane gal/ton 1.49 g 1.52 Propane, liquid, manufacture

and combustion/US U

Output

Sheller Grade Peanuts tons 1.0 kg 0.4 Peanuts, at Buying Points/US U a Farm Grade Peanut analysis was developed using University of Georgia peanut crop budgets, conventional

till/non-irrigated b Electricity use was calculated by summing the provided data for screening, cleaning, elevating, drying, and

building operations electricity on a per ton basis

Figure 2-4. Buying Point input/output flow diagram

20

necessary to transport the peanuts to the Buying Point. Trip distance from the Farm to the Buying Point

was then multiplied by the number of trips required and then converted to total amount of diesel used

by a applying a Semi and Wagon fuel efficiency estimate based on the percentage of Semis versus

Wagons. Reported yard diesel was combined with the transportation diesel.

Natural Gas and Liquid Propane use for drying peanuts was initially to be estimated using survey

data that requested the amounts of Natural Gas and Liquid Propane used on site. However, survey

response concerning these two factors was extremely low. We then developed a Natural Gas and Liquid

propane estimate from the ratio of Semis to Wagons, the total amount of peanuts dried, and a report

produced by the National Peanut Research Laboratory (NPRL) (Butts, 2010) indicating Natural Gas and

Liquid Propane consumption rates per ton of peanuts dried, assuming a standard initial moisture

content (IMC) of 17% and a cutoff moisture content of 10-11%.

Buying Point electricity use was developed by summing the estimated electricity consumption of

screening, cleaning, elevating, drying, and operations. Screening, cleaning, and elevation electricity use

was estimated using information provided by Larry Cunningham on standard motor sizes and specific

processing rates on a per ton basis (Cunningham, 2011). Drying Fan electricity on a per ton basis was

estimated based on research published in the Peanut Science journal (Blankenship & Chew, 1979).

Using a standard IMC of 17%, the following equation was used to determine the electricity requirements

on a per ton basis for drying fans where TEU is the Total Electricity Used in kWh per ton of peanuts

dried:

(Eq. 1)

Buying Point operations electricity was estimated from data from two specific buying points provided by

Larry Cunningham.

2.2.4.3 Sheller

Sheller model inputs were taken from returned survey data for the year 2010. Survey questions

were used to understand the basic inputs to the shelling process: Whole Peanuts, Electricity, Diesel,

Petrol, Liquid Propane, Polypropylene, Cleaners, and Pesticides. Sheller Grade Peanuts are peanuts that

have met grade at the Buying Points and are purchased by the Sheller. Five sheller surveys were

returned and all data was aggregated on a per ton basis. All of the inputs to the sheller can be seen in

Table 2-12. A flow chart diagraming the shelling processes is shown in Figure 2-5. Diesel and petrol

21

consumption by the sheller were calculated from the surveys using the combination of fuel used on-site

at the farmers stock storage, fuel used to transport the sheller grade peanuts from the farmer stock

storage to the shelling plant, fuel used to transport shelled peanuts to the shelled stock storage, and fuel

used to transport rejected peanuts back from the blancher.

Liquid Propane consumption from the survey was from forklift use at the shelling plant. The

total amount of Liquid Propane was reported for each plant and aggregated on a per ton of peanuts

processed basis. Electricity was a combination of reported electric forklift hours, shelling operations

electricity, and cold storage electricity. For facilities with operations other than shelling peanuts, an

allocation was assigned based on the reported percentage of energy used at the plant specifically for the

shelling of peanuts.

Table 2-12. Sheller model input and output processes



Input Sheller Grade

Peanuts1

ton 1.21 kg 0.50 Peants, at Buying Points/US U

Electricity KWh/ton 97.80 Wh 13.60 Electricity, low voltage, at grid, US NREL/US

Diesel gal/ton 0.58 g 0.26 Diesel, manufacture and combustion/US U

Gasoline gal/ton 0.16 g 0.06 Gasoline, manufacture and combustion/US U

Liquid Propane gal/ton 0.11 g 0.03 Propane, liquid, manufacture and combustion/US U

Cleaners2 kg/ton 7.36E-03 g 1.02E-03 Cleaners, combined value, at regional

storage/US U Pesticides

2 kg/ton 1.93E-02 g 2.68E-03 Pesticides, combined value, at regional

storage/US U

Output

Shelled Peanut ton 1.00 kg 0.41 Peanuts, shelled, at sheller/US U 1 Shelling Grade Peanut analysis was developed using survey data from the U.S. Peanut Shelling Industry

2 All pesticides, insecticides, and cleaners used were condensed into a single value and represented in the model using the

surrogates "Pesticides" and "Cleaners". Mass conversion assumes the density of water.

22

Cleaners and Pesticide use was also reported on the survey and shown in Table 2-13. Each

cleaner and pesticide was aggregated on a per-ton of peanuts processed basis, based on their active

ingredient. Because of the wide variety of cleaners and pesticides available, most were combined into

categories with similar active ingredients. SimaPro®, however, does not contain processes for most of

the chemicals used at the shelling plant, therefore, placeholders were developed using generic chemical

processes for pesticides and a cleaner ratio of Sodium Hypochlorite and Sodium Hydroxide that was

representative of the survey data.

Figure 2-5. Sheller input/output flow diagram

23

2.2.4.4 Blanch and Roasting

Blanching and Roasting are separate processes; however, all of the survey results were from

facilities that accomplished both. This created a unique problem when separating out the energy and

materials embodied in each individual process. This was done by assigning a weight factor to each input

that was shared by each process and is explained in the following paragraphs. Five surveys were

returned containing blanching and roasting data that was aggregated on a per ton basis. All of the

inputs to the blanching and roasting processes can be seen in Table 2-14 & Table 2-15. Flow charts

diagraming the blanching and roasting processes are shown in Figure 2-6 & Figure 2-7.

Diesel consumption for blanching and roasting was from the transport of peanuts from the

previous spot in the supply chain. For the majority of the survey respondents, this was the sheller.

Diesel use for each process, whether blanching or roasting, was allocated based on how my major

processes were accomplished at the facility. If the facility is simply a blancher/roaster, then each

process was assigned 50% of the diesel impact. If the facility is a blancher/roaster/peanut butter

manufacturer, then each process was assigned 33% of the diesel impact. Natural gas was reported for

each returned survey. Determining which process was assigned the impact from natural gas was based

on the assumption that all natural gas reported was for the roasting of peanuts.

Table 2-13. Sheller chemical and pesticide inputs

Active Ingredient Units Total Active (kg) kg/Ton

Cleaners

Hypochlorite kg 1559.59 7.31E-03

sodium hydroxide kg 4.54 5.14E-05

Total Cleaners kg - 7.36E-03

Pesticides

beta-cyfluthrin kg 2.38 4.32E-06

cyfluthrin kg 3.99 4.52E-05

Pyrethrum kg 74.95 8.48E-04

Indoxacarb kg 2.50E-03 2.00E-08

Aluminum Phosphide kg 3889.04 1.83E-02

Entec 10/Unknown Pest kg 3.79 3.03E-05

Methyl Bromide/Unknown Pest kg 4.54 5.22E-05

Total Pesticides kg - 1.93E-02

24

Facility electricity consumption was part of the survey response as well. In order to assign

electricity use for each process accomplished at the facility, we used the survey question, “To the best of

your knowledge, what percentage of your total energy consumption was used for the blanching or

roasting of peanuts at your facility in 2010”. This percentage was used to determine how much

electricity was assigned to each process.

