Carbon Footprint Methodology
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CARBON FOOTPRINT MEASUREMENT METHODOLOGY METHODOLOG Y REPORT
CENTRE FOR WATER AND WASTE TECHNOLOGY UNIVERSITY OF NSW DR SVEN LUNDIE, MATTHIAS SCHULZ, DR GREG PETERS
IN CO-OPERATION WITH SCION DR BARBARA NEBEL
AGRESEARCH DR STEWART LEDGARD LEDGARD
FOR FONTERRA CO-OPERATIVE GROUP LIMITED
12 JANUARY 2009
DOCUMENT STATUS RECORD
Project Title:
Carbon Footprint Measurement
Client:
Fonterra Co-operative Group Limited
©
Document Title
Methodology Report
Document File Name:
0812 081204_CW 04_CW WT SCION AGRESEARCH Methodology for carbon footprint
DISCLAIMER: 1. The Centre for Water Water and Waste T Technology echnology has taken all reasonable steps to ensure ensure that the information contained in this publication is accurate at the time of production. In some cases, we have relied on information supplied by the client.
2. This report has been prepared in accordance with good professional practice. No other warranty, warranty, expressed or implied, is made as to the professional advice given in this report. 3. The Centre for Water and Waste Waste Technology Technology maintains no responsibility for the misrepresentation of results due to incorrect use of information contained within this report. 4. This report should remain together and be read as a whole. 5. This report has been prepared prepared solely for the benefit of the client listed above. No liability is accepted by the Centre for Water and Waste Technology with respect to the use of this report by third parties without prior written approval.
COPYRIGHT: © The Centre for Water and Waste Technology Technology,, Scion and AgResearch
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CARBON FOOTPRINT METHODOLOGY REPORT
TABLE OF CONTENTS
CONTENTS CONTENTS CONTENTS CONTENTS CONTENTS
1
Executive Summary
6
2
Overview of the project
8
3
Methodology for calculating the carbon footprint
9
3.1 3.1
9
Standards and references 3.1.1 Existing ISO Standards related to carbon footprint ass assessment essment 3.1.2 Carbon Trust Trust – BSI – DEFRA initiative 3.1.3 IPCC guidelines
3.2
General overview
11
3.3
life cycle assessment calculation procedure
12
3.4
Carbon footprint calculation
12
3.5
Methodology for the farm
12
3.5.1 Overview of published published studies on carbon footprint and life cycle assessment for dairy farm systems to the farm-gate stage 3.5.2 Goal and scope: functional unit, sys system tem boundaries, allocation rules 3.5.3 Inventory data 3.5.4 Carbon footprint calculation 3.6
Methodology for processing
21
3.6.1 Goal 3.6.2 Scope 3.6.3 Functional unit 3.6.4 System boundary 3.6.5 Life cycle inventory data 3.6.6 Allocation procedure 3.7
Methodology for distribution
27
3.7.1 Goal 3.7.2 Scope 3.7.3 Functional unit 3.7.4 System boundaries 3.7.5 Life cycle inventory data 4
References
31
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INDEX TO FIGURES
CONTENTS CONTENTS CONTENTS CONTENTS CONTENTS
Figure 1:
Life cycle assessment framework as described in ISO standards 14040 and 14044
9
Figure 2:
System boundaries of the carbon footprint calculation with mass and energy flows
11
Figure 3.
Simplified flowchart of the “grass-to-farm-gate” NZ production system
14
Figure 4:
Flow chart of "grass-t-farm-gate" life cycle for the range of milk suppliers
15
Figure 5.
System boundaries of product carbon footprints
22
Figure 6:
System boundaries of distribution carbon footprints
28
INDEX TO TABLES
4
Table 1.
Districts included in each regionally-adjusted category for this study study..
17
Table 2.
Technical description of Nort Northland, hland, W Waikato, aikato, Bay of Plenty Plenty,, Taranaki, Taranaki, Lower North Island, Westland, Marlborough + Canterbury, Otago + Southland and weighted NZ average dairy farms (year 2004/2005).
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Table 7.
Key emission factors and parameters according to the IPCC-based methodology
20
Table 8:
Industry-specific physico-chemical allocation matrix
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CARBON FOOTPRINT METHODOLOGY REPORT
ABBREVIATIONS
AMF
Anhydrous milkfat
BSi
British Standards Institution
A M F CF
Carbon footprint
CO2-eq
Carbon dioxide equivalents
CWWT
Centre for Water and Waste Technology echnol ogy
Defra
Department for Environment, Food and Rural Affairs
DM
Dry matter
EEIO
Environmentally extended input–output
FU
Functional unit
GHG
Greenhouse gas
GWP
Global Warming Potential
IPCC
Intergovernmental Panel on Climate Change
LCA
Life Cycle Assessment
LCI
Life Cycle Inventory
LCIA
Life Cycle Impact Assessment
LDPE
Low density polyethylene
LLDPE
Linear low density polyethylene
LUC
Land use change
MP
Milk powder
MPC
Milk protein concentrate
NZ
New Zealand
PAS
Publicly Available Specification
TEU
20 ft equivalent unit (shipping container)
WPC
whey protein concentrate
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1
EXECUTIVE SUMMARY
Fonterra has demonstrated a proactive stance on environmental management by commissioning independent analysis of the carbon footprint footprint of five key products throughout their value chain. Carbon footprint is a more recent term for global warming potential and refers to the total greenhouse gas emissions associated with a product or service. service. Emissions of different different individual greenhouses gases are converted into global warming potential and expressed in the common unit of CO 2-equivalents. The global warming potential reflects the atmospheric heat absorption capacity of individual greenhouse gases, most importantly methane (CH 4), nitrous oxide (N2O) and carbon dioxide (CO2) over a 100 year time horizon. A consortium of three independent research organisations (The University of New South Wales, AgResearch and SCION) were commissioned to calculate the carbon footprint (i.e. perform a greenhouse gas life cycle assessment) for butter butter,, milk powder (MP), milk protein concentrate (MPC), cheese and caseinate acr across oss their complete supply chains. This comprises all processes processes within the system starting with the on-farm and farm-related inputs where the raw milk is produced through to the finished product leaving the dairy processing sites and being transported to overseas dest destinations. inations. The farming operations were based on comprehensive data from 2004/2005 and both processing and distribution data were obtained for the 2006/2007 financial year. year. A robust and globally accepted methodology had to be applied that represents current world’s world’s best practice and science in this area. All three research research organisations are working at the forefront forefront of methodological developments in environmental system analysis, particularly life cycle assessment. The University of New South Wales and SCION participate in several national and international processes for methodological standardisation of assessment methods. methods. AgResearch coordinates a NZ review group and contribut contributes es to the Agricultural Working Group of the UK-based Carbon Trust methodology. Several standards serve as guidelines on how to perform a carbon footprint as part of a life cycle assessment. ISO 14040 and ISO 14044 (2006) are key standards which describe how the carbon footprint can be quantified at a product or service level. The recently published PAS 2050 methodology, methodology, an initiative from Carbon Trust, DEFRA and BSI in the UK, is aimed at industry users who are interested in a more consistent method for assessing the carbon footprint of products. The main goal of PAS PAS 2050 is to provide a common basis for the comparison and communication of results. PAS 2050 builds on pr previously eviously published standards but specifies certain technical requirements with regards to the carbon footprint measurement. The methodology used in the project undertaken for Fonterra is broadly consistent with PAS 2050 carbon footprint assessment specifications published in October 2008. As specified in PAS 2050, the calculation of the carbon footprint of Fonterra’s dairy products includes greenhouse gas emissions throughout the supply chain arising from the following sources: •
Emissions resulting from processes, including chemical reactions, livestock and other agricultural processes, refrigerant loss and other emissions sources, • Consumption of energy that has greenhouse gas emissions associated with it, • Consumption of energy carriers that were themselves created using processes that have greenhouse emissions associated with them (e.g. electricity), and • Wastes that produce greenhouse gas emissions. Indirect greenhouse gas emission offset mechanisms ar are e not included at any point in the supply chain. In cases where Fonterra uses renewable energy directly, the benefits are included in the calculations. The carbon footprint was calculated using the most recent figures as published by the Intergovernmental Panel for Climate Change in kg CO2-equivalent terms, i.e. with multiplication factors of CO2 1, N2O 298, CH4 25. As in many other life cycle assessment studies, allocation procedures play a critical role in determining the final carbon footprint result. Allocation refers to the rules applied to partitioning the inputs and outputs of a process or product between the product under study and other products. For the farming operations a physical allocation approach approach based on biological causality was adopted. This method wasinputs used to define the physiological feed requirements of and the animal to produce (consistent milk and meat, and specific and emissions were then calculated for the milk meat co-products with the highest priority method in PAS 2050). This was calculated to give an allocation ratio for milk and meat equivalent of 86%:14%. 6
CARBON FOOTPRINT METHODOLOGY REPORT
1
Executive Summary (continued)
With regards to the manufacturing processes, a physico-chemical allocation matrix was used. This type of allocation matrix was developed specifically for the dairy industry in Australia and reflects the actual relationships in the processing processing of dairy products at a high accuracy. accuracy. This approach is consistent with tthe he current PAS PAS 2050 specification as well as with other standards in carbon footprint and life cycle assessment methodology. With respect to the distribution of dairy products from the factory to overseas destinations, no allocation procedures were necessary since all emissions could be directly linked to the five individual products. At the grass-to-farm-gate stage, the system boundary covered the extraction, transportation and use of all raw materials associated with the dairy farm and with land used to grow dairy replacement animals and supplementary feed sources. Emissions of methane and nitrous oxide (predominantly from dairy cows and replacement animals) on- and off-farm were calculated using the IPCC-based NZ greenhouse gas Inventory methodology.. At the farm-gate-to-market-port stage, the system boundary ccovered methodology overed all material and energy flows and their associated emissions required for manufacturing and transportation of the products to the destination markets.
