LIFE CYCLE ASSESSMENT OF WILD ALASKA POLLOCK
FINAL ISO LCA REPORT

Prepared by:
Xinyue Zhang (Quantis) • Adam Kotin (Quantis) • Melissa Zgola (Quantis)

For:
Genuine Alaska Pollock Producers

Date:
July 2021

LCA of Wild Alaska Pollock

1

Contact:
Xinyue Zhang
Sustainability Consultant
xinyue.zhang@quantis-intl.com

Adam Kotin
Senior Sustainability Consultant
adam.kotin@quantis-intl.com

LCA of Wild Alaska Pollock

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Quantis is a leading life cycle assessment (LCA) consulting firm that specializes in supporting
companies to measure, understand, and manage the environmental impacts of theirproducts, services, and operations. Quantis is a global company with offices in the UnitedStates, Switzerland, Germany, France, and Italy. It employs over 200 people, with several
internationally renowned experts.
Quantis offers cutting-edge services in environmental footprinting (considering multiple
indicators including carbon, water, toxicity, resource use, etc.), eco-design, sustainablesupply chains, and environmental communication. Quantis also provides innovative andcustomized tools, which enable organizations to evaluate, analyze, and manage theirenvironmental footprint with ease. Fueled by its close ties with the scientific community andits strategic research collaborations, Quantis has a strong track record in applying its
knowledge and expertise to accompany clients in transforming LCA results into decisions andaction plans. More information can be found at www.quantis-intl.com.

This report has been prepared by the US office of Quantis. Please direct all questions
regarding this report to the contacts above.

Quantis US
240 Commercial Street, 3rd floor
Boston, MA, US, 02109
Tel: +1 857 239-9290
www.quantis-intl.com

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PROJECT INFORMATION
Project title

Life Cycle Assessment of Wild Alaska Pollock

Contracting organization Genuine Alaska Pollock Producers
Liability statement

Information contained in this report has been compiled from
and/or computed from sources believed to be credible. Data
quality analysis is provided within the report. Application of
the data is strictly at the discretion and the responsibility of
the reader. Quantis is not liable for any loss or damage arisingfrom the use of the information in this document.

VersionFinal ISO LCA report
July 2021

Project team

Adam Kotin (adam.kotin@quantis-intl.com) – Senior
Sustainability Consultant
Melissa Zgola (melissa.zgola@quantis-intl.com) – Senior
Sustainability Consultant
Xinyue Zhang (xinyue.zhang@quantis-intl.com) – Sustainability
Consultant

Client contacts

Craig Morris (craig.morris@alaskapollock.org) - Chief Executive
Officer
Ron Rogness (ron.rogness@alaskapollock.org) - Consultant

External reviewers

Peter Tyedmers (peter.tyedmers@dal.ca) – Professor, School
for Resource and Environmental Studies, Dalhousie University
Friederike Ziegler (friederike.ziegler@ri.se) – Senior scientist,
Research Institutes of Sweden
Ray Hilborn (hilbornr@gmail.com) – Professor, School ofAquatic and Fishery Sciences, University of Washington

Associated files

This report is associated with the following electronic files,
which are available upon request to info@alaskapollock.org:
GAPP_WAP LCA_Appendix.xlsx (includes Appendix A,
Appendix B, Appendix C, Appendix D, Appendix E)
GAPP_WAP LCA_Data Survey_Catching vessels for
Shore-based and MS_V1_Quantis.xlsx

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GAPP_WAP LCA_Data Survey_CatcherProcessors_V1_Quantis.xlsx
GAPP_WAP LCA_Data Survey_Shore-based andMothership processors_V1_Quantis.xlsx
Life Cycle Assessment Explanatory Notes Catchingvessels_V1.docx
Life Cycle Assessment Explanatory Notes CatcherProcessors_V1.docx
Life Cycle Assessment Explanatory Notes Shore-basedand Mothership processors_V1.docx
GAPP_WAP LCA_Data Survey_Catching vessels for
Shore-based and MS_V2_Quantis.xlsx
GAPP_WAP LCA_Data Survey_CatcherProcessors_V2_Quantis.xlsx
GAPP_WAP LCA_Data Survey_Shore-based andMothership processors_V2_Quantis.xlsx
Life Cycle Assessment Explanatory Notes Catchingvessels_V2.docx

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CONTENTS
LIST OF FIGURES ......................................................................................................................... 9
LIST OF TABLES ......................................................................................................................... 10
1 Introduction ........................................................................................................................ 12
2 Goal of the study................................................................................................................. 12
2.1 Objectives ........................................................................................................................ 13
2.2 Intended audiences ......................................................................................................... 13
2.3 Disclosures and declarations ........................................................................................... 14
3 Scope of the study .............................................................................................................. 14
3.1 General description of the products studied ................................................................... 14
3.2 Data collection and data representativeness .................................................................. 17
3.3 Comparative basis............................................................................................................ 18
3.3.1 Functions and functional unit ....................................................................................... 18
3.3.2 Reference flows ............................................................................................................ 19
3.4 System boundaries .......................................................................................................... 19
3.4.1 General system description .......................................................................................... 19
3.4.2 Temporal and geographic boundaries .......................................................................... 23
3.4.3 Cut-off criteria............................................................................................................... 23
4 Approach............................................................................................................................. 24
4.1 Life cycle inventory .......................................................................................................... 24
4.1.1 Data sources, assumptions and extrapolation .............................................................. 24
4.1.1.1

Primary and secondary data ...................................................................................... 24

4.1.1.2

Key assumptions ........................................................................................................ 25

4.1.1.3

Total production data (BSAI) ...................................................................................... 25

4.1.2 Data quality assessment ............................................................................................... 27
4.2 Allocation methodology ................................................................................................... 31
4.2.1 Recycled content and end-of-life recycling................................................................... 31
4.2.2 Incineration with Energy Recovery ............................................................................... 32
4.2.3 Freight transport ........................................................................................................... 32
LCA of Wild Alaska Pollock

64.2.4 Ecoinvent processes with allocation ............................................................................. 32
4.2.5 Allocation between Wild Alaska Pollock and other species and between key Wild Alaska
Pollock products and other (co-product allocation) ................................................................. 32
4.2.6 Diesel energy apportions for fish oil and fishmeal processing ...................................... 36
4.3 Impact Assessment .......................................................................................................... 36
4.3.1 Impact assessment method and indicators .................................................................. 36
4.3.2 Limitations of LCIA ........................................................................................................ 38
4.4 Calculation tool ................................................................................................................ 38
4.5 Contribution analysis ....................................................................................................... 38
4.6 Uncertainty in LCI and LCIA .............................................................................................. 39
4.6.1 Inventory data uncertainty analysis .............................................................................. 39
4.6.2 Characterization models uncertainty analysis .............................................................. 39
4.7 Sensitivity and scenario analysis ...................................................................................... 39
4.8 Critical Review.................................................................................................................. 40
5 Results................................................................................................................................. 40
5.1 Baseline results ................................................................................................................ 40
5.1.1 Climate change indicator results - Overall .................................................................... 40
5.1.2 Land use indicator results - Overall............................................................................... 42
5.1.3 Water consumption indicator results - Overall ............................................................. 43
5.1.4 Additional indicator results - Overall ............................................................................ 44
5.2 Contribution analysis – Catching and processing stage ................................................... 46
5.2.1 Climate change indicator results – Catching and processing stage .............................. 46
5.2.2 Land use indicator results – Catching and processing stage ......................................... 47
5.2.3 Water consumption indicator results – Catching and processing stage ....................... 48
5.3 Scenario and Sensitivity analysis ...................................................................................... 49
5.3.1 Scenario analysis with economic allocation .................................................................. 49
5.3.2 Scenario analysis with using ammonia as the only refrigerant ..................................... 50
5.3.3 Sensitivity analysis for diesel consumption................................................................... 51
6 Key Findings ........................................................................................................................ 52
7 Recommendations .............................................................................................................. 53
LCA of Wild Alaska Pollock

78 Limitations .......................................................................................................................... 54
9 References .......................................................................................................................... 56
10

Appendices ....................................................................................................................... 58

10.1

Appendix 1 – Description of impact categories ............................................................. 58

10.2

Appendix 2 – External Critical Review Verification Letter .............................................. 62

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LIST OF FIGURES
Figure 1. Fishery locations of Wild Alaska Pollock ................................................................................. 15
Figure 2. System boundary of Wild Alaska Pollock products evaluated in this study ........................... 20
Figure 3. Graphic representation of cut-off allocation methodology (Source: Quantis) ....................... 31
Figure 4. Production Flow Chart of BSAI Fishery in metric tons (MT), all catches 2016 to 2018 .......... 35
Figure 5. Climate change indicator results for the six Wild Alaska Pollock functional units (based on PEF
methodology v1.4 (JRC-IES 2017)) ......................................................................................................... 41
Figure 6. Climate change indicator results for the four frozen product functional units (based on IPCC
2013)....................................................................................................................................................... 42
Figure 7. Land use indicator results for the six Wild Alaska Pollock functional units (based on Beck et al.
2010 and Bos et al. 2016) ....................................................................................................................... 43
Figure 8. Water consumption indicator results for the six Wild Alaska Pollock functional units (basedon Impact 2002+) ................................................................................................................................... 43
Figure 9. Full suite of normalized indicator results for 1 kg of average frozen Wild Alaska Pollock productdistributed to East coast US (based on PEF v1.4)................................................................................... 44
Figure 10. Full suite of normalized indicator results for 1 kg of fish oil distributed to East coast US (basedon PEF v1.4) ............................................................................................................................................ 45
Figure 11. Full suite of indicator results for 1 kg of fishmeal distributed to East coast US (based on PEFv1.4) ........................................................................................................................................................ 46
Figure 12. Catching and processing stage contribution analysis to the Climate change indicator for thesix Wild Alaska Pollock products (based on IPCC, 2013) ........................................................................ 47
Figure 13. Catching and processing stage contribution analysis to the Land use indicator for the six Wild
Alaska Pollock products (based on Beck et al. 2010 and Bos et al. 2016) ............................................. 48
Figure 14. Catching and processing stage contribution analysis to the Water consumption indicator forthe six Wild Alaska Pollock products (based on Impact 2002+) ............................................................ 49
Figure 15. Sensitivity test using an economic co-product allocation metric (based on IPCC 2013) ...... 50
Figure 16. Scenario results for using ammonia as the only refrigerant (IPCC 2013) ............................. 51
Figure 17. Sensitivity results for diesel fuel consumption (IPCC 2013).................................................. 52

LCA of Wild Alaska Pollock

9LIST OF TABLES
Table 1. Harvest information ................................................................................................................. 16
Table 2. Combined three-year production totals for years 2016-2018 by location, and Wild Alaska
Product type ........................................................................................................................................... 17
Table 3. Data representativeness........................................................................................................... 18
Table 4. 2016-2018 production by sector, and Wild Alaska Pollock product type ................................ 26
Table 5. Pedigree matrix used for data quality assessment .................................................................. 28
Table 6. Inventory and Data quality Assessment for fillet ..................................................................... 29
Table 7. Inventory and Data Quality Assessment for fish oil ................................................................. 30
Table 8. Yield rates and economic values (wholesale price) for Wild Alaska Pollock products ............ 34
Table 9. Indicators and related assessment models used ..................................................................... 37

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ABBREVIATIONS AND ACRONYMS
CO2
CPUE
EI
EOL
eq
GAPP
GWP
IPCC
ISO
kg
km
LCA
LCI
LCIA
m2
m3
MJ
MSW
PM
US
USEPA

Carbon Dioxide
Catch per unit effort
EcoinventEnd of Life
equivalents
Genuine Alaska Pollock Producers
Global Warming Potential
Intergovernmental Panel on Climate ChangeInternational Organization for Standardization
Kilogram = 1,000 grams (g) = 2.2 pounds (lbs)
Kilometer = 1000 meters (m)Life Cycle Assessment
Life Cycle Inventory
Life Cycle Impact Assessment
Square meter
Cubic meter
Megajoule = 1,000,000 joules, (948 Btu)
Municipal solid waste
Particulate Matter
United States
United States Environmental Protection Agency

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1 Introduction
Heightened concern around the environmental and social sustainability of society’s
consumption habits has focused attention on understanding and proactively managing thepotential environmental and societal consequences of production and consumption of
products and services. Nearly all major product producers now consider environmental andsocial impacts as key decision points in product design, including but not limited to material
selection, product manufacturing, distribution, use and disposal, and sustainability is arecognized point of differentiation in many industries, including food and agriculture.
The Association of Genuine Alaska Pollock Producers (GAPP), the organization for the world’s
largest certified sustainable fishery, has commissioned Quantis to use life cycle assessment 1(LCA) methodologies and practices to measure and document the estimated environmental
impacts of catching, processing, and delivering Wild Alaska Pollock products to primarycustomers.
Among other uses, LCA can be used to identify opportunities to improve the environmental
performance of products, inform decision-making, and support marketing, communications,and educational efforts. The importance of the life cycle view in sustainability decision-making
is sufficiently strong that over the past several decades it has become the principal approachto evaluate a broad range of environmental problems, identify social risks and to help makedecisions within the complex arena of socio-environmental sustainability.
It is the intention for this LCA to conform to ISO 14040 and 14044 standards (ISO 2006a; ISO
2006b) for public disclosure of comparative statements. In addition to alignment with ISO, thestudy will align with recommendations from the Publicly Available Standards (PAS) 2050-2 forseafood and other aquatic food products (BSI, 2012) with regard to scope and data boundary,time period for data collection, and other relative general instructions for LCAs in fisheries.
The study was peer-reviewed as a requirement of ISO LCA standards.

2 Goal of the study
This section describes the goal of the study, intended audience and declarations.

1 LCA is an internationally recognized approach that evaluates the relative potential environmental and human health impacts

of products and services throughout their life cycle, beginning with raw material extraction and including all aspects oftransportation, manufacturing, use, and end-of-life treatment.

LCA of Wild Alaska Pollock

122.1 Objectives
Key objectives of the initiative are to:
1. Provide internal knowledge to GAPP as to the industry-average life cycle
environmental impacts of Wild Alaska Pollock products (including fillet, surimi, roe, fishoil, and fishmeal, see Section 3.1 for more details) across several key impact categories,
such as Climate change, Land use, and Water consumption;
2. Enable GAPP members to provide their customers with credible production-weighted
average (see Section 3.3.1 for more details) environmental impact information on five
Wild Alaska Pollock products (and average of frozen product including fillet, surimi androe) that adheres to leading LCA standards (ISO 14040 and ISO 14044; PAS2050-2);
3. Identify improvement opportunities to further reduce environmental impacts of theWild Alaska Pollock fishing, processing, and delivery;
4. Gain a deeper understanding of where Wild Alaska Pollock products fall on the animal
protein continuum in terms of environmental impacts, without making competitive orderogatory claims about other forms of seafood or land-based animal protein.
The specific goals of this study are as follows:
1. Carry out an ISO 14040/14044 compliant LCA of products derived from Wild Alaska
Pollock produced by GAPP’s members;
2. Understand the contributions that production of Wild Alaska Pollock products make toresource depletion (e.g., energy use, water use, etc.) and environmental concerns(e.g., climate change). Identify environmental hotspots (top contributors ofenvironmental impacts) of Wild Alaska Pollock products and identify potential
improvement opportunities. Identify opportunities for further environmental impact
reduction of Wild Alaska Pollock fishing, processing & delivery.
3. Explore key data points, uncertainties and methodological choices that might influence
results;
4. Enable GAPP to communicate Wild Alaska Pollock product impacts credibly withinternal and external stakeholders, via use of leading LCA standards and practices;
5. Identify how to best add this information to Wild Alaska Pollock’s sustainability story,
without disparaging other sea and land protein sources.

2.2 Intended audiences
This project report is intended to provide the estimated contributions to resource depletionand environmental concerns of Wild Alaska Pollock products in a clear and useful manner, inorder to inform GAPP’s communication of environmental performance to internal andexternal audiences such as customers and suppliers of GAPP members, members of the media,
policymakers, and consumers. Communication options could include meetings withcustomers, marketing materials, and web tools, among others. The level and quality of support
for the conclusions has been evaluated during the critical review to ensure that the results areappropriate to support a public disclosure of the LCA findings.