Table 2-14. Blancher model input/output parameters



Input Shelled Peanut ton 1.00 kg 0.43 Peanuts, shelled, at sheller/US U



Natural Gas cf/ton 276.06 g 3.21 Natural Gas, manufacture and combustion/US U

Cleaners kg/ton 0.23 g 0.11 Cleaners, combined value, at regional storage/US U

Pesticides kg/ton 5.95E-05 g 2.84E-05 Pesticides, combined value, at regional storage/US U

Output

Blanched Peanuts ton 0.87 kg 0.41 Peanuts, blanched, at blancher/US U

Table 2-15. Roaster model input/output parameters



Input Blanched Peanuts ton 1.00 kg 0.42 Peanuts, blanched, at blancher/US U



Natural Gas cf/ton 1164.67 g 13.00 Natural Gas, manufacture and combustion/US U

Cleaners kg/ton 0.23 g 0.11 Cleaners, combined value, at regional storage/US U

Pesticides kg/ton 5.95E-05 g 2.73E-05 Pesticides, combined value, at regional storage/US U

Output

Roasted Peanuts ton 0.98 kg 0.41 Peanuts, roasted, at roaster/US U

25

Cleaners and Pesticide use reported on the survey are shown in Appendix A, Table A-3 and Table

A-4. Cleaners and pesticides were aggregated, combined by active ingredient, and replaced by

SimaPro® placeholders in a manner similar to the one used for the sheller.

2.2.4.5 Processing & Packaging Materials

Peanut butter processing and packaging for the purpose of this report will be considered a single

unit process, however, all surveys returned were for facilities where peanut butter processing was

accomplished alongside other processes, such as roasting and blanching. Similar to the blanching and

roasting section, we used a weighting factor where applicable in order to assign specific impacts to

individual processes. In order to input all of the processes into the SimaPro® model, we first broke

Figure 2-6. Blancher input/output flow diagram

Figure 2-7. Roaster input/output flow diagram

26

down processing and packaging into the ingredients and materials that make up a jar of peanut butter,

and then the final assembled product. The final assemble product including roasted peanuts, other

ingredients, and primary packaging is shown in Table 2-16. Flow charts diagraming the processing and

packaging process can be seen in (Figure 2-8).

Table 2-16. Summary of all material inputs to a 16 oz jar of peanut butter

Units Amount SimaPro® Process Name

Input

Roasted Peanuts kg 0.41 Peanuts, roasted, at Roaster/US U

Electricity Wh 129.52 Electricity, low voltage, at grid, US NREL/US

Diesel1 g 5.47 Diesel, manufacture and combustion/US U

Cleaners g 0.47 Cleaners, combined value, at regional storage/US U

Pesticides g 0.01 Pesticides, combined value, at regional storage/US U

Oil, Stabilizer g 6.80 Stabilizer, crude coconut oil at plant/US U

Sugar g 33.17 Sugar, from sugar beet at plant/US U

Salt g 5.67 Salt placeholder, at plant/US U

Label, Paper g 1.30 16oz product label, at plant/US U

Lid, Poly/Aluminum g 11.10 16oz product lid, at plant/US U

Jar, PET g 28.00 16oz product jar, at plant/US U

Output

16oz Peanut Butter kg 0.49 Peanut Butter, 16oz at Processor/US U 1 Diesel listed is for transportation of roasted peanuts, transportation of other materials is imbedded in that

materials unit process

27

Reported diesel use was for the transportation of roasted peanuts to the peanut butter

processor. Transportation diesel requirements for all other materials that make up a 16 oz jar of peanut

butter are inherent in that particular material’s unit process. The diesel amounts needed to move these

items are shown in Table 2-17.

Figure 2-8. Processing and packaging input/output flow diagram

Table 2-17. Transportation diesel totals for materials that make up a 16 oz peanut butter product on a per ton basis.

Product Assembly Amount

(ton)

Transport Diesel

(kg/ton)

Stabilizer Oil 1 26.69

Sugar 1 14.34

Salt 1 26.14

Jar 1 18.98

Label 1 60.96

Lid 1 26.98

Secondary Packaging 1 15.01

28

Cleaners and pesticide use in the processing phase did not have high reporting levels. It is

assumed in this report that similar types and amounts on a per ton basis of cleaners and pesticides were

used in the processing phase as were used at the roaster.

The additional peanut butter ingredients (oil, sugar, and salt) are represented as individual unit

processes. Each of these ingredients has a specific transportation diesel demand as well as

manufacturing flows within the SimaPro® Model. Table A-5 in Appendix A shows all of the individual

inputs for each ingredient.

Packaging includes primary (PET Jar, Polypropylene Lid, and Paper Label), secondary (cardboard

tray), and tertiary (palletized) packaging types. The types and associated mass of each material that

makes up the primary packaging can be seen in Table 2-1. Packaging was modeled using LCI data

collected from the American Peanut Council sustainability survey as well as similar packaging types used

in the Cheese LCA (Thoma, et al., 2012). Input flows and unit processes for each packaging type can be

seen in Appendix A, Table A-6.

2.2.4.6 Retail

After distribution from the processor to the retail gate, peanut butter is displayed on store

shelving for purchase by the consumer. During this phase, the two distinct emissions streams are

overhead electricity and overhead fuel. The Cheese LCA study was used to make estimates of the sales

volume, space occupancy, and energy demands of peanut butter. Cheese LCA electricity demand such

as ventilation, lighting, cooling, space heating, water heating, and other miscellaneous electrical loads

were applied to estimate overhead electricity demand for peanut butter at retail. For our purposes, we

assumed a similar shelf space allocation and final energy footprint for peanut butter while subtracting

the appropriate amount of energy that is used for the refrigeration of cheese and dairy products.

Peanut butter products were assumed to travel the same distance from the distributor as in the Cheese

LCA as well as use a similar amount of natural gas, according to shelf space (Thoma, et al., 2012). Table

2-18 contains the data that was used to populate the retail portion of the SimaPro® model. The retail

flow diagram can be seen in Figure 2-9.

29

Waste flows from the retail sector are from secondary and tertiary packaging and consist of

cardboard, plastics, and wood (Table 2-19). We assumed a plastic and cardboard recycling rate of 29%,

which is in accordance with the national average (EPA, 2009). Waste from broken pallets was assumed

to be 10%, or a 90% reuse rate, which is in accordance with reuse rates seen in other peanut industry

sectors. Reuse and recycling flows are modeled in SimaPro® as “avoided products” which allows the

model to reduce the total impact from these items.

Figure 2-9. Retail input/output flow diagram

Table 2-18. Retail model input/output parameters


Input

16oz Peanut Butter g 0.49 Peanut Butter, 16oz at Processor/US U

Distribution1 g 500 Distribution, from processor/US U

Natural Gas2 g 0.45 Natural Gas, manufacture and combustion/US U

Electricity3 Wh 37.6 Electricity, low voltage, at grid, US NREL/US

Output

Peanut Butter at Retail kg 0.494 Peanut Butter, 16oz at retail/US U 1 Value is equal to the transport allocation that was used in the Cheese LCA

2 Value is density converted from Cheese LCA

3 Value is equal to the retail electricity demand from the Cheese LCA minus 45% refrigeration energy (Thoma,

et al., 2012)

30

2.2.4.7 Consumer Use & Disposal

Impacts accounted for in this phase include transport from retail to home and cleaning energy

embodied in running a dishwasher. For the consumer transportation fuel impacts, we followed the

example of the Hogass Industrial Milk LCA (Hogass, 2002) and allocated fuel for consumer travel based

on an estimate of the weight percentage that peanut products represent in typical grocery purchases.

In allocating dishwasher space, we followed the example of the cheese LCA and allocated based and the

percent of a typical dishwasher cycle it would take to clean all of the dishes associated with a 16oz jar of

peanut butter (Thoma, et al., 2012). This method assumes a standard dishwasher capacity of eight place

settings and six serving pieces based on the Energy Star standard dishwasher criteria (Energy Star, 2010).