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2
OVERVIEW OF THE PROJECT
Fonterra commissioned the Centre for Water and Waste Technology (CWWT) at the University of New South Wales and AgResearch to measure the carbon footprint of a range of Fonterra’s Fonterra’s dairy products. CWWT is co-operating with SCION, a New Zealand Crown Research Institute. The project measures Fonterra’s carbon emissions across its complete supply chain which consists of the following distinct modules: •
Module 1 - On-farm: covers inputs and outputs related to the production of milk from the farming operation up until it leaves the on-farm milk vat,
•
Module 2 - Processing: includes the transportation of milk from the on-farm milk vat; the complete manufacturing process including packaging and storage at the factory site; through to the product loaded onto transport for delivery, and
•
Module 3 - Distribution: includes measuring the carbon emissions caused by the transportation of the product from the manufacturing site, to the warehouse and its shipping to key destinations internationally.
This report outlines the methodological approach applied in relation to Fonterra’s carbon footprint measurement. Section 3.1 will present the differ different ent standards and references references available, Section 3.2 will give a general overview of the methodological issues followed by specific life cycle assessment and carbon footprint calculation procedures (see Sections 3.3 and 3.4). Subsequently, Subsequently, the particular methodological items on the farm (see section 3.5), processing (see section 3.6) and distribution (see sectionreferring 3.7) willtobeprocesses described.
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CARBON FOOTPRINT METHODOLOGY REPORT
3
METHODOLOGY FOR CALCULATING THE CARBON FOOTPRINT
3.1
STANDARDS AND REFEREN REFERENCES CES
3.1.1 Existing ISO Standards related to carbon footprint assessment Several ISO standards relate to the assessment of a carbon footprint at different systems’ levels. The carbon footprint can be quantified either at a product or service level as in life cycle assessment described by ISO standards 14040 and 14044 (2006) or at an organisation or company level as described in ISO standard 14064-1 (2006).
i s s o
When assessed at the product level, the carbon footprint is one indicator amongst a basket of indicators (e.g.: eutrophication; land use; toxicity and energy use) that are quantified as part of life cycle assessment. Life cycle assessment consists of four steps as described in Figure 1: the the definition of the goal and scope of the study, the inventory analysis, the life cycle impact assessment (LCIA) and the interpretation.
LIFE CYCLE ASSESSMENT FRAMEWORK
GOAL AND SCOPE DEFINITION DIRECT APPLICA APPLICATIONS: TIONS: - Product development and improvement INVENTORY ANALYSIS
- Strategic planning INTERPRETATION - Public policy making - Marketing - Other
IMPACT ASSESSMENT
FIGURE 1: LIFE CYCLE ASSESSMENT FRAMEWORK AS DESCRIBED IN ISO STAND STANDARDS ARDS 14040 AND 14044 In the goal and scope phase, the purpose of the study, its scope (geographic, temporal, and technological), the studied function and corresponding system are are defined. The level of detail and data quality requirements of a life cycle assessment can vary significantly depending on its particular goal and the intended use of the study. study. The scope phase should define the type of critical review desired. Depending the scope of the stud study y, the critical review can be carried out by internal or external experts or byon a panel of interested parties.
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In the inventory analysis the resources consumed and the emissions to the environment are quantified at all stages of the life cycle of the product studied – from the extraction of resources, through to the production of materials, product parts and the product itself, and the use of the product, to its reuse, recycling or final disposal (Guinée et al., 2002). For each environmental impact in the life cycle impact assessment stage, a characterisation model is used to convert the inventory data contributing to this impact, into indicator results. This is done by multiplying the emissions of each substance by a characterisation factor for each impact category to which it may potentially contribute. Characterisation factors are substance-specific, quantitative representations of the additional environmental pres pressure sure per unit emission of of a substance. For a carbon footprint, these characterisation factors are called global warming potentials and should be based on IPCC guidelines. The interpretation phase is the final phase of the life cycle assessment procedure where the results are summarised and discussed as a basis for conclusions, recommendations and decision-making in accordance with the goal and scope definition (ISO 14040, 2006). The ISO standards for life cycle assessment do not actually describe a single method but allow organisations some flexibility. Application of life cycle assessment methodology in general and especially to food and farming systems is still the subject of on-going research and debate to achieve more harmonised and accurate practice across all practitioners even if the data quality requirements need to be adapted to the goal and scope of each study. The international and national IPCC guidelines represent the most formalised and widely accepted references for the the actual quantification of the greenhouse greenhouse gas emissions fro from m a system. Existing databases such as Eco-invent and life cycle assessment software tools such as GaBi or Simapro are essential tools for the implementation and accuracy of life cycle assessment/carbon footprint studies. IPCC guidelines will be more widely introduced later in this chapter and the actual assessment method for the carbon footprint of dairy products will be described specifically for each module in chapters 3.5, 3.6 and 3.7.
3.1.2 3.1.2
CARBON TRUST – BSI – DEFRA INITIATIVE INITIATIVE As already mentioned the implementation of the life cycle assessment methodology is specific to the goal and scope of each study and requires specific assumptions assumptions and data. The current initiative from the Carbon Trust, DEFRA and BSI in the UK is targeted at applying life cycle assessment over a wide range of products in a consistent manner for industry users, focusing only on the carbon footprint indicator indicator.. The goal and scope for this indicator has specific key assumptions (system boundaries, allocation rules…) and also requires some arbitrary assumptions and simplifications to get harmonised results across all product categories. The Carbon T Trust rust method P PAS AS 2050, published October 2008, for instance, distinguishes a business-to-consumer assessment and a business-to-business assessment. The business-to-consumer assessment includes the emissions from the full life cycle of the product (“cradle-to-grave”) whereas the business-to-business assessment includes the greenhouse gas emissions released up to and including the point where the input arrives at a new organisation (“cradle-to-gate”) (PAS (PAS 2050, 2008). The Carbon Trust Trust method is therefore industry or production focused and is therefore a relevant reference for Fonterra to analyse the carbon footprint of their dairy products from cradle to its shipping to key destinations internationally. internationally. The methods used for this project are broadly consistent with PAS 2050.
3.1.3 IPCC GUIDELINES The Intergovernmental Panel on Climate Change (IPCC) is responsible for assessing the science related to climate change at a global level. Countries which ratified the Kyoto Protocol Protocol committed themselves to individual, legally-binding targets to limit or reduce their greenhouse gas emissions. The goal for all countries which ratified the Kyoto Protocol is to reduce aggregate emissions by at least 5 percent below 1990 levels in the commitment period 2008 2008 to 2012. New Zealand’ Zealand’ss target in this group of nations is 100 percent percent of the level in 1990. To achieve their targets, those countries must put in place domestic policies and measures to address emissions, and are required to report their greenhouse gas inventory annually at the country level. IPCC guidelines provide methodologies for estimating national inventories of anthropogenic emissions by sources and removals by sinks of gr greenhouse eenhouse gases. Therefore the boundaries of the
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CARBON FOOTPRINT METHODOLOGY REPORT
studied system in those inventories correspond to the boundaries of the country and do not follow a life cycle approach. The 2006 IPCC guidelines generally provide advice on estimation methods at three levels of detail, from tier 1 (the default method) to tier 3 (the most detailed method). The advice consists of mathematical specification of the methods, information on emission factors or other parameters to use in generating the estimates, and sources of activity data to estimate the overall level of net emissions. emissions. The provision of different different tiers enables focus of efforts on the emissions and removals that contribute most significantly to the total greenhouse gas emissions (IPCC, 2006). Although the purpose and system boundaries of IPCC guidelines are are different, these emission factors and methods to estimate greenhouse gas emissions are also relevant for life cycle assessment studies. IPCC guidelines provide default emission factors for greenhouse gas emissions from all sectors, it also encourages the use of country-specific factors if the methods used to calculate them can be defended and withstand international inter national peer-review. peer-review. Consequently, specific research has been carried out in New Zealand and progressively integrated into IPCC-NZ guidelines for the main sources of GHG emissions, being nitrous oxide emission from nitrogen excreted from grazing animals and methane due to enteric fermentation (MfE, 2006).
3.2
GENERAL OVERVIEW In life cycle assessment methodology usually all inputs and outputs from the system are based on the ‘cradle-to-grave’ approach. This means that inputs into the system system should be flows from from the environment without any transformation from humans and outputs should be discarded to the environment without subsequent human transformation (ISO 14040, 2006). Each system considers upstream processes with regard to the extraction of raw materials and the manufacturing of products being used in the system and it considers downstream processes as well as all final emissions to the environment. In this study the system boundary encompasses all processes within the system starting with the on-farm and farm-related inputs where the raw milk is produced through to the finished product leaving the dairy processing sites and being transported transported to overseas destinations. This business-tobusiness assessment is broadly consistent with the recently published Carbon Trust methodology PAS PAS 2050:2008 and is referred to as a “cradle-to-gate” assessment assessment (P (PAS AS 2050, 2008). The system boundaries and flow diagram of the entire life cycle of Fonterra’s dairy products selected for this study are presented in Figure 2.
