Overview of Goal and Scope Definition in Life Cycle Assessment

Annexes

1.1 Annex A (pp 16–35): Example of a Comparative, Attributional Life Cycle Assessment to Support Government Decision Making

1.1.1 Life Cycle Assessment of Drinking Water Systems : Bottled Water , Tap Water , and Home/Office Delivery Water

1.1.1.1 Goal and Scope Definition (Recreated)

Preamble

In 2009, the Oregon Department of Environmental Quality (DEQ) commissioned an LCA study to compare alternative types of drinking water systems – water packaged in disposable bottles , tap water consumed from reusable drinking containers , and home/office delivery water consumed from reusable drinking containers. The study was conducted for DEQ by ERG as an independent contractor.

The project leader at ERG was Beverly J. Sauer, who served as primary life cycle analyst. Greg Schivley and Ann Marie Molen assisted with research tasks and development of the report appendices. Chris Dettore, a graduate student at the University of Michigan, provided assistance with research and contribution analysis tasks, with oversight by Greg Keoleian of the University of Michigan Center for Sustainable Systems. The project was peer reviewed by an expert panel consisting of Beth Quay, an independent consultant with expert knowledge of bottling systems (serving as review chair), David Allen of the University of Texas, and David Cornell, an independent consultant with expert knowledge of PET container systems.

This annex summarizes the goal and scope of the study as described in the full report, dated 22 October 2009. It is intended to suggest how a goal and scope definition document would have looked if one had been prepared for public view at the outset of the study. The final report from the completed study is publicly available and can be found on DEQ’s website: http://www.deq.state.or.us/lq/sw/wasteprevention/drinkingwater.htm.

The findings and conclusions presented in the report are strictly those of ERG. ERG makes no statements nor supports any conclusions other than those presented in the report. ERG and DEQ are not responsible for writing or preparing the following recreation of a goal and scope document.

1.1.1.2 1 Introduction

Bottled water offers consumers a clean, portable supply of drinking water for consumption at home or away from home. Some disposable water bottles are recyclable, and light weighting of bottles and bottled water packaging have reduced the amount of packaging waste associated with bottled water consumption. However, bottled water is frequently consumed at away from home locations where access to container recycling may be limited. In addition, while recycling of postconsumer bottles and packaging reduces consumption of virgin material resources, other resources are used and wastes created when packaging is manufactured and bottled water is transported.

Consumers have other drinking water options that do not involve disposable containers. These include consumption of tap water from a container that can be washed and reused many times, or consumption of water from a home/office delivery (HOD) system with the water dispensed into a reusable drinking container . However, while reusable systems require less use and disposal of material, these systems require washing of containers between uses, and in the case of HOD systems, transportation of the containers to and from the filler. These processes incur environmental burdens that may be higher or lower than the burdens for disposable container systems.

LCA has been recognized as a scientific method for making comprehensive, quantified evaluations of the environmental benefits and tradeoffs for the entire life cycle of a product system , beginning with raw material extraction and continuing through disposition at the end of its useful life. This LCA evaluates the environmental burdens for disposable and reusable systems for delivering drinking water.

1.1.1.3 2 Purpose of the Study

This LCA was commissioned by the Oregon Department of Environmental Quality (DEQ) to evaluate the environmental implications of various systems for delivery and consumption of drinking water, including bottled water, tap water consumed from reusable containers, and HOD water consumed from reusable containers. The analysis includes water processing, production of containers and packaging materials, filling, transport, and end-of-life management of containers and packaging. The analysis also looks at transportation of bottled water imported from several foreign locations. The results are not intended to be used to represent specific brands of bottled water or reusable containers available in the marketplace.

1.1.1.4 3 Intended Use

The primary intended use of the study results is to inform DEQ about the environmental burdens and tradeoffs associated with various options for providing drinking water to consumers and behavioral choices of consumers. DEQ is also interested in better understanding the environmental burdens and tradeoffs of end-of-life management options (recycling, composting, landfilling, etc.). This analysis contains comparative statements about the results for the drinking water subscenarios analyzed. Because DEQ will make the results of this study, including comparative statements, publicly available, this report is being peer reviewed in accordance with ISO standards for life cycle assessment.^{Footnote 12}

1.1.1.5 4 Systems Studied

The following types of drinking water systems are analyzed in this study:

• Bottled water packaged in and consumed from individual disposable bottles :

  • Virgin polyethylene terephthalate (PET) bottles (16.9 ounce, 8 ounce, and one liter)

  • PET bottles with a mix of virgin and recycled content (16.9 ounce)

  • Bottles made of virgin polylactide (PLA) resin derived from corn (16.9 ounce)

  • Glass bottles with a mix of virgin and recycled content (12 ounce)

• Tap water consumed from reusable containers:

  • Virgin aluminum bottle with plastic closure (20 ounce)

  • Virgin steel bottle with plastic closure (27 ounce)

  • Virgin plastic bottle with plastic closure (32 ounce)

  • Drinking glass with a mix of virgin and recycled content (16 ounce)

• Home/office delivery (HOD) water consumed from reusable containers

  • Virgin polycarbonate bottles

  • Virgin PET bottles

  • Same reusable containers listed under the Tap system.

Within these three general drinking water scenarios, a number of subscenarios will be evaluated for variations in container sizes, weights, transportation distances, recycled content and recycling rates, and many other variables. Forty-eight subscenarios are identified: 25 bottled water subscenarios (20 for PET bottles, 4 for PLA, 1 for glass), 12 subscenarios for tap water consumption using a variety of reusable drinking containers , and 11 subscenarios for HOD water consumed from reusable containers. Of the bottled water subscenarios, 5 include long-distance transport of water from another country or the Eastern U.S. to Oregon.

1.1.1.6 5 Functional Unit

In a life cycle study, systems are evaluated on the basis of providing a defined function (functional unit). The function of each system analyzed in this report is to deliver drinking water to consumers. The functional unit selected for this analysis is delivering 1000 gallons of drinking water to a consumer, including use of a bottle or reusable drinking container , and end-of-life management of the containers and packaging. To provide some perspective, 1000 gallons is the amount of water a person would consume in about 5.5 years if they drank eight 8-ounce servings of water a day.

The functional equivalence is based on delivering drinking water that meets water quality standards set by the Food and Drug Administration (FDA), EPA, and state governments. The scope of the analysis does not include evaluating other differences in the quality of the water (e.g., taste, fluoride or mineral content, etc.) or temperature of the water, or any potential health impacts that may be associated with the use of specific water container materials. Carbonated and flavored waters are excluded.

The functional unit is 1000 gallons of delivered water for several reasons:

1. 1.

   This basis produces results of a sufficient magnitude to be shown as whole numbers in the results tables and figures. Using a smaller unit, such as a liter of water, would produce results of a very small magnitude that would need to be shown in scientific notation.

2. 2.

   It is easier to understand reuse rates for 5-gallon HOD bottles when the functional unit is a multiple of the container volume (e.g., 1000 gallons = 200 HOD bottle trips).

3. 3.

   Bottled water is typically packaged and purchased in multi-container cases, so again it makes sense to use a basis that is a multiple of the functional unit (1000 gallons = 315 cases of 24 16.9 oz bottles) rather than a fraction of a purchasing unit (1 l = two 16.9 oz bottles, equivalent to 1/12 of a case, or 0.083 cases).

Results shown on the basis of 1000 gallons can easily be converted to any desired volume basis. For example, to convert results per 1000 gallons to result per liter, first divide the 1000 gallon results by 1000 (to arrive at results on a per gallon basis), then divide the per gallon results by 3.8 l per gallon to arrive at per liter results.

1.1.1.7 6 Scope and Boundaries

This study is a complete LCA as defined in the ISO standards 14040 and 14044 . As such, the study includes definition of goal and scope, life cycle inventory (LCI) , life cycle impact assessment (LCIA) , and interpretation of results.

The analysis includes all steps in the production of each drinking water container system, from extraction of raw materials through production of the materials used in the containers, fabrication of finished containers and closures, and transport to filling locations:

• Raw material extraction (e.g., extraction of petroleum and natural gas as feedstocks for plastic resins; growing corn used as a feedstock for polylactide resin, commonly referred to as PLA)

• Processing and fabrication steps to transform raw materials into containers and closures (water bottles, HOD bottles, reusable containers)

• Manufacture of materials used to package containers for retail shipment (corrugated trays, plastic film)

• Water treatment processes

• Container filling and washing operations (including industrial washing of HOD bottles and home washing of reusable drinking vessels)

• Distribution of filled containers

• Optional processes for chilling water

• End-of-life management of containers and packaging.

Treatment of municipal drinking water and additional processing steps used to purify bottled municipal water and natural water such as spring water are included in the analysis. Bottle filling and washing operations are included, as is production of secondary packaging used for shipment of filled containers, distribution of filled containers, washing of reusable containers, and end-of-life management of containers and associated packaging components. Various options for chilling water are also included in the model, including home refrigeration, use of ice, and HOD chiller units.

All washing of reusable personal drinking containers in this study is modeled based on use of a residential dishwasher, which is expected to be the most common method used by consumers for washing of these containers. Containers may also be hand-washed; however, water and detergent use for hand washing can vary widely based on the practices of individual consumers.

The scope of the study does not include analysis of scenarios for HOD and tap water consumed from disposable cups, nor does the study include scenarios in which disposable drinking water bottles sold filled with water were refilled by consumers and used as a reusable drinking container . Additional at-home purification of tap water, such as use of tap water filters, is not included in the scope of the analysis. The scope of the analysis does not include greenhouse gas effects of direct and indirect land use changes that may be associated with corn growing for PLA production.

1.1.1.8 7 Material Requirements

Once the LCI study boundaries have been defined and the individual processes identified, a material balance is performed for each individual process. This analysis identifies and quantifies the input raw materials required per standard unit of output, such as 1000 pounds, for each individual process included in the LCI. The purpose of the material balance is to determine the appropriate weight factors used in calculating the total energy requirements and environmental emissions associated with each process studied. Energy requirements and environmental emissions are determined for each process and expressed in terms of the standard unit of output.