Using the Cheese LCA, we assumed a place setting to consist of two plates, one bowl, six utensils, and

three glasses. Therefore, the total capacity would be 48 utensils (6 utensils x 8 place settings) and 54

non-utensils (2 plates + 1 bowl + 3 glasses x 8 place settings + 6 additional serving pieces). Further, it is

assumed that 10% of the water and energy is allocated to the utensil rack and 90% for the non-utensil

rack (Thoma, et al., 2012). Using these numbers, we assumed a 10% dishwasher allocation to clean

dishes resulting from the consumption of a 1lb jar of peanut butter (16 servings) which includes the use

of 16 utensils and 4 plates. Table 2-20 and Table 2-21 show the assumptions and final allocation

amounts that were developed for dishwasher use and consumer travel.

Table 2-19. Retail waste flows


Waste Cardboard g 11.7 Disposal, packaging cardboard, to inert material landfill/CH WITH

US ELECTRICITY U Plastics g 1.30 Disposal, plastics, mixture, to sanitary landfill/CH WITH US

ELECTRICITY U Wood g 1.37 Disposal, wood untreated, to sanitary landfill/CH WITH US

ELECTRICITY U

Avoided Products Cardboard g 2.17 Corrugated board, recycling fibre, double wall, at plant/CH WITH

US ELECTRICITY U Packaging Film g 0.24 Packaging film, LDPE, at plant/RER WITH US ELECTRICITY U

31

Using a national average rate of 9lb of peanut butter consumed per household per year

(USDA/Economic Research Service, 2011), the average price of $2.25 for one pound of peanut butter

(Mid-Atlantic Information Office, 2012), and an average annual family grocery budget of $6129 (BLS,

2011), we determined that the allocation of peanut butter for each grocery trip was 0.33%. The average

number of trips a family takes to the grocery per year is roughly 110 (Gordon, 2007), and the average

roundtrip distance is 10 miles (Economic Research Center, 2009), equating to 1100 grocery miles per

year. Using an economic allocation, if 0.33% of the average family budget is for 9 lb of peanut butter,

then we can assume that 0.33% of the miles used for groceries in a year were driven to purchase those 9

lb. Therefore, an allocation of 0.40 miles per year per 1 pound jar of peanut butter was used to model

the consumer peanut butter transportation load.

Table 2-20. National average dishwasher use data (Thoma, et al., 2012)

National Average Units Amount

Tap Water Use kg 22

Electricity kWh 1.5

Soap kg 0.025

Allocation of Single Cycle for 16oz Peanut Butter1 % 10%

1 Assumes a combined dishwasher capacity of 54 plates, cups or bowls, and 48

utensils. Assumes 16oz Peanut Butter contains 16 1oz servings requiring 16 utensils and 4 plates for an average capacity of 10% for a single cycle.

Table 2-21. National average peanut butter and consumer behavior data (Hogass, 2002)

National Average Units Amount

Trips to Grocery per Year1 # 110

Roundtrip Distance to Grocery2 miles 10

Annual Grocery Budget3 $ 6129

Annual Peanut Butter Consumption per Household4 lb 9

Cost of Peanut Butter per lb5 $/lb 2.25

Allocation of Each Grocery Trip to Peanut Butter % 0.33%

Annual Peanut Butter Miles per lb of Peanut Butter Miles 0.40 1 (Gordon, 2007)

2 (Economic Research Center, 2009)

3 (BLS, 2011)

4 (USDA/Economic Research Service, 2011)

5 (Mid-Atlantic Information Office, 2012)

32

There is a relatively small quantity of post-consumer waste generated, and it is modeled using

EcoInvent processes for landfill disposal. Recycled goods are attributed back to the model as an avoided

product, meaning that the recycling of certain goods lowers its overall impact. The package disposal

picture for a 16 oz peanut butter product is shown in Table 2-22.

Table 2-22. Summary of consumer disposal assumptions

Plastics % Amount (g)

Recycled1 32% 12.16

Incinerated1 14% 5.32

Landfilled1 54% 20.52

Aluminum

Recycled1 32% 0.35

Landfilled1 68% 0.75

1 (EPA, 2009)

33

3 Scan Level Results and Analysis

3.1 ReCiPe Life Cycle Impact Assessment

This model and life cycle inventory has been analyzed using an impact methodology called

ReCiPe Midpoint. There are 18 characterization categories within ReCiPe Midpoint which reflect issues

of direct environmental relevance. The midpoint impacts associated with the entire peanut butter life

cycle estimated by the ReCiPe Midpoint Model represent potential processes that could cause some

endpoint impact. In LCA the impact categories that directly derive from human activities and potentially

cause damage are midpoint impacts; the damage that ultimately results from these impact categories

are called endpoint categories. For the sake of consistency with the model analysis and comparison of

the results with other GHG studies, the results are presented for one kg of peanut butter (2.2 lb of

peanut butter), normalized for comparison to world values.

In the cradle to grave ReCiPe LCIA, the highest impact categories from peanut butter were human

human toxicity, freshwater ecotoxicity, and marine ecotoxicity characterization categories (

Figure 3-1). The life cycle phase with the greatest impact, Consumer Use and Disposal, is not in

the control domain of the peanut industry sector. The Consumer Use phase was influenced heavily by

the electricity demand used to clean dishes as part of peanut butter consumption. The peanut industry

has little to no control over how a product is used after it leaves the supply chain, and therefore no

power to reduce its environmental impact. The focus of the remaining ReCiPe impact analysis was the

supply chain up to and including retail (cradle to retail gate) with the remainder of the ReCiPe results

excluding consumer use and disposal. Figure 3-2 shows the ReCiPe impact assessment overview for

cradle to retail gate.

34

Figure 3-1. ReCiPe midpoint impact analysis, cradle to grave

0.

0.0005

0.001

0.0015

0.002

0.0025

0.003N

orm

aliz

ed

Imp

act

(Wo

rld

Fac

tors

)

Impact Characterization Categories

Consumer

Retail

Processor

Roaster

Blancher

Sheller

Buying Point

Farm

Analyzing 1 kg 'Consumer use phase/US U'; Method: ReCiPe Midpoint (H) V1.06 / World ReCiPe H /

35

Figure 3-2. Recipe midpoint impact analysis, cradle to retail gate

3.1.1 Highest Weighted Characterization Categories

In this section we will take a closer look at the top three characterization categories as analyzed

by ReCiPe for U.S. peanut butter production excluding consumer use and disposal. The top three as

previously discussed are human toxicity, marine ecotoxicity and freshwater ecotoxicity. All three share a

common unit of kg 1,4 Dichlorobenzene equivalent. 1,4 Dichlorobenzene is the reference chemical for

these characterization categories because it is anticipated to be a carcinogen in humans and animals and

because of its lipophilic properties, meaning it will accumulate in the fatty tissues of animals. In section

3.1.3 we will take a closer look at what peanut butter industry sector processes contribute to the

impacts shown below.

0.

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018N

orm

aliz

ed

Imp

act

(Wo

rld

Fac

tors

)

Impact Characterization Categories

Retail

Processor

Roaster

Blancher

Sheller

Buying Point

Farm

Analyzing 1 kg 'Peanut Butter, 16oz at retail/US U'; Method: ReCiPe Midpoint (H) V1.06 / World ReCiPe H / Normalization / Excluding infrastructure processes

36

3.1.1.1 Human Toxicity

The factor of human toxicity encompasses human health damages by a chemical using dose and

inherent toxicity of a chemical (Hertwich, Mateles, Pease, & McKone, 2009). This factor can be

accounted for and applied to the human population, N, using Disability Adjusted Life Years (DALYS). The

main processes affecting Human Toxicity in the peanut butter production chain are natural gas

extraction, crude oil production, disposal of spoil from coal mininig, and bituminous coal electricity

production (Figure 3-3). Natural gas use is most predominant at the roaster. Peanut butter processing

contributes the most towards the production of crude oil for the transportation of materials involved in

making peanut butter. Processes on the farm are most responsible for the disposal of coal byproducts.

