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
Contact:
Xinyue Zhang
Sustainability Consultant
[email protected]
Adam Kotin
Senior Sustainability Consultant
[email protected]
Quantis is a leading life cycle assessment (LCA) consulting firm that specializes in supporting
companies to measure, understand, and manage the environmental impacts of their
products, services, and operations. Quantis is a global company with offices in the United
States, 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, sustainable
supply chains, and environmental communication. Quantis also provides innovative and
customized tools, which enable organizations to evaluate, analyze, and manage their
environmental footprint with ease. Fueled by its close ties with the scientific community and
its strategic research collaborations, Quantis has a strong track record in applying its
knowledge and expertise to accompany clients in transforming LCA results into decisions and
action 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
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 arising from the use of the information in this document.
Version: Final ISO LCA report
July 2021
Project team
Adam Kotin ([email protected]) – Senior
Sustainability Consultant
Melissa Zgola ([email protected]) – Senior Sustainability Consultant
Xinyue Zhang ([email protected]) – Sustainability Consultant
Client contacts
Craig Morris ([email protected]) - Chief ExecutiveOfficer
Ron Rogness ([email protected]) - Consultant
External reviewers
Peter Tyedmers ([email protected]) – Professor, School for Resource and Environmental Studies, Dalhousie University
Friederike Ziegler ([email protected]) – Senior scientist, Research Institutes of Sweden
Ray Hilborn ([email protected]) – 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 [email protected]:
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 GAPP_WAP LCA_Data Survey_Catcher
Processors_V1_Quantis.xlsx
GAPP_WAP LCA_Data Survey_Shore-based and
Mothership processors_V1_Quantis.xlsx
Life Cycle Assessment Explanatory Notes Catching
vessels_V1.docx
Life Cycle Assessment Explanatory Notes Catcher
Processors_V1.docx
Life Cycle Assessment Explanatory Notes Shore-based
and Mothership processors_V1.docx
GAPP_WAP LCA_Data Survey_Catching vessels for
Shore-based and MS_V2_Quantis.xlsx
GAPP_WAP LCA_Data Survey_Catcher
Processors_V2_Quantis.xlsx
GAPP_WAP LCA_Data Survey_Shore-based and
Mothership processors_V2_Quantis.xlsx
Life Cycle Assessment Explanatory Notes Catching
vessels_V2.docx
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
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
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
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 (based
on Impact 2002+) ................................................................................................................................... 43
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)................................................................................... 44
Figure 10. Full suite of normalized indicator results for 1 kg of fish oil distributed to East coast US (based
on PEF v1.4) ............................................................................................................................................ 45
Figure 11. Full suite of indicator results for 1 kg of fishmeal distributed to East coast US (based on PEF
v1.4) ........................................................................................................................................................ 46
Figure 12. Catching and processing stage contribution analysis to the Climate change indicator for the
six 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 for
the 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
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
CO2 - carbon dioxide
CPUE - catch per unit effort
EI - ecoinvent
EOL - end of life
eq - equivalents
GAPP - genuine alaska pollock producers
GWP - global warming potential
IPCC - Intergovernmental Panel on Climate Change
ISO - International Organization for Standardization
kg - Kilogram = 1,000 grams (g) = 2.2 pounds (lbs)
km - Kilometer = 1000 meters (m)
LCA - Life Cycle Assessment
LCI - Life Cycle Inventory
LCIA - Life Cycle Impact Assessment
m2 - Square meter
m3 - Cubic meter
MJ - Megajoule = 1,000,000 joules, (948 Btu)
MSW - Municiple solid waste
PM - particulate matter
US - United States
USEPA - United States Environmental Protection Agency
1 Introduction
Heightened concern around the environmental and social sustainability of society’s
consumption habits has focused attention on understanding and proactively managing the
potential environmental and societal consequences of production and consumption of
products and services. Nearly all major product producers now consider environmental and
social impacts as key decision points in product design, including but not limited to material
selection, product manufacturing, distribution, use and disposal, and sustainability is a
recognized 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 assessment1
(LCA) methodologies and practices to measure and document the estimated environmental
impacts of catching, processing, and delivering Wild Alaska Pollock products to primary
customers.
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 approach
to evaluate a broad range of environmental problems, identify social risks and to help make
decisions 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, the
study will align with recommendations from the Publicly Available Standards (PAS) 2050-2 for
seafood 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.
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, fish
oil, 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 and
roe) that adheres to leading LCA standards (ISO 14040 and ISO 14044; PAS2050-2);
3. Identify improvement opportunities to further reduce environmental impacts of the
Wild 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 or
derogatory 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 to
resource depletion (e.g., energy use, water use, etc.) and environmental concerns
(e.g., climate change). Identify environmental hotspots (top contributors of
environmental 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 with
internal 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 depletion
and environmental concerns of Wild Alaska Pollock products in a clear and useful manner, in
order to inform GAPP’s communication of environmental performance to internal and
external audiences such as customers and suppliers of GAPP members, members of the media,
policymakers, and consumers. Communication options could include meetings with
customers, 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 are
appropriate to support a public disclosure of the LCA findings.