Once the detailed material balance has been established for a standard unit of output for each process included in the LCI, a comprehensive material balance for the entire life cycle of each product system is constructed. This analysis determines the quantity of materials required from each process to produce and dispose of the required quantity of each system component and is typically illustrated as a flow chart. Data must be gathered for each process shown in the flow diagram, and the weight relationships of inputs and outputs for the various processes must be developed.

1.1.1.9 8 Energy Requirements

The average energy requirements for each process identified in the LCI are first quantified in terms of fuel or electricity units, such as cubic feet of natural gas, gallons of diesel fuel, or kilowatt-hours (kWh) of electricity. The fuel used to transport raw materials to each process is included as a part of the LCI energy requirements. Transportation energy requirements for each step in the life cycle are developed in the conventional units of ton-miles by each transport mode (e.g. truck, rail, barge, etc.). Government statistical data for the average efficiency of each transportation mode are used to convert from ton-miles to fuel consumption.

Once the fuel consumption for each industrial process and transportation step is quantified, the fuel units are converted from their original units to an equivalent Btu value based on standard conversion factors.

The conversion factors have been developed to account for the energy required to extract, transport, and process the fuels and to account for the energy content of the fuels. The energy to extract, transport, and process fuels into a usable form is labeled precombustion energy. For electricity, precombustion energy calculations include adjustments for the average efficiency of conversion of fuel to electricity and for transmission losses in power lines based on national averages.

The LCI methodology assigns a fuel-energy equivalent to raw materials that are derived from fossil fuels. Therefore, the total energy requirement for coal, natural gas, or petroleum based materials includes the fuel-energy of the raw material (called energy of material resource or inherent energy). In this study, this applies to the crude oil and natural gas used to produce the plastic resins. No fuel-energy equivalent is assigned to combustible materials, such as wood, that are not major fuel sources in North America.

The Btu (British termo unit) values for fuels and electricity consumed in each industrial process are summed and categorized into an energy profile according to the six basic energy sources listed below:

• Natural gas

• Petroleum

• Coal

• Nuclear

• Hydropower

• Other

The ‘other’ category includes sources such as solar, biomass and geothermal energy. Also included in the LCI energy profile are the Btu values for all transportation steps and all fossil fuel-derived raw materials.

1.1.1.10 9 Environmental Emissions

Environmental emissions are categorized as atmospheric emissions, waterborne emissions, and solid wastes and represent discharges into the environment after the effluents pass through existing emission control devices. Similar to energy, environmental emissions associated with processing fuels into usable forms are also included in the inventory. When it is not possible to obtain actual industry emissions data, published emissions standards are used as the basis for determining environmental emissions.

The different categories of atmospheric and waterborne emissions are not totaled in this LCI because it is widely recognized that various substances emitted to the air and water differ greatly in their effect on the environment.

Atmospheric Emissions

These emissions include substances classified by regulatory agencies as pollutants, as well as selected non-regulated emissions such as carbon dioxide. For each process, atmospheric emissions associated with the combustion of fuel for process or transportation energy, as well as any emissions released from the process itself, are included in this LCI . The amounts reported represent actual discharges into the atmosphere after the effluents pass through existing emission control devices. Some of the more commonly reported atmospheric emissions are: carbon dioxide, carbon monoxide, non-methane hydrocarbons, nitrogen oxides, particulates, and sulfur oxides.

Waterborne Emissions

As with atmospheric emissions, waterborne emissions include all substances classified as pollutants. The values reported are the average quantity of pollutants still present in the wastewater stream after wastewater treatment and represent discharges into receiving waters. This includes both process-related and fuel-related waterborne emissions. Some of the most commonly reported waterborne emissions are: acid, ammonia, biochemical oxygen demand (BOD), chemical oxygen demand (COD), chromium, dissolved solids, iron, and suspended solids.

Solid Wastes

This category includes solid wastes generated from all sources that are landfilled or disposed of in some other way, such as incineration with or without energy recovery. These include industrial process- and fuel-related wastes, as well as the packaging components that are disposed when a container of product is emptied. Examples of industrial process wastes are residuals from chemical processes and manufacturing scrap that is not recycled or sold. Examples of fuel-related solid wastes are ash generated by burning coal to produce electricity, or particulates from fuel combustion that are collected in air pollution control devices.

1.1.1.11 10 LCI Practitioner Methodology Variation

There is general consensus among life cycle practitioners on the fundamental methodology for performing LCIs.^{Footnote 13} However, for some specific aspects of life cycle inventory , there can be variations in the methodology used by experienced practitioners. These areas include the method used to allocate energy requirements and environmental releases among more than one useful product produced by a process, the method used to account for the energy contained in material feedstocks, and the methodology used to allocate environmental burdens for postconsumer recycled content and end-of-life recovery of materials for recycling. LCI practitioners vary to some extent in their approaches to these issues. The following sections describe the approach to each issue used in this study.

1.1.1.12 11 Co-Product Credit

One unique feature of life cycle inventories is that the quantification of inputs and outputs are related to a specific amount of product from a process. However, it is sometimes difficult or impossible to identify which inputs and outputs are associated with individual products of interest resulting from a single process (or process sequence) that produces multiple useful products. The practice of allocating inputs and outputs among multiple products from a process is often referred to as ‘co-product credit’^{Footnote 14} or ‘partitioning’.^{Footnote 15}

Co-product credit is done out of necessity when raw materials and emissions cannot be directly attributed to one of several product outputs from a system. It has long been recognized that the practice of giving co-product credit is less desirable than being able to identify which inputs lead to particular outputs. In this study, co-product allocations are necessary because of multiple useful outputs from some of the ‘upstream’ chemical processes involved in producing the resins used to manufacture plastic packaging components.

Franklin Associates follows the guidelines for allocating co-product credit shown in the ISO 14044 :2006 standard on life cycle assessment requirements and guidelines. In this standard, the preferred hierarchy for handling allocation is (1) avoid allocation where possible, (2) allocate flows based on direct physical relationships to product outputs, (3) use some other relationship between elementary flows and product output. No single allocation method is suitable for every scenario. How product allocation is made will vary from one system to another but the choice of parameter is not arbitrary. ISO 14044 section 4.3.4.2 states “The inventory is based on material balances between input and output. Allocation procedures should therefore approximate as much as possible such fundamental input/output relationships and characteristics.”

Some processes lend themselves to physical allocation because they have physical parameters that provide a good representation of the environmental burdens of each co-product. Examples of various allocation methods are mass, stoichiometric, elemental, reaction enthalpy, and economic allocation. Simple mass and enthalpy allocation have been chosen as the common forms of allocation in this analysis. However, these allocation methods were not chosen as a default choice, but made on a case by case basis after due consideration of the chemistry and basis for production.

In the sequence of processes used to produce resins that are used in the plastic containers and closures, some processes produce material or energy co-products. When the co-product is heat or steam or a co-product sold for use as a fuel, the energy content of the exported heat, steam, or fuel is shown as an energy credit for that process. When the co-product is a material, the process inputs and emissions are allocated to the primary product and co-product material(s) on a mass basis. (Allocation based on economic value can also be used to partition process burdens among useful co-products; however, this approach is less preferred under ISO life cycle standards, as it depends on the economic market, which can change dramatically over time depending on many factors unrelated to the chemical and physical relationships between process inputs and outputs.)

In this study, corn grain is modeled as an input to production of PLA bottles. When corn grain is produced, corn stover (stalks and leaves) is coproduced. There are several ways in which corn stover can be managed. It may be left in the field to decompose, used for animal feed, or burned. In addition, there are some efforts to utilize corn stover as a source of biomass-derived energy. In this analysis, all of the corn growing burdens are allocated to the corn grain. It is assumed the stover is simply left in the field to decompose.

In the sequence of process steps used to convert corn into starch at a wet mill, coproducts corn gluten and corn oil are also produced. For each process step at the mill, the energy and emissions are to be allocated to corn starch and other coproducts on a weight basis.

1.1.1.13 12 Energy of Material Resource

For some raw materials, such as petroleum, natural gas, and coal, the amount consumed in all industrial applications as fuel far exceeds the amount consumed as raw materials (feedstock) for products. The primary use of these materials in the marketplace is for energy. The total amount of these materials can be viewed as an energy pool or reserve. This concept is illustrated in Fig. 1.1A.

The use of a certain amount of these materials as feedstocks for products, rather than as fuels, removes that amount of material from the energy pool, thereby reducing the amount of energy available for consumption. This use of available energy as feedstock is called the energy of material resource (EMR) and is included in the inventory. The energy of material resource represents the amount the energy pool is reduced by the consumption of fuel materials as raw materials in products and is quantified in energy units.

EMR is the energy content of the fuel materials input as raw materials or feedstocks. EMR assigned to a material is not the energy value of the final product, but is the energy value of the raw material at the point of extraction from its natural environment. For fossil fuels, this definition is straightforward. For instance, petroleum is extracted in the form of crude oil. Therefore, the EMR for petroleum is the higher heating value of crude oil.

Once the feedstock is converted to a product, there is energy content that could be recovered, for instance through combustion in a waste-to-energy waste disposal facility. The energy that can be recovered in this manner is always somewhat less than the feedstock energy because the steps to convert from a gas or liquid to a solid material reduce the amount of energy left in the product itself.

The materials which are primarily used as fuels (but that can also be used as material inputs) can change over time and with location. In the industrially developed countries included in this analysis, these materials are petroleum, natural gas, and coal. While some wood is burned for energy, the primary uses for wood are for products such as paper and lumber. Similarly, some oleochemical oils such as palm oils can be burned as fuel, often referred to as “bio-diesel.” However, as in the case of wood, their primary consumption is as raw materials for products such as soaps, surfactants, cosmetics, etc.

At this time, the predominant use of biomass crops is for food or material use rather than as an energy resource. However, biomass is increasingly being used as feedstock for fuels, e.g., corn-derived ethanol and soy-derived biodiesel. At some point in the future, the energy of material resource methodology may be applied to biomass resources as well as fossil resources.

1.1.1.14 13 Postconsumer Recycling Methodology

Some drinking water containers are recycled at end of life. Some containers also have recycled content. When material is used in one system and subsequently recovered, reprocessed, and used in another application, there is a reduction in the total amount of virgin material that must be produced to fulfill the two systems’ material needs. However, there are different methods by which the savings in virgin material production and disposal burdens can be assigned to the systems producing and using the recovered material. Material production, collection, reprocessing, and disposal burdens can be allocated over all the useful lives of the material, or boundaries can be drawn between each successive useful life of the material.