LCA of Wild Alaska Pollock

132.3 Disclosures and declarations
GAPP seeks to evaluate the environmental performance of Wild Alaska Pollock products. The
project conforms to the ISO 14040 and 14044 standards, including a critical review by a panelof independent experts.
It is the intention for this LCA to conform to ISO 14040 and 14044 standards (ISO 2006a; ISO
2006b) for public disclosure of comparative statements. In addition to alignment with ISO, thestudy will align with recommendations from the Publicly Available Standards (PAS) 2050-2 forseafood products (BSI, 2012) with regard to scope and data boundary, time period for datacollection, and other guidance for conducting LCA related to fisheries and seafood products.
The study was peer-reviewed as a requirement of ISO LCA standards.
Because the results of this study apply only to particular products (five Wild Alaska Pollock
products produced by GAPP members, see Section 3.1), the results of this study are notexpected to negatively affect any external interested parties. The results of this study will bemade public and may be used by GAPP or external parties to compare with other products. If
the results of this study are used to compare with the potential impacts of other products,
care must be taken to interpret the results in light of potential differences in scope (e.g.,system boundary, raw products versus consumer-ready products) and methodology.

3 Scope of the study
3.1 General description of the products studied
This section describes the scope of the assessment. It includes a description of the productfunctions and product systems, the system boundaries, data sources, and methodologicalframework. This section also outlines the requirements for data quality as well as review of
the analysis. Additional, specific data pertaining to each system can be found in Section 3.2.
The entire data inventory is included in Appendix D to the full report.
GAPP wishes to evaluate the potential environmental impacts of weighted industry average
Wild Alaska Pollock products, taking into account:•

Wild Alaska Pollock produced in two fishery locations: Bering Sea/Aleutian Islands(BSAI) and Gulf of Alaska (GOA);LCA of Wild Alaska Pollock

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Figure 1. Fishery locations of Wild Alaska Pollock••

Wild Alaska Pollock products produced by catching and three processing methods:
Catcher-processors, catcher vessels delivering to Mothership processors, and catcher
vessels delivering to Shore-based processors;
Five types of key Wild Alaska Pollock products: fillet, surimi, roe, fish oil, and fishmeal.

The goal of this LCA is to cover the entirety of the U.S. Alaska Pollock fishery. GOA covers 11.4%
of total catching volume (in which only Shore-based processors operate). In this study, allresponses are based on BSAI data and the results are extrapolated to represent the entire
production of BSAI and GOA. There are several factors that would influence the relative
impacts of GOA and BSAI fisheries including the following: 1) Catching vessels in the GOA tend
to have a shorter travel distance especially for the second half of the year, 2) The vast majorityof GOA processing uses almost 100% hydropower, and 3) Relative catch per unit effort (CPUE)
may be somewhat higher in the Gulf of Alaska. Depending on the relative magnitude of these
influences, there could be some over- or underestimation by using BSAI to represent GOA.
To review, the quota setting process in the Bering Sea is as follows:•••

From the initial quota, 10% is set aside as Community Development Quota (CDQ). This
CDQ is apportioned to regional associations of rural communities in Western Alaska.
These associations either partner with a company in the Catcher-processor sector thatcatches and processes the product, or in the case of one association, owns their own
Catcher-processor. In our model, the catch of this quota was apportioned to theappropriate participant in the Catcher-processor sector.
A small percentage of the initial quota is also set aside to account for bycatch infisheries other than the Directed Alaska Pollock fishery. After these deductions fromthe initial quota, the Directed Alaska Pollock Fishery quota is apportioned to thefollowing distinct sectors:o Shore-based sector – 50% of the directed fishery quota is apportioned to 71
vessels delivering to processing plants on shore.
o Catcher-processor sector – 40% of the directed fishery quota is apportioned tovessels that both catch and process that catch into primary products.
o Mothership sector – 10% of the directed fishery quota is apportioned to 15
vessels that deliver their catch to Mothership processors.
Overall, BSAI is 88.6% of the total catching volume of Wild Pollock Alaska and GOA
covers 11.4%. The overall harvest information is shown in Table 1 below.
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When factoring in CDQ harvests by Catcher-processors, the Catcher-processors harvested
46% of the total catch in the Bering Sea from 2016 – 2018. The Shore-based sector
accounted for 45% of the catch and the Mothership processors 9%.
Table 1. Harvest information

Fishery
location

Bering Sea/AleutianIslands (BSAI)

Number ofvessels

Sector’s shareof total
catching
volume forlocation

Catcherprocessors

14

46%

Mothershipprocessors

3

9%Shore-based
processors

6

45%

To
Motherships

15

N/A

To Shorebased

71

N/A

Less than 60
feet

5-7

No data

Greater thanor equal to 60
feet

24

No dataLess than 60
feet

8-11

No data

Greater thanor equal to 60
feet

41-45

No data

Sector: Catching &Processing
methodCatching vessels

Gulf of Alaska(GOA)

Shore-based
processors

Catching vessels

Fleet

Based on the guidance of PAS 2050-2, an assessment period of three years is used to take into
account biological and environmental variability (BSI, 2012). This study evaluates activities
over the three-year period spanning Jan 1, 2016 to Dec 31, 2018. For some members, 2019
data were still being finalized during the data collection phase of this project, and therefore,
for data consistency 2019 data are excluded from this study. Results are provided in alignment
with the functional units (see Section 3.3.1).
Table 2 presents the combined three-year production totals for 2016, 2017 and 2018 for eachof the products and fishery locations.

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Table 2. Combined three-year production totals for years 2016-2018 by location, and Wild Alaska Product type

Total production (MT)Wild Alaska Pollock productBering Sea /AleutianIslands (BSAI)

Gulf of Alaska(GOA)

Fillet485,867

39,840Surimi

584,076

33,781

Roe

53,316

4,010

Fish oil

80,155

1,434

Fishmeal

192,6132,498

Head & Gutted

73,77570,052

Minced80,996

3,674

Milt

2,934

1,055

Stomach

5,688

39

Bones

29,8320Whole fish

1,241

24,381

Belly flap

110

Other retained products

31

104

3.2 Data collection and data representativenessThe process to develop the inventory started with the items proposed within PAS 2050-2,
which are specific to evaluating potential GHG impacts. Further consideration was given to
any supplementary inventory data needed in order to represent all Catching and processing
activities, with emphasis on activities that might drive other indicators including those related
to ecosystem quality and human health. To identify a more complete set of inventory data,
GAPP member companies were consulted and asked to provide input. The external review
panel was also consulted to ensure a balance of completeness of inventory and response ratefrom the surveys.
Data surveys (version 1) were sent by GAPP to all GAPP members, who were pre-notified
about the goal of this study in December 2019. However, the initial response was low. To help
motivate GAPP members to participate in data collection and to obtain a higher response rate,
a simplified data collection file (version 2) was prepared to focus on the expected impact
drivers including energy and important consumables. This simplification solution was basedon input from the Expert Review Panel Chair, Dr. Tyedmers. The simplified data collection files
were sent out in May 2020. Please see associated files for the surveys. Where data was notprovided by a GAPP member, the production data from the other members were used toestimate activity for the non-respondent activity. The responses from catchers are used to
calculate potential catching impact per kg Wild Alaska Pollock product, and responses from
LCA of Wild Alaska Pollock

17each type of processing are used to calculate the potential impact per kg Wild Alaska Pollock
product caused by each type of processing. See Section 4.2.5 for more details.
The response rate is considered in relation to the combined three-year total production
volume for years 2016-2018 for each location and for each life cycle stage. The response rateis used to assess the data representativeness and is documented in Table 3. The results
calculated from the responses have been used to represent the impact caused by the activities
for each sector. The percentage of responses to GAPP total catching volume is used as the
response rate and/or data coverage rate in this study.

Table 3. Data representativenessFishery
locationLife cycle
stagesBering
Sea/AleutianIslands(BSAI)Catching

Processing

SectorCatching
vessels to
Shore-based
processors(n = 71)
Catching
vessels toMothershipprocessors(n = 15)
Catcherprocessors(n = 14)
Shore-based
processors(n = 6)
Mothershipprocessors(n = 3)Catching mass foryears 2016-2018
represented in
responses (MT)% of GAPP total
catching volume foryears 2016-2018(used as response
rate/data coveragerate in this study)

235,633

11%

35,21510%

1,442,767

79%

929,49454%

121,330

34%

3.3 Comparative basis
3.3.1 Functions and functional unit
Life cycle assessment relies on a “functional unit” (FU) for comparison of alternative productsthat may substitute each other in fulfilling a certain function for the user or consumer. The FU
describes this function in quantitative terms and serves as an anchor point for the comparison,
ensuring that the compared alternatives do indeed fulfill the same function. It is therefore

LCA of Wild Alaska Pollock

18

critical that this parameter is clearly defined and measurable. The functional units for thisstudy are:1) 1 kg of Wild Alaska Pollock fillet, distributed to first-tier customers;2) 1 kg of Wild Alaska Pollock surimi, distributed to first-tier customers;3) 1 kg of Wild Alaska Pollock roe, distributed to first-tier customers;4) 1 kg of average frozen Wild Alaska Pollock product (including fillet, surimi and roe)
distributed to first-tier customers.
5) 1 kg of Wild Alaska Pollock fish oil, distributed to first-tier customers;6) 1 kg of Wild Alaska Pollock fish meal, distributed to first-tier customers;
For each group, we consider three first-tier destinations: East coast US, Asia, and Europe. We
provide the results for the three destinations and six functional units.
The results of this study are general for the entire fishery and not specific to any one sector
within this fishery.
Functional unit number 4 represents a weighted average product based on the production
weight of the three key frozen Wild Alaska Pollock products (fillet, surimi, and roe) and basedon the relative contribution of each Catching and processing method. From there, distribution
to one of three first-tier customers is included, resulting in three final results.
3.3.2 Reference flows
To fulfill the functional unit, different quantities and types of material are required for Wild
Alaska Pollock products. The lists of inputs that provide the functional unit are identified as
reference flows. These reference flows are provided in Appendix D alongside the life cycle
inventory datasets to which they are mapped.

3.4 System boundaries
The system boundaries identify the life cycle stages, processes, and flows considered in the
LCA and should include all activities relevant to attaining the above-mentioned study
objectives. The following paragraphs present a general description of the system as well as
temporal and geographical boundaries of this study.
3.4.1 General system description
This study evaluates the cradle-to-gate life cycle of Wild Alaska Pollock products (including
the catching, processing, and packaging) in addition to distribution to a first-tier businesscustomer, as depicted in Figure 2. An effort is made to define the system boundary andcollect data on all activities outlined as key in the PAS 2050-2 standard (BSI, 2012). Fillet,
surimi, and roe are considered as Wild Alaska Pollock frozen products.

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Processing

Catching
Energy consumption

Energy consumption

Non-durable goods

Non-durable goods

Durable and semidurable goods

Durable and semidurable goods

Waste disposal

Additives for surimi;
Diesel for fishmeal
and fish oil
production;
EmployeeCommuting

PackagingDistribution
To East coast US

Primary packaging

To Asia

Secondary packaging

To Europe

Tertiary packaging

Figure 2. System boundary of Wild Alaska Pollock products evaluated in this study

LCA of Wild Alaska Pollock

20

As is generally done in LCA, within the above shown steps the assessment considers all
identifiable “upstream” activities to provide as comprehensive a view as possible of the
product’s cradle-to-gate life cycle. For example, when considering the environmental impactof transportation, not only are the emissions of the truck or ship considered, but also included
are the impacts of additional processes and inputs needed to produce the fuel and the vehicle.
In this way, the production chains of all inputs are traced back to the original extraction of raw
materials. Per PAS 2050-2 (BSI, 2012) no capital goods (e.g., infrastructure, buildings) of thereporting companies is included. However, capital goods for all material inputs are included in
the background life cycle inventory data. Employee commuting from Seattle to Dutch Harboris also included in this study; however, any commuting that occurs prior to employee arrivalin Seattle is not included.
Catching includes:
•

•

•

•

Energy consumption
o Diesel fuelo Purchased electricity, 100% diesel based on the regional informationo Fuel for trucks, if any (e.g., for transport)o etc.
Non-durable goods
o Refrigerants, including freon, CO2, ammonia, otherso Hydraulic fluido Purchased oil or other lubricantso Cleaning agentso Anti-fouling agentso Painto Heated storage (not at Shore-based processors)o etc.
Durable and semi-durable goods
o Netso Filterso Chains
o Cableso Trawl/doorso Other steel productso Rope/twine
o Electrical wireo Third wireo Batterieso etc.
Wasteso Solid waste delivered to landfill
o Waste burned at sea
o Waste oil delivered to recyclingo Other waste delivered to recyclingo etc.

Processing includes:
LCA of Wild Alaska Pollock

21

•

•

•

•

•

•

Energy consumption
o Dieselo Natural gas
o LPG
o Gasoline
o Purchased electricity
o etc.
Non-durable goods
o Refrigerants, including freon, CO2, ammonia, otherso Hydraulic fluido Purchased oil or other lubricantso Cleaning agentso Glues and other adhesiveso Adhesive tapeso Anti-fouling agentso Painto Lubricating oilo Fresh water not produced from desalination of sea watero etc.
Durable and semi-durable goods
o PVC pipeo Wood other than wood used in pallets or other packaging
o Chain and cableso Rope/twine
o Electrical wireo Batterieso etc.
Waste generationo Waste delivered to landfill
o Waste delivered to incinerationo Waste burned at sea
o Waste recycledo Fish waste discharged to sea
o etc.
Additives to surimi
o Sorbitolo Sugaro Sodium tripolyphosphateo Tetrasodium pyrophosphate
Employee commuting

Any purchased electricity is applied as 100% diesel-sourced, based on regional information.
The Packaging stage considers only materials (and upstream production of the materials) since
any assembly of packaging and packing of Wild Alaska Pollock products is mainly done
manually. Information taken into consideration is listed below:

LCA of Wild Alaska Pollock

22

•
•
•
•
•
•

Materials for packaging products together (e.g., box assembly that contains multiple
packed items for transport)
Amount of packaging per kg of specific product (e.g., materials required to pack 1 kgof fillet product)
Transport of packaging delivery
Recycled content of packaging materials
Loss rate during packaging activities
Lifespan if the packaging is reusable

The Distribution stage includes only the activities from processors to one of three first-tierbusiness customers. The main locations of the first-tier business customers are listed in Figure
2. Information taken into consideration is listed below:•

Transport mode (e.g., ship, truck), all reefer in this study•

Transport distance and mass for each product to each destination

The three first-tier business customer locations chosen for the distribution boundary
cumulatively represent the majority of Wild Alaska Pollock product distribution.
3.4.2 Temporal and geographic boundaries
This LCA aims to be representative of Wild Alaska Pollock products produced in BSAI and GOA
and sold to Asia, North America and Europe at the time the study is conducted (2019). As
described in Section 3.1, the primary data collection to support this work represents theperiod from January 1, 2016 to December 31, 2018. Data and assumptions are intended toreflect current equipment, processes, and market conditions. It should be noted, however,that some processes within the system boundaries might take place anywhere or anytime. For
example, the processes associated with the supply chain and with waste management cantake place in Asia, North America or elsewhere in the world. In addition, certain processes may
generate emissions over a longer period of time than the reference period. This applies tolandfilling, which causes emissions (biogas and leachate) over a period of time whose length(several decades to over a century/millennium) depends on the design and operation
parameters of the burial cells and how the emissions are modeled in the environment.
3.4.3 Cut-off criteria
Processes may be excluded if their contributions to the total system’s environmental impact
are less than 1%. All product components and production processes are included when thenecessary information is readily available, or a reasonable estimate can be made. To help us
understand which are the most important processes and activities (i.e., >1% of impact), we
have carried out a data quality assessment focusing on Climate change impact (see 4.1.2).
The following processes have been excluded from the study due to lack of reliable data andan expected contribution lower than the cut-off criteria. These exclusions are also
recommended in PAS2050-2 (BSI, 2012).
••

Production and maintenance of capital goods, including buildings, offices, vessels,
tractors, fork-lift truck, machinery and other equipment, etc.;
Production and maintenance of vehicles and aircraft used for transportation;
LCA of Wild Alaska Pollock

23

•
•
•

Production and maintenance of harbors, roads, pavement and other floor
coverings;
Employee commuting prior to arrival in the Seattle area (as mentioned above,
employee commuting between Seattle and Dutch Harbor is included in the study).
Cold storage in Dutch Harbor prior to shipment is not included in this study, since it
tends to be a minor contributor. The 2012 study showed this was only 0.3% of
impact (AS SBC report, 2017).