SYSTEM BOUNDARY
FARM
Milk
Chemicals
TRANSPORTATION
Ingredients
Water
to all processes within the system
Milk
PROCESSING PLANT WASTE MANAGEMENT
PRODUCTION OF PACKAGING MATERIAL
Energy (fuel,themal, electric energy)
WASTE MANAGEMENT
Packaging material Product and Packaging material
Distribution in NZ Product and Packaging material
Storage Port handling and cooling
International transport (overseas ports)
FIGURE 2: SYSTEM BOUNDARIES OF THE CARBON FOOTPRINT CALCULATION WITH MASS AND ENERGY FLOWS
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As described in PAS 2050, the calculation of the carbon footprint of Fonterra’s dairy products includes emissions throughout the supply chain arising from the following sources: •
Releases resulting from processes, including chemical reactions, livestock and other agricultural processes, refrigerant loss and other emissions sources, • Consumption of energy that has greenhouse emissions associated with it, • Consumption of energy carriers that were themselves created using processes that have greenhouse gas emissions associated with them (e.g. electricity), and • Wastes that produce greenhouse gas emissions. The detail of those emissions and their calculation is given for each module specifically. As required by the PAS 2050, indirect greenhouse emission offset mechanisms are not included at any point in the supply chain. Where Fonterra uses renewable energy directly, directly, the benefits are included in the calculations.
3.3
LIFE CYCLE ASSESSMENT CALCULATION CALCULATION PROCEDURE In principle, three life cycle assessment calculation procedures can be applied for calculating the carbon footprint of a product: 1) process-based life cycle assessment approach, 2) an input-outputbased life cycle assessment approach and 3) hybrid approach combining process analysis with inputoutput analysis (see Suh et al, 2004 for details). The first approach can be described as accurate on a process level, but it has truncation errors as it does not take into account all processes of the supply chain of a product. The second approach overcomes the problem associated with the truncation error by taking into account the entire supply supply chain, but it lacks of precision at a pr process ocess level. The hybrid approach ideally combines the advantages of both approaches, however, such a tool is currently not available to be used in a commercial environment in New Zealand. Therefore, a choice needed to be made between a process - or input-output-based approach. The project team decided to take a very detailed process-based approach because it allows quantification of the carbon footprint of processes at a very high level of precision. PA PAS S 2050 is also in line with this approach. This is of overarching importance because some of the processes, such as emissions from feedstock, may have a significant contribution to Fonterra’s carbon footprint. Accuracy at the process level outranks by far the advantage of the input-output approach (i.e. covering the complete c omplete supply chain).
3.4
CARBON FOOTPRINT CALCULA CALCULATION TION After discussion with all partners of this project, the carbon footprint (equivalent to global warming potential) for a 100 year time horizon (GWP100) was calculated according to the most recent IPCC reference in kg CO2-equivalent, i.e. with multiplication factors of CO2 1, N2O 298, CH4 25. Global warming potential corresponds to the impact of emissions on the heat radiation absorption of the atmosphere.
3.5
METHODOLOGY FOR THE FARM
3.5.1 OVERVIEW OF PUBLISHED STUDIES ON CARBON FOOTPRINT FOOTPRINT AND LIFE CYCLE ASSESSMENT FOR DAIRY FARM SYSTEMS TO THE FARM-GATE STAGE In the published carbon footprint and life cycle assessment studies on dairy systems, the goal and scope of the studies are properly defined as well as the stud studied ied system and system boundaries. The allocation rules used at the farm gate level between milk and meat are diverse and include: • •
no allocation at all (Haas et al., 2001), allocation based on biological causality (feed requirement for each function) (Cederberg and Mattsson, 2000; Williams et al., 2006; Basset-Mens et al., 2008), • economic allocation, which is the most used across all studies, and is based on the economic returns for the different products (Cederberg and Flysjö, 2004; Berlin, 2002; Eide, 2002; Hospido et al., 2003 & 2004; Casey and Holden, 2005; Thomassen et al., 2007). The design of the studied farm systems and their technical data are based on a range of approaches from the survey of a sample of farms (studies have used between 1 and 11 farms per production type) to the use of national statistics and database. Some studies have compared dif different ferent types of production such as organic versus conventional or conventional intensive versus conventional extensified. Conversely Conversely,, in other studies, a representative weighted average of all types of production at the national level is calculated (Williams et al. 2006). 12
CARBON FOOTPRINT METHODOLOGY REPORT
In published studies, details of the inventory of greenhouse gases are rarely fully described and a range of approaches can be found. Some studies applied the methods used in their national GHG inventories (Williams et al. 2006), while other studies used a mix of specific references on emissions factors for dairy farm systems in their countries and IPCC guidelines (Cederberg and Flysjö, 2004; Casey and Holden 2005; Thomassen et al., 2007). Some studies apparently used quite simplified inventory covering only the key aspects such as methane emissions from from cows (Hospido et al., 2003 and 2004). Williams et al. (2006) and Thomassen et al. (2007) appear to have carried out an exhaustive inventory but it is difficult to check the completeness of the GHG inventory for most studies since not all components of the GHG inventory were mentioned. mentioned. For instance, in Cederberg and Flysjö (2004), the direct nitrous oxide emission from N fertiliser and the indirect nitrous oxide emission due to leaching were not mentioned. Casey and Holden (2005) did not mention indir indirect ect nitrous oxide emis emissions sions and the methods used by Haas et al (2001) and Eide (2002) to estimate methane and nitrous oxide emissions were not described. The level of discrepancy in the methods used and the degree of completeness of the published greenhouse gas inventories is uncertain. A more in-depth study with all interested parties is required required to harmonise as much as possible the methods used and guarantee more accurate comparisons.
3.5.2 GOAL AND SCOPE: FUNCTIONAL UNIT UNIT,, SYSTEM BOUNDARIES, BOUNDARIES, ALLOCATION ALLOCATION RULES The goal of module 1 is to provide Fonterra with an assessment of the grass-to-farm-gate carbon footprint for a range of NZ dairy farm systems. This goal is motivated by Fonterra’s Fonterra’s commitment to sustainability and the increased global awareness of the detrimental effect of greenhouse gases on the environment. The scope of the current study covers estimation of the carbon footprint for a significant range of dairy farms supplying Fonterra depending on key regional differences across New Zealand. A wide range of milk suppliers were analysed in this study for the 2004-2005 season, using information mainly based on the DairyNZ’s 1 ProfitWatch survey of farms and LIC2 (2005) statistics. The farm types included: • •
All regional average dairy farms across New Zealand, An NZ average dairy farm, designed by weighting (on an on-farm ha basis) regional average data. The functional unit of the study is one kilogram of milksolids (milkfat plus protein). 3
The system boundaries (see Figures 3 and 4) were set up from grass-to-farm-gate, which included: •
• •
Production of milk on-farm including on-farm pasture production (methane and nitrous oxide from animals), cow management (diesel, petrol) and milk extraction, farm dairy effluent management and water supply (electricity), Production of supplementary feed, Off-farm pasture production for the dairy cow replacements and any cows grazed off over
•
winter, and Production and delivery of crop and pasture inputs.
1.
New Zealand Zealand dairy research and exten extension sion organi organisation. sation.
2.
LIC – Livestock Improvement Corporation Corporation which provide providess farm management in information, formation, herd testing, testing, artificial breedi breeding ng services, DNA analysis, farm advisory extension service, research to improve farm productivity. productivity.
3.
Milksolids is milksolid that contains milk fat plus protein. protein. Farmers get paid on the basis of milksolids supp supplied. lied. Milk solids (two words as opposed to one) is milk protein, plus fat and other elements in milk. FOR FONTERRA CO-OPERATIVE GROUP 2009
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Input production
Off-farm level
Off-farm grazing heffers EXTRACTION AND DELIVERY OF RAW MATERIALS AND ENERGY RESOURCES
Farm level Herd management
Fertilizers
Meat
Electricity
Pasture production
Milk Extraction
Diesel Off-farm maize silage
1 kg MS
Farm dairy effluent
FIGURE 3. SIMPLIFIED FLOWCHART OF THE “GRASS-TO-FARM-GATE” “GRASS-TO-FARM-GATE” NZ PRODUCTION SYSTEM
This should account for at least 99% of the likely life cycle emissions from grass-to-farm-gate ther thereby eby meeting one of the key requirements of the PAS 2050. Capital was excluded in calculations, as specified in the PAS 2050 (2008). In keeping with specifications in the PAS 2050 (2008), land use change was included by accounting for any changes in land use from January 1990 to to 2005 (the year of data used in the st study). udy). This used relevant land use change factors from the NZ Greenhouse Gas Inventory. Impacts were allocated between the co-products milk and meat according to a biological causality, which was based on the physiological feed requirements of the animal to produce milk and meat (Basset-Mens et al., 2008). This accounted for all specific specific inputs and greenhous greenhouse e gas emissions associated with the animal growth phase (from birth to mature live-weight) within the whole-farm system, which were allocated to meat production. production. Thus, it met the first priority for allocation in the PAS PAS 2050 (2008). It equated to a relative allocation between milk and meat of 86%:14%. This methodology is consistent with the highest priority method in PAS 2050 (2008) and was preferred over economic allocation which can vary over time due to changes in prices for milk and meat products.