Bituminous coal for electricity production is needed for processes with high electricity demand, mainly,

the farm, blancher, roaster and processor.

Figure 3-3. ReCiPe midpoint impact analysis for human toxicity characterization category, cradle to retail gate.

3.1.1.2 Marine Ecotoxicity

The fate of essential metals such as Cobalt, Copper, Manganese, Molybdenum, and Zinc in the

ocean are the main driving factors behind marine ecotoxicity. Contributions to marine ecotoxicity are

primarily from disposal of municipal waste, disposal spoil from coal mining, and natural gas production

(Figure 3-4). The disposal of municipal waste impact is from the blancher, roaster, and processor

0.

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

kg 1

,4-D

B e

q

Natural gas, at extraction site

Natural gas, unprocessed, at extraction

Crude oil, at production

Disposal, spoil from coal mining, in surface landfill

Electricity, bituminous coal, at power plant

Pesticide unspecified, at regional storehouse

Disposal, municipal solid waste, 22.9% water, to sanitarylandfillDisposal, spoil from lignite mining, in surface landfill

Mercury, liquid, at plant

Phosphorous chloride, at plant

Discharge, produced water, onshore

Disposal, municipal solid waste, 22.9% water, to municipalincinerationRemaining processes

37

sectors. Disposal of spoil from coal mining is resultant from processes at the farm. Natural gas

production is primarily for peanut roasting.

Figure 3-4. ReCiPe midpoint impact analysis for marine ecotoxicity characterization category, cradle to retail gate.

3.1.1.3 Freshwater Ecotoxicity

The characterization factor for freshwater ecotoxicity is comprised of three items:

environmental fate, accumulation in food chain, and the toxic effect of the chemical. A characterization

factor can be created for each chemical by the emission of one unit of chemical and its effect on human

toxicity and freshwater ecotoxicity (Berthoud, Maupu, Huet, & Poupart, 2011). The freshwater

ecotoxicity impact is dominated by unprocessed natural gas and pesticide use (Figure 3-5). The majority

of pesticides are used at the farm level, however, there are very small contributions made by buying

points, shelling, blanching, roasting, and processing. Natural gas demand is driven by the roasting

process.

0.

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

kg 1

,4-D

B e

q

Disposal, municipal solid waste, 22.9% water, to sanitarylandfillDisposal, spoil from coal mining, in surface landfill





Disposal, spoil from lignite mining, in surface landfill

Disposal, municipal solid waste, 22.9% water, to municipalincinerationOperation, lorry 7.5-16t

Discharge, produced water, offshore

Disposal, hard coal ash, 0% water, to residual materiallandfillDisposal, tailings from hard coal milling, in impoundment


Disposal, nickel smelter slag, 0% water, to residual materiallandfillPhosgene, liquid, at plant

Remaining processes

38

Figure 3-5. ReCiPe midpoint impact analysis for freshwater ecotoxicity characterization category, cradle to retail gate.

3.1.2 Lesser Characterization Categories

In this section we will take a broad look at some of the remaining characterization categories

that are significant to peanut butter production. These analyses will also exclude the consumer use and

disposal phase. The remaining characterization categories that are significant to peanut butter

production are fossil depletion, freshwater eutrophication, terrestrial acidification, climate change,

particulate matter formation, and photochemical oxidant formation. Detailed below are the process

flows in the peanut butter production model that contribute the most to these impacts. Climate change

is analyzed separately in section 3.2.

3.1.2.1 Fossil Depletion

This assessment is based on an increase in price of fossil fuels in a balanced market. As fossil

fuel use and production continues, humans will need to adapt to less conventional resources that will be

harder and more costly to obtain (Goedkoop, Heijungs, Schryver, Struijs, & Van Zelm, 2008). Fossil

Depletion is based on an equivalent kg of oil. The top three process flows contributing to Fossil

Depletion are bituminous coal production for processing peanut butter, natural gas production for

roasting peanuts, and crude oil production for farm use (Appendix B, Table B-1).

0.

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018

0.002kg

1,4

-DB

eq



Disposal, municipal solid waste, 22.9% water, tosanitary landfill

Disposal, spoil from coal mining, in surface landfill




Disposal, spoil from lignite mining, in surface landfill

Disposal, municipal solid waste, 22.9% water, tomunicipal incineration

Disposal, hard coal ash, 0% water, to residualmaterial landfill

Remaining processes

39

3.1.2.2 Freshwater Eutrophication

The major limiting nutrient in freshwater is phosphorus. Subsequently, when freshwater is

saturated with phosphorus, eutrophication occurs. Phosphorus emissions can be evaluated with a

damage factor for the eutrophic swing in the freshwater (Goedkoop, Heijungs, Schryver, Struijs, & Van

Zelm, 2008). Freshwater eutrophication is measured based on a kg of phosphorus equivalence. Coal

waste disposal, pesticide use, and mining waste disposal stem primarily from on farm processes.

(Appendix B, Table B-2).

3.1.2.3 Terrestrial Acidification

As sulfates, nitrates, phosphates and other inorganic substances undergo atmospheric

deposition, an acidic shift can occur in the soil, harming plants that thrive at a certain pH value in the

soil. An acidic deviation in this value could not only promote the decline of a target plant species, but

also cause a shift in species occurrence (Goedkoop, Heijungs, Schryver, Struijs, & Van Zelm, 2008). This

characterization category is measured by kg sulfur-dioxide equivalent. Sulfur-dioxide, when oxidized in

the atmosphere, produces H2SO4 which in high concentrations can lead to acid rain. Major contributors

to Terrestrial Acidification were the production of bituminous coal for electricity and natural gas

production (Appendix B, Table B-3).

3.1.2.4 Particulate Matter Formation

Particulate Matter Formation refers to a solid or liquid mixture of organic and non-organic

substances with a diameter of less than 10 μm (PM10). The impacts of Particulate Matter Formation

include respiratory issues from inhalation, aerosols as well as other assorted health problems

(Goedkoop, Heijungs, Schryver, Struijs, & Van Zelm, 2008). Particulate Matter Formation is measured

by kg PM10 equivalence. The main contributor is the production of electricity by bituminous coal for all

peanut industry process which are electricity intensive (Appendix B, Table B-4).

3.1.2.5 Photochemical Oxidant Formation

Photochemical reactions cause the formation of atmospheric ozone which is hazardous to

humans, and leads to a greater occurrence and severity of many respiratory diseases such as asthma

and Chronic Obstructive Pulmonary Diseases. DALY’s are used to determine the severity of

Photochemical Oxidant Formation (Goedkoop, Heijungs, Schryver, Struijs, & Van Zelm, 2008).

Photochemical oxidant formation is based on equivalence to kg non-methane volatile organic

compounds (NMVOC). NMVOC’s are organic chemicals with high vapor pressure at room temperature,

40

which means that a solid or liquid may release these as gases into the air. The production of electricity

by bituminous coal is the primary contributor to this impact.

3.2 Greenhouse Gas and Climate Change

Climate change potential is the measure of a specific greenhouse gas and its contribution to

positive radiative forcing. Radiative forcing is measured by the change in net irradiance, which is the

electromagnetic radiation per unit area incident on a surface. Certain gases, in increasing

concentrations will retain more thermal radiation from the sun, thus increasing the temperature of the

atmosphere. The main greenhouse gases emitted by humans are: Carbon Dioxide, Methane, Nitrous

Oxide, and Fluorinated Gases (EPA, 2012). Climate change affects human and ecosystem health, as well

as many other environmental mechanisms (Goedkoop, Heijungs, Schryver, Struijs, & Van Zelm, 2008).