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 panel
of 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, the
study will align with recommendations from the Publicly Available Standards (PAS) 2050-2 for
seafood products (BSI, 2012) with regard to scope and data boundary, time period for data
collection, 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 not
expected to negatively affect any external interested parties. The results of this study will be
made 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 product
functions and product systems, the system boundaries, data sources, and methodological
framework. 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);
• 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, all
responses 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 majority
of 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 that
catches 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 the
appropriate participant in the Catcher-processor sector.
• A small percentage of the initial quota is also set aside to account for bycatch in
fisheries other than the Directed Alaska Pollock fishery. After these deductions from
the initial quota, the Directed Alaska Pollock Fishery quota is apportioned to the
following 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 to
vessels 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.
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
Sector: Catching &
Processing
method
Fleet
Number of
vessels
Sector’s share
of total
catching
volume for
location
Bering Sea
/Aleutian
Islands (BSAI)
Catcher
processors
14
46%
Mothership
processors
3
9%
Shore-based
processors
6
45%
Catching vessels
To
Motherships
15
N/A
To Shore
based
71
N/A
Gulf of Alaska
(GOA)
Shore-based
processors
Less than 60
feet
5-7
No data
Greater than
or equal to 60
feet
24
No data
Catching vessels
Less than 60
feet
8-11
No data
Greater than
or equal to 60
feet
41-45
No data
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 each
of the products and fishery locations.
Table 2. Combined three-year production totals for years 2016-2018 by location, and Wild Alaska Product type
Wild Alaska Pollock product
Total production (MT)
Bering Sea /Aleutian
Islands (BSAI)
Gulf of Alaska
(GOA)
Fillet 485,867 39,840
Surimi 584,076 33,781
Roe 53,316 4,010
Fish oil 80,155 1,434
Fishmeal 192,613 2,498
Head & Gutted 73,775 70,052
Minced 80,996 3,674
Milt 2,934 1,055
Stomach 5,688 39
Bones 29,832 0
Whole fish 1,241 24,381
Belly flap 11 0
Other retained products 31 104
3.2 Data collection and data representativeness
The 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 rate
from 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 based
on 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 not
provided by a GAPP member, the production data from the other members were used to
estimate 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 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 rate
is 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.
Fishery
location
Life cycle
stages
Sector
Catching mass for
years 2016-2018
represented in
responses (MT)
% of GAPP total
catching volume for
years 2016-2018
(used as response
rate/data coverage
rate in this study)
Bering
Sea
/Aleutian
Islands
(BSAI)
Catching
Catching
vessels to
Shore-based
processors
(n = 71)
235,633
11%
Catching
vessels to
Mothership
processors
(n = 15)
35,215
10%
Processing
Catcher
processors
(n = 14)
1,442,767
79%
Shore-based
processors
(n = 6)
929,494
54%
Mothership
processors
(n = 3)
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 products
that 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 critical that this parameter is clearly defined and measurable. The functional units for this
study 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 based
on 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 business
customer, as depicted in Figure 2. An effort is made to define the system boundary and
collect 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.
Catching
Energy consumption
Non-durable goods
Durable and semi
durable goods
Distribution
To East coast US
To Asia
To Europe
Processing
Energy consumption
Non-durable goods
Durable and semi
durable goods
Waste disposal
Additives for surimi;
Diesel for fishmeal
and fish oil
production;
Employee
Commuting
Packaging
Primary packaging
Secondary packaging
Tertiary packaging
Figure 2. System boundary of Wild Alaska Pollock products evaluated in this study 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 impact
of 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 the
reporting 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 Harbor
is also included in this study; however, any commuting that occurs prior to employee arrival
in Seattle is not included.
Catching includes:
• Energy consumption
o Diesel fuel
o Purchased electricity, 100% diesel based on the regional information
o Fuel for trucks, if any (e.g., for transport)
o etc.
• Non-durable goods
o Refrigerants, including freon, CO2, ammonia, others
o Hydraulic fluid
o Purchased oil or other lubricants
o Cleaning agents
o Anti-fouling agents
o Paint
o Heated storage (not at Shore-based processors)
o etc.
• Durable and semi-durable goods
o Nets
o Filters
o Chains
o Cables
o Trawl/doors
o Other steel products
o Rope/twine
o Electrical wire
o Third wire
o Batteries
o etc.
• Wastes
o Solid waste delivered to landfill
o Waste burned at sea
o Waste oil delivered to recycling
o Other waste delivered to recycling
o etc.
Processing includes:
• Energy consumption
o Diesel
o Natural gas
o LPG
o Gasoline
o Purchased electricity
o etc.