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CARBON FOOTPRINT METHODOLOGY REPORT
EXTRACTION OF RAW MATERIALS P rock (North Africa)
Sulphur (Canada)
KCI (Canada)
Natural gas (NZ/Indonesia)
Crude Oil (overseas)
Natural gas/coal (NZ)
FERTILISER MANUFACTURING Road & sea transport
Road & sea transport SSP (NZ)
Urea (NZ/Indonesia)
KCI
Road & sea transport
Lime (NZ)
Petrol & Diesel
Electricity NZ
FEED SUPPLEMENTS FROM NZ Maize
Pasture
Barley grain
Road
silage (Sc. 1-5)
silage (all sc.)
(Sc. 6-8)
transport
Road, rail & sea transport
Road transport
FARM
Farm dairy effluent
Cow management
Pasture production
Road transport
Milk extraction
Milksolids
{ Wintering-off – Pasture (all scenarios except sc. 8) – Swedes/kale (sc. 8)
Scenarios legend: Sc. 1: Northland Sc. 2: Waikato Sc. 3: Bay of Plenty Sc. 4: Taranaki
Off-farm grazing of replacements (all scenarios)
Meat
Sc. 5: Lower North Island Sc. 6: Westland Sc. 7: Marlborough + Canterbury Sc. 8: Otago, Southland
FIGURE 4. FLOW CHART OF “GRASS-TO-FARM-G “GRASS-TO-FARM-GA ATE” LIFE CYCLE FOR THE RANGE OF MILK SUPPLIERS. FOR FONTERRA CO-OPERATIVE GROUP 2009
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3.5.3 INVENTORY DAT DATA A 3.5.3.1 Regional average and New Zealand weighted average dairy farms
LIC statistics (LIC, 2005) and regional ProfitWatch data provided by DairyNZ were the two main sources of data used to design an exhaustive and non-overlapping range of regional scenarios for the season 2004-2005. The data was aggregated aggregated in order to match LIC regional categories and ProfitWatch regional categories (Table 1). Cow numbers, milk production, milk quality and size of farms were derived from LIC statistics while replacement rate, cow weight and key input data such as fertiliser-nutrients fertiliser-nutr ients and feed supplements were obtained from the regional ProfitWatch database. The dry matter intake by animals was estimated by using the NZ inventory model of Clark et al. (2003) for both dairy cow and replacement animals. Technical data for the eight regional NZ dairy farms and the weighted NZ average dairy farm are presented in Table 2. For each region, specific data on wintering-off, stock replacement and broughtin feed was collected. The practices of farmers in each region and distances of transportation for animals grazed-off farm were based on a survey of consulting officers from from DairyNZ and experts fr from om PGG Wrightson Wrightson (PGGW). Data on the distances of fertiliser transportation for each region were provided by experts from fertiliser companies (Ballance® and Ravensdown®). Transportation distances for brought-in-feed supplements were obtained from experts from Pioneer® (maize marketing company), DairyNZ and PGGW. PGGW. Distances for transport of animals grazedoff farm were collected by surveying different experts from the consulting officers of DairyNZ and PGGW PGG W. The fuel consumption for all agricultural components including cow management, pasture production, supplementary feed production and delivery, was calculated from the analysis of all single operations needed specifically for each scenario and parameterised in the life cycle assessment assessment model (SIMAPRO). Electricity consumption was calculated as a function of cow numbers based on an NZ study by Sims et al. (2005) and as a function of irrigation based on a summary of types of irrigation systems, mm irrigation water applied and typical depth of pumping.
New Zealand Regions (source Ministry of the Environment) NORTH ISLAND
Northland Auckland Waikato Bay of Plenty Gisborne Hawke’s Bay Taranaki Manawatu/Wanganui Wellington
SOUTH ISLAND
Nelson Marlborough Tasman West Coast Canterbury Otago Southland
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CARBON FOOTPRINT METHODOLOGY REPORT
d n + l a o h g t d a n n t u o i a a l O S d h e h t n t u u u l o D C S + y r r h y r u u y g b u b u r h b g r r o e u e e r t o a r n t o t i r n a n o b u a k a b o C l a r C l C t i r k a h a a i h t a M M K N t S W d n a l t s e W
. Y D U T S S I H T R O F Y R O G E T A C D E T S U J D A Y L L A N O I G E R H C A E N I D E D U L C N I S T C I R T S I D . 1 E L B A T
t s a o C n a t s m e s a W T
h t r d o n y a n a N l a B o r I s s t p e a a r i g n k e w w l l i o L a e a H W W i k a n a r a T
i k a n a r a T
y t n e l P f o y a B
u a y e t t n l s e a t l P P l a o f a r C o t t y n a e s a B C E
o t a k i a W
s d n a d l n p a U l u a r n k a u c n k k i u r e u a l k s n p n A t a a r a h t e M P F S W
d n a l h t r o N
i e r h t a a y r g o n r a e n N a i d r h p a o a F W K R
d e d u l c i n s t c i r t s i D FOR FONTERRA CO-OPERATIVE GROUP 2009
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, Y R U B R E T N A C + H G U O R O B L R A M , D N A L T S E W , D N A L S I H T R O N R E W O L , I K A . N ) A 5 R 0 0 A T 2 / , 4 Y 0 T 0 N 2 E L R P A F E ( O Y Y S A M B R , A O F T Y A R K I I A A W E , D D G N A A R L E H V T A R Z O N N D F E O T H N G I O I E T W P I R D C N S A E D D L N A A L C I H N T H U C O E T + . S 2 O E G L B A T A T O 18
e n b a h o t t r d e e h t m ( o u s s s t a n e e r m e e l w + d n p y a p 7 9 7 9 6 9 4 9 0 0 7 e o l 1 2 u 3 4 0 5 6 3 2 6 8 7 g h s 4 6 h t t 1 4 . . 9 . . 1 s d 1 1 i 4 3 2 0 t a u e o o O e r S f a l l n a e c t s y h r a l h g u a t u b 8 5 5 5 6 6 6 9 6 0 2 9 n e o r 8 6 0 1 3 i o r . 4 3 . 5 . 7 6 . 2 g e 1 4 3 0 4 g a 0 9 4 3 1 o t 1 r e b n r e l v r a t a s a C a l Z M + N e e d r e h t t d h n e g i a 3 7 8 3 5 7 3 5 7 0 3 7 h l t e t 4 0 3 0 6 9 9 2 2 1 4 r . . 6 . . 1 s w 1 5 4 1 o 4 3 2 0 e f e h W e l t i r h o w f h t e d r g e o d a 6 1 4 2 6 4 1 4 0 8 0 l m i N n 9 1 5 7 2 5 7 5 9 4 3 9 s u e s 6 8 . . 3 . 8 4 . 0 r e I l s a 1 4 2 z a i w a s o a m L w e t b i , o i Z t k N a 7 0 2 0 0 2 3 5 2 9 4 0 d e n n 8 4 8 3 1 . 8 5 3 . 7 . 7 . 2 a 3 4 8 4 m i 3 5 0 2 r n u a s o i T s t a c u e r d e o r w p e f y d 1 4 2 4 4 8 7 1 4 4 0 g o t 0 n a 4 n 3 6 7 l . 2 5 4 . 4 . 6 6 . 2 a i y e 5 2 s a l 1 4 2 0 8 4 3 1 a B P e e r r u a t s y r a i p a d t l p o a e t t 5 6 1 4 7 4 7 8 6 2 0 c a 5 o x 9 5 9 2 0 7 5 1 4 4 5 t e s e 5 2 . 1 . 3 . 9 4 . 0 4 2 i k a t h t n W e f o m e l % p 0 p d 7 u n s a 4 9 0 2 1 8 9 3 7 4 6 0 d t u l 0 6 1 4 2 o . 8 3 5 . 4 . 8 . 2 e h 2 2 5 4 1 4 2 b t e 3 0 f r a l o t l a n N e , s s e o i r . r p e a e n r g s s e a s c e i n l l s o s l n i e a ) u g i z s n e s ) a r a o s a i s h d * e e g g a m e e v m n e / i i a i r g l e s m f m f i r a s / a e r i a s r l e t e t s i c a a g h s v s g s a f i e e r w r , k i a - p a f r m y t f ) , t s m N P e u l n s e t e z t e i s d n e r r o i % r h s s r r . a f i l a g a p h a b n e s m s e i ( i ( o e g w a i i m l s t r e l l a e i i i a % o s e r t e k m e t f p l c c r n t h c r r i w g r i M M M t l a n e a m t e D D D r y i e f e t r l y l e f f r - w i u S t p e A o l t o g g g g g f n o a e g u r r : s O C D R K B P K K K K K : a * a * b * p
e d g e a t r 6 6 3 6 8 6 9 5 * 4 * h e 3 4 * 2 5 7 . 0 4 * . 5 . 5 7 . 2 g v 6 5 0 4 2 1 i a 0 8 4 3 1 2 e 4 Z W N
CARBON FOOTPRINT METHODOLOGY REPORT
3.5.3.2
Supplementary feed production, off-farm grazing of replacements and wintering-off
For all scenarios, an average beef farm was assumed to be used for grazing replacements, based on the MAF Intensive Beef Monitor Farm (Ledgard et al. 2003). The pasture silage was also assumed to come from the beef farm where replacement animals were grazed. For scenarios using maize silage, it was assumed to be produced produced on a “typical” forage cropping cropping block off the farm. The technical data for maize silage production was provided by the NZ branch of an international seed company (Ian Williams, Pioneer, pers. comm.). Current practices were assumed for the use of inputs for the production of pastures and silages (Basset-Mens et al. 2008). Barley grain was assumed to be similar to triticale grain and thus a recent life cycle inventory for triticale grain in the South Island was used. For all scenarios (except Otago + Southland), wintering-off of dairy cows was assumed to be on pasture while for the Otago + Southland scenario, cows were assumed to be wintered-off on a brassica (swedes + kale) crop. Data for pasture land used for wintering off dairy cows was unavailable and therefore conservative information was used which was similar to that for the average NZ dairy farm (in practice this is likely to overestimate emissions).