Climate change is measured in kg of Carbon Dioxide equivalent (kg CO2e). This climate change impact

assessment was accomplished using the IPCC 2007 GWP method.

Table 3-1 and Figure 3-6 contain the complete cradle to grave LCIA as analyzed by the IPCC 2007

GWP impact method, which includes consumer use and disposal. Consumer use was the greatest

contributor to GHG due to a high electricity demand by the dishwasher. The farm was the next highest

contributor to GHG, with the main contributors being fuel for machinery, fertilizers and pesticides, and

electricity for pumping water. When comparing the GHG emissions for the entire life cycle of peanut

butter to that of other common food products, peanut butter GHG emissions are low (Figure 3-7). For

reasons explained in the ReCiPe impact assessment, the remainder of the IPCC 2007 GWP impact

analysis will consist only of cradle to retail gate phases.

41

Table 3-1. IPCC 2007 GWP cradle to grave impact analysis, kg CO2e per kg of peanut butter produced

kg CO2e/kg

peanut butter

Farm 0.511

Buying Point 0.064

Sheller 0.046

Blancher 0.265

Roaster1 0.369

Processor 0.406

Retail 0.204

Consumer Use & Disposal 0.762

Total 2.64 1 Dry roast process

Figure 3-6. IPCC 2007 GWP cradle to grave impact analysis, kg CO2e per kg peanut butter produced

42

The greatest impact on climate change from processes within the peanut butter industry,

excluding consumer use and disposal, stems from the production of electricity by bituminous coal

(Figure 3-8). This electricity is used to power all of the processes from the buying point to retail with its

most significant use at the peanut butter processor. The next highest contributor is from diesel use on

the farm. This can be seen by combining the preharvest and harvest machinery fuel use processes

(Figure 3-8).

Figure 3-7. Comparison of GHG emissions from various food types (EWG, 2011)

43

3.2.1 Cultivation Practice Comparison for Greenhouse Gases

In our final impact analysis, we examined the different peanut cultivation practices as described

by the Georgia crop budgets used to populate our model. The amount of CO2e for each cultivation

practice was again determined using the IPCC 2007 GWP method (Frischknecht & Jungbluth, 2007) in

SimaPro®. Table 3-2 shows the amount of CO2e produced for each production practice.

Table 3-2. Greenhouse Gas Protocol results by impact category and cultivation method

Conventional,

Irrigated Conventional, Non-irrigated

Strip, Irrigated

Strip, Non-irrigated

Average Practice

kg CO2 eq 0.320 0.301 0.281 0.279 0.305

The conventional till and irrigated scenarios resulted in higher emissions than the strip till and

non-irrigated scenarios, respectively (Figure 3-9). Processes that accounted for less than 1% of the

carbon dioxide produced were excluded. In all four cultivation scenarios, the input categories with the

largest contributions to carbon dioxide emissions were harvest and preharvest machinery fuel, as well as

insecticide applications.

Figure 3-8. IPCC 2007 GWP impact analysis process flow contributors, cradle to retail gate

44

3.3 Uncertainty Analysis

Uncertainty analysis was performed in order to determine the certainty of the results within a

prescribed confidence interval. Each source of data included in this model carried with it some form of

uncertainty, either from inherent process variability or from a lack of knowledge of the range and

tendencies of actual process data. There are several major factors in determining uncertainty, such as:

degree of expert data verification, representativeness of the data for the entire field of study, temporal

and geographical correlation, and sample size.

To determine uncertainty for the peanut butter model a database of all the data inputs were

characterized for process and knowledge uncertainty. For example, electricity consumption at the

sheller was based on industry survey responses and normalized on a per-ton of shelled peanuts basis.

For electricity consumption the data were based on measurements representative of the some of the

sites under study within three years difference from the reference year with a sample size of less than

10. Using these input characteristics SimaPro®’s uncertainty model was used to describe the data and

its distribution and determine an uncertainty value for electricity consumption at the sheller. Each input

Figure 3-9. IPCC 2007 GWP impact analysis for conventional and strip till cultivation practices in units of CO2e

45

to the model was parameterized using this process. Inputs with limited data (less than five

observations) were assigned a Lognormal distribution and a coefficient of variation of 1.22.

SimaPro® uses the Monte Carlo method, a computational method, to calculate uncertainty.

Monte Carlo uses random sampling of the input data characterized as probability distribution functions

(pdf) and returns results as a pdf (Figure 3-10). A 95% confidence interval was used to determine the

upper and lower boundaries of the kg CO2e produced per kg peanut butter. Uncertainty analysis was

performed on the entire peanut life cycle from cradle to grave. The results indicated that there is 95%

confidence that the GHG emissions for a kg of peanut butter was between 1.24 and 6.71 kg CO2e with a

mean of 2.91 and standard deviation of 1.61. This is comparable to the initial SimaPro® model results of

2.64 kg CO2e and within range of other estimates for emissions from consumer packaged goods (Figure

3-11).

Figure 3-10. Probability distribution function for GHG emissions for peanut butter (kg Peanut Butter/kg CO2e). 95% confidence interval displayed on chart as red vertical lines.

46

3.4 Summary of SimaPro® modeling

A complete model of the peanut butter supply chain accounting for extractions from nature

through final disposal of packaging materials was constructed and life cycle inventory data from

publically available sources was used to support an analysis of multiple environmental impacts. The

primary impact categories were found using the ReCiPe impact analysis model. ReCiPe identified

toxicity impacts as significant and driven by emissions from four major processes: natural gas

production, disposal of municipal waste, disposal of coal byproducts, and pesticide use. Natural gas

production and use was associated primarily with the buying point where drying occurs and the roaster.

Disposal of municipal waste is associated with blanching, roasting, and processing. Disposal of coal

byproducts is associated with processes on the farm. Pesticides are usually associated with the farm,

however, significant pesticide use was reported at the sheller, blancher, and roaster.

An initial evaluation of our SimaPro® model concluded that 1kg of peanut butter produces 2.64

kg CO2e. Further, Monte Carlo uncertainty analysis revealed a mean of 2.91 kg CO2e with a 95%

confidence interval of 1.24 to 6.71 kg CO2e. The greatest impact on climate change from within the

peanut butter life cycle occurs at the consumer, followed by the farm and processor. The highest

processes impact stems from the production of electricity by bituminous coal. This electricity is used to

power all of the processes from the buying point to retail with its most significant use at the peanut

Figure 3-11. Monte Carlo uncertainty analysis results, 95% confidence interval, as compared with the initial SimaPro® model output and industry estimates for various packaged goods (EWG, 2011).

47

butter processor. Conventional till, irrigated peanuts incur the greatest amount of CO2e, which is

resultant from the electricity used to pump water.

4 Conclusions

The purpose of this analysis was to perform a scan-level life cycle assessment and impact

analysis for peanut butter production in the US. Objectives for the LCA were: 1) a literature review of

the peanut industry, 2) a life cycle inventory (LCI) review, and 3) an assessment of a variety of impacts

including carbon. This report provides a description of the life cycle inventory, assessment, and impact

results. Objective 1, the Literature Review of the peanut industry, was prepared in a separate

document. The results of Objectives 2 and 3 were presented in this document.

The project scope included environmental effects at a national scale in order to put into context

the environmental impacts that can be attributed to the entire life cycle of a 1 lb jar of peanut butter in

the United States. In the report, we focused on the factors in the peanut butter life cycle that were the

highest contributors to environmental impacts, specifically human toxicity, marine and freshwater

ecotoxicity, as well as climate change, which was an important impact for the peanut industry

shareholders. Most of the ReCiPe midpoint and IPCC 2007 GWP analysis did not include the consumer

phase because of a lack of control over this phase by the peanut industry.