• Non-durable goods
o Refrigerants, including freon, CO2, ammonia, others
o Hydraulic fluid
o Purchased oil or other lubricants
o Cleaning agents
o Glues and other adhesives
o Adhesive tapes
o Anti-fouling agents
o Paint
o Lubricating oil
o Fresh water not produced from desalination of sea water
o etc.
• Durable and semi-durable goods
o PVC pipe
o Wood other than wood used in pallets or other packaging
o Chain and cables
o Rope/twine
o Electrical wire
o Batteries
o etc.
• Waste generation
o Waste delivered to landfill
o Waste delivered to incineration
o Waste burned at sea
o Waste recycled
o Fish waste discharged to sea
o etc.
• Additives to surimi
o Sorbitol
o Sugar
o Sodium tripolyphosphate
o 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:
• 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 kg
of 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-tier
business 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 the
period from January 1, 2016 to December 31, 2018. Data and assumptions are intended to
reflect 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 can
take 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 to
landfilling, 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 the
necessary 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 and
an 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;
• 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, in
conformity to usual practices in attributional LCA: labor, commuting of workers from other
states, 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 as
comprehensive 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 system
boundaries. Primary data were collected directly from GAPP’s member companies for the
materials 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 the
same 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) is
prioritized 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
(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, from
quantitative (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 a
preferable option for representing Asian and North and South American conditions relative to
other options available. The data’s geographic representativeness is one aspect evaluated as
part 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 and
operations 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 other
species. 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 catchers
and 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 the
catcher/processor responses within the same sector, we use the available data from
other responses in the same sector, normalized by catching volume, to represent the
data 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 to
differentiate 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 the
percent of catch they represented within their particular sector;
25
• Next, the results of those extrapolations across all three sectors were compared to
the 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 some
products, however, there were notable discrepancies for others (e.g., 20% difference for
fillets). 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 to
the 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
/Aleutian
Islands (BSAI)
Fillet
485,867
Surimi
584,076
Roe
53,316
Fish oil
80,155
Fishmeal
192,613
Head & Gutted 73,775
Minced
80,996
Milt
2,901
Stomach
5,688
Bones
29,832
Whole fish
1,241
Belly flap
11
Other retained
products
31
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 and
Wesnaes 1996) as prescribed in ISO 14044. A complete discussion of this topic can be found
in 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 confidence
of the results reported. Inventory and Data Quality Assessment results for fillet and fish oil are
provided 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 Catching
and 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 a
relatively good data coverage rate (see Table 3). For waste and some non-durable goods, the
data coverage is relatively low, but their contributions are low as well. Collecting more
comprehensive data to have a higher coverage rate in the future would be helpful to improve
the data quality. Based on this data quality assessment, considering the extrapolation for
diesel 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, and
• 0.09 grams of leaked refrigerants are associated with Catching and processing. Of this
leakage, 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 metric
ton of catch; note that this includes fuel used to process fish aboard Catcher-processors, and
therefore it overstates the amount of fuel used for catching only. According to Parker and
Tyedmers (2014), the median fuel use intensity of global fishery records since 1990 was 639
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
Reliability
5
Verified data
based on
measurements
4
Verified data
partly based on
assumptions or
non-verified
data based on
measurements
3
Non-verified
data partly
based on
qualified
estimates
2
Qualified
estimate (e.g.
by industrial
expert)
1
Non-qualified
estimate
Completeness
Representative
data from all
sites relevant
to the market
considered,
over an
adequate
period to even
out normal
fluctuations
Representative
data from >50
of the sites
relevant for the
market
considered,
over an
adequate
period to even
out 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 the
market
considered or
some sites but
from shorter
periods
Representativeness
unknown or
incomplete data
from a smaller
number of sites
and from shorter
periods
Temporal
correlation
Less than 3
years of
difference to
the time-period
of the dataset
Less than 6
years
difference to
the time-period
of the dataset
Less than 10
years
difference to
the time-period
of the dataset
Less than 15
years
difference to
the time-period
of the dataset
Age of data
unknown or more
than 15 years of
difference to the
time-period of the
dataset
Geographical
correlation
Data from area
under study
Average data
from larger
area in which
the area under
study is
included
Data from area
with similar
production
conditions
Data from area
with slightly
similar
production
conditions
Data from
unknown or
distinctly different
area
Further
technological
correlation
Data from
enterprises,
processes and
materials under
study
Data from
processes and
materials under
study but from
different
enterprises
Data from
processes and
materials under
study but from
different
technology
Data on related
processes or
materials
Data on related
processes on
laboratory scale or
from different
technology
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 are
linked 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 been
developed, 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 a
material 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. The choice of allocation approach for recycled content and end-of-life recycling is not
expected 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 by
incineration 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 for
recycling, 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 cargo
within 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) will
reach 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, and
allocation 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 and
reported 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 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 other
species 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 or
apportioned by number of days fished.