Moreover, the following processes have been excluded from the system boundaries, inconformity to usual practices in attributional LCA: labor, commuting of workers from otherstates, and administrative work.
It should be noted that the capital equipment and infrastructure available in the ecoinvent
database (v3.4) is included in the background data for this study in order to be ascomprehensive as possible.

4 Approach
4.1 Life cycle inventory
The quality of LCA results depends on the quality of data used in the evaluation. Every effort
has been made to implement the most credible and representative information available.
4.1.1 Data sources, assumptions and extrapolation
4.1.1.1 Primary and secondary data
Life cycle inventory (LCI) data collection mainly concerns the materials used, the energy
consumed, and the wastes and emissions generated by each process included in the systemboundaries. Primary data were collected directly from GAPP’s member companies for thematerials and energy consumption, primary and secondary packaging materials and weights,
as well as data related to transportation distances, modes, and efficiency. These primary data
were collected via a survey sent to GAPP member companies in the winter of 2019. See
associated files starting with “GAPP_WAP LCA_Data Survey” for all data surveys.
Certain companies could only provide incomplete data for non-durable goods, durable goods,
waste, and/or refrigerant. In these cases, complete data from companies who operate thesame type of vessels were used in their place, assuming the same consumption per unit
production.
The ecoinvent database v3.4 using the cut-off by classification approach (SCLCI, 2017) isprioritized as the default source of background data. Some of these ecoinvent datasets may
be adapted to improve water balances and enable them to be compatible with the AWARE
impact assessment method for estimating water availability impacts (WULCA). Ecoinvent 3.6
LCA of Wild Alaska Pollock

24

(SCLCI, 2019) is used in addition to ecoinvent 3.4 (AWARE adapted, Quantis modified) where
inventory data are only available in the later update.
Ecoinvent is recognized as one of the most complete background LCI databases available, fromquantitative (number of included processes) and qualitative (quality of the validation
processes, data completeness, etc.) perspectives. Historically focused on European production
activities, it has reached a global coverage of thousands of commodities and industrial
processes. It is believed that the credibility and transparency of this database make it apreferable option for representing Asian and North and South American conditions relative toother options available. The data’s geographic representativeness is one aspect evaluated aspart of the data quality assessment.
A full list of data mapping is available in Appendix D.
4.1.1.2 Key assumptions
The following key assumptions are made in the LCA model for Wild Alaska Pollock products:
•

•
•

•

Per PAS 2050-2 (BSI, 2012), the impacts that arise from building infrastructure andoperations represent less than 1% of final life cycle impacts and on this basis are
excluded. In addition, this study includes all flows recommended for inclusion by PAS
2050-2 (BSI, 2012), even if their contributions were less than 1% of final life cycle
impacts.
During the Wild Alaska Pollock catching season there is minimal by-catch of otherspecies. Therefore, we attribute the full impact of catching activities to Wild Alaska
Pollock.
Where one or several pieces of data are missing across catcher/processor responses
within the same sector, average data based on the responses from other sectors,
normalized by catching volume, is used as a proxy. Specifically, for Mothership catchersand processors some durable and non-durable goods are missing, and therefore
normalized data from Shore-based catchers and processors are used.
Where one or several pieces of data are missing from a portion of thecatcher/processor responses within the same sector, we use the available data fromother responses in the same sector, normalized by catching volume, to represent thedata for the whole group.

Other assumptions are based on the professional judgment of the modelers and are held
constant for all Wild Alaska Pollock products under study where a clear basis does not exist todifferentiate among systems. All assumptions are documented in Appendix E.
4.1.1.3 Total production data (BSAI)
Production data across the three sectors of Wild Alaska Pollock used the extrapolation
method described below.
The following mass balance test was performed:•

Respondents’ production of the products listed above were extrapolated by using thepercent of catch they represented within their particular sector;
LCA of Wild Alaska Pollock

25

•

Next, the results of those extrapolations across all three sectors were compared tothe actual cumulative production quantities collected and reported by the National
Marine Fisheries Service (NMFS). (NMFS does not report production of processed
products by sector due to confidentiality constraints)

The resulting extrapolations were reasonably close to the NMFS production for someproducts, however, there were notable discrepancies for others (e.g., 20% difference forfillets). Given this, the production of processed products by sector is estimated using
extrapolations of survey data for the Catcher-processing and Mothership processing sectors.
We used the Mothership processing sector despite an overall lower response rate, as
Mothership processors represent 9% of the quota and thus errors would be less impactful tothe results. After the extrapolation of the production for the two sectors, the remaining
production of each processed product was apportioned to the Shore-based sector. See Table
4 for the total production data.
Table 4. 2016-2018 production by Wild Alaska Pollock product type

Fishery location

Wild Alaska
Pollock
product

Total
production(MT)

Bering Sea/AleutianIslands (BSAI)

Fillet485,867

Surimi

584,076

Roe

53,316

Fish oil

80,155

Fishmeal

192,613

Head & Gutted

73,775

Minced80,996

Milt

2,901

Stomach

5,688

Bones

29,832Whole fish

1,241

Belly flap

11

Other retained
products

31

LCA of Wild Alaska Pollock26

4.1.2 Data quality assessment
The reliability of the results and conclusions of an LCA depend on the quality of the data used.
It is therefore important to ensure that the information is adequate to meet the objectives of
the report.
The quality of foreground processes and data used in this study are assessed qualitatively on
a 1 to 5 scale, with a score of 5 being most favorable and a score of 1 being least favorable.
Quality considerations are based on those outlined by the pedigree matrix, including
reliability, completeness, temporal correlation, and geographical correlation (Weidema andWesnaes 1996) as prescribed in ISO 14044. A complete discussion of this topic can be foundin Weidema et al. (2013). The pedigree matrix for rating inventory data appears in Table 4
below.
The full Inventory and Data Quality Assessment results are included in Appendix D, which lists
all life cycle processes and ratings for those data that contribute at least 1% to one or more of
the most relevant impact indicators. The importance of the data to the total system results
may be examined using sensitivity testing and an explanation of influence on the confidenceof the results reported. Inventory and Data Quality Assessment results for fillet and fish oil areprovided in Table 6 and Table 7, respectively; the full data quality assessment and data quality
assessments for other functional units considered also appear in Appendix D and the patterns
found across all are very similar.
Table 6 shows a snapshot of the data quality assessment carried out for fillet as an example.
Through this data quality assessment, it was identified that diesel consumption for Catchingand processing, and refrigerant leakage (especially Freon) are hotspots to the Climate change
indicator of fillet production. All the data are primary data, collected from GAPP with arelatively good data coverage rate (see Table 3). For waste and some non-durable goods, thedata coverage is relatively low, but their contributions are low as well. Collecting morecomprehensive data to have a higher coverage rate in the future would be helpful to improvethe data quality. Based on this data quality assessment, considering the extrapolation fordiesel consumption (see Section 4.2.6), a sensitivity test on diesel consumption is provided in
the study (see Section 5.3.2).
Based on the Inventory and Data Quality Assessment data in Table 6, for every 1 kg of fillet(equivalent to 1 kg of pollock landed):••

3.5 MJ of diesel fuel are consumed during Catching and processing, contributing 36%
to the total Climate change impact, and0.09 grams of leaked refrigerants are associated with Catching and processing. Of thisleakage, 0.02 grams are CFC-12, which contributes 31% to the total Climate change
impact. (CFC-12 was chosen to represent freon in this study, as it presents a
conservative estimate and our data did not specify the gas used in freon systems. In
addition, a scenario analysis using ammonia as the only refrigerant was completed in
Section 5.3.2.)

The mean fuel use intensity for catching activities across sectors is 16.7 gallons per metricton of catch; note that this includes fuel used to process fish aboard Catcher-processors, andtherefore it overstates the amount of fuel used for catching only. According to Parker andTyedmers (2014), the median fuel use intensity of global fishery records since 1990 was 639
LCA of Wild Alaska Pollock

27

litres (168.8 gallons) per metric ton of catch. These data suggest that the U.S. Alaska Pollock
fishery is among the most fuel-efficient fisheries in the world.

Table 5. Pedigree matrix used for data quality assessment

INDICATOR
SCORE

5

4

3

2

1

Reliability

Verified data
based onmeasurements

Verified data
partly based onassumptions or
non-verified
data based on
measurements

Non-verified
data partly
based onqualifiedestimates

Qualifiedestimate (e.g.
by industrial
expert)

Non-qualified
estimate

Completeness

Representative
data from allsites relevantto the market
considered,over anadequate
period to evenout normal
fluctuations

Representative
data from >50of the sites
relevant for themarketconsidered,over anadequate
period to evenout normal
fluctuations

Representative
data from only
some sites(<<50) relevant
for the market
considered or>50 of sites but
from shorter
periods

Representative
data from only
one sites
relevant for themarketconsidered orsome sites but
from shorter
periods

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

Temporalcorrelation

Less than 3
years ofdifference tothe time-periodof the dataset

Less than 6
years
difference tothe time-periodof the dataset

Less than 10
years
difference tothe time-periodof the dataset

Less than 15
years
difference tothe time-periodof the dataset

Age of data
unknown or morethan 15 years ofdifference to thetime-period of thedatasetGeographicalcorrelation

Data from area
under study

Average datafrom larger
area in which
the area understudy isincluded

Data from areawith similar
production
conditions

Data from areawith slightly
similarproduction
conditions

Data fromunknown or
distinctly different
area

Furthertechnologicalcorrelation

Data fromenterprises,
processes andmaterials understudy

Data fromprocesses andmaterials understudy but fromdifferent
enterprises

Data fromprocesses andmaterials understudy but fromdifferent
technology

Data on related
processes ormaterials

Data on related
processes onlaboratory scale orfrom different
technology

LCA of Wild Alaska Pollock28

Table 6. Inventory and Data quality Assessment for fillet. The full Inventory and Data Quality Assessment results for
all functional units are provided in Appendix D; as noted above, fillet results are very similar to the other frozen
product functional units.
LCA of Wild Alaska Pollock

29

Table 7. Inventory and Data Quality Assessment for fish oil. The full Inventory and Data Quality Assessment results
for all functional units are provided in Appendix D; fish oil results are very similar to fishmeal results.

LCA of Wild Alaska Pollock30

4.2 Allocation methodology
A common methodological decision point in LCA occurs when the system being studied is
directly connected to a past or future system or produces co-products. When systems arelinked in this manner, the boundaries of the system of interest must be widened to include
the adjoining system, or the impacts of the linking items must be distributed—or allocated—
across the systems. While there is no clear scientific consensus regarding an optimal method
for handling this in all cases (Reap et al. 2008), many possible approaches have beendeveloped, and each may have a greater level of appropriateness in certain circumstances.
ISO 14044 prioritizes the methodologies related to applying allocation. It is best to avoid
allocation through system subdivision or expansion. If that is not possible, then one should
perform allocation using an underlying physical relationship. If using a physical relationship is
not possible or does not make sense, then one can use another relationship. Any allocations
made during calculations are stated throughout the report.
4.2.1 Recycled content and end-of-life recycling
When a system donates or receives a material or energy source from an upstream or
downstream system, respectively, a decision must be made to assign an amount of impact or
benefit to the systems involved.
In this study employs the “cut-off” approach (Ekvall and Tillman 1997), which is represented
in Figure 3. In the case of recycled content and recycling at end of life, use of the cut-off
approach entails modeling the production of input materials with appropriate virgin
production and recycling processes, depending on the average recycled content rate of amaterial in the relevant market assumed in this study. End-of-life is modeled by including only
the portions of disposal that do not result in reclaimed materials or energy. Specifically, landfill
and incineration processes are included, but recycling is excluded as it is considered a
production process for a subsequent system.

Figure 3. Graphic representation of cut-off allocation methodology (Source: Quantis)

LCA of Wild Alaska Pollock

31

The choice of allocation approach for recycled content and end-of-life recycling is notexpected to have a meaningful influence on the results since these activities do not apply to
the Wild Alaska Pollock products themselves, only packaging and perhaps other materials to
support Wild Alaska Pollock products.
4.2.2 Incineration with Energy Recovery
An allocation decision must also be made regarding the additional functions provided byincineration with energy recovery (or WtE), and landfilling with methane capture, which
provide an energy source for use by another system. Following the cut-off methodology forrecycling, the energy provided by the end of life (EoL) treatment is credited to a downstream
system. For the purposes of this study, end of life figures from the USEPA (Advancing
Sustainable Materials Management: 2014 Tables and Figures, December 2016) is used for the
distinct types of materials.
4.2.3 Freight transport
In this study, all transport is assumed to be weight-limited and the transportation of the cargowithin the vehicle is therefore allocated based on its weight.
Transport vehicles have both a weight capacity and a volume capacity. These are important
aspects to consider when allocating the impacts of an entire transportation journey to one
product. Vehicles transporting products with a high density (high mass-per-volume ratio) willreach their weight capacity before reaching their volume capacity. Vehicles transporting
products with a low density (low mass-per-volume ratio) will reach their volume capacity
before reaching their weight capacity. Therefore, the density of the product is critical for
determining whether to model transportation as volume-limited or weight-limited.
4.2.4 Ecoinvent processes with allocation
Many of the processes in the ecoinvent database also provide multiple functions, andallocation is required to provide inventory data per function (or per process) (Weidema et al.
2013). In this study, the ecoinvent database v3.4 using the cut-off by classification allocation
model is used (Weidema et al. 2013). Some additional datasets from ecoinvent database v3.6
using the cut-off by classification allocation model are also used as a supplement for v3.4. The
allocation model is aligned with the cut-off approach used in the foreground modeling (e.g.,treatment of recycled content in incoming materials).
4.2.5 Allocation between Wild Alaska Pollock and other species and between key Wild Alaska
Pollock products and other (co-product allocation)
This study is specific to five key Wild Alaska Pollock products and excludes activities relating
to catching and processing other species, as well as other Wild Alaska Pollock co-products.
Data relating to Catching and processing activities, such as fuel use, tend to be collected andreported by the industry on an annual basis and may account not only for Wild Alaska Pollock
activities, but also those for other species. To apportion these activity data to Wild Alaska
Pollock, we have asked catchers and processors to separate data based on their best

LCA of Wild Alaska Pollock

32knowledge. Described below are the allocation approaches that have been applied for this
work.
•

•

Allocation of catching activities to Wild Alaska Pollock relative to catching of otherspecies was done by each catcher company based on the number of days fished, where
needed. If the vessel participated in fisheries other than the directed Alaska Pollock
fishery, the catchers apportioned inputs between those other species and Wild Alaska
Pollock by having separate meters for Wild Alaska Pollock catching activities orapportioned by number of days fished.
Allocation of processing activities to Wild Alaska Pollock relative to the processing ofother species was done by each processor company by the amount of production,
where needed. If other species were processed and stored at the facility, theprocessors apportioned inputs and energy consumption between those other species
and Wild Alaska Pollock by having separate meters for Wild Alaska Pollock processing
facilities, or apportioned by product volume/mass, etc. based on data availability.