4.1 LCIA Using ReCiPe Midpoint

ReCiPe midpoint impact analysis showed that human toxicity, marine ecotoxicity and freshwater

ecotoxicity were the three highest weighted characterization categories. All three share a common unit

of kg 1,4 Dichlorobenzene equivalent. The main processes affecting Human Toxicity in the peanut

butter production chain were natural gas extraction, crude oil production, disposal of coal byproducts,

and bituminous coal electricity production. Natural gas use was most predominant at the roaster.

Peanut butter processing contributed the most towards the production of crude oil for the

transportation of materials involved in making peanut butter. Processes on the farm were most

responsible for the disposal of coal byproducts. Bituminous coal for electricity production was needed

for processes with high electricity demand, mainly, the farm, blancher, roaster and processor. 31% of

the total Human Toxicity impact occurred at the farm and another 26% occurred at the roaster.

Contributions to marine ecotoxicity were primarily from disposal of municipal waste, disposal of

coal byproducts, and natural gas production. The disposal of municipal waste impact was from the

blancher, roaster, and processor sectors. Disposal of coal byproducts was resultant from processes at

48

the farm. The freshwater ecotoxicity impact was dominated by unprocessed natural gas and pesticide

use. The majority of pesticides were used at the farm level. Natural gas demand was driven by the

roasting process. The farm accounted for 35% of freshwater ecotoxicity and 35% of the marine

ecotoxicity impacts.

4.2 LCIA Using IPCC 2007 GWP

4.2.1 Cradle to Grave

This climate change impact assessment was accomplished using the IPCC 2007 GWP method

which uses units of kg CO2e. Consumer use was the greatest contributor to GHG due to a high electricity

demand by the dishwasher. However, consumer use was not included in the majority of the greenhouse

gas analysis. IPCC 2007 GWP assessed that the greatest impact on climate change from processes within

the peanut butter industry excluding consumer use stems from the production of electricity by

bituminous coal. This electricity is used to power all of the processes from the buying point to retail

with its most significant use at the peanut butter processor. Natural gas production for the roasting of

peanuts and on-farm diesel use were also significant contributors to the total CO2e produced.

Discounting consumer use, farm gate impact was roughly 27% of the total greenhouse gas emissions

from the supply chain with the next highest contributor being the peanut butter processor at 22%. The

complete cradle to grave life cycle impact assessment for peanut butter shows a GHG impact of 2.64 kg

CO2e. When comparing the GHG emissions for the entire life cycle of peanut butter to that of other

common food products, peanut butter GHG emissions were low.

We used the Monte Carlo method to determine the uncertainty associated with our model. A

95% confidence interval was used to determine the upper and lower boundaries of the kg CO2e

produced per kg peanut butter. Uncertainty analysis was performed on the entire peanut life cycle from

cradle to grave. The results indicated that there is 95% confidence that the GHG emissions for a kg of

peanut butter was between 1.24 and 6.71 kg CO2e with a mean of 2.91 and standard deviation of 1.61.

This is comparable to the initial SimaPro® model results of 2.64 kg CO2e and within range of other

estimates for emissions from consumer packaged goods.

49

4.2.2 Peanut Production Scenarios

In our final impact analysis, we examined the different peanut cultivation practices as described

by the Georgia crop budgets used to populate our model. The amount of CO2e for each cultivation

practice was again determined using the IPCC 2007 GWP method (Frischknecht & Jungbluth, 2007) in

SimaPro®. The conventional till and irrigated scenarios resulted in higher emissions than the strip till

and non-irrigated scenarios, respectively (Figure 3-9). Conventional tillage required more preharvest

diesel than strip till which stems from the greater amount of field preparation required in conventional

till. Irrigation required the use of electricity to drive the water pumps where as non-irrigated did not.

Non-irrigated practices used less herbicides and pesticides than irrigated peanuts, according to Georgia

crop budgets, however, it was not enough to offset CO2e produced through electricity production. In all

four cultivation scenarios, the input categories with the largest contributions to carbon dioxide

emissions were harvest and preharvest machinery fuel, as well as insecticide applications.

50

References

Berthoud, A., Maupu, P., Huet, C., & Poupart, A. (2011). Assessing freshwater ecotoxicity of agricultural

products. Springer-Verlag.

Blankenship, P. D., & Chew, V. (1979). Peanut Drying Energy Consumption. Peanut Science, 6, 10-13.

BLS. (2011). Average Energy Prices in Atlanta - September 2011. U.S. Bureau of Labor Statistics Southeast

Information Office. News Release. BLS.

BLS. (2011). Consumer Expenditures--2010. Bureau of Labor and Statistics.

Butts, C. (2010). 2010 Peanut Drying Research Summary Report. USDA, ARS, National Peanut Research

Laboratory.

Cunningham, L. (2011, November). R.L. Cunningham and Sons. (J. McCarty, Interviewer)

Deru, M., & Torcellini, P. (2007). "Source energy and emissions factors for energy use in buildings",

Technical Report NREL/TP-550-38617. National Renewable Energy Laboratory, June 2001.

Economic Research Center. (2009). Access to Affordable and Nutritious Food. U.S. Department of

Agriculture.

Energy Star. (2010). Dishwashers Key Product Criteria. Retrieved May 22, 2012, from Energy Star:

http://www.energystar.gov/index.cfm?c=dishwash.pr_crit_dishwashers

EPA. (1995). AP-42 Compilation of Air Pollutant Emission Factors. Office of Air Quality Planning and

Standards. EPA.

EPA. (1998). Compilation of Air Pollution Emission Factors, Publication AP-42, Section 2.4 Municipal Solid

Waste Landfills.

EPA. (2008). Direct Emissions from Mobile Combustion Sources. Office of Air and Radiation. Washington:

EPA.

EPA. (2009). Municipal Solid Waste Generation, Recycling,. United States Environmental Protection

Agency.

EPA. (2012, February 27). Climate Change - Greenhouse Gas Emissions. Retrieved May 15, 2012, from

EPA: http://www.epa.gov/climatechange/emissions/

EWG. (2011). Environmental Working Group Meat Eaters Guide: Methodology 2011. Environmental

Working Group.

FAO. (2010). Greenhouse Gas Emissions from the Dairy Sector - A Life Cycle Assessment. Food and

Agriculture Organization of the United Nations.

51

Frischknecht, R., & Jungbluth, N. (2007). Implementation of Life Cycle Impact Assessment Methods.

Duebendorf, Switzerland: EcoInvent Center.

Goedkoop, M., Heijungs, R., Schryver, A. D., Struijs, J., & Van Zelm, R. (2008). A life cycle impact

assessment method. ReCiPe.

Gordon, J. (2007). Grocery Store Trends and Strategies For Arlington, Virginia. Arlington Economic

Development.

Heijungs, R., Goedkoop, M., Strujis, J., Effting, S., Sevenster, M., & Huppes, G. (2010). Towards a life

cycle impact assessment method which comprises category indicators at the midpoint and the

endpoint level Report of the first project phase: Design of the new method.

Hertwich, E. G., Mateles, S. F., Pease, W. S., & McKone, T. E. (2009). Human toxicity potentials for life-

cycle assessment and toxics release inventory risk screening. SETAC.

Hogass, E. (2002). Life Cycle Assessment of Industrial Milk Production. International Journal of Life Cycle

Assessment, 7(2), 115-126.