• Allocation of processing activities to Wild Alaska Pollock relative to the processing of
other species was done by each processor company by the amount of production,
where needed. If other species were processed and stored at the facility, the
processors 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 estimated
by GAPP, using a conversion rate for each product. These conversion rates are expressed as
yields (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 product
based 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 of
each Wild Alaska Pollock product is applied to allocate the impact from fishing and
processing.
A production flowchart representing the distribution of total catch through to final product
form is represented in Figure 4. To reconcile total production to total catch weight
equivalents 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 and
oil producers is 3.5%. Therefore, the remaining catch weight (total catch weight sent to
fishmeal 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 between fishmeal and fish oil is performed based on the production volume (Product
Weight).
For those processors that do not produce fishmeal or fish oil, the amount of catch weight
that 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 and
processing environmental impact. All impacts are attributed to Wild Alaska Pollock products.
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 be
able to apportion energy between dividing whole fish and processing fish oil and fishmeal, a
survey 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 aggregated
consumption 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 into
and out of each product system by the type of impact their use or release has on the
environment. The method to be used here to evaluate environmental impact is the Product
Environmental 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 reach
consensus. 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 Commission
2013).
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.
IMPACT
CATEGORY OR
LCI INDICATOR
MODEL UNIT SOURCE CLASS
Climate change Bern model – Global Warming
potentials (GWP) over a 100-year
time horizon
kg CO2 eq IPCC, 2013 I
Ozone depletion EDIP model based on the ODPs
of the WMO w/ infinite time
horizon
kg CFC-11 eq WMO, 1999 I
Human toxicity –
non-cancer effects
USEtox model CTUh Rosenbaum et
al., 2008
III
(interim)
Human toxicity –
cancer effects
USEtox model CTUh Rosenbaum et
al., 2008
III
(interim)
Particulate matter PM method recom-mended by
UNEP
Deaths/kg
PM2.5emitted
UNEP 2016 I
Ionising radiation Human Health effect model kg U235 eq Dreicer et al.,
1995
II
Photochemical
ozone formation
LOTOS-EUROS model kg NMVOC eq van Zelm et al.,
2008
II
Acidification Accumulated Exceedance model mol H+ eq Seppälä et
al.,2006; Posch
et al., 2008
II
Terrestrial
eutrophication
Accumulated Exceedance model mol N eq Seppälä et
al.,2006; Posch
et al., 2008
II
Freshwater
eutrophication
EUTREND model kg P eq Struijs et al.,
2009
II
Marine
eutrophication
EUTREND model kg N eq Struijs et al.,
2009
II
Freshwater
ecotoxicity
USEtox model CTUe Rosenbaum et
al., 2008
III
(interim)
Mineral & metal
resource depletion
CML 2002 model (abiotic
depletion – ultimate reserves)
kg Sb eq Guinée et al.,
2002 and van
Oers et al. 2002
III
Non-renewable
energy resource
depletion
CML 2002 model (abiotic
depletion – fossil)
MJ Guinée et al.,
2002 and van
Oers et al. 2002
Land use
Soil Quality Index (based on the
LANCA model)
points
Beck et al. 2010
and Bos et al.
2016
III
Water scarcity
footprint
AWARE 100 model
m3 water
deprived 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 the
reference 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 in
Appendix 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 with
human activities. Impacts such as noise, odors, electromagnetic fields and others are not
included in the present assessment. The methodological developments regarding such
impacts 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 the
relevant characterization factors. The final LCI results are calculated combining foreground
data (intermediate products and elementary flows) with generic datasets providing cradle-to
gate 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 process
modeled 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 a
great influence on the study results should be characterized by high-quality information. In this study, the contribution analysis is a simple observation of the relative importance of the
different 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; and
Characterization 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 to
the input uncertainty and data variability, data quality assessment results are included in
Appendix 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 the
influence of the uncertainty in these characterization models within the assessments made
here. Without consideration of the uncertainty in LCIA characterization factors, the
uncertainty assessment results derived here should be seen as something like a lower bound
on 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 high
potential impacts or uncertainty:
• Energy consumption data, including fuels (used in Catching and processing) and
electricity (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, a
university-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 to
the 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 of
this 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 be
accessed 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 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 high
end. 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 is
another contributor to the Climate change indicator results for these two products.
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 Catching
and 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 the
pollock 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 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 is
relatively small.
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 and
processing stage. The driver of this impact is from the additives and non-fish ingredients that
supplement the pollock to make the surimi product.
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 the
Catching 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 and
processing.
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 this
study, 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, and
fishmeal, 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 by
refrigerant leakage. Other than Land use, most of the indicator results follow the patterns of
the Climate change indicator results.
For fish oil distributed to East coast US, the Catching and processing stage contributes at
least 85% across all indicators evaluated.
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.
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 these
Wild 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 half
tends 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.
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 maintenance
material, and the production of water filters, which are used to desalinize the sea water.