Because the choice of recycling allocation methodology can significantly influence the LCI results, several approaches will be explored in this analysis, including sharing the burdens for a given quantity of resin equally between multiple uses of the resin (Method 1), assigning the resin production burdens to the system first using the virgin resin (Method 2), or transferring the resin production burdens from the system first using the virgin resin to the system that uses the recovered resin (Method 3). In all cases, the allocated burdens include the energy of material resource embodied in the plastic material.

Each recycling approach used in this analysis is described in more detail in the sections below. In these descriptions, the system from which the material is recovered is referred to as the ‘producer’ system, and the system utilizing recovered material is referred to as the ‘user’ system. It should be noted that all recycling allocations are based only on the burdens for the resin material and do not include any allocation of the burdens associated with fabricating the resin into a bottle or any other product. Thus, there are no inherent assumptions about the product in which resin is used before or after the resin’s use in the bottle system.

Method 1: Open-Loop Allocation

The recycling methodology designated method 1 in this analysis is an open-loop allocation approach. In this approach, all environmental burdens associated with a quantity of recycled material are shared equally between the systems producing and using the material, resulting in reduced burdens for both systems. The producer and user systems share the burdens for virgin material production, collection, reprocessing, and disposal, so that both systems share equally in the benefits of recycling.

For bottles that contain recycled material, the recycled resin content of the bottle comes into the bottle system with half of its virgin production burdens (as well as half of the burdens for collecting and reprocessing the material and disposing of the material at end of life). The other half is allocated to the original product system that used the material, which is outside the boundaries of this analysis. For example, if a bottle had recycled content ‘r’, the recycled material in the bottle would carry half of the burdens required to produce, collect, reprocess, and dispose of that material, or r/2 * (V + PC + D), where ‘V’ is virgin material production burdens, ‘PC’ is postconsumer collection and reprocessing burdens, and ‘D’ is disposal burdens. The virgin percentage of the bottle would carry full burdens for material production and disposal, or (1–r)*(V + D). Adding these together, the total virgin production burdens allocated to the recycled content bottle are (r/2)*V + (1−r)*V, or (1−r/2)*V. Similarly, the material disposal burdens allocated to the recycled content bottle are (r/2)*D + (1−r)*D, or (1−r/2)*D. The collection and reprocessing burdens for the recycled content allocated to the bottle are r/2*PC.

A similar allocation approach is used for virgin bottles that are recycled after use. If ‘R’ percent of virgin bottles are recycled at end of life, with half the virgin burdens for the bottle material going to a subsequent use outside the boundaries of the bottle system, then the virgin burdens allocated to the bottle system for the recycled bottles are R/2*(V+ PC + D) for the bottles that are recycled + (1–R)*(V + D) for the material in the bottles that are not recycled. The total virgin production burdens allocated to the bottle are (R/2)*V+ (1−R)*V, or (1−R/2)*V, the allocated disposal burdens are (R/2)*D + (1−R)*D, or (1−R/2)*D, and the collection and reprocessing burdens are R/2*PC.

For bottles that contain recycled material and are recycled after use, allocation becomes more complicated. For an example of bottles with recycled content r and recycling rate R, the virgin burdens for the material in the bottle are (1 − r/2)*(V), as described above. Some of these burdens must then be allocated to the next use of the material, using the (1–R/2) allocation. The net virgin burdens assigned to the bottle system, taking into account both the recycled content and the postconsumer recycling rate, are (1–r/2)*V*(1–R/2). The allocated disposal burdens are (1 − r/2)*D*(1 − R/2). The share of recycling burdens allocated to the bottle system is r/2*PC*R/2.

No further projections are made about the fate of the material after the end of its recycled use. For example, if a product made from recycled bottle material is subsequently recycled at the end of its life, then the material would have three uses rather than two. This analysis uses a conservative approach and takes into account only the known number of useful lives of the bottle material (i.e., one prior use for recycled material used in bottles that have recycled content; one subsequent use for bottle material that is recycled at end of life).

The other two recycling approaches are less complicated to model, as they draw boundaries between successive lives of the material, with burdens for specific steps allocated to either the producer system or the user system. When postconsumer material from one system is used in a second system, different perspectives can be taken as to whether the producer or user system deserves the credit for the reductions in virgin material production and material disposal due to recycling.

Method 2: User Credit Allocation

Recycling methodology 2 can be called the user credit method. In this approach the boundaries between successive uses of the material are drawn so that the system using the recycled material gets the credit for avoiding production of more virgin material. In method 2, all virgin material burdens for initially producing material are allocated to the first system using the material (e.g., a virgin water bottle), and the next system using the recovered material (resin from recovered bottles) takes all the burdens for collection and reprocessing of the material, as well as the burdens for disposing of the material (unless it is recycled again after use in the second system). The benefit to the producer system (in this example, the bottle system) is limited to avoided disposal burdens for the material that goes on to the secondary user. Using the same variables as above, the allocations are as follows:

For a bottle with recycled content r and recycling rate R, the virgin material production burdens assigned to the bottle are (1−r)*V, the recycling burdens are r*PC, and the disposal burdens are (1−R)*D.

Method 3: Producer Credit Allocation

Recycling method 3 can be referred to as the producer credit method. In this approach, the system generating the recovered material gets the credit for avoiding the need to produce more virgin material. Because the material is not disposed but goes on to a subsequent use, the producer system is assigned burdens for collecting and reprocessing the material in order to deliver it to the next user (in lieu of the burdens that would otherwise be incurred for disposing of the material). The virgin burdens for producing the material and the burdens for disposing of the material are transferred to the next system using the material, which may in turn pass these burdens on to a subsequent use if that product is recovered and recycled at end of life. Using the same variables as above, the allocations are as follows: For a bottle with recycled content r and recycling rate R, the virgin material production burdens assigned to the bottle are V*(1−R), the recycling burdens are R*PC, and the disposal burdens are D*(1−R).

System Expansion

Another approach that can be used to allocate burdens for coproducts or recycled products is system expansion, in which credit is given for a product or material that is displaced by the product or material of interest. In order to use system expansion, it is important to know the specific application that is being displaced, as different uses of material have different reprocessing requirements and different fabrication requirements. As noted previously, the recycling allocations in this analysis are applied only to the burdens associated with the resin material. The recycling allocations do not include additional processing to prepare the resin for a specific end use or fabricate it into a specific product (e.g., a food-grade application or production of carpet fiber) before or after its use in the bottle system, nor were any assumptions made about the previous or subsequent products in which the bottle resin would be used. The recycling burdens in this study are based on collection and mechanical recycling of PET bottles into ‘generic’ clean flake, and not on displacement of any specific product.

1.1.1.15 14 Life Cycle Inventory Data

The accuracy of the study is directly related to the quality of input data. Data necessary for conducting this analysis are separated into two categories: process-related data and fuel-related data.

1.1.1.15.1 14.1 Process Data

Methodology for Collection/Verification

The process of gathering data is an iterative one. The data-gathering process for each system begins with a literature search to identify raw materials and processes necessary to produce the final product. The search is then extended to identify the raw materials and processes used to produce these raw materials. In this way, a flow diagram is systematically constructed to represent the production pathway of each system. Each process identified during the construction of the flow diagram is then researched to identify potential industry sources for data.

Confidentiality

Franklin Associates takes care to protect data that is considered confidential by individual data providers. This can be done by aggregating data with data sets from other sources for the same unit process or aggregating the data with other sequential life cycle unit processes.

Objectivity

Each unit process in the life cycle study is researched independently of all other processes. No calculations are performed to link processes together with the production of their raw materials until after data gathering and review are complete. This allows objective review of individual data sets before their contribution to the overall life cycle results has been determined. Also, because these data are reviewed individually, assumptions are reviewed based on their relevance to the process rather than their effect on the overall outcome of the study.

1.1.1.15.2 14.2 Fuel Data

When fuels are used for process or transportation energy, there are energy and emissions associated with the production and delivery of the fuels as well as the energy and emissions released when the fuels are burned. Before each fuel is usable, it must be mined, as in the case of coal or uranium, or extracted from the earth in some manner. Further processing is often necessary before the fuel is usable. For example, coal is crushed or pulverized and sometimes cleaned. Crude oil is refined to produce fuel oils, and ‘wet’ natural gas is processed to produce natural gas liquids for fuel or feedstock.

To distinguish between environmental emissions from the combustion of fuels and emissions associated with the production of fuels, different terms are used to describe the different emissions. The combustion products of fuels are defined as combustion data. Energy consumption and emissions which result from the mining, refining, and transportation of fuels are defined as precombustion data. Precombustion data and combustion data together are referred to as fuel-related data.

Fuel-related data are developed for fuels that are burned directly in industrial furnaces, boilers, and transport vehicles. Fuel-related data are also developed for the production of electricity. These data are assembled into a database from which the energy requirements and environmental emissions for the production and combustion of process fuels are calculated.

Energy data are developed in the form of units of each primary fuel required per unit of each fuel type. For electricity production, federal government statistical records provided data for the amount of fuel required to produce electricity from each fuel source, and the total amount of electricity generated from petroleum, natural gas, coal, nuclear, hydropower, and other (solar, geothermal, etc.). In this study, the Oregon grid will be used to model electricity used for processes taking place in Oregon.

1.1.1.16 15 Trip Allocation for Purchases of Bottled Water

Unlike consumption of tap water, which requires no travel on the part of the consumer, and consumption of HOD water, which is delivered by a truck used specifically for this purpose, bottled water is most often picked up by the consumer on an outing that may have several purposes. The consumer is likely to run more than one errand on the same outing, and it is also likely that additional items will be purchased at the same location when the consumer purchases bottled water.

This analysis uses a modeling approach that is based on bottled water being purchased one case at a time, with 24 bottles per case. The number of trips required to purchase 1000 gallons of water depends on the volume of water in an individual bottle and the number of bottles in the case, both of which can be varied in the model. Each time a trip is made to purchase water, it is assumed that the case of water is purchased on an outing that includes one other errand in addition to the stop where water is purchased. The round-trip distance from the consumer’s home to the purchasing location is scaled up to account for the additional distance traveled to include the second stop (home to stop 1, stop 1 to stop 2, and stop 2 back to home). The overall distance traveled is divided by two to allocate half to each stop made.