IPCC. (2006). Intergovernmental Panel on Climate Change. Retrieved from IPCC Guidelines for National

Greenhouse Gas Inventories: http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol5.html

Lamb, D. M. (2012, May 23). Supervisory Research Food Technologist. (J. McCarty, Interviewer)

Mid-Atlantic Information Office. (2012). Average retail food and energy prices, U.S. city average and

Midwest region. Bureau of Labor Statistics.

NREL. (2012, January 5). National Renewable Energy Laboratory. Retrieved March 2012, from U.S. Life

Cycle Inventory Database: http://www.nrel.gov/lci/

Peer, R., Thorneloe, S., & Epperson, D. (1993). A Comparison of Methods for Estimating Global Methane

Emissions from Landfills. Chemosphere, 26(1-4), 387-400.

PE-International. (2012). Life Cycle Assessment LCA Methodology. Retrieved May 15, 2012, from PE-

International: http://www.pe-international.com/topics/life-cycle-assessment-lca-methodology/

Pooley, P. (2005). Report on the Feasability of a Peanut Processing Facility in Santa Rosa County, Florida.

Santa Rosa: TEAM Santa Rosa Economic Development Council.

Roy, P., Nei, D., Orikasa, R., Xu, Q., Okadome, H., Nakamura, N., et al. (2009). A review of life cycle

assessment (LCA) on some food products. Journal of Food Engineering(90), 1-10.

SWANA. (1998). Comparision of Models for Predicting Landfill Methane Recovery, Publication No. GR-LG

0075.

The Consumer Goods Forum. (2010). Economic Concerns and Consumer Demand Remain Top Priorities

for Consumer Goods Industry. Press Release, Paris.

52

The Peanut Institute. (2010). The Peanut Institute. Retrieved April 2012, from Peanut Butter:

http://www.peanut-institute.org/peanut-products/peanut-butter.asp

Thoma, G., Matlock, M., Shonnard, D., Cummins, E., Neiderman, Z., Cothren, J., et al. (2010). National

Scan-Level Beyond Carbon Life Cycle Study for Production of US Dairy. Fayetteville: University of

Arkansas.

Thoma, G., Nutter, D., Ulrich, R., Kim, D. S., Norris, G., Milani, F., et al. (2012). Comprehensive Life Cycle

Assessment for Cheese and Whey Products. Fayetteville: University of Arkansas.

UGA. (2011). 2011 Peanut Update. Retrieved from The University of Georgia Cooperative Extension:

http://www.caes.uga.edu/commodities/fieldcrops/peanuts/2011peanutupdate/index2011.html

.

USDA. (2011, December 19). USDA-ERS. Retrieved April 2012, from Major Land Uses: Glossary:

http://www.ers.usda.gov/data/majorlanduses/glossary.htm

USDA/Economic Research Service. (2011). Peanuts: Per Capita availability, by type of product. USDA/

Economic Research Service.

USDA-ERS. (2011). Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food

Availability Data. USDA.

53

A. Additional Model Parameters for Industry Sectors

Table A-1. Farm chemical inputs

Product Name Active Ingredient Amount Units US EcoInvent Unit Process

Fertilizers

Boron - - - Borax, anhydrous, powder, at plant

Lime/Gypsum - - - Lime, from carbonation

Fungicides

Abound azoxystrobin 2.08 lb/gal Pesticide unspecified, at regional storehouse

Artisan flutolanil 3 lb/gal Pesticide unspecified, at regional storehouse

n/a propiconazole 0.6 lb/gal Pesticide unspecified, at regional storehouse

Bravo chlorothalonil 4.17 lb/gal chlorothalonil, at regional storage

Chlorothalonil chlorothalonil 6 lb/gal chlorothalonil, at regional storage

Tebuconazole tebuconazole 3.6 lb/gal Pesticide unspecified, at regional storehouse

Tilt/Bravo propiconazole 0.3 lb/gal Pesticide unspecified, at regional storehouse

n/a chlorothalonil 4 lb/gal chlorothalonil, at regional storage

Herbicides

2,4-D Amine Dimethylamine salt of 2,4-D-Dichlorophenoxyacetic acid

3.8 lb/gal 2, 4-D at regional storehouse

2,4-DB dimethylamine salt of butyric acid 1.75 lb/gal phenoxy-compounds, at regional storehouse

Cadre ammonium salt of imazapic 2 lb/gal Pyridine-compounds, at regional storehouse

Dual Magnum S-metolachlor 7.62 lb/gal Metolachlor, at regional storehouse

Glyphosate glyphosate 4 lb/gal Glyphosate, at regional storehouse

Gramaxone Inteon paraquat dichloride 2.762 lb/gal Bipyridylium-compounds, at regional storehouse

Prowl pendimethalin 3.8 lb/gal Pendimethalin, at regional storage

Select clethodim 0.97 lb/gal Pesticide unspecified, at regional storehouse

Sonolan ethalfluralin 3 lb/gal Dinitroaniline-compunds, at regional storehouse

54

Table A-2. Farm chemical inputs, continued

Product Name Active Ingredient Amount Units US EcoInvent Unit Process

Herbicides

Storm sodium salt of bentazon 2.67 lb/gal Benzo[thia]diazole-compounds, at regional storehouse

Storm (2nd a.i.) sodium salt of acifluorfen 1.33 lb/gal Diphenylether-compounds, at regional storehouse

Valor flumioxazin 51 % by mass Pesticide unspecified, at regional storehouse

Insecticides

Chlorpyrifos chloropyrifos - - organophosphorus-compounds, at regional warehouse

Karate Z lambda-cyhalothrin 2.08 lb/gal pyretroid-compounds, at regional storehouse

Steward indoxacarb 1.25 lb/gal diazine-compounds, at regional storehouse

Temik aldicarb 15 % by mass [thio]carbamate-compounds, at regional storehouse

Tracer spinosad 4 lb/gal Pesticide unspecified, at regional storehouse

55

Table A-3. Blancher and roaster cleaner use per ton of peanuts produced

Active Ingredient Total Active (kg) kg/ton

Cleaners

Hypochlorite 9.19E+03 3.66E-02

sodium hydroxide 1.15E+05 2.14E-01

Potassium Hydroxide 6.39E+02 1.56E-02

Tripropylene glycol monomethyl ether 6.39E+02 1.56E-02

Sodium metasilicate 1.79E+02 4.37E-03

Disodium Trioxosilicate 3.38E+02 8.24E-03

Quaternary Ammonia 1.26E+03 7.81E-03

Sodium metasilicate pentahydrate 1.03E+03 2.51E-02

Sodium nitrilo-triacetate (40%) 5.15E+02 1.26E-02

Tomadol Surfactants 3.09E+03 2.51E-02

Phosphoric Acid 4.98E+03 3.08E-02

Butoxydiglycol 4.63E+02 8.62E-03

Ethoxydiglycol 4.63E+02 8.62E-03

Sodium Xylene Sulfonate 9.96E+01 3.71E-03

Benzenesulfonic acid 9.96E+01 3.71E-03

Cocamine Oxide 9.96E+01 3.71E-03

Sodium Percarbonate 2.14E+02 3.56E-03

Sodium Metasilicate Anhydrous 8.57E+00 1.42E-04

2-Butoxyethanol 4.10E+02 3.41E-03

Sodium tripolyphosphate 6.27E+01 1.04E-03

Nonyl phenol ethoxylate surfactant 6.59E+02 1.09E-02

Nitric Acid 1.34E+02 2.23E-03

Hydrochloric Acid 1.86E+01 3.09E-04

Isopropanol 1.20E+03 1.99E-02

Ethanol 2.23E+02 3.70E-03

Total Cleaners kg/ton 4.70E-01

56

Table A-4. Blancher and roaster pesticide use per ton of peanuts produced

Active Ingredient Total Active (kg) kg/ton

Pesticides

Pyrethrins 1.85E-01 2.73E-06

cyfluthrin 3.48E-01 5.13E-06

Piperonyl Butoxide 9.07E-01 3.38E-05

Pyriproxyfen 3.63E-02 1.35E-06

Magnesium Phosphide 2.22E-01 8.26E-06

Imidacloprid 1.13E-01 4.22E-06

Z-9 Tricosene 2.27E-02 8.45E-07

Deltamethrin 5.09E-01 1.90E-05

Bromethalin 4.95E-05 1.84E-09

Brodifacoum 8.14E-04 3.03E-08

Difethialone 3.84E-04 1.43E-08

Aluminum Phosphide 2.62E+00 4.35E-05

d-trans Alethrin 7.68E-03 1.27E-07

Phenothrin 7.14E-03 1.19E-07

Brodifacoum 2.49E-04 4.14E-09

9, 12-tetradecadien-1-yl acetate 1.11E-03 1.84E-08

Total Pesticides kg/ton 1.19E-04

57

Table A-5. Processing ingredient inputs and diesel transport requirement for 1 ton of ingredients