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 is
purchased 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 ingredients
and 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.
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 for
both the mass allocation and the economic allocation. Overall, the use of a mass metric
results 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 and
processing 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.
• 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 these
charts, 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, and
2018. When the relative economic values change significantly within these co-products, the
result 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 with
low global warming potential, a scenario analysis using ammonia as the only refrigerant is
carried out in this study.
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%
5.3.3 Sensitivity analysis for diesel consumption
Given the importance of energy-related activities (i.e., fuel and electricity use) to the
indicator results across the Wild Alaska Pollock products, a sensitivity test has been carried
out 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 to
apportion 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 the
data quality.
With diesel consumption fluctuating 10%, Climate change indicator results for surimi and
average frozen product rise or fall 3%, while for fillet, roe, fish oil, and fishmeal it would rise
or 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, that
in future work, diesel data collection be emphasized for its importance.
6 Key Findings
6.38 6.62 6.15 6.38 6.61 6.15
0.83 0.86 0.80 1.01 1.04 0.98 0.83 0.86 0.80 0.93 0.95 0.89
Baseline
+10% diesel
Fillet-10% diesel
Baseline
+10% diesel-10% diesel
Surimi
Baseline
+10% diesel
Roe
Catching and processing-10% diesel
Baseline
+10% diesel-10% diesel
Average of 1 kg
WAP frozen
product
Packaging
Baseline
+10% diesel
Fish oil-10% diesel
Distribution
Baseline
Figure 17. Sensitivity results for diesel fuel consumption (IPCC 2013)
+10% diesel-10% diesel
Fishmeal
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 impact
of 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 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 majority
of the Land use indicator results comes from wood pallets. Improving the reuse rate and
introducing recycled content would help reduce the impact.
Within Water consumption, the Catching and processing life cycle stage dominates the
impact 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 the
baseline results of this study, a mass allocation metric was used to apportion the impacts of
Catching 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) carried
out 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 with
potentially different scopes, system boundaries, and data quality; however, it helps to
validate, 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 the
following activities:
• Obtaining a higher data collection / survey response rate, and obtaining product
specific activity data to minimize the need for allocation-, apportionment- and
extrapolation-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.
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 in
terms 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 with
the 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 be
considered 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 the
functional 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 be
some over- or underestimation, as the catching vessels in GOA tend to have a shorter
route than those of BSAI. However, the vessel catching efficiency (catch per unit of
effort) might be different, so there is no clear conclusion on whether there is any 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 to
resource depletion and environmental concerns, from an LCA perspective, and in order
to 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 are
caught 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, these
categories do not cover all the environmental impacts associated with human
activities. Impacts such as noise, odors, electromagnetic fields and others are not
included in the present assessment. The methodological developments regarding such
impacts 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 clear
that 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 discussion
of spatial aspects, as well as other considerations in life cycle impact assessment, can
be found in Verones et al. (2017). In the context of the indicators evaluated in this
study, 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 regional
variability 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 the
use, which may be correlated to geographic regions due to regulations.
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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 capacity
of 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 emissions
of 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 and
weakened 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 the
intake 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 by
particulate 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 in
the total human population).
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 the
intake 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 in
morbidity 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 in
deaths 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 the
routine 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 is
expressed 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 the
troposphere caused by photochemical oxidation of Volatile Organic Compounds (VOCs) and
carbon monoxide (CO) in the presence of nitrogen oxides (NOx) and sunlight. High
concentrations of ground-level tropospheric ozone damage vegetation, human respiratory
tracts and manmade materials through reaction with organic materials. The impact metric is
expressed in kg NMVOC-eq (non-methane volatile organic carbon to air equivalents).
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 releases
of hydrogen ions (H+) when the gases are mineralized. The protons contribute to the
acidification 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 in
mole 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 in
soil. 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 (nitrogen
equivalents).
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 other
vegetation in freshwater. The degradation of organic material consumes oxygen resulting in
oxygen 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 other
vegetation in marine water. The degradation of organic material consumes oxygen resulting in oxygen deficiency. In marine environments, nitrate (NO3) 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 a
variety of different toxicological mechanisms caused by the release of substances with a
direct 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 fraction
of 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 de
accumulation 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 from
energy 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 into
a single Soil Quality Index. The LANCA
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, the
more likely another user will be deprived. It is based on the AWARE 100 model, the
recommended 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 to
model 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).
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 of
Washington
• 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 we
have 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 we
were 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 of
data acquisition and handling issues that the Quantis team encountered in
undertaking 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 that
would 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 to
make clear. If it is based on data provided by one or more firms then this
should be indicated. This is an important issue as refrigerant loss has been
found 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 more
detailed LCI data are being withheld purposefully to limit disclosure of
sensitive business operational details this should be stated. If they are not
being 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 around
key 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. Well
done 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. Integrated Comments from all Reviewers Decision Updates
1.1 Fourth Key objective does not seem to be addressed in the report Yes Added some language in
the recommendations
section about important
considerations when
comparing to other
proteins.