Furthermore, it is reasonable to assume that any item purchased on a trip to a grocery or other retail store could warrant an individual trip to the store if the item were not purchased together with other items as part of a combined purchase. Therefore, the burdens for making the stop at the store can be allocated over the number of items purchased. For example, if 25 items are purchased on a trip to a store, each item would be allocated 4 % of the burdens for making the stop at the store. For purchasing bottled water on a two-errand outing, most modeling scenarios in this analysis use a trip allocation of 4 %, although one scenario models a two-errand trip in which only water is purchased on the stop at the grocery store, so that 100 % of the burdens for that stop are allocated to water. The 25-item purchase is an estimate by the LCA practitioner , since no data were readily available for consumer purchasing patterns on an individual shopping trip basis.

In addition to allocating a portion of the total vehicle fuel use to bottled water, the analysis also accounts for the marginal increase in the loaded vehicle weight due to a case of water and the associated slight decrease in fuel economy over the distance the water is transported from store to home. The baseline fuel economy used for the consumer vehicle was 19.9 miles per gallon.^{Footnote 16}

1.1.1.17 16 System Components Not Included

The following components of each system are not to be included in this LCI study:

Water Use

There is currently a lack of water use data on a unit process level for life cycle inventories. In addition, water use data that are available from different sources do not use a consistent method of distinguishing between consumptive use and non-consumptive use of water or clearly identifying the water sources used (freshwater versus saltwater, groundwater versus surface water).

Capital Equipment

The energy and wastes associated with the manufacture and installation of capital equipment and infrastructure are not included. This includes equipment to manufacture buildings, motor vehicles, and industrial machinery, and the installation of water distribution piping. The energy and emissions associated with such capital equipment generally, for 1000 pounds of materials, become negligible when averaged over the millions of pounds of product manufactured over the useful lifetime of the capital equipment.

Space Conditioning

The fuels and power consumed to heat, cool, and light manufacturing establishments are omitted from the calculations in most cases. For manufacturing plants that carry out thermal processing or otherwise consume large amounts of energy, space conditioning energy is quite low compared to process energy. Energy consumed for space conditioning is usually less than 1 % of the total energy consumption for the manufacturing process. This assumption has been checked in the past by Franklin Associates staff using confidential data from manufacturing plants. In this analysis, bottled water purchased in retail stores has not been assigned any share of the store’s general space conditioning energy.

Support Personnel Requirements

The energy and wastes associated with research and development, sales, and administrative personnel or related activities have not been included in this study. Similar to space conditioning, energy requirements and related emissions are assumed to be quite small for support personnel activities.

Miscellaneous Materials and Additives

Selected materials such as catalysts, pigments, or other additives which individually account for less than 1 % by weight of the net process inputs are typically not included in the assessment unless inventory data for their production are readily available or there is reason to believe that these additives have environmental impacts that are very high in relation to their mass.

Rebound Effect

The analysis does not include any analysis of the environmental impacts of changes in consumer behavior that may be associated with choosing one water delivery system over another. For example, if consumers choose to drink tap water rather than purchasing bottled water, they may choose to save or invest the money that they do not spend on bottled water, or they may choose to spend the money on a different item or activity. Conversely, if consumers purchase bottled water, this will reduce the money they have available to spend on other items and activities. Alternative purchased items or activities may have environmental impacts that are greater or lesser than the impact of purchasing bottled water. It is beyond the scope of this analysis to make projections about the environmental impacts of alternative uses of consumers’ spending dollars that are currently used to purchase bottled water.

1.1.1.18 17 Data Sources

Data from credible published sources or licensable databases will be used wherever possible in order to maximize transparency. Wherever possible the study uses Oregon-specific data and assumptions. For processes and materials where reliable current published data are not available, data sets from Franklin Associates’ United States industry average database will be used. This database has been developed over a period of years through research for many LCI projects encompassing a wide variety of products and materials. Another advantage of the database is that it is continually updated. For each ongoing LCI project, verification and updating is carried out for the portions of the database that are accessed by that project.

1.1.1.19 18 Data Quality Goals for This Study

ISO standard 14044 :2006 states that “Data quality requirements shall be specified to enable the goal and scope of the LCA to be met.” Data quality requirements include time-related coverage, geographical coverage, technology coverage, and more. The data quality goal for this study is to maximize transparency by using life cycle data from credible publicly available sources to the extent possible, and to model all systems to reflect Oregon-specific conditions and practices, where appropriate. Where publicly available life cycle data are not available, processes and materials in this study are modeled based on Franklin Associates’ LCI database. The quality of individual data sets vary in terms of age, representativeness, measured values or estimates, etc.; however, all materials and process data sets used in this study will be thoroughly reviewed for accuracy and currency and updated to the best of our capabilities for this analysis.

1.1.1.19.1 18.1 Data Accuracy

An important issue to consider when using LCI study results is the reliability of the data. In a complex study with literally thousands of numeric entries, the accuracy of the data and how it affects conclusions is truly a complex subject, and one that does not lend itself to standard error analysis techniques. Techniques such as Monte Carlo analysis can be used to study uncertainty , but the greatest challenge is the lack of uncertainty data or probability distributions for key parameters, which are often only available as single point estimates. However, the reliability of the study can be assessed in other ways.

A key question is whether the LCI profiles are accurate and study conclusions are correct. The accuracy of an environmental profile depends on the accuracy of the numbers that are combined to arrive at that conclusion. Because of the many processes required to produce each container or packaging material, many numbers in the LCI are added together for a total numeric result. Each number by itself may contribute little to the total, so the accuracy of each number by itself has a small effect on the overall accuracy of the total. There is no widely accepted analytical method for assessing the accuracy of each number to any degree of confidence. For many chemical processes, the data sets are based on actual plant data reported by plant personnel. The data reported may represent operations for the previous year or may be representative of engineering and/or accounting methods. All data received are evaluated to determine whether or not they are representative of the typical industry practices for that operation or process being evaluated.

There are several other important points with regard to data accuracy. Each number generally contributes a small part to the total value, so a large error in one data point does not necessarily create a problem. For process steps that make a larger than average contribution to the total, special care is taken with the data quality.

There is another dimension to the reliability of the data. Certain numbers do not stand alone, but rather affect several numbers in the system. An example is the amount of material required for a process. This number will affect every step in the production sequence prior to the process. Errors such as this that propagate throughout the system are more significant in steps that are closest to the end of the production sequence. For example, changing the weight of an input to the final fabrication step for a plastic component changes the amounts of resin inputs to that process, and so on back to the quantities of crude oil and natural gas extracted.

In addition to the accuracy of the underlying data sets used to model each unit process, an added dimension to the drinking water analysis is the unlimited possibilities for variations in container weights, recycled content, fabrication energy, transportation distances, consumer use behavior, etc. for the drinking water systems studied. Because of this, the life cycle model was set up as a dynamic model capable of evaluating a wide range of user-defined scenarios. The program TopRank will also be used to evaluate the sensitivity of results to variations in individual modeling parameters.

1.1.1.19.2 18.2 Geographic Scope

Data for foreign processes are generally not available. This is usually only a consideration for the production of oil that is obtained from overseas. In cases such as this, the energy requirements and emissions are assumed to be the same as if the materials originated in the United States. Since foreign standards and regulations vary from those of the United States, it is acknowledged that this assumption may introduce some error. Transportation of crude oil used for petroleum fuels and plastic resins is modeled based on the current mix of domestic and imported crude oil used.

Other processes in this analysis modeled as occurring outside the United States include production of virgin aluminum and steel reusable drinking containers and the processing and bottling of water imported from several countries. Fabrication of the bottles used to package imported water was assumed to occur in the country in which the water was bottled. Recovered PET bottles are assumed to be exported to China for recycling, so PET resin production emissions are based on the U.S. grid, while credits for recycled resin are based on PET production using the Chinese electricity grid. (Recovered metals, glass, and corrugated were assumed to be recycled in the U.S.) For processes occurring outside the U.S., U.S. process energy requirements are used, but production of process electricity was modeled based on that country’s electricity grid.

The following table summarizes the model settings for the three example scenarios. For each drinking water system, the example scenario represents only one of the many combinations of parameters that can be modeled for each of the drinking water systems and is not meant to be interpreted as the most likely or most representative scenario for that system. Parameters that are modeled consistently for all systems (e.g., wastewater treatment) are not shown in Table 1.1A.

1.1.1.20 19 Selection of Subscenarios

48 subscenarios meet the following goals:

• To capture scenarios that are believed to best represent typical practices

• To demonstrate ‘best case’ or ‘worst case’ scenarios for selected systems to see if results for the different drinking water systems (bottled, tap, HOD) overlap at practical extremes

• To explore compounding or offsetting effects of simultaneous variations in key parameters within systems

• To identify parameters that have a large effect on results

• To identify parameters which do not have a large effect on results at any level.

In most cases the selected subscenarios use conservative baseline estimates or assumptions for the bottled water system and less favorable baseline assumptions (e.g., 1-year useful life, washing container after each use) for the reusable tap and HOD systems, to see if overlap is expected within the ranges of parameters that could occur for the different systems.

1.1.1.21 20 Life Cycle Impact Assessment

The U.S. EPA’s TRACI methodology was selected as the impact assessment methodology to be used, since it was developed to represent U.S. conditions (e.g., for fate and transport of chemical releases).

1.2 Annex B (pp 36–46): Example of a Comparative, Attributional Life Cycle Assessment to Support Product Manufacturer Decision Making

1.2.1 Comparative Life Cycle Assessment of an Artificial Christmas Tree and a Natural Christmas Tree

1.2.1.1 Goal and Scope Definition (Recreated)

Preamble

In 2010, the American Christmas Tree Association (ACTA) engaged thinkstep Americas to conduct a Life Cycle Assessment (LCA) that compares the most common artificial tree and the most common natural tree across a range of environmental impacts. Thinkstep is an independent consultancy with extensive experience in conducting LCA studies and facilitating critical stakeholder review processes.