With regard to Wild Alaska Pollock-specific product-related activity data versus that for
excluded co-products: Allocation for processing different Wild Alaska Pollock products which
are not included in the system boundary of this study are done by mass where needed.
During Wild Alaska Pollock processing, various Wild Alaska Pollock products are produced
simultaneously. The mass of each Wild Alaska Pollock product is reported by each company
and the catching amount needed to produce each Wild Alaska Pollock product is estimatedby GAPP, using a conversion rate for each product. These conversion rates are expressed asyields (percent recovered from fresh pollock biomass in final product mass) in Table 8. The
potential impacts from Catching and processing activities are apportioned to each productbased on its relative catching mass.
Given that the choice of allocation factors among Wild Alaska Pollock products is likely to be
influential to the results, a sensitivity analysis using an economic allocation metric(wholesale price, see Table 8) has been carried out. The economic values are for sold pollock
products in their final form; since the pollock parts before transformation are not products
and never get sold, the price for pre-transformation pollock is not available. The three-year
weighted average price is used in this study. The economic value of the entire production ofeach Wild Alaska Pollock product is applied to allocate the impact from fishing andprocessing.
A production flowchart representing the distribution of total catch through to final productform is represented in Figure 4. To reconcile total production to total catch weightequivalents in our mass balance exercise, the fresh pollock biomass to final product mass
ratios for all frozen products are used for this study. 1:1 ratios (100% yield rate) are applied
to all frozen products other than surimi. Since surimi has non-fish additives, a 0.91:1 ratio(109.9% yield rate) is used to convert production to catch weight.
As represented in Figure 4, fishmeal and fish oil are co-products through recovery plants.
Based on industry interviews, the estimated overall processing waste rate for fishmeal andoil producers is 3.5%. Therefore, the remaining catch weight (total catch weight sent tofishmeal and oil producers, minus a 3.5% processing waste rate, minus the amount used to
produce frozen products) are the raw materials to produce fishmeal and fish oil. The
production processes for fishmeal and fish oil cannot be divided, therefore mass allocation
LCA of Wild Alaska Pollock

33

between fishmeal and fish oil is performed based on the production volume (ProductWeight).
For those processors that do not produce fishmeal or fish oil, the amount of catch weightthat would have gone into fishmeal and fish oil production had they had that capability is
assumed to be processing waste returned to the sea. Waste does not carry any Catching andprocessing environmental impact. All impacts are attributed to Wild Alaska Pollock products.

Table 8. Yield rates and economic values (wholesale price) for Wild Alaska Pollock products

Fishery
location

Bering Sea/AleutianIslands (BSAI)

Yield rates (percent
recovered from fresh
pollock biomass in final
product mass)

Economic value(wholesale price)

Average of all sectors$ per MT

Fillet100%$2,580.39

Surimi109.9%$2,412.27

Roe

100%

$6,756.11

Fish oil

3.57%$1,316.67

Fishmeal

8.59%$1,893.33

Head &
Gutted100%$2,055.95

Minced100%$2,553.05

Milt

100%

$1,316.67

Stomach100%$2,403.33

Bones

100%

$1,316.67

Whole fish100%

$1,316.67

Belly flap

100%

$1,316.67

Otherretained
products

100%$1,316.67

Wild Alaska
Pollock
product

LCA of Wild Alaska Pollock

34

NOTE: The only
frozen products
assigned afunctional unit inthis study are fillet,
surimi, and roe.

Figure 4. Production Flow Chart of BSAI Fishery in metric tons (MT), all catches 2016 to 2018

LCA of Wild Alaska Pollock

35

4.2.6 Diesel energy apportions for fish oil and fishmeal processing
The energy data collected from each respondent represent aggregate consumption, including
cutting and dividing the fish, as well as the processing of fish oil (cooking, pressing,decantation, and centrifugation) and fishmeal (cooking, pressing, drying, and grinding). To beable to apportion energy between dividing whole fish and processing fish oil and fishmeal, asurvey of the industry to identify the incremental energy (in the form of diesel fuel) consumed
to produce fishmeal and fish oil has been done. We identified participants in each sector that
produce fishmeal and fish oil to include in the survey.
1) Shore-based: All Shore-based processors produce fishmeal and oil;2) Catcher-processors: 8 of the 11 Catcher-processors produce fishmeal and fish oil; and
3) Motherships: 2 of the 3 Motherships produce fishmeal and fish oil.
Using the responses, mass allocation between fishmeal and fish oil (based on production
volume) is used to determine energy consumption per kg of fishmeal and fish oil production.
We deducted the energy consumed to produce fishmeal and fish oil from the aggregatedconsumption to determine the amount of energy consumed to produce the other primary
products for each company.

4.3 Impact Assessment
4.3.1 Impact assessment method and indicators
Impact assessment classifies and combines the flows of materials, energy, and emissions intoand out of each product system by the type of impact their use or release has on theenvironment. The method to be used here to evaluate environmental impact is the ProductEnvironmental Footprint (PEF) method (JRC-IES 2017). This method assesses 16 different
potential impact categories (midpoint). It is the result of a project for the European
Commission that analyzed several life cycle impact assessment (LCIA) methodologies to reachconsensus. It is the official method to be used in the Product Environmental Footprint (PEF)
context of the Single Market for Green Products (SMGP) initiative (European Commission2013).
Table 9 describes the models used for each of the 16 indicators considered in the present
study. More detailed description is listed in Appendix 1, included at the end of this report.
The European Commission Joint Research Centre (JRC) classifies every impact category
according to the maturity and reliability of its underlying model:•••

Level I: recommended and satisfactory
Level II: recommended, but in need of some improvements
Level III: recommended, but to be applied with caution

Models classified at Level III are likely to evolve in the near future.

LCA of Wild Alaska Pollock

36

Table 9. Indicators and related assessment models used

IMPACT
CATEGORY OR
LCI INDICATOR

MODEL

UNIT

SOURCE

CLASSClimate change

Bern model – Global Warming
potentials (GWP) over a 100-yeartime horizon

kg CO2 eq

IPCC, 2013

I

Ozone depletion

EDIP model based on the ODPsof the WMO w/ infinite time
horizonkg CFC-11 eq

WMO, 1999

I

Human toxicity –non-cancer effects

USEtox model

CTUhRosenbaum etal., 2008

III(interim)

Human toxicity –cancer effects

USEtox model

CTUhRosenbaum etal., 2008

III(interim)

Particulate matter

PM method recom-mended by
UNEP

Deaths/kg
PM2.5emitted

UNEP 2016

I

Ionising radiation

Human Health effect modelkg U235 eq

Dreicer et al.,
1995

II

Photochemicalozone formation

LOTOS-EUROS modelkg NMVOC eq

van Zelm et al.,
2008

II

Acidification

Accumulated Exceedance modelmol H+ eq

Seppälä etal.,2006; Poschet al., 2008

II

Terrestrial
eutrophication

Accumulated Exceedance model

mol N eq

Seppälä etal.,2006; Poschet al., 2008

II

Freshwater
eutrophication

EUTREND modelkg P eq

Struijs et al.,
2009

II

Marineeutrophication

EUTREND model

kg N eq

Struijs et al.,
2009

II

Freshwater
ecotoxicity

USEtox model

CTUe

Rosenbaum etal., 2008

III(interim)

Mineral & metal
resource depletion

CML 2002 model (abiotic
depletion – ultimate reserves)

kg Sb eq

Guinée et al.,2002 and vanOers et al. 2002

III

Non-renewableenergy resource
depletion

CML 2002 model (abiotic
depletion – fossil)

MJ

Guinée et al.,2002 and vanOers et al. 2002

III

LCA of Wild Alaska Pollock

37

Land use

Soil Quality Index (based on the
LANCA model)

pointsBeck et al. 2010
and Bos et al.
2016

III

Water scarcity
footprint

AWARE 100 modelm3 waterdeprived eq

Boulay et al.
2016

III

Water consumption(W-R)

Impact 2002+

m3

Jolliet et al. 2003

I

No normalization of the results against an external reference is carried out, but an internal
normalization is performed presenting results on a relative basis (%) compared to thereference for each system. No weighting of the impact categories is done; they are presented
individually and not as a single score, as there is no objective method by which to achieve this.
We expect to focus on Climate change, Land use and Water consumption because these are
the most relevant ones to GAPP’s industry. All results for all indicators are provided inAppendix A, B and C.
4.3.2 Limitations of LCIA
Life cycle impact assessment results present potential and not actual environmental impacts.
Additionally, these categories do not cover all the environmental impacts associated withhuman activities. Impacts such as noise, odors, electromagnetic fields and others are notincluded in the present assessment. The methodological developments regarding suchimpacts are not sufficient to allow for their consideration within life cycle assessment. Other
impacts, such as potential benefits or adverse effects on biodiversity, are also only partly
covered by current impact categories.

4.4 Calculation tool
SimaPro 8.6 software, developed by PRé Consultants (www.pre.nl) has been used to assist the
LCA modelling and link the reference flows with the LCI database and link the LCI flows to therelevant characterization factors. The final LCI results are calculated combining foregrounddata (intermediate products and elementary flows) with generic datasets providing cradle-togate background elementary flows to create a complete inventory of the Wild Alaska Pollock
systems.

4.5 Contribution analysis
A contribution analysis has been performed to determine the extent to which each processmodeled contributes to the overall impact of the system under study. Lower quality data may
be suitable in the case of a process whose contribution is minimal. Similarly, processes with agreat influence on the study results should be characterized by high-quality information. In

LCA of Wild Alaska Pollock

38this study, the contribution analysis is a simple observation of the relative importance of thedifferent processes to the overall potential impact.

4.6 Uncertainty in LCI and LCIA
There are two types of uncertainty related to the LCA model:
Inventory data uncertainty; andCharacterization models uncertainty, which translate the inventory into environmental
impacts.
4.6.1 Inventory data uncertainty analysis
To quantify the uncertainty introduced in the results of a life cycle inventory analysis due tothe input uncertainty and data variability, data quality assessment results are included inAppendix D, listing each indicator score for processes and flows that contribute at least 1% to
one or more of the impact indicators. See more details in Section 4.1.2.
4.6.2 Characterization models uncertainty analysis
In addition to the inventory data uncertainty described above, there is also uncertainty related
to the LCIA method, which is about the characterization of the LCI results into mid-point
indicators. The accuracy of characterization factors depends on the ongoing research in the
many scientific fields behind life cycle impact modeling, as well as on the integration of current
findings within operational LCIA methods.
There are presently no systematic methods available for quantifying or evaluating theinfluence of the uncertainty in these characterization models within the assessments madehere. Without consideration of the uncertainty in LCIA characterization factors, theuncertainty assessment results derived here should be seen as something like a lower boundon the level of uncertainty in the systems and the uncertainty would be higher if also
considering the uncertainty in these characterization factors.

4.7 Sensitivity and scenario analysis
The parameters, methodological choices and assumptions used when modeling the systems
present a certain degree of uncertainty and variability. It is important to evaluate whether the
choice of parameters, methods, and assumptions significantly influences the study’s
conclusions and to what extent the findings are dependent upon certain sets of conditions.
Following the ISO 14044 standard, a series of sensitivity analyses are used to study the
influence of the uncertainty and variability of modeling assumptions and data on the results
and conclusions, thereby evaluating their robustness and reliability. Sensitivity analyses help
in the interpretation phase to understand the uncertainty of results and identify limitations.
The following parameters and choices are subjects for sensitivity analyses due to their highpotential impacts or uncertainty:

LCA of Wild Alaska Pollock

39

•
•
•

Energy consumption data, including fuels (used in Catching and processing) andelectricity (used in processing, if there is any);
Co-product allocation assumptions (e.g., economic metric);
Refrigerant type (e.g., using ammonia as the only refrigerant).

4.8 Critical Review
A critical review has been conducted by a review panel, including Dr. Peter Tyedmers, auniversity-based food system LCA expert and the chairman of the review panel; Dr. Friederike
Ziegler; and Dr. Ray Hilborn. This review process is instrumental in confirming that the study
has followed the stipulations set forth in the ISO 14040 and 14044 standards (ISO 2006a,
2006b), as well as PAS 2050-2 (BSI, 2012).
The critical review process was carried out in several steps:
1) Goal and scope report review (June 2020);
2) Full report review (June 2021);
3) Clarification of and response to points raised by the reviewers (June/July 2021);
4) Review of response in Step 3 and final comments by reviewers (July 2021).
The external critical review verification letter, as well as Quantis’ comments and responses tothe review report are presented in Appendix 2.

5 Results
5.1 Baseline results
This section provides baseline indicator results profiles per 1 kg of Wild Alaska Pollock
product evaluated in this study: fillet, surimi, roe, an average frozen Wild Alaska Pollock
product (i.e., combination of fillet, surimi, and roe), fish oil and fishmeal. For the purposes ofthis results summary, the Distribution stage reflects the East coast US as the first-tier
destination, although full results for all three destinations (including Asia and Europe) can beaccessed in Appendix A. Similarly, while only three indicators are highlighted in this
discussion (Climate change, Land use, and Water consumption) based on GAPP’s interests,
the full set of indicator results can be accessed in Appendix A.
5.1.1 Climate change indicator results - Overall
Figure 5 shows the Climate change indicator results for each functional unit. Among the life
cycle stages considered, the Catching and processing stage dominates the potential impact.
The Packaging and Distribution stages contribute much less to the life cycle potential
LCA of Wild Alaska Pollock

40impacts. The potential Climate change impacts span 0.83 to 6.38 kg CO2 eq per kg of
distributed product, with roe and fillet on the low end and fish oil and fishmeal on the highend. The relatively high impact of fish oil and fishmeal relative to the other products is a
result of the use of a mass metric for the allocation of co-products resulting from Wild Alaska
Pollock processing into usable products. Due to the high pollock mass requirements to
produce a unit of fish oil and fishmeal (see Section 4.2.5 for additional discussion), fish oil
and fishmeal are apportioned much of the impacts from Catching and processing.
Additionally, the energy that is utilized during the processing of fishmeal and fish oil isanother contributor to the Climate change indicator results for these two products.

kg CO2eq/kg of WAP productClimate change indicator result per kg of Wild
Alaska Pollock product
7

6.38

6.38

Fish oilFishmeal

6
5
4
3

0.83

1.01

0.83

0.93

FilletSurimiRoe

Average of 1
kg WAPfrozenproduct2
10Catching and processing

PackagingDistribution

Figure 5. Climate change indicator results for the six Wild Alaska Pollock functional units (based on PEF methodology
v1.4 (JRC-IES 2017))

The Catching and processing life cycle stage contributes to more than 70% of the fillet,
surimi, roe and average frozen product Climate change indicator. Catching and processing
contributes 97% to the fish oil and fishmeal Climate change indicator.
The Distribution stage contributes to about 20% of the fillet, surimi, roe and average frozen
product Climate change indicator, and 3% to the fish oil and fishmeal Climate change
indicator results.
Figure 6 presents close-up profile results specifically for the four frozen product functional
units. The main differences seen among frozen products are due to differences in Catchingand processing. Surimi has higher results for Catching and processing (0.78 kg CO2eq/kg),
mainly because of the production of additives and non-fish ingredients that supplement thepollock to make the surimi product. There are also differences in the Packaging life-cycle
stage, but these are not significant.
The fish oil and fishmeal functional units have identical indicator results in this study
because, due to the use of mass allocation, they are allocated the exact same Catching and
LCA of Wild Alaska Pollock

41

processing impact (including energy use at the recovery plant) per unit of production (kg of
final product). Packaging results do differ between fish oil and fishmeal but the difference isrelatively small.

kg CO2eq/kg of WAP productClimate change indicator result per kg of Wild
Alaska Pollock frozen product
1.2
1.0

0.83

1.01

0.83

0.93

Roe

Average of 1 kg WAP
frozen product

0.8

0.6
0.4
0.2
0.0

FilletSurimiCatching and processing

PackagingDistribution

Figure 6. Climate change indicator results for the four frozen product functional units (based on IPCC 2013)

Supporting data for Figure 5 and Figure 6 can be found in Appendix A.
5.1.2 Land use indicator results - Overall
Figure 7 shows the Land use indicator results for the six Wild Alaska Pollock functional units.
Packaging is the top contributing life cycle stage to the Land use indicator overall. For fillet
and roe, Packaging contributes 99% of the total Land use result. For surimi, average frozen
product, fish oil and fishmeal, Packaging contributes 66%, 77%, 10%, and 0.5% respectively.
The majority comes from the wood-based packaging, including pallets and cardboard boxes.
For fish oil and fishmeal, Catching and processing dominates the result.
Relative to the other frozen products, surimi has a large contribution from Catching andprocessing stage. The driver of this impact is from the additives and non-fish ingredients that
supplement the pollock to make the surimi product.