1.2 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. - No action needed.
1.3 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 contributions
of 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%.
Yes Modified this language to
clarify that we collected
all data points based on
PAS2050-2, included all
data when available or
able to be estimated
(even some data points
with <1% contribution).
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 the
modeling and if so, they need to be described somewhere and ideally
included in an inventory table
Yes The non-fish inputs to
surimi are characterized
in the modeling (see
Appendix D_Data Quality
line 100-103). Added a
paragrah/bullet points in
3.4.1, Processing
inventory data, on non
fish inputs to surimi.
1.5 the description of the mass balance test that was performed and how data
from one sub-sector were used to characterize another was quite opaque.
This could be argued as a writing sytle issue and there be beyond our
purview but I would encourage effort to be a straightforward transparent
with your methods descriptoin as very useful for all readers.
Yes Updated language to
clarify. 1.6 re the yield rate of fish oil. The ratio is really unclear. Moreover, we're all
very 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 oil
from mass of fresh fish tissue is simply impossibe. The highest fish oil yield
rate 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 a
ratio of 1:50
1.7 Re Table 6. this will be a very useful Table but the column heading "catch
weight to wild Ak Pollock weight ratio" is very cryptic and I think an incorrect
statement of what is represented. I think what you are trying to report in the
column 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 is
also 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.
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 ideally
needed in any subsequent economic allocation undertaken is the value of
the 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 suspect
overrepresent the value of these co-products in an economic allocaiton
model
1.9 I'm on the fence as to whether to raise this but here goes. First I think it
very good that more than just GHG emissions are being modeled and
reported. 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 Use
and perhaps water consumption. There is no need I think to remove these
from 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
The mass allocation
approach and
corresponding results
have been modified to
reflect fishmeal and fish
oil as co-products,
following approval for
our modified approach
from the panel via email.
The discussion of yield
rates has been updated
and clarified in Sec 4.2.5,
Table 3, and Figure 5 of
the report.
Yes
Yes
Yes
Renamed column to
reflect percentage yields
rather than ratios and
updated yields based on
revised approach (see
comment above).
Since the pollock parts
before transformation
are not products and
never get sold, the price
for pre-transformation
pollock is not available.
The best data available is
the price of final
products. We updated
the language in 4.2.5 to
be clear.
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 a
table of key inventory results per tonne of WAP landed in the text (I can find
the LCI table hidden in Appendix D "Data quality"), either for each type of
fishery and year or just a merged one that at least shows the average and
range in fuel use intensity and the amount of refrigerant leaking per tonne
of fish landed, since these two data points determine so much of the
results. 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 improve
transparency and facilitate easier interpretation of the characterized results
1.1 Intimately related to the issues identified above re to the apparent yields of
fishmeal and oil are then the results of the GHG emissions associated with
these two products. If in what are typical situations where the yield of
fishmeal is in the range of 20% from wet weight biomass and oil is in the
range 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 to
provide 1 kg of meal. Consequently, the GHG emissions associated with
catching 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 yield
rates are truly very unusual (oil yield rate is much higher than fishmeal yield
rate 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)
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 the
enormous role that refrigerant losses play in overall GHG emisiosns from
these products. This is a very unusual finding - but not completely unheard
of, particularly when the fishery is a low fuel intensive fishery. Would you do
us a favour though, please double check the refrigerant loss data and
associated calculations. and then the simapro model to confirm the
numbers. 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, this
is definitely an important finding to frame recommendaitons around given
the scope of emission reduction potentials that appear possible. FZ adds to
this comment: It seems that the importance of the refrigerants is entirely
due to the modelling as emission of CFC-12. Do you actually have
information 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.
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 this
being described. What was the scope of this input?
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 refrigerant
leakage. 2. Data
coverage rate for each
sector breakdown by
categories (e.g. energy,
non-durable goods) is
added as confidential
appendix E
Yes
Yes
Yes
The mass allocation
approach and
corresponding results
have been modified to
reflect fishmeal and fish
oil as co-products,
following approval for
our modified approach
from the panel via email.
In this case the mass
allocation approach
generates identical
results for fishmeal and
fish oil functional units.
Added discussion on
this throughout,
particularly Sec 4.2.5 and
Sec 5.1.1.
1. The data we collected
from each company
includes the Freon
consumption. A
conservative assumption
on CFC-12 is used in this
project. The calculation
has been checked. 2. A
scenario analysis using
Ammonia as the only
refrigerent has been
added in the report.
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 of
the co-product revenue stream (wholesale price times mass of each co
product produced in a year)? When the scale of the co-product streams are
very 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.
We don't understand the negative water use from waste. It needs to be
explained in text and in the caption.