This annex summarizes the goal and scope of the study as described in the full report, dated November 2010. It is intended to suggest how a goal and scope definition document would have looked if one had been prepared for public view at the outset of the study. The report is publicly available and can be found on thinkstep’s website: http://www.thinkstep.com/resources/studies/detail/study-comparative-life-cycle-assessment-of-an-artificial-and-a-natural-christmas-tree/

ACTA and thinkstep are not responsible for writing or preparing the following recreation of a goal and scope document.

1.2.1.2 1 Project Context and Study Goals

The American Christmas Tree Association (ACTA) is interested in understanding the ‘cradle-to-grave’ environmental impacts of artificial and natural Christmas trees that are sold and used in the United States. To accomplish this, the American Christmas Tree Association has engaged thinkstep to conduct an LCA that compares the most common artificial tree and the most common natural tree across a range of environmental impacts. Thinkstep is an independent consultancy with extensive experience in conducting LCA studies and facilitating critical stakeholder review processes.

ACTA is an industry association with many members of the artificial tree industry. This comparative study is expected to be released to the public by the ACTA to refute myths and misconceptions about the relative difference in environmental impact by real and artificial trees. The findings of the study are intended to be used as a basis for educated external communication and marketing aimed at the American Christmas tree consumer. As required by the ISO 14040 -series standards, for the public dissemination of comparative LCAs a third party critical review panel has been asked to verify the LCA results.

The goal of this LCA is to understand the environmental impacts of both the most common artificial Christmas tree and the most common natural Christmas tree, and to analyze how their environmental impacts compare. To enable this comparison, a cradle-to-grave LCA was conducted of the most commonly sold artificial and the most commonly sold natural Christmas tree in the United States.

Understanding that there is a wide range of Christmas tree products available (for both natural and artificial trees), the study goal does not include the comparison of every species of natural tree to every model of artificial tree available on the market. It also does not compare the average artificial tree to the average natural tree. Rather, the two products are chosen because they are the most common artificial and natural Christmas tree purchased in the United States.

Note that the two Christmas trees modeled in this study are not comparable in appearance or physical properties (weight, fullness, character). It is understood that the consumer’s decision to purchase an artificial tree or a natural tree is based primarily on factors such as tradition, convenience, maintenance, and geography. Because of this, and because there is already a division between artificial and natural tree owners, it is not expected that consumers will compare a similar looking artificial and natural trees. Rather, data shows which trees are most common among the natural tree constituency and the artificial tree constituency.

1.2.1.3 2 Scope of the Study

The following section describes the general scope of the project to achieve the stated goals. This includes the identification of the specific products that were assessed, the supporting product systems , the boundaries of the study, the allocation procedures, and the cut-off criteria used.

1.2.1.3.1 2.1 Definition of Product Systems

LCA evaluates the complete life cycle environmental impacts of the following two product systems, which represent the most common artificial and natural Christmas tree purchased in the United States (Fig. 1.1B).

1.2.1.3.1.1 2.1.1 Artificial Tree

The most commonly purchased artificial tree is manufactured at a large facility in China. Primary plant data for the manufacturing of this tree were collected in 2009. After manufacturing, the tree is shipped to the US and is distributed by a major big box retailer. The artificial tree including the tree stand is made of metal and plastic parts, is 6.5 ft tall, and weighs 5.1 kg (11.2 lb) out of the box.

According USA TRADE 2009, over ten million artificial trees have been imported to the United States each year for the years 2005–2008. According the ACTA, of this ten million, four million are 6.5 ft trees, and two million (or roughly 20 %) of the trees sold in the US are the same SKU (Stock Keeping Unit) as the tree modeled in this study. Therefore, this study models the environmental impact of the most common artificial tree, which represents approximately 20 % of the US artificial tree market.

Similar models to this artificial tree are sold at other major big box retailers making this artificial tree extremely representative.

1.2.1.3.1.2 2.1.2 Natural Tree

The most commonly purchased natural tree is a Fraser fir. This tree is modeled using literature and industry data for a 6.5 ft Christmas tree cultivated on wholesale natural tree farms, and distributed to the consumer through large retailers. The natural tree has a dry mass of 6 kg, and a total mass of 15 kg with a water content of 60 %. The accompanying tree stand is 10 % metal and 90 % plastic. Therefore, this study models the environmental impact of an American-grown Fraser fir, the most common natural tree grown in the United States.

1.2.1.3.2 2.2 System Description Overview

The environmental indicators analyzed in this study include: Primary Energy Demand, Global Warming Potential, Eutrophication, Acidification and Smog. Environmental indicators are calculated for the artificial tree and compared to the natural tree for three scenarios:

• 1-year: Assuming the artificial tree is only used for 1 year, the comparative natural tree scenario is the use of one natural tree. This scenario includes the production of 1/10th of a natural tree stand, assuming the tree stand will last 10 years. The artificial tree stand is assumed to have a lifetime equal to that of the artificial tree in all scenarios.

• 5-year: Assuming the artificial tree is used for 5 years before disposal, the comparative natural tree scenario is the purchase of a new natural tree every year for 5 years or in total, five natural trees over 5 years. This scenario includes 5/10th of a natural tree stand, assuming the tree stand will last 10 years.

• 10-year: Assuming the artificial tree is used for 10 years before disposal, the comparative scenario is the purchase of a new natural tree every year for 10 years or in total, ten natural trees over 10 years. This scenario includes one natural tree stand, assuming the tree stand will last 10 years.

Note that for the artificial tree, the tree stand is included in the product, and is assumed to have a lifetime equal to that of the artificial tree. For comparison purposes, the natural tree model includes a Christmas tree stand that is purchased separately by the user. It is assumed that the natural tree stand will last for 10 years. Therefore the impacts of the natural tree stand are allocated based on the number of years the artificial tree is kept. For instance, in the 1-year scenario, 1/10th of the tree stand impact is included in the overall natural tree life cycle. A detailed breakdown of impacts is summarized in this report for the artificial tree and for the 1-year scenario for the natural tree. The 5-year and 10-year natural tree scenarios are scaled from the 1-year baseline, such that relative impacts will be consistent between the three natural tree scenarios. Additionally, sensitivity analyses are performed by varying key parameters to test their significance to the model.

1.2.1.3.3 2.3 Functional Unit

All impacts were related to the functional unit, which is displaying one unlit, undecorated Christmas tree with tree stand in the home during one holiday season.

Although the most common artificial tree sold is a pre-lit tree, the material and impacts associated with the lights have been removed from the study boundaries. It is assumed that the lighting and decorations on each tree would be equivalent, and are therefore excluded from the study.

1.2.1.3.4 2.4 Study Boundaries

This study includes the cradle-to-grave environmental impacts of producing and using a Christmas tree in the home during one holiday season.

For the artificial tree the system boundary includes:

• Cradle-to-gate material environmental impacts;

• The production of the artificial tree with tree stand in China;

• Transportation of the tree and stand to a US retailer, and subsequently a customer’s home; and

• Disposal of the tree and all packaging.

For the natural tree, the system boundary includes:

• Cradle-to-gate material environmental impacts;

• Cultivation including initial growth of the tree in a nursery, transplant of the seedling to the field, harvesting the full size tree, and post harvest treatment of the tree;

• Transportation from the farm to retailer, and subsequently to a customer’s home;

• Use phase watering;

• Disposal of the tree and all packaging; and

• Cradle-to-grave impacts of a natural tree stand.

Both tree models include all impacts associated with the upstream production of all materials and energy used.

Foreground datasets used in this assessment do not account for production and maintenance of infrastructure (streets, buildings and machinery). That means that impacts associated with building and maintaining infrastructure are excluded. In other words, mechanical processing on farms accounts for fuel use but not production or maintenance of the tractor. Odor, biodiversity aspects, noise and human activities are also excluded from the system analysis.

Additionally, overhead warehouse and retail impacts are excluded from this study. The only impact included at the retailer is the disposal of shipment packaging.

Table 1.1B summarizes what is included and excluded in this study.

1.2.1.3.5 2.5 Geographic and Time Coverage

For manufacturing of the artificial tree, the electricity grid mix and fuel datasets used in the model represent Chinese boundary conditions. For the US distribution, use, and disposal of artificial trees, all background datasets chosen are based on US boundary conditions. For the natural tree, all background datasets are based on US boundary conditions with cultivation of the tree on a natural tree farm.

This study evaluates Christmas trees used in the United States in the years 2003–2009. The artificial trees are modeled using primary data collected in 2009. Data describing natural tree production in 2009 was not available at the time of this study; therefore the most recent published data available (2003–2008) are used in this model.

1.2.1.3.6 2.6 Selection of Impact Assessment Categories

The US EPA TRACI (Tool for the Reduction and Assessment of Chemical and other Environmental Impacts) impact assessment methodology was chosen because the geographical coverage of this study is the United States, and the TRACI methodology was developed specifically for US environmental conditions. Since TRACI does not include an index for the consumption of renewable or fossil energy sources, Primary Energy Demand is included as an additional environmental indicator. Specifically this study looks at Primary Energy from non-renewable resources, as this is more important environmentally than total Primary Energy Demand.

1.2.1.3.6.1 2.6.1 Included Impact Categories

Use of fossil energy sources and Global Warming Potential are included in the study because of their growing importance to the global environmental and political/economic realm.

Acidification, Eutrophication, Photochemical Ozone Creation Potential/ Smog are included because they reflect the environmental impact of regulated and additional emissions of interest by industry and the public, e.g. SO₂, NO_X, CO, and hydrocarbons. These categories have been used as key indicators to determine the environmental performance of the different trees. A short description of each impact category is shown in Table 1.2B.

1.2.1.3.6.2 2.6.2 Common Excluded Impact Categories

The following impact categories are not included in this study.

1.2.1.3.6.2.1 2.6.2.1 Ozone Depletion Potential (ODP)

ODP has not been selected as it is only relevant once cooling fluid is consumed in a high quantity. As this is not the case in either manufacturing process, ODP has not been included in the study.