3.5.3.3
Inventory-based greenhouse gas emissions from agricultural land
The inventory of greenhouse gas emissions covering methane (CH4) from enteric fermentation by cows, CH4 and nitrous oxide (N2O) from excreta deposited on pasture and from farm dairy effluent, and CO2 emissions from lime and urea application was based on IPCC (2006) and specific IPCC-NZ methodology (de Klein et al. 2001; Clark 2001; Saggar et al. 2003, MfE 2007). 3.5.3.3.1 Intake model The animal feed intake model used in estimating CH4 and N2O emissions was that used in the Tier 2 approach of the NZ IPCC inventory (Clark et al. 2003). It is a comprehensive model that operates at a monthly time step and utilises data on livestock numbers, livestock performance and diet quality. quality. Within the dairy category, the model subdivides population into animal sub-categories such as dairy cows in milk, heifers from 0 to 1 year old and heifers from 1 to 2 years old. Dry matter intake was estimated by calculating the energy energy required to meet the assumed levels of performance (MJ metabolisable energy (ME) per day) and dividing this value by the energy concentration of the diet consumed (MJ ME per kg dry matter). For dairy cattle, energy requirements requirements were calculated using the algorithms presented in the publication “Feeding The standards for Australian livestock: Ruminants (CSIRO, 1990)” . intake model from the NZ inventory described above was used for each scenario by entering data on milk production, milk composition and cow liveweight and by adjusting the feed quality (metabolisable energy energy,, digestibility and N concentrations), on a monthly basis to account for all feed supplements used in addition to pasture. pasture. The monthly rates of supplementary supplementary feeding were based on the team’s team’s expertise.
3.5.3.3.2 N excreted The N excreted by animals used the NZ inventory methodology, which was based on principles in the OVERSEER® model (Wheeler et al. 2003). Dry matter intake was based on the method outlined in the pr previous evious section. This was then multiplied by the average NZ pasture N concentration (from a review of data for the NZ inventory) to calculate N intake. For pasture silage, maize silage and barley grain, N concentrations were obtained from the OVERSEER ® nutrient budget model (Wheeler et al. 2003) and are based on the mean of samples submitted to a major NZ laboratory, FeedTech. FeedTech. The N in milk or meat products (based on the NZ inventory) was subtracted from total N intake in order to calculate the amount of N excreted. In the IPCC-NZ inventory inventory,, 95% of the N excreted by milking cows is assumed to be applied onto pastures during grazing and 5% to be stored in an anaerobic lagoon before being reapplied onto pastures. The same was assumed in this study for all scenarios.
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3.5.3.3.3 Methane emissions Methane emissions from enteric fermentation were calculated from the product of energy and dry matter intake by animals using the NZ Inventory model and the IPCC-NZ emission emission factors (Table (Table 3). Methane emissions due to waste management were calculated by multiplying faecal dry matter (1- digestibility of feed) by specific emission factors according to Saggar et al. (2003) and MfE (2007) for faecal dry matter deposited on pastures and for effluent stored in an anaerobic lagoon respectively.
3.5.3.3.4 Nitrous oxide emissions Direct N2O emissions were calculated by multiplying N inputs by IPCC (2006) and NZ-specific emission factors corresponding to the fraction emitted to the atmosphere as N2O (de Klein et al. 2001; Table 3). In particular, the emission factor for N2O from excreta-N on grazed pasture is 1% in the NZ context (MfE 2007) compared with a default emission factor of 2% for the IPCC methodology (IPCC 2006). 2006). Considerable research effort has gone gone into establishing a country-specific value for this emission factor (de Klein et al. 2003; de Klein et al. 2004; Kelliher et al. 2005). Indirect N2O emissions were calculated using the IPCC (2006) and IPCC-NZ N source and emission factors, with the NZ-specific factors developed from research and reviews carried out by scientists in our project team. Key emission factors and parameters from IPCC (2006) and the IPCC-NZ inventory are summarised in Table 3.
3.5. 3.5.3. 3.3. 3.5 5 CO2 emissions from lime and urea application Direct CO2 emissions from soils due to lime and urea application were calculated according to the default IPCC emission factor (IPCC 2006). The CO2 absorbed by plants was not taken into account since it is in equilibrium with losses from the grazing cycle and plant respiration.
TA TABLE BLE 3. KEY EMISSION FACTORS AND PA PARAMETERS RAMETERS ACCORDING TO THE IPCC-BASED METHODOLOGY Process kg of CH4 emitted per tonne of DM ingested
Mean
References for mean
21. 6
Clark (2001)
Due to N fertiliser use (EF1)
0. 01
IPCC (2006)
Due to excreta deposited during grazing (EF3) Due to excreta stored in an anaerobic lagoon (EF3 AL)
0.01
De K Kllein et et a all. (2 (2001) & Kelliher e ett a all. (2 (2005)
0.001
IPCC (2006)
Due to atmospheric deposition of NH 3-N (EF4)
0. 01
IPCC (2006)
Due to leaching and runoff of NO 3-N (EF5)
0.0075
IPCC (2006)
kg of NO3-N emitted per kg of N excreted or N fertiliser applied (FRACLEACH)
0. 07
Thomas et al. (2005)
kg of NH3-N (and NOx-N) emitted per kg of N excreted (FRACGASM)
0. 20
IPCC (2006)
kg of NH3-N emitted per kg of N fertiliser applied (FRACGASF)
0. 10
IPCC (2006)
kg of N2O-N emitted per kg of N:
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CARBON FOOTPRINT METHODOLOGY REPORT
3.5.4 CARBON FOOTPRINT CALCULA CALCULATION TION The same calculation methods for total greenhouse gas emissions (i.e. the carbon footprint) were used across all partners of the project for the production and delivery of NZ diesel, petrol and electricity.. These were pr electricity provided ovided by SCION. The carbon footprint calculations for the manufacturing and delivery of fertilisers were based on the Eco-Invent database (Frischknecht et al. 2005) adjusted as far as possible to the NZ situation in terms of distances and technology used. A brief summary was done of the use of refrigerants (mainly associated with vats for chilling milk on farm prior to collection) after after discussion with a local expert (D. (D. Grey pers. comm.). The estimate of emissions associated with HFCs and carbon footprint CFCs equated to roughly 0.012 kg CO2-equivalent/kg milksolids. This represented represented about 0.1% of the total total carbon footprint and was incorporated in the carbon footprint estimates in keeping with the goal of including all contributors. The Inventory approach, system boundaries, and sources of GHG emissions were peer-reviewed by experts from overseas (Dr David Chadwick from UK for CH4 and N 2O specifics and Dr Christel Cederberg for overall life cycle assessment methodology).
3.6
METHODOLOGY FOR PROCESSING Greenhouse gas emissions that are included in the carbon footprint calculations are determined with reference to Life Cycle Assessment Assessment techniques. In this Section relevant methodological issues are described in order to make the carbon footprint calculation procedure of the processing phase (module 2) transparent.
3.6.1 GOAL The goal of Module 2 is to provide Fonterra with an assessment of the farm-to-factory- gate carbon footprint for 5 products which are predominantly sent to overseas markets. This goal is motivated by Fonterra’ Fonterra’ss commitment to sustainability and the increased global awareness of the detrimental effect of greenhouse gases on the environment.
3.6.2 SCOPE All processes are included in this module that cover the transportation of the milk from the on-farm milk vat, the complete manufacturing process,(including packaging and storage at the factory site), to the final product loaded on distribution transport for delivery. All processes take place in New Zealand. The 5 products included in the scope of this study are: • butter, • milk powder, • milk protein concentrate, • cheese • caseinate.
3.6.3 FUNCTIONAL UNIT The functional unit (FU) for each product is 1 tonne product leaving the factory gate. The functional unit describes the main function fulfilled by a product system and is the basis for all calculations.