LCA of Wild Alaska Pollock

42

Pt/kg of WAP productLand use indicator result per kg of Wild Alaska
Pollock product
17.1

20
16

13.7
10.4

9.94

12
8
4

1.26

1.14

Fish oilFishmeal

0FilletSurimiRoe

Catching and processing

Average of 1
kg WAPfrozenproduct
PackagingDistribution

Figure 7. Land use indicator results for the six Wild Alaska Pollock functional units (based on Beck et al. 2010 and
Bos et al. 2016)

Supporting data for Figure 7 can be found in Appendix A.
5.1.3 Water consumption indicator results - Overall
Shown in Figure 8 are the Water consumption indicator results for the six Wild Alaska
Pollock functional units. Most of the Water consumption indicator impact comes from theCatching and processing stage. Surimi has the highest Water consumption (14.0 L/kg), and
95% of it is from Catching and processing. Fish oil and fishmeal have the next largest Water
consumption indicator (5.89 L/kg and 5.82 L/kg), of which 92% and 94% is from Catching andprocessing.

L/kg of WAP product

Water consumption result per kg of Wild Alaska
Pollock product
16
14
12
10
8
6
4
2
0

13.97

1.45

Fillet

7.93

5.89

5.82

Average of 1
kg WAPfrozenproduct

Fish oilFishmeal

1.40

SurimiRoe

Catching and processing

PackagingDistribution

Figure 8. Water consumption indicator results for the six Wild Alaska Pollock functional units (based on Impact
2002+)LCA of Wild Alaska Pollock

43

Supporting data for Figure 8 can be found in Appendix A.
5.1.4 Additional indicator results - Overall
To demonstrate the life cycle contributions across the full set of indicators evaluated in thisstudy, Figure 9, Figure 10, and Figure 11 show the life cycle stage contributions normalized
to each indicator’s total, for an average frozen Wild Alaska Pollock product, fish oil, andfishmeal, respectively. The Distribution indicator result shown is specific to distribution to
the East coast US. Results for other destinations can be find in Appendix A.
For the average frozen Wild Alaska Pollock product, Catching and processing contributes
more than 60% to all indicators except Land use. Ozone depletion is driven primarily byrefrigerant leakage. Other than Land use, most of the indicator results follow the patterns of
the Climate change indicator results.

Contribution of all environmental indicators per kg frozen Wild
Alaska Pollock product% contribution of life cycle stage to each indicator's overall
result

100%

90%
80%

70%
60%
50%
40%
30%
20%
10%
0%

Catching & Processing

PackagingDistribution to East coast US

Figure 9. Full suite of normalized indicator results for 1 kg of average frozen Wild Alaska Pollock product distributed
to East coast US (based on PEF v1.4)

For fish oil distributed to East coast US, the Catching and processing stage contributes atleast 85% across all indicators evaluated.

LCA of Wild Alaska Pollock

44

% contribution of life cycle stage to each indicator's overall
resultContribution of all environmental indicators per kg fish oil
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Catching & Processing

PackagingDistribution to East coast US

Figure 10. Full suite of normalized indicator results for 1 kg of fish oil distributed to East coast US (based on PEF
v1.4)

For fishmeal distributed to the East coast US, the same pattern is more pronounced—
Catching and processing contributes to over 90% of potential indicator results. The
contribution of Catching and processing is slightly more pronounced compared with fish oil
due to the lower impact from Packaging.

LCA of Wild Alaska Pollock

45

% contribution of life cycle stage to each indicator's overall
resultContribution of all environmental indicators per kg fishmeal
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Catching & Processing

PackagingDistribution to East coast US

Figure 11. Full suite of indicator results for 1 kg of fishmeal distributed to East coast US (based on PEF v1.4)

Supporting data for Figure 9, Figure 10 and Figure 11 can be found in Appendix A.

5.2 Contribution analysis – Catching and processing stage
Given the dominance of the Catching and processing stage to the indicator results of theseWild Alaska Pollock products, a contribution analysis of this life cycle stage is discussed
below.
5.2.1 Climate change indicator results – Catching and processing stage
Across all functional units except surimi, energy-related activities tend to contribute to about
half of the Catching and processing stage Climate change indicator results. The other halftends to be driven by refrigerant leakage, and for surimi only, a portion is due to the
upstream impacts of producing surimi product additives and ingredients. Activities like
commuting and waste disposal tend to be negligible to the Climate change indicator results.

LCA of Wild Alaska Pollock46

kg CO2eq/kg of Wild Alaska Pollock productContributions to the Catching and processing stage,
Climate change indicator
7
6.19

6.19

Commuting

6

Waste
5

Refrigerant leakage

4

Additives for Surimi

3

Non-durable goods

2

Durable and semi-durable
goods
Energy

1

0.59

0.78

0.59

FilletSurimi

Roe

0.69

0

Averageof 1 kg
WAP
frozenproduct

Fish oil Fishmeal

Figure 12. Catching and processing stage contribution analysis to the Climate change indicator for the six Wild
Alaska Pollock products (based on IPCC, 2013)

5.2.2 Land use indicator results – Catching and processing stage
The Land use indicator results are highest for surimi, which is driven by the upstream
impacts of additives and ingredients incorporated into the surimi product. Thus, these
impacts are unrelated to the Wild Alaska Pollock, and are attributed to the additives.
Additives with high Land use results tend to be agricultural products, especially sugar, that
require either land occupation or land transformation during their cultivation.
For other products, the impact mainly comes from the production of paint as a maintenancematerial, and the production of water filters, which are used to desalinize the sea water.

LCA of Wild Alaska Pollock

47

Contributions to the Catching and processing stage, Landuse indicator
75.85

Pt/kg of Wild Alaska Pollock product6

Commuting
5

Waste

4

Refrigerant leakage
3.10

Additives for Surimi

3

Non-durable goods

2
1.12

1.12

1
0.12

Durable and semi-durable
goods
Energy0.120FilletSurimiRoe

Averageof 1 kg
WAP
frozenproduct

Fish oil Fishmeal

Figure 13. Catching and processing stage contribution analysis to the Land use indicator for the six Wild Alaska
Pollock products (based on Beck et al. 2010 and Bos et al. 2016)

5.2.3 Water consumption indicator results – Catching and processing stage
Figure 14 presents the Water consumption indicator results for the six functional units.
Because some activities within the Catching and processing stage are net positive water
consumers and others are net negative, the red dot indicates the overall net Water
consumption value for this stage. The main contributor under non-durable goods ispurchased tap water. The negative Water consumption values result from wastewater sent
to a wastewater treatment plant, which means during the wastewater treatment process
there is water sent back to the environment. Notably, the Additives for surimi are relatively
large consumers of water, likely due to the agricultural origins of the additive ingredientsand the water demands of cultivation. Otherwise, Non-durable goods are a meaningful
contributor to Water consumption, mainly due to the purchased water used during
processing for cleaning.

LCA of Wild Alaska Pollock

48

Contributions to the Catching and processing stage, Water
consumption indicator
30

5.45

5.45

Commuting
Waste

20

L/kg of Wild Alaska Pollock product13.28

Refrigerant leakage
7.26

10

Non-durable goods
0.74

0.74

Durable and semi-durable
goods
Energy0Fillet-10Additives for Surimi

SurimiRoe

Average Fish oil Fishmealof 1 kg
WAP
frozenproduct

Total-20
Figure 14. Catching and processing stage contribution analysis to the Water consumption indicator for the six Wild
Alaska Pollock products (based on Impact 2002+)

5.3 Scenario and Sensitivity analysis
5.3.1 Scenario analysis with economic allocation
Allocation among co-products from the processing of Wild Alaska Pollock plays a significant
role in this study. To test the sensitivity of the results to the choice of allocation
methodology, results with an economic allocation metric among co-products are provided
below.
Shown in Figure 15 are the Climate change indicator results for the six functional units forboth the mass allocation and the economic allocation. Overall, the use of a mass metricresults in a relatively lower impact (as compared to economic allocation) for the frozen Wild
Alaska Pollock products (fillet, surimi, and roe), by pushing more of the Catching andprocessing impact to fishmeal and fish oil, which have a relatively low unit economic value
but consume a higher volume of fish parts per unit of final product.

LCA of Wild Alaska Pollock

49

6.38

6.38

3.64

FilletSurimiRoe

Catching and processing

Mass

Average of 1
kg WAPfrozenproduct

Fish oil

PackagingDistribution

Economic

1.55

1.26

Mass

Economic

Economic

Mass

0.93 1.71

0.83

Economic

1.68

Mass

1.01

Economic

1.54

Mass

0.83

Economic

7
6
5
4
3
2
1
0

Mass

kg CO2eq/kg Wild Alaska Pollock productClimate change result by mass and economic
allocation

Fishmeal

Figure 15. Sensitivity test using an economic co-product allocation metric (based on IPCC 2013)•

Fillet – 186% of mass allocation/baseline result•

Surimi – 166%•

Roe – 439%•

Average frozen product – 185%•

Fish oil – 20%•

Fishmeal – 24%

The underlying mass and economic metrics, as well as the results data to support thesecharts, can be found in Appendix B. In this study, 3-year weighted average prices are applied
to take into account price variability, using economic values from the years 2016, 2017, and2018. When the relative economic values change significantly within these co-products, theresult would be expected to change as well, due to the use of economic allocation.
5.3.2 Scenario analysis with using ammonia as the only refrigerant
Refrigerant leakage is another big contributor to the total Climate change result, mainly
because of the consumption of freon. As noted above, CFC-12, which has a high global
warming potential, is used to represent freon refrigerants in this study. There are several
different types of refrigerant consumed in this industry, including freon, R507, CO2, R22,
R134A, R404A, and ammonia. To test the potential impact of substituting refrigerants withlow global warming potential, a scenario analysis using ammonia as the only refrigerant iscarried out in this study.

LCA of Wild Alaska Pollock

50Due to the relatively low global warming potential of ammonia, and the relatively low impact
during the production stage, the total Climate change result goes down about 30%
compared with the baseline result.
•

Fillet – 69% of using ammonia as the only refrigerant/baseline result•

Surimi – 77%•

Roe – 69%••

Average frozen product – 73%
Fish oil – 58%•

Fishmeal – 58%

6.38

6.38
3.68

FilletRoe

Catching and processing

Average of 1
kg WAPfrozenproduct
Packaging

Fish oilAmmonia as
refrigerant

Baseline

Ammonia as
refrigerant

Ammonia as
refrigerant

Surimi3.68

0.68

Baseline

0.57

Ammonia as
refrigerant

0.83

Baseline

0.78

Baseline

0.93

1.01

Ammonia as
refrigerant

0.57

Baseline

0.83

Ammonia as
refrigerant

7
6
5
4
3
2
1
0

Baseline

kg CO2eq/kg Wild Alaska Pollock productClimate change result by mass and economic
allocation

Fishmeal

Distribution

Figure 16. Scenario results for using ammonia as the only refrigerant (IPCC 2013)

5.3.3 Sensitivity analysis for diesel consumption
Given the importance of energy-related activities (i.e., fuel and electricity use) to theindicator results across the Wild Alaska Pollock products, a sensitivity test has been carriedout on the quantity of diesel consumption during Catching and processing (this excludes
diesel consumption used to process fishmeal and oil). Diesel is the top contributor to the
Climate change indicator, and given that some data extrapolation has been done toapportion the total diesel consumption to processing fishmeal and fish oil (see Section 4.2.6
for more details), there is a degree of uncertainty in data quality.
Shown in Figure 17 are the results under the baseline scenario as well as plus and minus 10%(+/-10 ) diesel consumption quantity. The choice of +/-10 was based on consideration of thedata quality.
LCA of Wild Alaska Pollock

51

With diesel consumption fluctuating 10%, Climate change indicator results for surimi andaverage frozen product rise or fall 3%, while for fillet, roe, fish oil, and fishmeal it would riseor fall 4%. From this analysis, it can be concluded that the results across the six Wild Alaska
Pollock products would not change substantially. It should be recommended, however, thatin future work, diesel data collection be emphasized for its importance.

6.38 6.62 6.15 6.38 6.61 6.15

7
6
5
4
3
2
1

0.83 0.86 0.80 1.01 1.04 0.98 0.83 0.86 0.80 0.93 0.95 0.89

FilletSurimiRoe

Catching and processing

Average of 1 kg
WAP frozen
product

Packaging

Fish oil-10% diesel+10% diesel

Baseline-10% diesel+10% diesel

Baseline-10% diesel+10% diesel

Baseline-10% diesel+10% diesel

Baseline-10% diesel+10% diesel

Baseline-10% diesel+10% diesel

0
Baseline

kg CO2eq/kg WAP productClimate change sensitivity test result per kg Wild
Alaska Pollock productFishmeal

Distribution

Figure 17. Sensitivity results for diesel fuel consumption (IPCC 2013)

6 Key Findings
The results of this cradle-to-distributor life cycle assessment of various Wild Alaska Pollock
products reveal that per kg of distributed product (to the East coast of the US):•••

Climate change indicator results may vary from 0.83 to 6.38 kg CO2eq (for roe to fish
oil and fishmeal, respectively);
Land use indicator results may vary from 1.14 to 17.1 Pt (for fishmeal to surimi,
respectively);
Water consumption indicator results may vary from 1.40 to 13.97 L (for roe to surimi,
respectively).

Within the Climate change indicator results, Catching and Processing dominates the impactof the product. Energy, mainly diesel, is the top contributor to the Climate change indicator.
Focusing on reducing diesel consumption, or seeking low-carbon alternatives, would help
reduce the impact. Refrigerant leakage is another big contributor to the Climate change
LCA of Wild Alaska Pollock52

indicator. Focusing on reducing leakage and seeking low-carbon alternatives would help
reduce the impact.
Within Land use, Packaging is the top contributor to the impact of the product. The majorityof the Land use indicator results comes from wood pallets. Improving the reuse rate andintroducing recycled content would help reduce the impact.
Within Water consumption, the Catching and processing life cycle stage dominates theimpact of the product. Surimi has a significant higher Water consumption results due to the
production of additives. Water consumption for other products is mainly from the purchased
tap water used for cleaning.
These results are highly sensitive to the choice of co-product allocation metric. In thebaseline results of this study, a mass allocation metric was used to apportion the impacts ofCatching and processing across the various useful outputs of processing. When an economic
allocation with wholesale price metric is used, the results of all frozen products significantly
increase. For 1 kg of average frozen product, the Climate change indicator result shifts from
0.93 to 1.71 kg CO2eq, and for roe it shifts dramatically from 0.83 to 3.64 kg CO2eq. For fish
oil and fishmeal, results go down with economic allocation. For fish oil, the Climate change
indicator result shifts from 6.38 to 1.26 kg CO2eq, and for fishmeal it shifts from 6.38 to 1.55
kg CO2eq.
The results of this work are on the same order of magnitude as work Fulton (2010) carriedout to estimate the Climate change potential of Wild Alaska Pollock fillets (0.59 kg CO2eq per
kg fillet product). Care must be taken when comparing results of LCAs carried out withpotentially different scopes, system boundaries, and data quality; however, it helps tovalidate, in broad strokes, that the orders of magnitude are similar, as one would expect.

7 Recommendations
Future environmental footprinting of Wild Alaska Pollock can be improved through thefollowing activities:••

Obtaining a higher data collection / survey response rate, and obtaining productspecific activity data to minimize the need for allocation-, apportionment- andextrapolation-related modeling decisions.
Obtaining survey data from a more deliberately representative sampling of companies,
e.g., from companies operating in the Gulf of Alaska.

•

Consider including a plastic leakage indicator among the metrics evaluated to evaluate
the potential impact of lost nets in the sea.

•

Consider bolstering the evaluation of impacts on biodiversity, both direct and indirect.
Over-fishing might be a consideration for wild caught species.
LCA of Wild Alaska Pollock

53

Improvement of Wild Alaska Pollock’s environmental footprint can be made by:•

Focusing on reducing diesel consumption, or seeking low-GWP alternatives, would
help reduce the impact.

•

Focusing on reducing refrigerant leakage and seeking low-GWP alternatives, e.g.,ammonia, for refrigerant would help reduce the impact.
Focusing on packaging innovation to reduce the Land use impact, e.g., improving
recycled content, improving reuse rate, exploring low-wood content alternatives.