1.2.1.3.6.2.2 2.6.2.2 Toxicity

In 2004 a group of environmental leaders released a report, the Apeldoorn Declaration,^{Footnote 17} describing the shortcomings of toxicity and hazard characterization within LCA. As per this declaration, it is the position of this study that “even though LCIA can use models and methodologies developed for Risk Assessments, LCA is designed to compare different products and systems and not to predict the maximal risks associated with single substances.” Human and eco-toxicology results are best suited to case- and site-specific studies that accurately model dispersion pathways, rates, and receptor conditions. As a result of this declaration, the LCIA categories of human health toxicity (cancer and non-cancer) and ecological toxicity were not included in the study.

1.2.1.3.6.2.3 2.6.2.3 Fossil Fuel Depletion

This impact category will not be included as part of this study as the non-renewable Primary Energy Demand (PED) indicator will succinctly communicate the impact of fossil fuel depletion through non-renewable energy consumption. In addition, the endpoint methodology is not readily understood by a variety of audiences, technical and non-technical alike.

1.2.1.3.6.3 2.6.3 Normalization, Grouping and Weighting

Additional optional Life Cycle Impact Assessment (LCIA) steps include normalization, grouping and weighting. Due to uncertainties associated with the incongruence between the normalization boundary associated with readily available datasets and the boundary of impact, normalization is not included as part of this study. Further, due to the subjective nature of grouping impact categories and/or applying value-based weights, the impact results that are included will be communicated in disaggregated form.

1.2.1.3.7 2.7 Data Collection

In modeling a product system , it helps to consider the foreground system and the background system separately. For the foreground system, primary data from the artificial tree manufacturing plant in China and published literature describing natural tree production in the United States will be collected. For all background data (production of materials, energy carriers, services, etc.) the GaBi databases 2006 will be used. In modeling, the product flows of the foreground system are connected to the background datasets of the respective products. In doing so, the quantities of the background datasets are scaled to the amount required by the foreground system.

1.2.1.3.7.1 2.7.1 Artificial Tree Production

Data for the production and transportation of an artificial tree will be collected for a manufacturing facility in China. At this plant, Christmas trees and stands are produced in the summer and then shipped to the US for distribution and sale during the winter holiday season. This plant is one of the largest Chinese artificial tree manufacturers. Production line data was collected from equipment dedicated to tree production by an ACTA member

1.2.1.3.7.2 2.7.2 Natural Tree Cultivation

US impacts from agricultural production depend upon local conditions such as climate, soil type, fertility, indigenous pests and also on available technology (degree of mechanization, use of fertilizers and pesticides, etc.). The data used for modeling a Fraser fir, the most commonly sold natural Christmas tree, will be collected from literature, international electronic databases, and personal interviews.

1.2.1.3.7.3 2.7.3 Transportation

The GaBi database for transportation vehicles and fuels will be used to model the transportation associated with both the artificial and natural tree. US average fuels will be used for all transportation within the US. Chinese fuels will be used for all transportation within China and originating from China. The transport of the artificial tree from China is modeled using a global truck (factory to port) and container ship (Chinese port to US port). All truck transportation within the United States will be modeled using the GaBi 4 US truck transportation datasets. In accordance with the US CENSUS BUREAU 2002, Vehicle Inventory and Use Survey results:

• Seedlings (assumed to be similar to grains) are transported in a dump truck;

• Wood and agriculture products (including cultivation intermediary steps) are transported using a US flatbed or platform truck, however the finished trees product are transported from farm to retailer using a pole, logging, pulpwood, or pine truck;

• Fertilizers are transported using a US liquid or gas tanker truck;

• Wastes are transported using a US dump truck; and

• Raw materials and artificial tree products are transported using a basic enclosed trailer.

The vehicle types, fuel usage, and emissions for each truck model were developed using a GaBi model based on the most recent US Census Bureau Vehicle Inventory and Use Survey (2002) and US EPA emissions standards for heavy trucks in 2007. The 2002 VIUS survey is the most current available data describing truck transportation fuel consumption and utilization ratios in the US, and the 2007 EPA emissions standards are considered by this study’s authors to be the most appropriate data available for describing current US truck emissions.

For each modeled truck, the utilization ratio can be varied. The utilization ratio can be thought of either as the percentage of miles while carrying the maximum cargo load, or the percentage of the maximum cargo load which is being carried during an average mile. The three trucks used in this model are listed in Table 1.3B.

1.2.1.3.7.4 2.7.4 Fuels and Energy – Upstream Data

National and regional averages for fuel inputs and electricity grid mixes were obtained from the GaBi databases 2006. For activities occurring in China, the fuel and energy models were based on Chinese boundary conditions. For all activities occurring within the United States, national average electricity and fuel datasets were chosen.

1.2.1.3.7.5 2.7.5 Raw and Process Materials

LCI data for all upstream raw and process materials were obtained from the GaBi databases 2006.

1.2.1.3.7.6 2.7.6 Co-product and By-product Allocation

A process, sub-system or system may produce co-products in excess of the specified functional unit . Such co-products leave the system to be used in other systems yet should carry a portion of the burden of their production system. In some cases materials leaving the system are considered ‘free of burden’. To allocate burden in a meaningful way between co-products, several procedures are possible (e.g. allocation by mass, market value, heating value, etc.). Whenever allocation was necessary, the method was chosen based upon the original intent of the process in need of allocation. For instance, in the case of mining precious metals where the desired object (e.g. gold) is only a small fraction of the total mass of products produced (e.g. gravel), it is illogical to allocate the burdens of mining based upon mass. However, for transportation processes where the amount of cargo carried per trip is determined by weight limits, mass allocation is appropriate.

In this study, no allocation is necessary for the manufacturing processes associated with the production of the trees as the artificial tree data were collected during the tree producing season from equipment dedicated to tree production. All recycling and disposal of scrap materials associated with the artificial tree production is included in the model.

The by-products of the natural tree that were produced in the system (stem wood, cutting, root system and pruning) were also included inside the system boundaries and assumed to be disposed in a landfill. In later stages of the life cycle, some by-products occur (e.g. organic material). In these cases allocation is avoided by system expansion. An overview of the by-products and the substituted product is given in Table 1.4B.

1.2.1.3.8 2.8 Critical Review

The applicable ISO standards require a critical review in cases where a comparative assertion is being made and communicated publicly. The primary goals of a critical review are to provide an independent evaluation of the LCA study and to provide input to the study proponents on how to improve the quality and transparency of the study. The benefits of employing a critical review are the following:

• To provide precise instructions in the numerous situations where documented approaches described in appropriate reference materials were deficient of detail;

• Identification and assurance that the most significant inputs and outputs of the system studied are identified; and

• Assure that the data collected, the models developed, and the sensitivity analyses performed are of sufficient quality, both qualitatively and quantitatively, to ensure that the system assessed is truly represented and supports the claims made.

If applicable, the peer review panel can serve to comment on suggested priorities for improvement potential.

1.3 Annex C (pp 47–60): Example of a Comparative, Consequential Life Cycle Assessment to Support Government Decision Making

1.3.1 Life Cycle Assessment of Aluminum Production in New Alcoa Smelter in Greenland

1.3.1.1 Goal and Scope Definition (Recreated)

Preamble

The Government of Greenland commissioned an LCA study of a planned aluminum smelter with an annual capacity of 360,000 tonnes in West Greenland. The target audience was all interested parties, directly or indirectly involved in the Strategic Environmental Assessment (SEA) process. This included the Government of Greenland, NGOs, Alcoa, the citizens of Greenland, and thecitizens of Maniitsoq in West Greenland, where the proposed aluminum smelter is to be situated. The results of the LCA study are also of interest to the negotiating parties, including Denmark and Greenland, in the new climate agreement, which is to replace the Kyoto Protocol. The study was conducted during the period from October 2008 to April 2009.

The study applied LCA methodology with a focus on greenhouse gas (GHG) emissions , also known as a carbon footprint . The focus on GHG emissions is partly a result of the requirements from the commissioner of the study and partly due to the fact that the LCA forms part of an SEA in which other types of impacts are assessed separately. Other impact categories such as ozone depletion, acidification, eutrophication, eco-toxicity, and human toxicity are included in the present study and presented as part of the results, but are not assessed as detailed as GHG emissions and are therefore subject to considerable uncertainties .

This annex summarizes the goal and scope of the study as described in the full report published by the Government of Greenland in 2009. It is intended to suggest how a goal and scope definition document would have looked if one had been prepared for public view at the outset of the study. The report is publicly available and can be found on 2.-0 LCA Consultants’ webpage: http://lca-net.com/publications/show/life-cycle-assessment-aluminum-production-new-alcoa-smelter-greenland/

The Government of Greenland and the contractors who conducted the study are not responsible for writing or preparing the following recreation of a goal and scope document.

1.3.1.2 1 Background

Aluminum is a non-ferrous metal and its production requires a significant amount of electricity. According to the International Aluminum Institute (IAI), 1 ton of virgin aluminum represents, on average, an emission of 10 tonnes of CO₂e, including mining and alumina production. This corresponds approximately to the GHG emissions from one average person during 1 year in Europe. Hence, according to the IAI data, the proposed smelter represents GHG emissions equivalent to the emissions from approximately 360,000 persons in Europe during 1 year (or 3.6 million tonnes of CO₂e annually). This is a significant contribution to Greenland’s total Carbon Footprint (GHG emissions), and one of the reasons for the commissioning of the present study.

Electricity generation for the planned smelter will be based on two hydropower plants, which will be constructed for the same purpose. In terms of global warming, this is a great advantage, but the construction and operation of hydropower plants also produce GHG emissions . Furthermore, emissions also take place at other life cycle stages, as well as during the production of auxiliary materials (e.g. anodes), during transport, and during the construction of capital goods, such as buildings, machinery, and other types of infrastructure required.

To obtain a reliable assessment, it is therefore necessary to make a comprehensive analysis that unveils a representative set of consequences, at all lifecycle stages, and in a larger perspective in which we include aluminum production that is avoided (globally) due to the construction of the Greenland smelter.

1.3.1.3 2 Strategic Environmental Assessment

The LCA is made as part of a Strategic Environmental Assessment (SEA). SEAs require that the main alternative is compared with reasonable alternatives (Directive 2001/42/EC of the European Parliament and the Council on the Assessment of the Effects of Certain Plans and Programmes on the Environment).