3.6.4 SYSTEM BOUNDAR BOUNDARY Y In this study the system boundary encompasses all processes within the system starting with • • • •
the transport of the raw milk to the processing sites and inter-factory intermediate product transport, multi-product manufacturing at 22 production and 2 transfer sites under consideration of all products even if they are not part of the analysis, production, delivery and consumption of operating materials, i.e. chemicals, packaging materials, ingredients, electrical and thermal energy production on-site and off-site, and
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• freshwater delivery and wastewater treatment. The system boundaries and flow diagram of the entire life cycle of Fonterra’s dairy products selected for this study are presented in Figure 5.
SYSTEM BOUNDARY
Chemicals
Ingredients
Water
Energy (fuel, thermal, electrical energy)
Milk
Farm
Transportion
To all processes within the system
Milk
Processing plant Waste management
Production of packaging material
Pack. material
Waste management
Prod. + Pack. material
Distribution in NZ Prod. + Pack. material
Storage Port handling + cooling SYSTEM BOUNDARY FOR PROCESSING
International transport (overseas ports)
FIGURE 5. SYSTEM BOUNDARIES OF PRODUCT CARBON FOOTPRINTS
As described in PAS 2050 (2008), the calculation of the carbon footprint of Fonterra’ Fonterra’ss dairy products includes emissions throughout the supply chain arising from the following sources: • •
Releases resulting from processes, including chemical and ingredients production, refrigerant manufacturing and losses and other emissions sources, Usage of energy that has greenhouse gas emissions associated with it,
•
Consumption ofemissions energy carriers thatwith werethem themselves created and using processes that have greenhouse has associated (e.g. electricity), • Wastes that produce greenhouse gas emissions. As also required by PAS 2050, indirect greenhouse gas emission offset mechanisms are not included at any point in the supply chain. Where Fonterra uses renewable energy directly, directly, the benefits are included in the calculations. The carbon footprint calculation excludes ‘greenhouse gas emissions from the manufacture and ongoing maintenance of capital goods, such as plant machinery, transport equipment, electricity generating plant, etc., used in the manufacture of the product; and transport of employees to their normal place of work’ (PAS 2050 (2007), p 8).
3.6.5 LIFE CYCLE INVENTORY DAT DATA A Life cycle inventory data were collected in close close cooperation with Fonterra. At the beginning of the project a detailed data collection questionnaire questionnaire was sent to Fonterra staff. All data the project CWWT / SCION team requested was provided by Fonterra and additional data sources were considered if no company specific information was available. Secondary data are used for so-called background processes, such as refinery of crude oil which is processed in diesel and electricity generation. Sources for secondary data from internationally agr agreed eed datasets are contained in the life cycle assessment software tool GaBi. Additionally Additionally,, data from peer reviewed journal articles ar are e used. 22
CARBON FOOTPRINT METHODOLOGY REPORT
CWWT/SCION staff visited several sites in order to get an impression of the manufacturing practices and processing technologies in use. All data was checked by the project team and an independent reviewer with regard to consistency and completeness. The data was then incorporated into an Excel-spr Excel-spreadsheet eadsheet that built the basis for the carbon footprint calculation in life cycle assessment-software tool GaBi. All processes are included in this module starting from the transportation of the milk from the on-farm milk vat, the complete manufacturing process, including packaging and storage at the factory site and the loading of the final product on distribution distribution transport for delivery delivery.. All processes for the production of the dairy products take place in New Zealand. The life cycle inventory data, used for calculating the carbon footprint of the processing phase, contains information on raw milk or other intermediate products processed at 22 manufacturing sites, inter-factory intermediate product transfer (i.e. a total of 46 intermediate products), all products manufactured (all types of fat products, milk powders, cheese, caseinate based products, whey products, concentrated milk), electricity and thermal energy consumption figures, ingredients and chemicals usage, water consumption figures and refrigerant usage, packaging materials and waste management. Transport effort is taken into account of raw milk from the farms to the sites, chemicals and packaging materials including truck size and load efficiencies as well as transport impacts. Data used in the carbon footprint measurement for processing are described in more detail below: •
•
•
•
•
•
•
Transport of raw milk from the farm to the manufacturing sites is taken into account by reporting the quantity of diesel diesel required for trans transportation. portation. Two types of information are used for calculating transport effort: 1) diesel fuel consumption figures of the transport fleet as well as 2) transport distances multiplied by the mass transported in order to calculate the tonne-kilometres (tkm) associated with delivery (Font (Fonterra, erra, 2008). These figures take into account the type and size of the truck as well as the average load factor (see Mueller & Baas, 2004). There are large amounts of different intermediate products transported between manufacturing sites. The logistics between the sites is very complex, since most sites both receive and send various amounts of different different intermediate products. products. These inter inter-site -site intermediate produ product ct transfers have been fully considered since only through these additional milk solids flows between the sites the actual production of dairy products is ensured. Final annual production figures of all Fonterra dairy products are included, i.e. fat products (butter,, anhydrous milk fat, fat blend), milk powders (skim, whole and butter milk, nutritional (butter and speciality powders, MPC42, 56, 70 and 85), cheese (dry and brine salt, mozzarella and cream cheese types), casein based products (caseinate and casein) and whey products (whey powder,, WPC, whey fractions, lactose, lactalbumin, alamin and ethanol). All dairy products powder need to be taken into account for the calculation of the 5 dairy products under study. In addition, chemical consumption figures are considered for each plant: In total 28 different types of chemicals are used on-site out of which 4 chemicals che micals (caustic soda, nitric acid, triplex, sodium hypochlorite) contribute 96·1% in mass to the total total chemical usage. Therefore, only these 4 chemicals are included in the carbon footprint calculation. Some of these chemicals are directly allocated to specific products, e.g. caustic and nitric acid to butter production (Fonterra, 2008). Other chemical consumption figures, e.g. chemicals for raw milk tr treatment, eatment, can not be directly attributed to one product. These chemicals are allocated according to the procedure described in Chapter 3.6.6 based on classification as acidic or basic chemicals. Transport of chemicals is included in the analysis if distances were provided by Fonterra. Beside the intermediate products, which are fully considered, more than one hundred different ingredients go into into the production of Fonterra dairy products. In most cases, the amounts of ingredients are rather small and can be neglected. Salt and palm oil are the most important ingredients and were included in the study. study. The transport effort has been included in the analysis if distances were provided (Fonterra, 2008). Company data are provided for electricity and thermal energy (black coal, natural gas, oil, LPG and biogas). For all energy carriers LCI data are ttaken aken from the GaBi database except for LPG. LPG is substitut substituted ed with natural gas. There are several sites which have on-site co-generation. For these plants detailed information was provided to the UNSW team with regard to primary energy inputs and electricity and thermal energy outputs. Primary energy inputs and emissions are allocated to electricity and steam following the guidelines of WRI WBCSD (2006).
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These cogeneration sites generate electricity surpluses which are fed fed into the New Zealand grid. In these cases the amount of electricity and its associated emissions are subtracted from the primary energy inputs of the co-generation plants. The remaining primary energy inputs and emis emissions sions are allocated to the products manufactured. The remaining manufacturing sites are using electricity from from the NZ grid. On this account the specific emission profile for the NZ grid mix are utilized in the model. • •
•
• •
•
Electricity consumption of offsite stores are excluded from the analysis because it can not be attributed to the specific specific products. This electricity demand equals ~1.5% of the total. The quantity of packaging materials and their respective material compositions (predominantly low density polyethylene and linear low density polyethylene) are included in this study. Transport of packaging materials by road and rail is included if distance were provided by Fonterra. Some of the packaging material can be allocated dir directly ectly to dairy products. If that is not the case, packaging materials are allocated according to Feitz et al. (2005, see also Chapter 3.6.6). Nitrogen and carbon dioxide are predominantly used in the packaging process for milk powder and to a lesser extend for anhydrous a nhydrous milk fat. Hence, these gases were directly linked to the production of these products. Data on refrigerants are reported for several sites. The refrigerants are allocated on a mass basis between butter and cheese (Somers, 2008, pers. comm.). Water and wastewater quantities are allocated on a milk solids basis to the products. Information on the wastewater treatment level attained at processing sites has been provided by Fonterra. The electricity intensity of wastewater treatment depends on the level of treatment that has been calculated based on Lundie et al. (2005). Solids waste management and material recycling have been excluded from the analysis due to 1) their small quantities and 2) difficulties of allocating the waste quantities to individual products.