Stabilizer Oil Input

Coconut Oil ton 1.00 Crude coconut oil, at plant/PH WITH US ELECTRICITY U

Diesel kg 26.69 Diesel, manufacture and combustion/US U

Sugar Input Sugar ton 1.00 Sugar, from sugar beet, at sugar refinery/CH WITH US ELECTRICITY U


Salt Input

Salt ton 1.00 No SimaPro® process available


58

Table A-6. Packaging material inputs and diesel transport requirements for 1 ton of materials


Jar Input Jar, PET ton 1.00 Polyethylene terephthalate, granulate, bottle grade, at plant/RER WITH US Diesel,

manufacture and combustion/US U

Moulding Process ton 1.00 Injection moulding/RER WITH US ELECTRICITY U


Label Input Label, Paper ton 0.99 Paper, wood-containing, LWC, at regional storage/CH WITH US ELECTRICITY U

Label, Adhesive ton 0.01 Proxy_Adhesives and binders, at plant NREL /US


Lid Input

Polypropylene ton 0.90 Polypropylene, granulate, at plant/RER WITH US ELECTRICITY U

Aluminum ton 0.10 Aluminum, secondary, rolled NREL /RNA

Moulding Process ton 1.00 Injection moulding/RER WITH US ELECTRICITY U


Secondary Packaging Input

Packaging Film lb 10.00 Packaging film, LDPE, at plant/RER WITH US ELECTRICITY U Corrugated Box lb 90.00 Corrugated board, recycling fibre, double wall, at plant/CH WITH US ELECTRICITY U

Pallet p 1.00 EUR-flat pallet/RER WITH US ELECTRICITY U


Output

Stabilizer Oil ton 1.00 Stabilizer, crude coconut oil at plant/US U

Sugar ton 1.00 Sugar,from sugar beet at plant/US U

Salt ton 1.00 Salt placeholder, at plant/US U

Jar, PET ton 1.00 16oz product jar, at plant/US U

Label, Paper ton 1.00 16oz product label, at plant/US U

Lid, Poly and Aluminum ton 1.00 16oz product lid, at plant/US U

Secondary Packaging lb 150.00 16oz product, secondary packaging, at plant/US U

59

B. Additional Results Tables and Figures

Table B-7. Fossil depletion process flow impacts for 1 kg peanut butter

Process Name Units

Fossil Depletion kg Oil eq

Bituminous coal, at mine 0.1705

Natural gas, at extraction site 0.0978

Natural gas, unprocessed, at extraction 0.0564

Crude oil, at production onshore 0.0361

Crude oil, at production offshore 0.0292


Natural gas, at production onshore 0.025


Crude oil, at production offshore 0.0243

Hard coal, at mine 0.0175

Crude oil, at production 0.0143

Crude oil, at production 0.0133

Lignite coal, at surface mine 0.0129


Natural gas, at production offshore 0.0113


Remaining processes 0.0387

Table B-8. Freshwater eutrophication process flow impacts for 1 kg peanut butter

Process Name Units

Freshwater Eutrophication kg P eq

Disposal, spoil from coal mining, in surface landfill 4.11E-05

Pesticide unspecified, at regional storehouse 2.23E-05

Disposal, spoil from lignite mining, in surface landfill 1.04E-05 Disposal, basic oxygen furnace wastes, 0% water, to residual material landfill 1.32E-06

Remaining processes 4.33E-06

60

Table B-9. Terrestrial Acidification process flow impacts for 1 kg of peanut butter

Process Name Units

Terrestrial Acidification kg SO2 eq

Electricity, bituminous coal, at power plant 0.0044

Natural gas, at production 0.001

Natural gas, processed, at plant 0.001

Pesticide unspecified, at regional storehouse 0.0004

Operation, lorry 20-28t, fleet average 0.0004

Operation, lorry 7.5-16t 0.0004

Electricity, lignite coal, at power plant 0.0003

Natural gas, sour, burned in production flare/MJ 0.0003

Hard coal, burned in power plant 0.0002

Hard coal, burned in power plant 0.0001


Table B-10. Particulate matter formation process flow impacts for 1kg of peanut butter

Process Name Units

Particulate Matter Formation kg PM10 eq



Natural gas, processed, at plant 0.0002



Pesticide unspecified, at regional storehouse 9.26E-05

Electricity, lignite coal, at power plant 7.69E-05

Natural gas, sour, burned in production flare/MJ 5.31E-05

Hard coal, burned in power plant 3.86E-05

Transport, train, diesel powered 3.01E-05

Hard coal, burned in power plant 2.95E-05


61

Table B-11. Photochemical oxidant formation process flow impacts for 1kg of peanut butter

Process Name Units

Photochemical Oxidant Formation kg NMVOC




Electricity, biomass, at power plant 0.0006

Natural gas, vented 0.0001


Transport, train, diesel powered 0.0001

Electricity, lignite coal, at power plant 0.0001

Pesticide unspecified, at regional storehouse 0.0001

Electricity, natural gas, at power plant 8.32E-05

Natural gas, processed, at plant 8.02E-05


62

C. Cradle to Roaster Emissions Analysis

In addition to the main emissions analysis, which included the raw materials production through

disposal phases for peanut butter manufacturing, a secondary assessment was performed for the

emissions from the cradle to roaster stages. This analysis is valuable for industries that require roasted

nuts for uses other than peanut butter production. The included life cycle stages were farming, buying

points, shelling, blanching, and roasting. Using the SimaPro model developed for peanut butter, the

total amount of CO2e emissions from cradle to roaster was calculated using the IPCC 2007 GWP impact

assessment method. The functional unit was 1 kg of roasted peanuts, and infrastructure processes were

excluded from the assessment.

The total amount of CO2e emissions estimated using the IPCC method was 1.08 kg CO2e per kg

roasted peanuts with the farm and roaster being the two highest contributors at 0.41 and 0.38 kg CO2e

respectively (Figure C-1). Both of these stages were energy intensive, with relatively large fuel

requirements for planting and harvesting machinery on the farm, and large electricity and natural gas

requirements for the peanut roasters. Figure C-2 shows the percent emissions contribution of each

production stage.

Figure C-1. kg CO2e emissions per kg roasted nuts produced. IPCC 2007 GWP method

63

Figure C-2. Percentage of total CO2e emissions by life cycle stage

64

Figure C-3 shows the CO2e production from each input in the process, excluding inputs that

contributed to less than 1% of the total emissions. The inputs resulting in the largest amounts of CO2e

were electricity production, natural gas production and combustion, as well as farm fuel for planting and

harvesting machinery.

Figure C-3. CO2e emissions by input. Life cycle inputs contributing less than 1%

of the total emissions are not shown.

national scan-level life cycle assessment for production

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