•

It is not our intention to make competitive or derogatory claims about other forms of
seafood or land-based animal protein, however if any audience would like to gain a deeper
understanding of where Wild Alaska Pollock products fall on the animal protein continuum interms of environmental impacts, please acknowledge the following:•

The scope of studies comparing different animal proteins can be different. This study
is a cradle-to-gate study. Distribution after the first-tier customer, consumer use
phase, and end-of-life of food waste impacts are excluded. Other studies may have a
different scope.

•

Wild Alaska Pollock are wild caught fish. There is no cultivation impact associated withthe fish catch. Other cultivated proteins (e.g., beef) could have a cultivation impact,
including land use and land use change.
The methodologies used in different studies can vary, including impact calculation
methods, data year, allocation method among co-products, and functional unit
definitions.

•

8 Limitations
When using the information provided by this study, the following limitations should beconsidered along with the context described in earlier sections of this report:•

The direct comparison of this study to other studies may not be meaningful unless thefunctional unit and goal and scope assumptions are aligned, such as assumptions
around the life cycle stages considered and the co-product allocation metric.

•

Notable extrapolation has been done to fill data gaps. It is recommended to update
the study when data are more representative of the overall GAPP population,
especially for key activity data such as energy consumption and refrigerant use.
This study uses BSAI data to represent both BSAI and GOA. There could potentially besome over- or underestimation, as the catching vessels in GOA tend to have a shorterroute than those of BSAI. However, the vessel catching efficiency (catch per unit ofeffort) might be different, so there is no clear conclusion on whether there is any•

LCA of Wild Alaska Pollock

54•overestimation. It is recommended to revisit the study with data from GOA to better
represent the fishing information geographically.
The natural fisheries system results in Wild Alaska Pollock’s lower contributions toresource depletion and environmental concerns, from an LCA perspective, and in orderto sustain this natural capital the ecosystem enabling this system to function must be
protected. Although the health of ecosystem in which the Wild Alaska Pollock arecaught was not directly assessed in this study due to the limitations of our
methodologies, the relatively low environmental footprint of Wild Alaska Pollock
depends greatly on the health of its ecosystem and the services it provides.

•

This is a cradle to gate LCA. Downstream activity is not included, including use stage
and potentially influential consumer behaviors.

•

LCIA results present potential and not actual environmental impacts. They are relative
expressions, which are not intended to predict the final impact or risk on the natural
media or whether standards or safety margins are exceeded. Additionally, thesecategories do not cover all the environmental impacts associated with humanactivities. Impacts such as noise, odors, electromagnetic fields and others are notincluded in the present assessment. The methodological developments regarding suchimpacts are not sufficient to allow for their consideration within LCA.

•In the impact assessment models and life cycle inventory data underlying LCA, there
are different types of uncertainty, such as parameter uncertainty, model uncertainty,
or value choices (Huijbregts 1998; Hertwich and Hammitt 2001a,b). Although it is clearthat uncertainties in models and data exist, LCIA methods rarely report uncertainties
for their characterization factors. Spatial variability, and the limitations within methods
to be spatially explicit regarding emissions that have location-dependent impacts, is an
important source of uncertainty to consider in the context of LCA. Further discussionof spatial aspects, as well as other considerations in life cycle impact assessment, canbe found in Verones et al. (2017). In the context of the indicators evaluated in thisstudy, some have low spatial uncertainty, namely, GWP, and therefore the reported
indicator results can safely be considered representative for any geographic region
where emissions might take place. For other indicators, there is high regionalvariability that is not carried through in the average data considered by the method.
Examples of this include the Water consumption inventory, which does not consider
variable local scarcity, and acidification, which is highly dependent on the buffer
capacity of soils to neutralize acid rain, which is a regional issue. For other LCIA
indicators, uncertainty in the results is driven by limitations in the inventory. For
instance, for ozone formation and the effect on terrestrial ecosystems, much
uncertainty stems from the type of diesel fuel used and emissions controls around theuse, which may be correlated to geographic regions due to regulations.

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9 References
American Seafoods, Sustainable Business Consulting. 2017. Carbon Footprint Report for
Calendar Years 2010A, 2010B, 2011A, 2011B, and 2012A.
Boulay A, Bare J, Benini L, et al. 2017. The WULCA consensus characterization model for water
scarcity footprints: assessing impacts of water consumption based on available waterremaining (AWARE). Int J Life Cycle Assess 1–23. doi: 10.1007/s11367-017-1333-8
BSI 2008. PAS 2050 - Assessing the life cycle greenhouse gas emissions of goods and services.
http://www.bsigroup.com/en/Standards-and-Publications/How-we-can-helpyou/Professional-Standards-Service/PAS-2050/
BSI 2012. PAS 2050-2:2012 Assessment of life cycle greenhouse gas emissions. Supplemental
requirements for the application of PAS 2050:2011 to seafood and other aquatic foodproducts. British Standard Institute. London, UK
Dreicer, M; Tort, V; Manen, P. 1995. Nuclear fuel cycle: estimation of physical impacts andmonetary valuation for priority pathways. Feb 1, 1995.
Ekvall T, Tillman A. 1997. Open-loop recycling: Criteria for allocation procedures. Int J Life Cycle
Assess 2(3): 155-162
European Commission 2013. 2013/179/EU: Commission Recommendation of 9 April 2013 on
the use of common methods to measure and communicate the life cycle environmental
performance of products and organisations. Annex III: Organisation Environmental Footprint(OEF) Guide. Official Journal of the European Union, L 124, Volume 56, May 4th, 2013.
Fulton, S. 2010. Fish and Fuel: Life cycle greenhouse gas emissions associated with Icelandic
cod, Alaskan pollock, and Alaskan pink salmon fillets delivered to the United Kingdom [M.S.
Thesis]. Halifax, Nova Scotia: Dalhousie University, Environmental Studies. Available athttps://dalspace.library.dal.ca/ handle/10222/13042. Accessed June 2, 2021.
Goedkoop, M., Heijungs, R., Huijbregts, M. A. J., Schryver, A. De, Struijs, J., & van Zelm, R. 2013.
ReCiPe 2008: A life cycle impact assessment method which comprises harmonised category
indicators at the midpoint and endpoint level. First edition (version 1.08). Report I:
Characterisation
Hellweg S, Demou E, Bruzzi R, Meijer A, Rosenbaum R, Huijbregts M, et al. 2009. Integrating
human indoor air pollutant exposure within life cycle impact assessment. Environ Sci &Technol 43(6): 1670-1679
IPCC 2013. Adoption and acceptance of the “2013 supplement to the 2006 guidelines:
Wetlands” (Vol. 2). Geneva, Switzerland.
ISO 2006a. 14040: Environmental management – life cycle assessment – principles andframework. International Organization for Standardization, Geneva, Switzerland.
ISO 2006b. 14044: Environmental management – life cycle assessment – requirements andguidelines. International Organization for Standardization, Geneva, Switzerland.
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JRC-IES 2017. Product Environmental Footprint Category Rules Guidance. Version 6.2, June
2017. European Commission-Joint Research Centre - Institute for Environment andSustainability.
Jolliet, O., Margni, M., Charles, R., Humbert, S., Payet, J., Rebitzer, G., Rosenbaum, R. 2003.
Impact 2002+: A new life cycle impact assessment methodology. Int J Life Cycle Assess
8(6):324-330
Parker, R.W.R., Tyedmers P.H. 2015. Fuel consumption of global fishing fleets: current
understanding and knowledge gaps. Fish and Fisheries, 16: 684-696. doi:10.1111/faf.12087
Reap, J., Roman, F., Duncan, S., Bras, B. 2008. A survey of unresolved problems in life cycle
assessment. Part 1: goal and scope and inventory analysis. Int J Life Cycle Assess 13(4): 290300
Rosenbaum, R. K., Bachmann, T. M., Gold, L. S., Huijbregts, M. A. J., Jolliet, O., Juraske, R.,.
Hauschild, M. Z. 2008. USEtox—the UNEP-SETAC toxicity model: recommended
characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact
assessment. The International Journal of Life Cycle Assessment, 13(7), 532–546.
Rosenbaum, R. K., Anton, A., Bengoa, X., Bjorn, A., Brain, R., Bulle, C., … Wallman, M. 2015.
The Glasgow consensus on the delineation between pesticide emission inventory and impact
assessment for LCA. The International Journal of Life Cycle Assessment, 20, 765–775.
doi:10.1007/s11367-015-0871-1
Swiss Center for Life Cycle Inventories (SCLCI). 2017. ecoinvent database v3.4.
http://www.ecoinvent.org/home/.
Swiss Center for Life Cycle Inventories (SCLCI). 2019. ecoinvent database v3.4.
http://www.ecoinvent.org/home/.
Udo de Haes, H.A., Finnveden, G., Goedkoop, M. 2002. Life-Cycle Impact Assessment: Striving
towards best practice. Soc Environ Toxicol & Chem: 272
USEPA (2016) Advancing Sustainable Materials Management: 2014 Facts and Figures.
https://www.epa.gov/sites/production/files/201611/documents/2014_smm_tablesfigures_508.pdf
Van Oers et al. 2002. Abiotic resource depletion in LCA. Improving characterization factor for
abiotic resource depletion as recommended in the new Dutch LCA handbook. 25 June 2002.
Weidema, B.P., Wesnaes, M.S. 1996. Data quality management for life cycle inventories – anexample of using data quality indicators. J Clean Prod 4(3-4): 167-174
Weidema, B.P., Bauer, C., Hischier, R., Mutel, C., Nemecek, T., Reinhard, J., Vadenbo, C.O.,
Wernet, G. 2013. Overview and methodology. Data quality guideline for the ecoinvent
database version 3. Ecoinvent Report 1(v3). The ecoinvent Centre. St. Gallen, Switzerland
WULCA. AWARE methodology. http://www.wulca-waterlca.org/aware.html

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10 Appendices
For Appendices A, B, C, D, and E, see file “GAPP_WAP LCA_Appendix.xlsx”.

10.1 Appendix 1 – Description of impact categories
Climate change
Model: Bern model – Global Warming potentials (GWP) over a 100-year time horizon (IPCC
2013)
Unit: kg CO2 eq
Impact category that accounts for radiative forcing caused by greenhouse gas (GHG)
emissions such as carbon dioxide (CO2), methane (CH4) or nitrous oxide (N2O). The capacityof a greenhouse gas to influence radiative forcing is expressed in terms of a reference
substance (carbon dioxide equivalents) and considers a time horizon of 100 years following
the guidelines from the Intergovernmental Panel on Climate Change (IPCC 2013). Radiative
forcing is the mechanism responsible for global warming.

Ozone depletion
Model: EDIP model based on the ODPs of the WMO with infinite time horizon (WMO 1999)
Unit: kg CFC-11 eq
Impact category that accounts for the degradation of stratospheric ozone due to emissionsof ozone-depleting substances, for example long-lived chlorine and bromine containing
gases (e.g. CFCs, HCFCs, Halons). The emission factors are calculated using Ozone Depletion
Potentials (ODP) reported by the World Meteorological Organization. The ODP is a relative
measure for the potency of a substance to destroy the ozone layer. Stratospheric ozone
filters out most of the sun's potentially harmful shortwave ultraviolet (UV) radiation. When
this ozone becomes depleted, more UV rays reach the earth. Exposure to higher amounts of
UV radiation can causes damages to human health such as skin cancer, cataract andweakened immune system. The impact metric is expressed in kg CFC-11-eq (CFC-11 to air
equivalents).

Human toxicity, non- cancer effects
USEtox model (Rosenbaum et al. 2008)
Unit: CTUh
Impact category that accounts for the adverse health effects on human beings caused by theintake of toxic substances through inhalation of air, food/water ingestion, penetration
through the skin insofar as they are related to non-cancer effects that are not caused byparticulate matter or ionizing radiation. The impact metric is expressed in CTUh (i.e.
comparative toxic units for humans in terms of cases, the estimated increase in morbidity inthe total human population).
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Human toxicity, cancer effects
USEtox model (Rosenbaum et al. 2008)
Unit: CTUh
Impact category that accounts for the adverse health effects on human beings caused by theintake of toxic substances through inhalation of air, food/water ingestion, penetration
through the skin insofar as they are related to cancer. The impact metric is expressed in
CTUh (i.e. comparative toxic units for humans in terms of cases, the estimated increase inmorbidity in the total human population).

Particulate matter
Model: PM method recommended by UNEP (UNEP 2016)
Unit: deaths per kg PM2.5-emitted
Sometimes named respiratory effects, respiratory inorganics or winter smog, this impact
category measures the potential impact on human health (such as acute and chronic
respiratory diseases and asthma attacks) caused by emissions of inorganic particles. It takes
into account the adverse health effects on human health caused by emissions of Particulate
Matter (PM) and its precursors (NOx, SOx, NH3) into the air. The impact metric is expressed indeaths per kg PM2.5-emitted (PM2.5 covers all particles < 2.5 µm).

Ionising radiation
Model: Human Health effect model (Dreicer et al. 1995)
Unit: kg U235-eq
Impact category that accounts for the adverse health effects on human health caused by theroutine releases of radioactive material into air and water. The model describes the routine
14 atmospheric and liquid discharges in the French nuclear fuel cycle. The impact metric isexpressed in kg U235-eq (Uranium 235 to air equivalents).

Photochemical ozone formation
Model: LOTOS-EUROS model (van Zelm et al., 2008)
Unit: kg NMVOC-eq
Impact category that accounts for the formation of ozone at the ground level of thetroposphere caused by photochemical oxidation of Volatile Organic Compounds (VOCs) andcarbon monoxide (CO) in the presence of nitrogen oxides (NO x) and sunlight. Highconcentrations of ground-level tropospheric ozone damage vegetation, human respiratory
tracts and manmade materials through reaction with organic materials. The impact metric isexpressed in kg NMVOC-eq (non-methane volatile organic carbon to air equivalents).
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Acidification
Model: Accumulated Exceedance model (Seppälä et al.2006; Posch et al. 2008)
Unit: mol H+ -eq
Impact category that addresses impacts due to acidifying substances in the environment.
Emissions of nitrogen oxides (NOx), ammonia (NH3) and sulphur oxides (SOx) lead to releasesof hydrogen ions (H+) when the gases are mineralized. The protons contribute to theacidification of soils and water when they are released in areas where the buffering capacity
is low, resulting in forest decline and lake acidification. The impact metric is expressed inmole H+-eq (hydrogen ions to soil and water equivalents).

Terrestrial eutrophication
Model: Accumulated Exceedance model (Seppälä et al.2006; Posch et al. 2008)
Unit: mol N-eq
Impact category that addresses impacts from nutrients (mainly nitrogen and phosphorus)
from sewage outfalls and fertilized farmland which accelerate the growth of vegetation insoil. The degradation of organic material consumes oxygen resulting in oxygen deficiency.
With respect to terrestrial eutrophication, only the concentration of nitrogen is the limiting
factor and hence important. The impact metric is expressed in mole N-eq (nitrogenequivalents).

Freshwater eutrophication
Model: EUTREND model (Struijs et al. 2009)
Unit: kg P-eq
Impact category that addresses impacts from nutrients (mainly nitrogen and phosphorus)
from sewage outfalls and fertilized farmland which accelerate the growth of algae and othervegetation in freshwater. The degradation of organic material consumes oxygen resulting inoxygen deficiency. In freshwater environments, phosphorus is considered the limiting factor.
The impact metric is expressed in kg P-eq (kg phosphorous to freshwater equivalents).

Marine eutrophication
Model: EUTREND model (Struijs et al. 2009)
Unit: kg N-eq
Impact category that addresses impacts from nutrients (mainly nitrogen and phosphorus)
from sewage outfalls and fertilized farmland which accelerate the growth of algae and othervegetation in marine water. The degradation of organic material consumes oxygen resulting

LCA of Wild Alaska Pollock60

in oxygen deficiency. In marine environments, nitrate (NO 3) is considered the limiting factor.
The impact metric is expressed in kg N-eq (kg nitrogen to water equivalents).