3.6.6 ALLOCA ALLOCATION TION PROCEDURE There are two principle approaches dealing with this multi-functional situation, i.e. system boundary expansion or allocation methods. In this case, only allocation is described in detail as system boundary expansion does not seem to be a practical solution for this project. This is because: 1)
System boundary expansion generally introduces new multi-functional processes (Guinée et al, 2002); some sort of allocation is still needed in order to collect the necessary background data. 2) Broadening the system boundaries makes the process of data collection much more complicated; not only are more data needed, but appropriate data are needed (Curran, 2006). 3) Larger systems run the risk of being less transparent in that there is more detail on how data were arrived at than can be conveyed conveniently (Curran, 2006). See also Lundie et al (2007) for more details. Below the three major allocation procedures are briefly described: •
•
Allocation based on physical properties: Mass, molar flows, energy contents or volume are physical properties which are used to allocate the inputs and outputs of the product / service under study. study. This approach may, may, but most likely does not reflect the causal relationship (see also Section 2.2.2. in Lundie et al (2007) for more details). Guinée et al (2002) go even further.. They discredit this approach for a lack of justification (as do Huppes and Schneider further 1994), as there is no causality involved, e.g. the mass of outputs cannot cause inputs by physical causation. At best, allocation based on mas masss or energy can be used as a proxy for allocation based on economic value (Guinée et al 2004). Physico-chemical allocation: One way of solving the allocation problem is to generate a product or industry specific physico-chemical allocation matrix which reflects the actual allocation of resources resources in a whole plant. Feitz et al (2007) developed systematically an industry specific physico-chemical allocation matrix for the dairy industry that reflects closely the causalities in the the dairy manufacturing indus industry try.. It may be seen seen as an optimisat optimisation ion procedure for producing a changed output within the production functions constraints and the variation in production technologies in the system. In this sense, it uses physico-chemical relations, as part of production functions. This approach has been developed at a sector level, linking in to new developments in life cycle assessment.
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CARBON FOOTPRINT METHODOLOGY REPORT
•
Feitz et al (2007) recommend that such physico-chemical allocation matrices may be developed for other industrial sectors which do have similar production processes; for example, agriculture (e.g. the meat industry); construction (e.g. sand, gravel and other construction materials); mining (e.g. gold and lead) and petrochemical industries (e.g. automotive fuels; see Feitz et al (2007) for details how to calculate such a physico-chemical allocation matrix). Economic allocation: A systematic approach to allocation has been suggested by Guinée et al (2002). Guinée et al (2002) recommend recommend economic allocation as a baseline method for most
detailed life cycle assessment applications, because it seems the only generally applicable method (Guinée et al 2004). This avoids the problem that differences between alternatives are caused by different allocation methods applied instead of being due to the underlying reality.. This position may seem to contradict th reality the e ISO 14044 recommendation that allocation should preferentially be done on the basis of physical relationships. There is no universal consensus on which type of allocation should be used for a particular application. ISO 14044 (2006) and Lundie et al. (2007) recommend allocation based on physicalproperties and physico-chemical physico-chemical relationships. Guinée et al. (2002) favour economic allocation, but only if physico-chemical relationships cannot be established. PAS 2050 2050:2008 :2008 recommends dividing the unit processes to be allocated into two or more sub-processes, which could be done using a physico-chemical allocation procedure (PAS 2050, 2008). Fonterra staff and the project team decided to select physico-chemical allocation for two reasons: 1) this type of allocation matrix was developed particularly for the dairy industry in Australia reflecting the actual relationships in the processing of dairy products at a high accuracy, and 2) it comes closest to the recommendations of ISO and Lundie et al. (2007). Applying this to Fonterra Cooperative Group Group is very complex. There are 22 manufacturing sites that produce two or more of the dairy products as listed above. There are only 6 mono-product plants and there are substantial substantial intersite-intermediate product transfers. Only the intermediate product transfers ensure that the milk balance closes on a plant level. Because of this complicated situation the inputs and outputs of all plants need to be allocated on a plant level if they could not be attributed to individual products (direct attribution to products is possible for some ingredients, chemicals and some of the the packaging materials). Once the inputs and outputs were allocated, weighted averages per product group were calculated. The allocation of raw milk is important in dairy product life cycle assessments as this step allocates upstream dairy farming processes (e.g. feed supplements, fertilisers, herbicides, water, etc.) to different products. products. Using the approach developed by Feitz et al. (2005), the amount of raw milk is assigned on a milk solids basis, which includes fat, protein, lactose and ash, and the degree of milk solids concentration in the final product (see Table 1 in Feitz et al., 2005). In the course of this project, NZ specific data became available so that the raw milk allocation all ocation to different products could be done using NZ specific allocation factors. Since the raw milk transport efforts are directly rrelated elated to the raw milk content of the different products, these allocation factors are also specific to the Fonterra situation. For the allocation of all other processes, the allocation factors as determined in Feitz et al. (2005) were applied.
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For example, process energy coefficients for a plant that only produced milk powders (the cream was sent off site) was then used to update the butter process energy coefficients for a plant that only produced butter and milk powders. Once an estimate for butter was obtained, this was used used to determine the AMF process energy coefficients in a plant that produced milk powders, butter and AMF, AMF, and so on. By applying this procedure for 17 plants for many different pr products oducts (using the same level of technology), a very good estimate of the average process energy figures (and resource efficiency efficiency coefficients) for different different products could be obtained. The coefficients could be further refined by using an approach similar to the residual allocation system (RAS) method, used for optimising input coefficients in input-output tables (Stone, 1963; Bacharach, 1970; Parikh, 1979; van der Linden & Dietzenbacher, 2000). The allocation matrix given in Table 4 enables the allocation of the primary resources for any combination of dairy products based on physico-chemical principles. By normalising the coefficients to milk powder, for example, the relative resource use for each product is maintained and the allocation matrix is capable of accounting for differences in overall plant efficiency (e.g. using less efficient coal rather than natural gas gas for thermal process energy). Normalising the average reso resource urce data also ensures company confidentiality. confidentiality. The percent allocation is determined by multiplying the annual production of a product by its unique coefficient (or allocation factor; AF given in Table 4 and then dividing by the sum of all products multiplied by its specific AF, i.e. Allocation(%)i = Productioni × AFi / ∑ Productionij × AFij with:
i = product under investigation and
j = all products that are produced at the manufacturing site
The determined percentage allocation is multiplied by the input or output flow of interest, e.g. fuel use or wastewater emission. In this particular case the milk balances for Fonterra dairy processing plants are complicated due to the transfer of intermediate products between factories. To account for these inter-factory inter-factory transfers, intermediate products going into factories were considered an additional milk solids input reflecting the particular milk solids concentration of the respective respective intermediate product. Therefore, factory inputs are based on the sum of both intermediate product product and raw milk milk solids. Some intermediate products, can only be used for the manufacturing of certain products (e.g. cream for the production of butter, anhydrous milk fat (AMF) and other fat products or skim milk for the production of skim milk powder). Hence, this kind of intermediate product product is only allocated to the respective products and the equivalent quantity of final product is deducted from the total amount of product manufactured at the plant. For example, Site Kauri produces 22,422 tonnes of butter and 15,729 tonnes of AMF per year. year. In addition to its raw milk inputs, Kauri also receives 30,841 tonnes of cream from other sites which represents an additional milk solids input of 14,323 tonnes (milk solids content of cream = 46.4%). Since the butter vs. AMF production ratio at Kauri is 59% vs. 41%, the milk solids coming from the cream are distributed accordingly. accordingly. As a result, Kauri produces 10,266 tonnes of butter and 5,905 tonnes of AMF from from its cream imports. The remaining amount of butter is produced from incoming raw milk milk solids. For other intermediate products (e.g. milk concentrate c oncentrate or whey) this direct allocation procedure was not necessary since these intermediate products can be used for the whole Fonterra product range. Consequently, the milk solids from these types of intermediate products were added to the milk solids coming from the plant’s raw milk intake. The handling of the intermediate products as described above was an important distinction because otherwise the raw milk would be distributed over all products, whereas in reality it is primarily used for particular products only and other products products have high intakes o off intermediate products. The net effect would be an under-prediction of the raw milk required for products which don’t require intermediate product inputs. In this study the allocation is carried out for butter, milk powder, milk protein concentrate, cheese and caseinate. While allocation factors ar are e available for butter butter,, milk powder and cheese, similar products needed to be selected for milk protein concentrate and caseinate. In consultation with Fonterra staff, whey protein concentrate/lactose was chosen for milk protein concentrate and whey powder for caseinate (Barnett, 2008, 2008, pers. comm.). With regards to to milk protein concentrate, the specific thermal energy requirements were taken from the Fonterra Engineering Cost Models and allocated directly to MPC on a product basis (Barnett, 2008, pers. comm.).
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CARBON FOOTPRINT METHODOLOGY REPORT
The resulting final factory balance is as follows:
INPUTS
OUTPUTS
Raw milk in (t) x 13.82% milk solids + intermediate product imports (t) x respective % milk solids
Products out x products’ individual milk
=
solids content + intermediate product exports x respective milk solids content
3.7 METHODOLOGY FOR DISTRIBUTION 3.7.1 GOAL The goal of module 3 is to provide Fonterra with an assessment the carbon footprint of the distribution of its products from the manufacturing sites to the destination markets.
3.7.2 SCOPE The finished goods supply chain commences on loading of the finished product onto distribution transport at the weighted average manufacturing site and continues until the defined destinations, i.e. butter to Zeebrugge (Belgium), Arhus (Denmark) and Damietta (Egypt), milk powder to Manila (Philippines), Colombo (Sri Lanka), Port Kelang (Malaysia), Puerto Cabello (Venezuela), Apapa (Nigeria) and Toronto (Canada), MPC to Philadelphia (USA), cheese to Zeebrugge (Belgium) and Yokohama (Japan), and caseinate to Hamburg (Germany).
3.7.3 FUNCTIONAL UNIT The Functional unit (FU) for each product is 1 tonne product leaving the factory gate.
3.7.4 SYSTEM BOUNDARIES In this study the system boundary encompasses all processes within the system starting with •
the transport by truck and train of the finished products from NZ manufacturing sites to a
• • •
store (port or non port), storage of products including energy (electricity and fuel) and chemical at the warehouse, port handling in New Zealand and at the destination country country,, and sea transport to the destination country. country.