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 the
consequences of say a straight substiution of a refrigerant is poteniallly
much more achievable and results potentially much more dramatic and
would likely result in variable effects between impact categories
1.15 The entire recommendations section seems to be surprisingly brief.
Separately, the final bulleted recommendation regardign the potential effect
of 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.
1.16 Re the references. It may have been intentional but it is interesting that
you've choosen to not try and compare, despite the cautions needed, to
make 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 or
comparison might be beyond the scope of what you agreed to do.
2. Specific comments
Nr.
Integrated Comments from all Reviewers
2.1 Peter Tyedmers home department name is School for Resource and
Environmental Studies
perhaps refer to estimated impacts rather than potential impacts
Thanks for raising this.
We used wholesale price
* production to allocate
the catching and
processing impact
across wild alaska
products, then
normalized the results to
per kg product.
Reworded language in
4.2.5 and section 6(page
49 lines 11-12) and make
it clear.
Yes
Yes
Yes
Yes
Deci
sion
Yes
Yes
Added a sentence to
5.2.3, 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.
See 5.3.2. We added a
scenario analysis using
ammonia as the only
refrigerant to the report.
Added 5.3.2 to support
the refrigerant
recomendation. Added a
bullet point for Land use
recommendation.
Indeed we have
deliberately avoided
comparisons as being
beyond scope here but
we have added a
reference to Fulton
(2010), along with a
caution about making
comparisons between
studies.
Updates
Typo fixed
Language changed
2.2 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.
Not sure what "leading" means in this context
2.3 PAS 2050-2 applies to seafood and other aquatic food products generally
not just those from fisheries
2.4 As a comment above, mid-point indicators don't tell us what the
environmental impacts are. Better phrasing might be: Understand the
contributions that produciton of Wild Alaska Pollock products make to
resource depletion (e.g. energy use, water use etc0 and environmental
concerns (e.g. climate change, etc). ...
2.5 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.
when referring to catch-processor and shore based processors would it not be
better to say catcher processor companies and shore based companies
2.6 again, not evaluating environmental impacts - but again, I may be being
pedantic
2.7 looks like you have an extra 'and' in the sentence. Should be '… catcher
vessels delivering to mothership processors, ...
2.8 this is a confusing paragraph as it starts out as if it is describing the fishery
and 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?
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 to
100 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)
2.1 Pollock and Alaska are reversed.
2.11 Table 1 is very useful but the data represented in the far right column is
confusing. The description suggests it’s a % of the catch in each region -
essentially describing the % of the regionally available catch by each
sector. 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 latter
but it's confusing.
Table 1: very confusing re Gulf of Alaska – why are there no catching volumes and
what is the split in vessels??
2.12 the sentence that starts "The three-year period data are used…" doesn't
seem to add anything
2.13 functional units should be plural
2.14 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 more
detail in the Table caption would be useful.
"External" review panel rather than "peer"
For clarity of purpose to the
lay reader, we will keep as is
here, but have nuanced the
language at several other
places in the report.
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Removed the word 'leading'
Specified using the title of
the standard
Substituted in the suggested
language
Nuanced the language here
in response to comment
Added 'companies'.
Kept as is in this case, but
point taken.
Typo fixed
It appears as though the
reviewer has misunderstood
the percentages - they refer
to the % of total catch that is
processed by each sector.
We have made some minor
text adjustments to help
clarify.
The 'extra' 10% is due to the
Community Development
Quota. We have re-written
this section to clarify.
Typo fixed
Changed table headings and
lead-in sentence to clarify.
Specified 'no data' instead of
N/A. GOA was not used in
the analysis.
We have removed this
sentence.
Typo fixed
Clarified with lead-in
sentence and table caption.
Substituted 'external' for
'peer'
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 the
versions of the surveys) If they are also to be made available, perhaps ideal
to also place them into an Appendix
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
Table 3 number of plants/vessels would be useful
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 then
calculate an average than to aggregate the data first.
which three functional units are going to be considered or does that depend on
markets?
2.17 you indicate three functional unitis but there are now six. Text looks like a
carry over from the scope document and wasn't fully updated. It should be
specified that some of the products are frozen in the FU definition
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?
2.20 one too many packaging. Did you mean something else?
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
2.21 re transport mode do you mean that regardless of mode (ship, truck etc0 all
containers are refrigerated?
Confirmed that Appendices
are referenced consistently
and accurately throughout,
and Associated Files are as
delineated under 'Project
Information'.
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Changed table headings and
lead-in sentences to clarify
that it's three years of data
together
This info is covered in Table
1 but we added in the # of
vessels per sector for ease of
reference
The info is from 3 years
combined, which is the
basis for the analysis.
PAS 2050-2 calls for an
assessment period of
three years to take into
account biological and
environmetnal variability.
Fixed - this was a
holdover from a previous
draft when there were
only three functional
units.
See response to Comment
2.17. Added 'frozen' to the
description of FU #4.
This is a required data point
based on PAS 2050-2, which
means we should consider
this data point during data
collection. Added some text
to the description to the
item description, for
clarification.