Freshwater ecotoxicity
USEtox model (Rosenbaum et al. 2008)
Unit: CTUe
Impact category that addresses the toxic impacts on an ecosystem, which damage individual
species and change the structure and function of the ecosystem. Ecotoxicity is a result of avariety of different toxicological mechanisms caused by the release of substances with adirect effect on the health of the ecosystem. The impact metric is expressed in CTUe (i.e.
comparative toxic unit for ecosystems in terms of the estimated potentially affected fractionof species (PAF) integrated over volume and time, i.e. PAF*m3*y).

Resource use, minerals and metals
Model: CML 2002 model (Guinée et al., 2002 and van Oers et al. 2002)
Unit: kg Sb eq
Category that measures the potential impact on resource depletion from mineral and metals
resource use. The emission factors are determined on an ultimate reserves and rate of deaccumulation approach. The impact metric is expressed in kg Sb-eq (kg antimony
equivalents).
Resource use, energy carriers
Model: CML 2002 model (Guinée et al., 2002 and van Oers et al. 2002)
Unit: MJ
Category that measures the potential impact on non-renewable resource depletion fromenergy carriers (i.e., fossil fuels and uranium). The impact metric is expressed in MJ(megajoules).

Land use
Model: Soil quality index based on LANCA model (Beck et al. 2010 and Bos et al. 2016)
Unit: points (dimensionless)
The LANCA (Land Use Indicator Value Calculation in Life Cycle Assessment) model assesses
the environmental impact from land occupation and land transformation through four
indicators: biotic production, erosion resistance, mechanical filtration and groundwater
replenishment. The European Commission Joint Research Centre (JRC) aggregated these intoa single Soil Quality Index. The LANCALCA of Wild Alaska Pollock

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Water scarcity footprint
Model: AWARE 100 (Boulay et al., 2016)
Unit: m3 water deprived-eq
This impact indicator assesses the potential of water deprivation, to either humans or
ecosystems, building on the assumption that the less water remaining available per area, themore likely another user will be deprived. It is based on the AWARE 100 model, therecommended method from WULCA for water consumption impact assessment in LCA.

10.2 Appendix 2 – External Critical Review Verification Letter
Appended below is the final critical review verification letter from the expert panel, dated
July 16, 2021, followed by the panel’s earlier comments and Quantis’ responses.
Prior to finalization of the report, Quantis made the following additional changes in an
attempt to, in part, address the residual issues identified in the panel’s letter:
1. Added text to clarify that the primary data used in this study only record when freon
was used but do not specify CFC-12 vs. other options. Therefore, Quantis chose tomodel using CFC-12 for freon as a conservative assumption. Quantis also added
additional mention of the scenario analysis using ammonia as the sole refrigerant.
2. Expanded Table 6 to include all Inventory results (beyond just refrigerants and energy),
and added Table 7 to show Inventory results for fish oil.
3. Added text to further explain the approach used to model employee commuting(Section 3.4.1).

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Ms. Sarah Beaubien
Director, Quantis US
240 Commercial Street #3B
Boston, MA 02109
July 16, 2021

Dear Ms. Beaubien,
We write as the three-member Review Panel commissioned to: 1) provide guidance
to the process of completing, and 2) undertake a final critical review of Quantis’ Life
Cycle Assessment of Wild Alaska Pollock: Final ISO LCA Report, dated July 15,
2021, to assure it conforms with International Organization for Standardization (ISO)14040 and 14044 guidance documents for conducting life cycle assessment (LCA).
This letter communicates the overall assessment of our review and provides a final
short list of additional details that could be addressed as the report is finalized.
Completion of these would be ideal but are not mandatory.
Members of the Review Panel and signatories of this letter are:• Ray Hilborn, Professor, School of Aquatic and Fishery Sciences, University ofWashington• Friederike Ziegler, Senior scientist, Research Institutes of Sweden, and• Peter Tyedmers, Professor, School for Resource and Environmental Studies,
Dalhousie University (chair of the Review Panel).
The Review Panel was established and initiated work in April, 2020. As a panel wehave reviewed and provided detailed feedback on an initial Goal and Scope
document provided by the Quantis analysts leading this work in June of 2020. In
June of 2021, we reviewed and provided detailed feedback on a Draft final report that
was accompanied by a set of Appendices and related files. Then on July 15, 2021 wewere provided with a revised version of the final report that we have all now
reviewed. Before and between these major review activities, members of the Review
Panel were also frequently consulted and provided advice related to a wide range ofdata acquisition and handling issues that the Quantis team encountered inundertaking this LCA.
Overall, we concur that the LCA study described above meets ISO 14040 and 14044
requirements for the public disclosure of comparative statements.
There are residual issues that we have identified in our review of the revised final
report that have been previously communicated to the Quantis team via e-mail but
are briefly reproduced below. In our view, addressing these issues in the report would
be ideal but are not essential for the report to meet ISO standards. The issues thatwould ideally be addressed are:
• In the report it appears that it is an assumption that the main Freon refrigerant
lost on fishing boats is CFC-12. If it is an assumption this would be good tomake clear. If it is based on data provided by one or more firms then thisshould be indicated. This is an important issue as refrigerant loss has beenfound to represent a substantial source of life cycle greenhouse gas emissions

••from the Alaska pollock supply chain and there are many Freon refrigerants
also in wide usage and some have much lower global warming potentials than
CFC-12.
In our feedback on the draft final report, we’d urged inclusion of detailed life
cycle inventory (LCI) data in the body of the final report itself. Though some
important LCI data have now been included in the report (related to fuel use
and refrigerant loss when fishing), these reported LCI data are limited. If moredetailed LCI data are being withheld purposefully to limit disclosure ofsensitive business operational details this should be stated. If they are notbeing withheld for these reasons, we would urge greater inclusion of a wider
set of LCA data related to at least some of the major sub-systems.
The revised final report has substantially improved the transparency aroundkey issues, like the mass flows through pollock processing stages, that had
been previously difficult to follow. However, other details of data analysis steps
and assumptions employed to quantify other inputs to the system being
modelled, but that often make smaller contributions to overall results, remain
obscure. Some of these can be discerned from a careful read of some of the
supporting materials but others can not. In the interests of greater
methodological transparency, it would be ideal if more of the analytical steps
and assumptions made were evident in the body of the final report or in an
appendix.

This has been an interesting project to have been part of. We applaud the entire
team that has been central to seeing this project through to completion, particularly
given the unusual challenges that everyone has encountered along the way. Welldone Xinyue, Ron, Adam and Melissa.

Sincerely,

Ray Hillborn, PhD

Friederike Ziegler, PhD

Peter Tyedmers, PhD

Review of the study "Life cycle assessment of Wild Alaska Pollock"
Compilation of Reviewer Comments and Quantis Updates
Commissioner: GAPP; Author: Quantis team
Reviewers: Prof. Peter Tyedmers (Dalhousie University), Dr. Friederike Ziegler (RISE Research Institutes of
Sweden), Prof. Ray Hilborn (University of Washington)
Date:

July 8, 2021

1. General/overall comments
Nr.
1.1

1.2
1.3

Integrated Comments from all Reviewers

Decision

Updates

Fourth Key objective does not seem to be addressed in the report

Yes

Added some language inthe recommendations
section about importantconsiderations whencomparing to otherproteins.

too bad that ultimately no GoA data were either forthcoming or useable. But
the share of total catch is small and as such overall results would not likely
be affected much.
In the discussion of cut offs, you indicate that a test was undertaken to try
and identify those inputs which make trivial (<1%) contributions to LCIA
results. It's unclear though what came of this effort. I don't want to overly
complicate things but it would also be useful to know if the <1% was only in
relation to GHG emissions or did you also look at the relative contributionsof detailed inputs across other imapct categories included? In additon,
please specify if single processes were excluded if estimated to represent<1% or if all processes excluded together should represent less than 1%.

-

No action needed.

Yes

Modified this language toclarify that we collected
all data points based on
PAS2050-2, included alldata when available orable to be estimated(even some data points
with <1% contribution).

The non-fish inputs tosurimi are characterized
in the modeling (seeAppendix D_Data Qualityline 100-103). Added aparagrah/bullet points in
3.4.1, Processing
inventory data, on nonfish inputs to surimi.
Updated language toclarify.

1.4

Not exactly sure where this should appear but after reading all the way
through the inventory section, I realsed I saw no details on the non-fish
inputs to surimi other then they only account for 9% of the mass of the final
surimi product. I'm hoping that these inputs have been characterized in themodeling and if so, they need to be described somewhere and ideally
included in an inventory table

Yes

1.5

the description of the mass balance test that was performed and how datafrom one sub-sector were used to characterize another was quite opaque.
This could be argued as a writing sytle issue and there be beyond ourpurview but I would encourage effort to be a straightforward transparent
with your methods descriptoin as very useful for all readers.

Yes

1.6

re the yield rate of fish oil. The ratio is really unclear. Moreover, we're allvery concerned with the implied yield rate of oil from fresh material mass. A
1:2.5 ratio implies a yield of oil from live weight mass of fish tissue of 40% or are we completely misreading this ratio?? Such a high yield rate of oilfrom mass of fresh fish tissue is simply impossibe. The highest fish oil yieldrate from whole fish biomass we have come across is ~20% - this would
equate to a ratio of 1:5. But that is in menhaden. In whitefish species like
AK pollock, fish oil yield rates from whole fish tends to be in the mid single
digit range (say 5-6%) while oil yield rates from trimmings from whitefish
tends to be lower still and can be as low as 2%- this latter value would be aratio of 1:50

Yes

The mass allocation
approach andcorresponding results
have been modified toreflect fishmeal and fish
oil as co-products,
following approval forour modified approachfrom the panel via email.
The discussion of yieldrates has been updated
and clarified in Sec 4.2.5,
Table 3, and Figure 5 of
the report.

1.7

Re Table 6. this will be a very useful Table but the column heading "catchweight to wild Ak Pollock weight ratio" is very cryptic and I think an incorrectstatement of what is represented. I think what you are trying to report in thecolumn below is the ratio of the final product form mass to fresh pollock
biomass ratio but what is described is something else. Separately, in
addition to the very serious problem with the fish oil to fresh pollock
biomass ratio (1:2.5) as noted above, the ratio you report for fishmeal isalso very confusing to verging on the highly unlikely. In most reduction
plants that process round fish, you see a fish meal yield rate of 20 to 22% this would equate to a ratio of 1:5. The ratio that you report (1:10.5) implies
a yield rate of 9.5% which is very low.

Yes

Renamed column toreflect percentage yields
rather than ratios andupdated yields based onrevised approach (seecomment above).

1.8

Re the wholesale prices in Table 6. Great that these are available as they
are often not but there might be a challenge with the use of the wholesale
prices for fishmeal and oil in the context of allocation. What is ideallyneeded in any subsequent economic allocation undertaken is the value ofthe pollock processing co-products at the point of initial processing - where
the portions of the pollock that are destined for different products separate,
and before their further transformation (e.g. reduction to meal and oil)happens. Using the wholesale prices of meal and oil will I suspectoverrepresent the value of these co-products in an economic allocaiton
model

Yes

Since the pollock parts
before transformation
are not products andnever get sold, the pricefor pre-transformation
pollock is not available.
The best data available isthe price of final
products. We updated
the language in 4.2.5 tobe clear.

1.9

I'm on the fence as to whether to raise this but here goes. First I think itvery good that more than just GHG emissions are being modeled andreported. But choice of additional impact categories to include seems
automatic and non-specific or tailored to the product and related concerns.
So on the one hand, great that you've adopted the suite of IC used in the
PEF but some seem highly irrelevant in the case of a set of fisheries
products. The most obvious ICs that seem less than useful are Land Useand perhaps water consumption. There is no need I think to remove thesefrom the results but an aspect of good practice that seems to be
increasingly skipped over is the throughtful identificaiton and motivation of
the specific IC of concern to be addressed

Yes

Added that these ICs
were chosen 'based on
GAPP's interests…' in
5.1

In order to interpret the characterised results, there is a clear need for atable of key inventory results per tonne of WAP landed in the text (I can findthe LCI table hidden in Appendix D "Data quality"), either for each type offishery and year or just a merged one that at least shows the average andrange in fuel use intensity and the amount of refrigerant leaking per tonneof fish landed, since these two data points determine so much of theresults. For each data point it would also be good to know how much data
was available, just assuming that data on fuel use was easier to get than
refrigerant use. One or two well constructed inventory tables, or if easier
one inventory table per individual FU would substantially improvetransparency and facilitate easier interpretation of the characterized results

Yes

1. Added a sentence in
4.1.2. Per ton of WAP
landed (fish caught), 3.5
MJ of diesel fuel and 0.09
grams of refrigerantleakage. 2. Data
coverage rate for eachsector breakdown by
categories (e.g. energy,
non-durable goods) is
added as confidential
appendix E

1.1

Intimately related to the issues identified above re to the apparent yields offishmeal and oil are then the results of the GHG emissions associated withthese two products. If in what are typical situations where the yield offishmeal is in the range of 20% from wet weight biomass and oil is in therange of say 5% then we would expect that the biomass required to provide
1 kg of oil is substantially larger (actually 4x larger) than that required toprovide 1 kg of meal. Consequently, the GHG emissions associated withcatching the greater fish biomass would also be substantially higher (4x) for
oil then for fishmeal. But this is not what we see in Fig 5. So either the yieldrates are truly very unusual (oil yield rate is much higher than fishmeal yieldrate for the first time in all reduction fisheries) OR you have got things very
mixed up/confused in the modeling of yields and hence GHG emissions. To
provide some context for the relative scale of typcial oil and meal GHG
emission intensities for fishmeal and oil (including when derived form AK
pollock) see Cashion Parker and Tyedmers 2017 Global reduction fisheries
and their products in the context of sustainable limits. in Fish and Fisheries.
Though we raise this in the context of GHG emissions, the effect of
odd/incorrect yield rates for meal and oil will also affect results for the other
IC considered, particulalry when the catching and processing phase is a
hotspot (as it seems to be for most if not all)Yes

The mass allocation
approach andcorresponding results
have been modified toreflect fishmeal and fish
oil as co-products,
following approval forour modified approachfrom the panel via email.
In this case the mass
allocation approach
generates identicalresults for fishmeal andfish oil functional units.
Added discussion on
this throughout,particularly Sec 4.2.5 andSec 5.1.1.

1.11

setting aside here the concern re the relative scale of the emissions
associated with fishmeal and oil, in this Figure, we're all struck by theenormous role that refrigerant losses play in overall GHG emisiosns fromthese products. This is a very unusual finding - but not completely unheardof, particularly when the fishery is a low fuel intensive fishery. Would you do
us a favour though, please double check the refrigerant loss data andassociated calculations. and then the simapro model to confirm thenumbers. It's just product to undertake a double check like this when there
is such an anomolous hotspot in a system like this. If the data stand up, thisis definitely an important finding to frame recommendaitons around given
the scope of emission reduction potentials that appear possible. FZ adds tothis comment: It seems that the importance of the refrigerants is entirely
due to the modelling as emission of CFC-12. Do you actually haveinformation this is a refrigerant used or was it an assumption for the general"freon" category? It would also be good to understand if you got data on
this from all respondents or from fewer than those that provided fuel use
data e.g.

Yes

1. The data we collectedfrom each company
includes the Freon
consumption. A
conservative assumption
on CFC-12 is used in thisproject. The calculation
has been checked. 2. A
scenario analysis using
Ammonia as the only
refrigerent has been
added in the report.

1.12

Also related to Figure 12 (and 13) is the appearance of commuting amongst
the sub-system activities/inputs modeled. I don't recall anything about thisbeing described. What was the scope of this input?

Yes

Added a paragrah/bullet
points in 3.4.1,
Catching/Processing
inventory data, on
employee commuting

Regarding the economic allocation modelling. We want to check whether
you used just the relative value of the co-products or the relative value ofthe co-product revenue stream (wholesale price times mass of each coproduct produced in a year)? When the scale of the co-product streams arevery different as are here, and their unit wholesale prices are also different,
the difference in these two approaches can be very significant! I've just now
read page 49 lines 11-12 where you indicate that you used wholesale
prices without first multiplying them to the size of the co-product streams. If
this is indeed the case, this needs to be re-visited.