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The system boundaries and flow diagram of the distribution phase of Fonterra’s dairy products selected for this study are presented in Figure 6.
transport (truck or train)
manufacturing site
port store
port store
transport (truck or train)
transport (truck or train)
diesel
port handling
shipping
port handling
FIGURE 6: SYSTEM BOUNDARIES OF DISTRIBUTION CARBON FOOTPRINTS As described in PAS 2050 (2008) the calculation of the carbon footprint of Fonterra’s dairy products includes emissions throughout the entire logistic chain arising from the following sources: • •
Releases resulting from products handling processes, Usage energy that has greenhouse gas emissions associated with it (transportation etc.), and • Consumption of energy carriers that were themselves created using processes that have greenhouse emissions associated with them (e.g. electricity). As required by the PAS 2050 as well, indirect greenhouse gas emission offset mechanisms are not included at any point in the supply chain. No renewable energy directly is used by Fonterra; hence no benefits thereof are included in the calculations. The carbon footprint calculation excludes ‘greenhouse gas emissions from the manufacture and ongoing maintenance of capital goods, such as plant machinery, transport equipment, electricity generating plant, etc., used in the manufacture of the product; and transport of employees to their normal place of work’ (PAS (PAS 2050 (2007), p 8). This is in line with other studies, such as Lundie et al (2001).
3.7.5 LIFE CYCLE INVENTORY DAT DATA A Life cycle inventory data were collected in close cooperation with Fonterra. At the beginning of the project a detailed data collection questionnaire questionnaire was sent to Fonterra Fonterra staff. All data the project CWWT / SCION team requested was provided by Fonterra, additional data sources were considered if no company specific information was available.
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CARBON FOOTPRINT METHODOLOGY REPORT
All data was checked by the project team and an independent reviewer with regard to consistency and completeness. The data was then incorporated into an Excel-spr Excel-spreadsheet eadsheet that built the basis for the carbon footprint calculation in life cycle assessment-software tool GaBi. Data used in the carbon footprint measurement for distribution are described in more detail below: •
• • • • •
•
All products have been manually aggregated for this analysis. The caseinate aggregation includes calcium caseinate and sodium caseinate. The milk protein concentrate aggregation includes Milk Protein Isolates (MPI) etc. Butter aggregation does not include anhydrous milk fat or other oils. All volumes are in metric tonnes. he average metric tonnes per TEU (20ft equivalent unit) for the products in scope are 15.6 mt for Milk Powder, MPC and caseinate and 18 mt for cheese and butter. All calculations are provided per mt rather than per container. container. It is presumed that all products leaving sites leave either on pallets or in containers. The domestic distances were determined with the help of Google maps. Depending on the different distances between destinations in NZ (factory to port store or non-port store; port store to port or other non-port store; non-port store to port store) for each product, average distances were calculated. These were then weighted according according to the volume or product travelling on each corridor. corridor. Uncertainty may be introduced in some cases by port stor store e to port transportation. These moves are typically less than 5 km and thus negligible relative to the much larger distances included in the model. No product refrigeration is required for domestic transportation. The insulation capability of the containers is sufficient for short domestic transportation ensuring that the temperature
within the container remains cold enough. Transportation to the domestic customer is excluded. Movements from factories have not been split further into movements to stores versus movements to ports since an average distance was calculated. • All the distances are weighted. • Trucking is calculated based on modified information from Müller and Baas (2004) assuming a split of 12% highway, 18% urban and 70% rural 4. Data regar regarding ding trucks with a capacity of 34-40 tonnes are taken from the GaBi life cycle assessment database. • For transport by train an average utilisation rate of 50% is assumed, while the rate is 60% for trucks (Fonterra, 2008). • The average power demand of a cooling unit for a 40ft reefer container has been calculated with 3.1 kW based on (Thermoking, 2008; Climatecontrol 2008). • No air freight takes place. 3.7.5.1 Storage of products The on-site storage of products is included in the total annual energy consumption figures • •
which in turn are included in the carbon footprint calculation. In 2007, 30% of the butter make and 34% of the cheese make were were held off site (Jones 2008). These emissions were not covered in the calculation for the following reasons: Warehouse Warehouse capacity data and vvarious arious utilisation rates for different time periods were provided by Fonterra but no information was available on the energy consumption of the warehouses. Public data on product sstorage torage at warehouses is very rare. Lundie et al. (2004) calculated some generic energy consumption consumption figures for storage at the supermarket. However, However, these figures appear to be an overestimate compared to the situation in commercial commercial warehouses. Hence, energy consumption of off-s off-site ite warehouse is not considered in this study due to its poor data quality.
3.7.5.2 Port handling Containers are stored at the harbour for approximately 7 days (point of departure and destination). During this period of time cooling is required for butter and cheese. No power requirements are available for the harbours in New Zealand nor for any destination harbour. Therefore, we calculated the average power demand of 3.1 kW for a 40ft reefer container (Thermoking, 2008; Climatecontrol 2008) with an assumed utilisation rate of 60% resulting in an electrical power demand of 8.7 kWh el.
4. Travel on gravel roads (i.e. 35%) are classified as rural transportation. FOR FONTERRA CO-OPERATIVE GROUP 2009
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Containers that are shipped to the destination market need to be handled at the port in New Zealand and at the destination. Containers are transported within the harbour by vancarriers. These vehicles usually have diesel-electric engines. Diesel consumption by modern vancarriers is about 19 L/hr. L/hr. Vancarriers are used for loading and unloading trucks and ships as well as for movements of containers within dockyard operations. Depending on deployment, travel distances differ, i.e. ~1 km for un/loading trucks, ~3 km for un/loading ships and ~4-5 km for dockyard hinterland movements. The average speed depends on the actual model, i.e. between 25-30 km (Winter, 2008). Assuming an equal split between the three travel distances
an average travel time can be calculated (i.e. 0.2 hr) plus idle time of 0.17 hr (Winter, 2008). 3.7.5.3 Overseas shipping For the overseas transportation of products to the destination markets Fonterra data was used. The overseas transport includes various destination countries for milk powder powder,, butter and cheese, while caseinate and MPC are investigated only for one destination. The explanations and assumptions are reproduced below. Additional information obtained from different sources and the GaBi database is listed as well: • Products modelled in our analysis have been assigned to one or more routes, i.e. milk powder to Manila (Philippines), Colombo (Sri Lanka), Port Kelang (Malaysia), Puerto Cabello (Venezuela), Apapa (Nigeria) and Toronto Toronto (Canada) and butter to Zeebrugge (Belgium), Arhus (Denmark) and Damietta (Egypt). Caseinate is delivered to Hamburg (Germany), cheese to Zeebrugge (Belgium) and Yokohama Yokohama (Japan) and MPC to Philadelphia (USA). MSL (Maersk) provided assistance to Fonterra in calculating the transport dist distances. ances. Maersk provided Fonterra with data regarding the days of steaming and the transship delays. •
Jones (2008) consulted some confidential materials to determine that the average sized ship is approximately 6,000 TEU.
•
In arriving at the emission levels for ocean freight a days steaming approach has been taken. For scheduled advertised services the days steaming approach is considered the most accurate and auditable approach, reflective of actual consumption rather than trying to estimate the distance between various port pairs, an approach that may be imprecise. The data used in calculating the emissions also includes an allowance for “empty slot legs”, this reflects the fact that frequently capacity and hence size of vessel (and resulting consumption) con sumption) will be determined by the primary leg capacity needs, the result on the secondary leg being spare capacity “empty slots”. When legs on a service are identified has having empty slots, it has been assumed that cargo being carried in both directions has to burden a share of emissions associated with the “empty slots”, this reflects a conservative approach ensuring emissions are not underestimated. Other key features of the approach taken for calculating emissions for ocean freight include: o
Known vessel sizes have been used when available e.g. ex New Zealand,
o
Averages have been used for the size of subsequent transhipment vessels,
o
Fuel consumption per days have been used using publicly available information and information provided directly by the carrier,
o
Load utilisation and hence empty slots legs have been arrived at using published trade line data from “Containerisation International”,
o
Scheduled at sea days have been used for calculating total fuel consumption, excluded from the calculation are days at transhipment port, and
o
Allowances have been made for the additional consumption associated with refrigerated cargo.
This approach is different to the one that is usually taken in life cycle assessment studies: In the life cycle assessment software tools the transport effort is calculated in two steps: 1) quantifying the tonne-kilometers (tkm) of the FU and 2) selection and adaptation of the most appropriate ship for transportation. The approach taken using days steaming and the transship delays is based on company data, i.e. MSL (Maersk) services, and appears to be more reliable than the typical life cycle assessment calculation procedure. Where a shipment is a reefer route (for cheese and butter) separate diesel consumption was quantified. This 42 kg d diesel iesel per TEU slot per day. day. This number has only been applied to days when thefigure ship isequals steaming. The backhaul ratio varies on a lane by lane basis (Fonterra, 2008; Containerisation International, 2008). 30
CARBON FOOTPRINT METHODOLOGY REPORT
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References
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