Hence, the primary purpose of the LCA is to assess and to document the potential environmental impacts with a focus on GHG emissions from the following alternatives:

• Alternative 1: the establishment of an aluminum smelter in Greenland (Alcoa)

• Alternative 0: not establishing the aluminum smelter in Greenland; this means that an equivalent capacity will be installed in another location in the world, and that it may be commissioned by another company. This is also referred to as the marginal production in the present analysis.

Alternative (1) above refers to the main alternative in the strategic environmental assessment carried out by the Government of Greenland, and (0) refers to the 0 alternative. The fact that the 0 alternative is represented by aluminum production in another location in the world is based on the assumption that aluminum production is driven by the global demand for aluminum. Thus, the decision to approve the aluminum smelter in Greenland will have the effect that a corresponding capacity will not be installed elsewhere. The 0 alternative represents the most likely location and technology that will be installed if the Greenland smelter is not installed. Alcoa may be able to identify another location with access to renewable energy as in the Greenland case, and thereby achieve similar low GHG emissions . However, it is out of the scope of the present study to determine whether Alcoa will search for another location if the Greenland smelter is not approved. Therefore, the present study only compares the specific proposed smelter in Greenland (alternative 1) with the most likely alternative capacity that will be installed elsewhere by an unspecified actor on the market (alternative 0).

Hence, the outcome of any decision made as part of the strategic environmental assessment process in Greenland can only affect local alternatives, such as local location and waste treatment, etc., in the location in which new aluminum smelter capacity is installed.

It should be noted that a decision of establishing the smelter in Greenland (Alternative 1) also means that Alternative 0 is avoided, according to the mentioned assumptions about the global supply and demand situation on the aluminum market. The global change in GHG emissions , which results from placing an aluminum smelter in Greenland, is therefore Alternative 1 minus Alternative 0.

1.3.1.4 3 Definition of Goal and Scope

The LCA is carried out in accordance with the ISO standards on LCA: ISO 14040 (2006) and ISO 14044 (2006). According to the ISO standards, an LCA consists of four phases:

1. 1.

   Definition of goal and scope

2. 2.

   Life cycle inventory (LCI)

3. 3.

   Life cycle impact assessment (LCIA)

4. 4.

   Life cycle interpretation

This section documents the first phase of the LCA of aluminum production in Greenland. The first phase includes a description of the purpose of the study, a definition of the functional unit , an overview of the applied methods, and an overview of the relevant processes (system boundary ). This also includes important methodological choices affecting the other phases of the LCA, e.g., the system boundaries affect the data to be collected in phase 2, and the method used for LCIA affects the results calculated in phase 3.

1.3.1.4.1 3.1 Purpose of the Study

The overall purpose of the present study is to provide decision support in the environmental impact assessment (EIA) process of a new aluminum smelter in Greenland. The main decision to be supported is whether the aluminum smelter should be approved or not. Usually, EIAs do not contain life cycle information. As a supplement to the conventional information provided in the EIA process, the Government of Greenland has requested life cycle information, especially for GHG emissions .

The main question to be answered by the LCA is: “What is the environmental impact of installing the new smelter in Greenland”? In EIA, the environmental impact of the proposed project and possibly some alternatives is assessed in comparison with the so-called zero alternative, which represents a situation in which the proposed project is not implemented. In the following, the zero alternative is referred to as Alternative 0.

It is relatively easy to define the situation in which the proposed project is implemented, which simply corresponds to the scenario proposed by the project commissioner. But when it comes to the zero alternative, it may be more difficult. In the present study, the zero alternative is defined as the situation in which the new aluminum smelter is not installed in Greenland and a corresponding amount of capacity is installed somewhere else in the world. Thus, Alternative 0 is equivalent to the installation of the capacity and annual production of 360,000 tonnes of aluminum somewhere in the world. It is obvious that the identification of the technology and location of Alternative 0 is subject to significant uncertainties . Therefore, several possible versions of Alternative 0 are identified. But all the identified scenarios represent Alternative 0.

It should be noted that the present study does not include any concrete alternatives to the proposed project – only Alternative 0. It is obvious that Alcoa may choose to install new capacity somewhere else in the world if the proposed project is not chosen. Since information on Alcoa’s future plans for capacity expansion is confidential, no additional alternatives have been included in the LCA. Therefore, the proposed project in Greenland is compared to a situation in which Alcoa does not install specific capacity in another location. It is clear that Alcoa could achieve an environmental impact similar to the impact of the Greenland smelter if they choose to install a capacity which uses the same technology in another region, e.g., a smelter based on 100 % hydro power in Russia. But the assessment of such alternatives lies outside the scope of the present study.

As follows from the above described reasoning, the installation of the Greenland smelter will have the effect that Alternative 0 is avoided and, if the Greenland smelter is not established, then Alternative 0 is affected. The fact that the zero alternative is represented by aluminum production in another location in the world is due to the assumption that aluminum production is driven by the global demand for aluminum, i.e. full elasticity of supply is assumed. In reality, there may be intermediate price differences. The effect of such price differences could be modelled by general economic equilibrium modeling. This would lead to lower impacts of any decision or any change compared to what is modelled in an LCA, but the direction of the change would be the same. It should also be noted that full elastic supply and inelastic demand represent the default assumption in all LCAs.

1.3.1.4.2 3.2 Assessed Alternatives in the Comparative LCA

Thus, the primary purpose of the LCA is to assess and to document the potential environmental impacts from:

• Alternative (1) the establishment of the aluminum smelter in Greenland (Alcoa)

• Alternative (0) not establishing the aluminum smelter in Greenland, which means that an equivalent capacity will be installed in another location in the world and will possibly be commissioned by another company.

In addition to the two alternatives, Alcoa’s existing production in two smelters is included for comparison. This production is analyzed in two scenarios; Scenario 2a: Alcoa Deschambault in Canada and Scenario 2b: Alcoa Iceland. It should be noted that these scenarios do not represent actual alternatives to the Greenland smelter, but are included for illustrative and comparative purposes, since most of the data collection is based on data from these two smelters. Furthermore, an alternative could be the establishment of an increased collection of aluminum scrap and an additional capacity for the processing of scrap into new aluminum. This could eliminate the need for new facilities for the production of virgin aluminum. However, it should be noted that this alternative is out of the scope of both the Government of Greenland and Alcoa – it is more related to structural changes in economy, which may also be regarded as out of scope of this study.

1.3.1.4.3 3.3 Included Scenarios Representing the Proposed Project and the Zero Alternative

Alternative (1) above refers to the main alternative in the strategic environmental assessment carried out by the Government of Greenland, and (0) refers to the 0 alternative. The fact that the 0 alternative is represented by aluminum production in another location in the world is based on the assumption that aluminum production is driven by the global demand for aluminum. Hence, the outcome of any decision made as part of the strategic environmental assessment process in Greenland can only affect the location of the new aluminum smelter capacity.

It should be noted that a decision of establishing the smelter in Greenland (Alternative 1) also means that alternative 0 is avoided, according to our assumptions about the global supply and demand situation on the aluminum market. The global change in GHG emissions as a result of placing an aluminum smelter in Greenland is therefore Alternative 1 minus Alternative 0.

Alternative 1 is analyzed using two different scenarios; a main scenario (Sc1) applying modern technology in the smelter, and an alternative scenario (Sc1a) applying world average existing technology in the smelter. Correspondingly, alternative 0 is analyzed using different scenarios. The main scenario (Sc0) applies a mix of aluminum produced in China, CIS/Russia, and Middle East using an identified marginal electricity mix. To evaluate the uncertainties in identifying the marginal location and electricity mix, a broad range of sensitivity scenarios are applied, i.e. scenarios Sc0a to Sc0o. For all these scenarios, new smelter technology has been applied. This is supplemented with a scenario (Sc0p) which analyses scenario Sc0, but with existing smelter technology. The two scenarios representing the existing Alcoa smelter in Deschambault in Canada and the smelter in Iceland are termed Sc2a and Sc2b, respectively (Fig. 1.1C).

1.3.1.4.4 3.4 Functional Unit : 1 kg of Basic Aluminum

The function of the product of interest is to supply basic aluminum to the world market, which faces an increased demand. The functional unit is defined as 1 kg of virgin aluminum (ingots) supplied at a plant (100 % aluminum, 0 % alloying metals). Further processing, downstream manufacture of the aluminum, the use stage and the disposal stage including recycling are not included in the study because these stages are not related to the production of basic aluminum.

1.3.1.4.5 3.5 Method for System Delimitation

This LCA uses the consequential approach. The attributional approach represents the traditional way of defining system boundaries in LCA, while the consequential approach is developed in light of the fact that cause-effect mechanisms are missing in attributional LCA .

This difference means that the consequential approach only includes the affected processes (or so called marginal suppliers) and avoids co-product allocation by system expansion.

1. 1.

   The best^{Footnote 18} available IO data on primary aluminum is identified. This data is presented in monetary units; i.e., the functional unit or reference flow for the data set is EUR or USD aluminum.

2. 2.

   Price information on primary aluminum for the relevant period of time and geographical region is identified.

3. 3.

   A process is expressed in physical units (kg), transforming the monetary reference flow in (1) into mass using the price information in (2).

4. 4.

   A number of inputs and outputs of the IO data set for the aluminum smelter process are replaced by more detailed process-based LCA data based on:

5. (a)

   a screening of the process (contribution analysis made by use of LCA software) and the literature review presented in section 1, and

   1. (b)

      an identification of the processes of which it is desirable to be able to make detailed modeling (e.g. if it is desirable to be able to make detailed modeling of bauxite production, energy inputs, transportation, or other inputs and outputs which may be either special in the case of Greenland or they may be relevant as parameters in defining alternative technologies to be included in the study)

The procedure of converting an original IO data set into a hybrid data set represents an iterative process which can continue as far as desired. However, each time an IO-based input is replaced by a process-based input, it must be considered if the process-based input is significantly less complete than the IO-based input which it replaces. If so, steps (1) to (4) must also be carried out for this specific input.

Currently, such LCI databases do not exist, and it would be a major task to construct such a database. Such a task is not in the scope of this LCA study of aluminum production in Greenland. Instead, it is chosen to describe the anticipated most important processes with both process data and IO data, and then use only process data for the remaining processes.