Yes

Thanks for raising this.
We used wholesale price* production to allocate
the catching andprocessing impact
across wild alaska
products, thennormalized the results to
per kg product.
Reworded language in
4.2.5 and section 6(page49 lines 11-12) and makeit clear.

We don't understand the negative water use from waste. It needs to be
explained in text and in the caption.

Yes

Added a sentence to
5.2.3, The negative water
consumption values
result from wastewater
sent to a wastewater
treatment plant, whichmeans during thewastewater treatment
process there is water
sent back to theenvironment.

1.14

great that you looked at the effect of an increase or decrease in the fuel use
intensity of the fishery on LCIA results but I'm surprised that you didn't also
consider modelling a scenario in which a different refrigerant is used as theconsequences of say a straight substiution of a refrigerant is poteniallly
much more achievable and results potentially much more dramatic andwould likely result in variable effects between impact categories

Yes

See 5.3.2. We added ascenario analysis using
ammonia as the only
refrigerant to the report.

1.15

The entire recommendations section seems to be surprisingly brief.
Separately, the final bulleted recommendation regardign the potential effectof reducing loss of refrigerants or substiution with those with a low or zero
GWP would ideally be supported with a scenario analysis as noted above.
Improvements are only suggested for climate.

Yes

Added 5.3.2 to support
the refrigerant
recomendation. Added abullet point for Land use
recommendation.

1.16

Re the references. It may have been intentional but it is interesting thatyou've choosen to not try and compare, despite the cautions needed, tomake any sort of comparison with previously published results. In addiiton
to the GHG emisison estimates for fishmeal and oil derived from AK pollock
reported in Cashion Parker and Tyedmers 2017 Global reduction fisheries
and their products in the context of sustainable limits. in Fish and Fisheries,
there is a paper by McKuin 2019 (Climate forcing by battered and breaded
fillets and crab-flavoured sticks form Alaska pollock. Elementa) that could
provide a pre-existing source of GHG emisiosn numbers for surimi that
could be used as a basis of comparison. But this context making orcomparison might be beyond the scope of what you agreed to do.

Yes

Indeed we havedeliberately avoided
comparisons as being
beyond scope here but
we have added areference to Fulton(2010), along with acaution about making
comparisons between
studies.

Deci
sion

UpdatesPeter Tyedmers home department name is School for Resource andEnvironmental Studies

Yes

Typo fixedperhaps refer to estimated impacts rather than potential impacts

Yes

Language changed

1.13

2. Specific comments
Nr.
2.1

Integrated Comments from all Reviewers

as noted later in the report, you aren't assessing environmental impacts
when using mid-point indicators but contributions to prenomena of concern.
This is admittedly a very nuanced observation and you can address it or
not.

No

For clarity of purpose to thelay reader, we will keep as ishere, but have nuanced thelanguage at several otherplaces in the report.

Not sure what "leading" means in this context

Yes

Removed the word 'leading'

2.3

PAS 2050-2 applies to seafood and other aquatic food products generally
not just those from fisheries

Yes

Specified using the title ofthe standard

2.4

As a comment above, mid-point indicators don't tell us what theenvironmental impacts are. Better phrasing might be: Understand thecontributions that produciton of Wild Alaska Pollock products make toresource depletion (e.g. energy use, water use etc0 and environmental
concerns (e.g. climate change, etc). ...
Sorry if I'm a pedant (this is PT) but again, not measuring environmental
impacts - a point that you do make later in the report.

Yes

Substituted in the suggested
language

Yes

Nuanced the language here
in response to comment

when referring to catch-processor and shore based processors would it not bebetter to say catcher processor companies and shore based companies
again, not evaluating environmental impacts - but again, I may be being
pedantic

Yes

Added 'companies'.

No

Kept as is in this case, but
point taken.

2.7

looks like you have an extra 'and' in the sentence. Should be '… catcher
vessels delivering to mothership processors, ...

Yes

Typo fixed

2.8

this is a confusing paragraph as it starts out as if it is describing the fisheryand it's breakdown into sub-fleets but it is really describing the data
coverage that you have (data from 6 of 14 catcher-processors etc)..
Perhaps better to first in one paragraph describe the compisiiton of the
fleets (total number of units, % of total catch etc) and then separately
describe your data coverage from those secors in the BSAI. When you
state "total" here, do you mean total BSAI?

Yes

It appears as though thereviewer has misunderstood
the percentages - they referto the % of total catch that isprocessed by each sector.
We have made some minor
text adjustments to help
clarify.

2.9

The bulleted breakdown of the sub-sectors in BSAI, the fractions of the total
quota sum to 110% of the total. Revisit and make the total quota sum to100 but then indicate within each how they are harvested. The issue here
may be that you are conflating who owns quota (one set of percentages)
and who fishes available quota (another set of percentages)

Yes

The 'extra' 10% is due to theCommunity Development
Quota. We have re-written
this section to clarify.

2.1
2.11

Pollock and Alaska are reversed.

Yes

Typo fixed

Table 1 is very useful but the data represented in the far right column isconfusing. The description suggests it’s a % of the catch in each region essentially describing the % of the regionally available catch by eachsector. But then the NA make no sense. OR are you actually representing
say the % of where you received data from? I don't thinnk that it is the latterbut it's confusing.

Yes

Changed table headings andlead-in sentence to clarify.

Table 1: very confusing re Gulf of Alaska – why are there no catching volumes andwhat is the split in vessels??

Yes

Specified 'no data' instead of
N/A. GOA was not used in
the analysis.

2.12

the sentence that starts "The three-year period data are used…" doesn't
seem to add anythingYes

We have removed thissentence.

2.13
2.14

functional units should be plural

Yes

Typo fixed

Data in table 2 are unclear. Are these sums of the total three year
produciotn voluems or averages of the three years. Either way, a bit moredetail in the Table caption would be useful.

Yes

Clarified with lead-insentence and table caption.

"External" review panel rather than "peer"

Yes

Substituted 'external' for'peer'

2.2

2.5

2.6

2.15

In this first paragraph on this page there is frequent reference to supporting
materials. Some are referenced as Appendices and others are not (like theversions of the surveys) If they are also to be made available, perhaps ideal
to also place them into an Appendix

No

Confirmed that Appendices
are referenced consistently
and accurately throughout,and Associated Files are asdelineated under 'Project
Information'.

2.16

Table 3. Really good to have these data reported but the Landed 'volumes'(really masses) represented are unclear. Are they tonnes of live weight
landings across the three years being characterized or in just one year

Yes

Changed table headings andlead-in sentences to clarify
that it's three years of data
together

Table 3 number of plants/vessels would be usefulYes

This info is covered in Table
1 but we added in the # ofvessels per sector for ease ofreference

Related to the above: If the data originally was provided per year for three
years, it is much more interesting to present annual results and thencalculate an average than to aggregate the data first.

No

The info is from 3 years
combined, which is thebasis for the analysis.
PAS 2050-2 calls for anassessment period of
three years to take into
account biological andenvironmetnal variability.

which three functional units are going to be considered or does that depend on
markets?

Yes

Fixed - this was aholdover from a previous
draft when there were
only three functional
units.

2.17

you indicate three functional unitis but there are now six. Text looks like acarry over from the scope document and wasn't fully updated. It should bespecified that some of the products are frozen in the FU definition

Yes

See response to Comment
2.17. Added 'frozen' to thedescription of FU #4.

2.18

a minor point but it's unclear how trucks are used in the catching of pollock.
Is it transport of catch to shore-based processor location?

Yes

This is a required data point
based on PAS 2050-2, whichmeans we should consider
this data point during datacollection. Added some text
to the description to theitem description, forclarification.

2.20

one too many packaging. Did you mean something else?

Yes

Changed this language toclarify the two types of
packaging included in theanalysis.

how spatially disaggregated is the WECC grid mix for energy – A lot of Alaska has
hydropower but I suspect all the shore based processors are 100% diesel
generated

Yes

Thanks that is correct, theupdated results will reflect
100% diesel for purchased
electricity.

re transport mode do you mean that regardless of mode (ship, truck etc0 all
containers are refrigerated?

Yes

Yes, updated

2.21

2.22

you indicate here that commuting of workers was excluded but I thinksomewhere in the results you have indicated contributions from labour
travel by air from homes in the lower 48 states. This was something that
had been discussed/encouraged earlier but so far it's not been mentioned.
You mention here attributional LCA, but have not stated the type of LCA
done here or explained the term and the difference to alternative ways of
LCA modelling.

No

Based on PAS2050-2, there isno indication that thecommuting should be
included. We've included the
air travel from Seattle toDutch Habor to represent
the employee commuting, as
virtually all of the companyarranged travel to and from
Dutch Harbor is from
Seattle. Commuting fromother states is not included.

2.23

instead of saying see associated files starting with xxxxx would it not bebetter if these materials were in an Appendix as are other supporting
materials?

No

The survey files etc. aresupplementary materials
and we feel they are best
kept separate from
Appendices for ease ofreference.

Should all excluded processes together represent less than 1% or eachone? Please specify

Yes

UpdatedIn the lower part of Table 4, Belly flip shouold be Belly flap

Yes

fixed (in table)

Yes

fixed the language for clarity
and accuracy here.

2.26

in the space of three sentences, you describe the pedigree matrix scale
twice and actually then also reverse the direction in one of them. Is a scoreof 5 best as initially suggested and in line with table 5 or is 5 worst as
described at the top of page 28??
Trends are patterns that occur over time. You are not describing a trend but
a simple pattern of resutls. More generally can you make the meaning of
the sentence a bit clearer? Something like The data quality assessment forother functional units considered also appear in Appendix D and thepatterns found across all are very similar.

Yes

substituted in the suggested
sentence.

2.27

data are plural. You've previously recognized that data are plural but here
you use is instead of are.

Yes

fixed

2.28

We like that you've included part of the data quality assessment result for
one product as an illustrative item in the report but it's simply unreadable atthe scale it is.

2.29

a great Figure but the source is not indicatedYes

Source = quantis. Added tocaption.

2.3

to what does the 1:1 ratio refer? Similarly in the next sentence, to what
does the 1:0.91 ratio refer? In the first case (1:1) I think it's fresh productmass to frozen product mass (not additions of glazing water etc) In thesecond (1:0.91), I think it's surimi mass to skinless boneless fresh pollock
meat. But please make it clear for the reader.

Yes

See comments above aboutchanges to yield discussion
and mass allocation
approach.

2.31

Unclear why you have not reported the apparent fish meal to live weightratio when you have for oil (which seems problematically high) and it'sseemingly reported in Table 6.

Yes

See comments above aboutchanges to yield discussion
and mass allocation
approach.

2.24
2.25

We will enlarge the image
and flip this page horizontal
once we've made otheredits.

2.32

It is not clear to me if the proportion of by-products that us wasted wasquantified and if this affected the calculations or if it was assumed that all
was processed to fish meal/oil. So that the WAP products from the plants
without fish meal factory is higher than from others.

Yes

Language updated. Yes you
are right that the WAP
products from thecompanies without meal
plant is higher than thosewith meal plants (if all other
inputs are the same), sincewaste does not carry any
enviromental impact. In thisstudy we report the results
as an average industry
results so the result forcompanies with or without
meal plants won't bereported separately.

2.32

Regarding the sentence that reflects on the reason behind the relatively
high GHG emission intensity of fishmeal. You indicate that it is due to theuse of mass allocation and indeed given that 1 kg of fishmeal typicallyrequires 5 kg of wet fish biomass to produce, right away you are going tohave higher emission intensities compared to those resulting from fillets
when using mass allocation, however, it is also a function of the additionalenergy invested in the dewatering, cooking and grinding of the meal.
Finally, if the yield rate used is incorrectly low - as we suspect it is here
given the 1:10.5 product to wet fish biomass ratio that you reported, thenyou are going to be over-reporting the energy use and ultimately the GHG
emisison intensity of the fishmeal production. This highlights why it's
REALLY important to get the fishmeal and oil yield rates right.

Yes

See comments above aboutchanges to yield discussion
and mass allocation
approach.

2.33

Re the data reported in Fig 5. Just as I think that the fishmeal GHG
emission values may be being overestimated (perhaps by 2x) I suspect thatthe fish oil emisiosn intensity may be being underestimated as the seeming
yield rate that you have used (1:2.5 or 40%) is crazy high.

Yes

See comments above aboutchanges to yield discussion
and mass allocation
approach.

2.34

Re the sources of land use impacts of fishmeal and oil. Can you indicate
what the source of the land use that arises from fish catching andprocessing? On it's face, it will be hard for the average reader to imagine
how there is any sort of land use dependency, let alone impact that arises
from fishing.
Re results of the land use modeling, setting aside if this is even a useful IC
to report, I think that the 3x higher land use for meal over oil is suspect
given the seeming role of catching and processing in both and the issues
identified with yield rates

Yes

Added language here. See
5.2.2

Yes

See comments above aboutchanges to yield discussion
and mass allocation
approach.

2.36

Re results of the water consumption modeling, setting aside if this is even auseful IC to report, I think that the 3x higher water use for meal over oil issuspect given the seeming role of catching and processing in both and theissues identified with yield rates

Yes

See comments above aboutchanges to yield discussion
and mass allocation
approach.

2.37

And specifically in relation to the data reported in Fig 10 at the top of p 42, I
think that once the fish oil yield rate concern is addressed (from above weare concerned that you are using a yield rate value that is far too high) thatthe catching and processing hotspot will increas in importance across all of
the ICs that you are reporting for.
this is minor but the pattern that you are seeing in the contribution analysis
is not a trend as it does not vary over time. I know that in common parlance
everything is now a 'trend', and indeed many things are (like the popularityof a meme over time) but patterns as you are describing in Figs 10, 11 etc
are simply patterns and not trends.

Yes

See comments above aboutchanges to yield discussion
and mass allocation
approach.

Yes

Trend' has been changed to'pattern' throughout the
document.

2.35

2.38

2.39

And specifically in relation to the data reported in Fig 11 at the top of p 43, I
think that once the fishmel yield rate concern is addressed (from above weare concerned that you are using a yield rate value that is far too low) thatthe catching and processing hotspot will will decrease in importance acrossall of the ICs that you are reporting for. However, it is likely to remain the
hotspot but just not account for 90+ % of total contributions.

Yes

See comments above aboutchanges to yield discussion
and mass allocation
approach.

2.4

The presumption re the role of land occupation and land use change as thedrivers underpinning the high land use values arising from agricultural
inputs to surimi should not be necessary. If you dig into the underying
sources of land use for the ingredients used in the background data you
have drawn upon you should be able to confirm that this is the case or notYes

Language added in 5.2.2 that
discusses the sources of land
use impacts.

2.41

The use of the word 'benefits' in relation to the relative emisison intensity offrozen products in relation to fishmeal and oil seems inappropriate as it
seems to be ascribing a preference or desirability for a specific outcome.
Ultimately we are attempting to model as far as possible an objective
understanding of the world. This is hard enough without introducing thesense that the results can be shaped to accomodate certain desired
outcomes.

Yes

We have changed thelanguage in this paragraphto remove the use of'benefit', as well as to clarify
the meaning of thisparagraph overall.

2.42

re the description of the impact of economic allocation. We're surprised that
there is no discussion of the highly unstable nature of the econ allocation
results. Wholesale prices of the co-products are going to vary of time and inparticualr with respect to each other. These changes would have immediate
affect on the econ allocaiotn-based results. Indeed, it is likely that they are
no longer valid given the time between when the wholesale unit price data
were collected/reported and when the report finally is released.

Yes

Added discussion of the
impact of price variability
and relative economic values
when using economic
allocation.

2.43

describing refrigerants as low-carbon is problematic as most contain zerocarbon. This is the result of using 'low-carbon' to mean low GHG emission
intense. Sometimes this verbal conflation is OK but here's an instance
where its misleading. So substitute the phrase low-carbon with another thatconveys the idea of low GWP or low GHG emission source of refrigerant.

Yes

Substituted low GWP for low
carbon in both the fuel andrefrigeration bullet points

2.44

definitely no cultivation going on with AK pollock

Yes

Removed 'cultivation' andalso removed 'benefits' (andsubstituted in some
additional language).