Changed this language to
clarify the two types of
packaging included in the
analysis.
Thanks that is correct, the
updated results will reflect
100% diesel for purchased
electricity.
Yes, updated
2.22 you indicate here that commuting of workers was excluded but I think
somewhere 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.
2.23 instead of saying see associated files starting with xxxxx would it not be
better if these materials were in an Appendix as are other supporting
materials?
Should all excluded processes together represent less than 1% or each
one? Please specify
2.24 In the lower part of Table 4, Belly flip shouold be Belly flap
2.25 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 score
of 5 best as initially suggested and in line with table 5 or is 5 worst as
described at the top of page 28??
2.26 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 for
other functional units considered also appear in Appendix D and the
patterns found across all are very similar.
2.27 data are plural. You've previously recognized that data are plural but here
you use is instead of are.
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 at
the scale it is.
2.29 a great Figure but the source is not indicated
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 product
mass to frozen product mass (not additions of glazing water etc) In the
second (1:0.91), I think it's surimi mass to skinless boneless fresh pollock
meat. But please make it clear for the reader.
2.31 Unclear why you have not reported the apparent fish meal to live weight
ratio when you have for oil (which seems problematically high) and it's
seemingly reported in Table 6.
Based on PAS2050-2, there is
no indication that the
commuting should be
included. We've included the
air travel from Seattle to
Dutch Habor to represent
the employee commuting, as
virtually all of the company
arranged travel to and from
Dutch Harbor is from
Seattle. Commuting from
other states is not included.
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
The survey files etc. are
supplementary materials
and we feel they are best
kept separate from
Appendices for ease of
reference.
Updated
fixed (in table)
fixed the language for clarity
and accuracy here.
substituted in the suggested
sentence.
fixed
We will enlarge the image
and flip this page horizontal
once we've made other
edits.
Source = quantis. Added to
caption.
See comments above about
changes to yield discussion
and mass allocation
approach.
See comments above about
changes to yield discussion
and mass allocation
approach.
2.32 It is not clear to me if the proportion of by-products that us wasted was
quantified 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.
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 the
use of mass allocation and indeed given that 1 kg of fishmeal typically
requires 5 kg of wet fish biomass to produce, right away you are going to
have higher emission intensities compared to those resulting from fillets
when using mass allocation, however, it is also a function of the additional
energy 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, then
you 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.
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 that
the 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.
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 and
processing? 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.
2.35 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
2.36 Re results of the water consumption modeling, setting aside if this is even a
useful IC to report, I think that the 3x higher water use for meal over oil is
suspect given the seeming role of catching and processing in both and the
issues identified with yield rates
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 we
are concerned that you are using a yield rate value that is far too high) that
the catching and processing hotspot will increas in importance across all of
the ICs that you are reporting for.
2.38 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 popularity
of a meme over time) but patterns as you are describing in Figs 10, 11 etc
are simply patterns and not trends.
Language updated. Yes you
are right that the WAP
products from the
companies without meal
plant is higher than those
with meal plants (if all other
inputs are the same), since
waste does not carry any
enviromental impact. In this
study we report the results
as an average industry
results so the result for
companies with or without
meal plants won't be
reported separately.
Yes
Yes
Yes
Yes
Yes
Yes
Yes
See comments above about
changes to yield discussion
and mass allocation
approach.
See comments above about
changes to yield discussion
and mass allocation
approach.
Added language here. See
5.2.2
See comments above about
changes to yield discussion
and mass allocation
approach.
See comments above about
changes to yield discussion
and mass allocation
approach.
See comments above about
changes to yield discussion
and mass allocation
approach.
Trend' has been changed to
'pattern' throughout the
document.
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 we
are concerned that you are using a yield rate value that is far too low) that
the catching and processing hotspot will will decrease in importance across
all 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.
2.4 The presumption re the role of land occupation and land use change as the
drivers 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 not
2.41 The use of the word 'benefits' in relation to the relative emisison intensity of
frozen 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 the
sense that the results can be shaped to accomodate certain desired
outcomes.
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 in
particualr 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.
2.43 describing refrigerants as low-carbon is problematic as most contain zero
carbon. 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 that
conveys the idea of low GWP or low GHG emission source of refrigerant.
2.44 definitely no cultivation going on with AK pollock
See comments above about
changes to yield discussion
and mass allocation
approach.
Yes
Yes
Yes
Yes
Yes
Language added in 5.2.2 that
discusses the sources of land
use impacts.
We have changed the
language in this paragraph
to remove the use of
'benefit', as well as to clarify
the meaning of this
paragraph overall.
Added discussion of the
impact of price variability
and relative economic values
when using economic
allocation.
Substituted low GWP for low
carbon in both the fuel and
refrigeration bullet points
Removed 'cultivation' and
also removed 'benefits' (and
substituted in some
additional language).