1.3.1.4.6 3.6 System Boundary : Life Cycle Stages and Included Processes

The production of aluminum can be divided into three main stages: (1) Bauxite mining, (2) Production of alumina (Al2O3), and (3) Aluminum smelter (electrolysis). The downstream processes concerning further processing, final use and disposal are not included in this LCA study. The Alcoa aluminum smelter in Greenland only concerns the stage: (3) Aluminum smelter (electrolysis). The included life cycle stages and the system boundary are illustrated in Fig. 1.2C.

The left side of Fig. 1.2C represents Alternative 1: the Greenland smelter, and the right side represents Alternative 0: alternative production of aluminum.

1.3.1.4.6.1 3.6.1 Cut-off Criteria

The hybrid approach adopted in this study implies that the cut-off criterion is 0 % for some processes which are selected as the most important ones (IO data combined with process data), while the cut-off criterion is >0 % for other processes (only process data is used).

Using IO data implies that many product inputs are described with relatively aggregated product categories, and that these inputs are based on relatively old data. As described in section 3.6, the applied IO data is the US98 IO table, which represents the US economy in 1998 (Suh 2004). Though the data is old, we argue that it is better to have data for 1998 rather than having no data at all for the inputs covered by the IO data.

Based on the literature review and a screening of the ecoinvent (2007) process of primary aluminum production, the following processes have been identified as the most significant contributors to GHG emissions : Electricity, Aluminum smelter, and Alumina production (where process heat is most significant). Based on this, it is chosen to create hybrid processes for these three processes. In order to have consistent modeling of the feedstock chain from bauxite to aluminum, it is also decided to create a hybrid process for bauxite production.

All other product inputs in the product system of aluminum production will be described using only process data.

It should be noted that the cast house process often includes alloying metals for a few per cent of the product output . However, since the input of alloying metals relate to the specific purpose of the use of the aluminum, which is not considered in this study, the input of alloying metals has been eliminated and the analyzed product output is assumed to be 100 % pure aluminum.

The processes and the product flow presented in Fig. 1.3C form the backbone and the starting point for the LCA. By using only the data presented in this figure, the LCA would be a pure IO-LCA. As described previously, this data is not suitable for the purpose of this LCA for the following reasons:

• Aluminum smelter stage: The process Primary aluminum in the US98 IO table includes virgin as well as recycled aluminum.

• Alumina production stage: The process Industrial inorganic and organic chemicals in the US98 IO table represents the average of the US chemical industry, which is not a desirable level of detail for describing alumina production.

• Bauxite production stage: The process Nonferrous metal ores, except copper in the US98 IO table represents the average of the US mining of nonferrous metal ores (except copper), which is not a desirable level of detail for describing bauxite mining.

Therefore, in the above-mentioned processes, for all product and resource inputs as well as emissions outputs where more detailed data is available, these exchanges have been replaced with the better process-based data.

1.3.1.4.7 3.5 Method for Life Cycle Impact Assessment

The life cycle impact assessment (LCIA) phase is the third phase of an LCA. In this phase, the interventions (or emissions) per functional unit are transformed into easier interpretable impact categories . The interventions per functional unit are calculated through the life cycle inventory phase – phase 2 in the LCA. The number of interventions included in an LCA is typically several hundred, while the number of included impact categories is more limited. Therefore, LCIA is normally necessary in order to be able to interpret the results. Figure 1.4C provides an overview of the most commonly included impact categories in LCA as well as examples of some typical interventions.

The LCIA consists of three steps: (1). Characterization, (2). Normalization, (3). Weighting.

Characterization

Here, the interventions are transformed into impact categories and the results are presented as impact indicators (Fig. 1.5C).

Normalization

Here, the normalized results are divided by a reference (typically the total contribution to the impact category per citizen per year). Hereby, the magnitude of the environmental impact can better be assessed. The unit of the normalized results is person equivalents. It is often easier to have an impression of the magnitude of the contribution from 1 kg of aluminum to acidification if it is expressed in terms of person equivalents than in terms of kg of SO₂-eq.

Weighting

In this step, the normalized results are multiplied by a factor representing the relative importance of the impact category to the other impact categories . Hereby, the magnitude of the different impact categories can directly be compared, and it is possible to point out the most significant impact categories. Sometimes the normalization step and the weighting step are carried out as one single step.

1.3.1.4.7.1 3.5.1 Presentation of Results

The presentation and interpretation of results will be at the level of characterized results. Since normalization and weighting imply that additional factors are multiplied by the characterized results, these results will be more uncertain. Therefore, these results will not be used for presenting the results of the LCA. However, the weighted results are used for identifying the most significant impact categories .

1.3.1.4.7.2 3.5.2 LCIA Method: Stepwise v1.2

The applied LCIA method in the present study is the Stepwise 2006 method, version 1.2. The method is described and documented in Weidema et al. (2007) and Weidema (2009). This method is developed by selecting the best principles of the Danish EDIP2003 method (Hauschild and Potting 2005) and the Impact 2002 + method (Jolliet et al. 2003). Weidema et al. (2007) is available at http://www.lca-net.com/publications/.

In the assessment of environmental impacts of aluminum production, special attention is given to the impact category of global warming. There are several reasons for focusing on GHG emissions :

• This specific focus is of particular interest to the Government of Greenland.

• GHG emissions of an aluminum smelter in Greenland will increase Greenland’s domestic GHG emissions significantly, but may lead to avoided emissions in other places which need to be addressed and quantified to get a complete picture of the consequences.

• Other types of impacts, especially other types of local impacts, are assessed as a part of the strategic environmental assessment (SEA) , of which the present LCA forms part.

• GHG emissions represent a major environmental issue on the global agenda and GHG emissions calculated by the use of LCA corresponds to carbon footprint of products (CFP), which is an eco label undergoing rapid development these years (EPLCA 2007; PAS2050 2008).

Apart from the detailed assessment of GHG emissions , the study includes a screening of local human health impacts, which was requested by the commissioner of the study. After GHG emissions, which are given first priority in the assessment, the study gives second priority to human health impacts, which include respiratory organics and inorganics as well as human toxicity carcinogenic and non-carcinogenic (Table 1.1C).

Third priority is given to ‘other’ impact categories included in the Stepwise method. The latter is therefore only considered at a screening level. This does not mean, however, that these issues are of a trivial character (especially not in a pristine environment such as Greenland), but merely that they are not addressed at a detailed level in the present study. Readers who are interested in more detailed assessments of other impacts are referred to the information provided in the SEA.

1.3.1.4.8 3.6 Data Collection

The data collection concerns three types of data, i.e. data on (1) processes within Alcoa, (2) processes outside Alcoa, and (3) input-output data (IO data) which covers the data not included in the first two data types.

Processes Within the Alcoa Aluminum Smelter

The data collection for Alcoa processes is mainly based on specific requested data provided by Alcoa. Data which is not available from Alcoa is estimated on the basis of other data sources; personal communication with Chris Bayliss from IAI (2009) and Eirik Nordheim from EAA as well as other LCA studies such as EAA 2008^{Footnote 19} and ecoinvent 2007.

Company Visit at Alcoa’s Deschambault Plant in Quebec

Data collection has taken place in collaboration with Alcoa. A company visit took place at the Alcoa smelter in Deschambault in Quebec from the 10th to the 13th of February 2009. The factory tour took place on the 10th of February and meetings with key staff members took place on the 10th and 11th of February.

Processes Outside Alcoa

Upstream processes to the aluminum smelter as well as smelter data for Alternative 0 are based on existing LCA data (EAA 2008; ecoinvent 2007), personal communication (see above), statistical information (IAI 2009), energy outlooks (IEA 2008), as well as general industry information (European Commission 2001).

IO Data

All the inputs to the processes in the product system which could not be covered by the above-mentioned process- specific data collection are covered by general IO data for the USA in 1998 (Suh 2004). This relatively old data represents the best compromise between level of detail, regional coverage, and updated data.

1.3.1.4.9 3.7 Critical Review

According to the ISO 14044 standard, an LCA study should undergo a critical panel review if the results are meant to be used to support a comparative assertion intended for public disclosure. The final report will, therefore, be subjected to a panel review.

References to Annex C

• EPLCA (2007) Carbon Footprint – what it is and how to measure it. European Platform on Life Cycle Assessment (EPLCA), European Commission, JRC, Institute for Environment and Sustainability, Ispra

• European Commission (2001) Integrated Pollution Prevention and Control (IPPC) Reference document on best available techniques in the NON ferrous metals industries. European Commission, Brussels

• Hauschild M, Potting J (2005) Spatial differentiation in Life Cycle impact assessment – The EDIP2003 methodology. Environmental news No. 80 2005, Danish Environmental Protection Agency, Copenhagen

• IAI (2009) Story of aluminum. Homepage of the International Aluminum Institute (IAI)

• IEA (2008) World energy outlook 2008, International Energy Agency (IEA), Organisation for Economic Co-operation and Development (OECD), Paris

• Jolliet O, Margni M, Charles R, Humbert S, Payet J, Rebitzer G, Rosenbaum R (2003) Impact 2002+: a new life cycle impact assessment methodology. Int J Life Cycle Assess 8(6): 324–330

• PAS 2050 (2008) Publicity available specification: PAS 2050 – Specification for the measurement of the embodied greenhouse gas emissions in products and services. Revised in 2011, BSI British Standards. www.bsigroup.com/PAS2050

• Schmidt JH (2007) Life assessment of rapeseed oil and palm oil. Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil. Department of Development and Planning, Aalborg University, Aalborg

• Thrane M, Schmidt JH (2007) Tools for sustainable development. http://www.lca-net.com/publications/

• Suh S (2004) Materials and energy flows in industry and ecosystem networks. Life cycle assessment, input-output analysis, material flow analysis, and their combinations for industrial ecology. CML, Leiden

• Weidema BP (2009) Using the budget constraint to monetarize impact assessment results. Ecol Econ 68(6):1591–1598 (Together with Weidema et al. (2007), this publication provides a complete presentation of the Stepwise2006 impact assessment method)

• Weidema BP, Hauschild MZ, Jolliet O (2007) Preparing characterization methods for endpoint impact assessment. Available at: http://www.lca-net.com/publications/ (Together with Weidema (2009), this publication provides a complete presentation of the Stepwise2006 impact assessment method)