How Green is That Product Lecture Notes Week 5

Lecture Notes for How Green is That Product? © 2015 Northwestern University.

How Green is That Product?

An Introduction to Life Cycle Environmental Assessment

Coursera Lecture Notes

March 2015

Prepared by:

Eric Masanet and Yuan Chang

McCormick School of Engineering and Applied Science

Northwestern University

Evanston, IL, USA

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Table of Contents About these lecture notes ...................................................................................................................... 2

Lecture 1: The life-cycle perspective and course goals .......................................................................... 3

Lecture 1 Supplement ............................................................................................................................ 7

Lecture 2: Understanding unit processes ............................................................................................... 9

Lecture 2 Supplement .......................................................................................................................... 15

Lecture 3: Constructing unit process inventories: Part 1 ..................................................................... 17


Lecture 4: Constructing unit process inventories: Part 2 ..................................................................... 24


Lecture 5: Energy flow basics ............................................................................................................... 32


Lecture 6: Mass balances .................................................................................................................... 38


Lecture 7: Goal definition ..................................................................................................................... 47

Lecture 8: Scope definition: functional units ....................................................................................... 51


Lecture 9: Scope definition: initial system boundaries ....................................................................... 58

Lecture 10: Scope definition: requirements for data and data quality ................................................ 66

Lecture 11: Scope definition: review and reporting ............................................................................. 72

Lecture 12: Life cycle inventories: the basics ...................................................................................... 76

Lecture 13: Life cycle inventories: mass flows and cut off criteria ..................................................... 81

Lecture 14: Life cycle inventories: data estimation .............................................................................. 86

Lecture 15: Life cycle inventories: multi-functionality ........................................................................ 91

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About these lecture notes There are many useful resources for learning the life-cycle assessment (LCA) methodology, including

books, websites, case studies, publicly-available lecture materials, and LCA standards and

guidebooks. Rather than choose one particular resource as the assigned reading, the course staff

has prepared this compendium of lecture notes, which will serve as your primary reference for this

course. These notes make use of elements of key online LCA resources that are available to

students, and refer you to them where appropriate for additional information on different LCA

topics. Additional readings will be assigned or suggested throughout our MOOC as part of the

homework assignments, through the discussion forums, and when discussing specific LCA case

studies.

Each chapter relates to a single video lecture. The first section in each chapter contains a full

transcript of the video lecture. These transcripts will allow you to read along with the lectures as

you watch them, to write down comments at different points in a lecture, and to refer to the lecture

content when you are offline.

In many chapters, a second section has been provided, which contains additional notes that expand

upon points made within the lecture and refer you to other LCA resources as appropriate. Because

Coursera video lectures are inherently short, we’ve made use of the additional notes sections to

provide you with supporting information that couldn’t be included in the video lectures due to time

constraints. We’ve also added additional notes to further discuss topics that proved particularly

interesting or challenging in past offerings of the MOOC. Within the transcript section, you’ll see

blue arrows in the left hand margin that look like this:

This symbol indicates that additional notes have been provided. Each additional note has been

assigned a number, which also appears in the blue arrow symbol (in our example above, this

number is 1.1). The numbered blue arrows will allow you to easily jump back and forth between the

transcript and the additional information that is relevant to a particular topic.

Lecture notes will be released on a week-by-week basis.

We hope these lecture notes can serve as a basic, useful reference for you in your learning

experience. Suggestions for improving or expanding these lecture notes for future offerings of this

course are heartily welcomed. We hope you enjoy our journey together learning about and applying

the LCA methodology. Let’s get started!

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Lecture 1: The life-cycle perspective and course goals Transcript

Hello, and welcome to “How Green is that Product? An Introduction to Life Cycle Assessment.” I’m

Eric Masanet, and I’ll be your instructor for this course. I hope you’ve been looking forward to this

as much as I have.

This course will provide you with a basic working knowledge of life cycle assessment, or “LCA” for

short. Now, you won’t become a certified LCA practitioner in only nine weeks. However, you will

learn how to construct LCA studies that provide transparent results, to build basic LCA models in

spreadsheets, and to collect, analyze, and interpret environmental data in a structured manner for

better decisions.

But perhaps most importantly, you’ll learn that -- whatever the product -- everything has

environmental impacts and that understanding these impacts requires sound data and thorough

analysis. If you stick with me, you’ll be equipped with the basic skills to conduct such analyses and

begin answering environmental questions of your own.

So what exactly is LCA? LCA is a method to assess the environmental impacts of a product, process,

or service that involves four major steps:

1. Determine the goals and scope of the LCA;

2. Compile an inventory of energy and mass

inputs and outputs across all relevant life

cycle stages;

3. Evaluate relevant environmental impacts

associated with the life-cycle inputs and

releases; and

4. Interpret the results to lead to a more

informed decision.

Let’s first discuss what is meant by “life cycle stages” using this plastic bag as an example. In this

course, we’ll refer to five distinct stages of the product life cycle:

1. Raw materials acquisition, which includes processes related to raw materials extraction and

refining. For our plastic bag, which is made of a plastic called high-density polyethylene or

“HDPE” for short, raw materials acquisition would include extracting and processing natural

gas and transporting it to a chemicals plant.

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2. Manufacturing, which includes processes that convert raw materials to finished products.

In our case, plastic bags are manufactured by producing plastic pellets, melting them into a

film, and forming the bags.

3. Distribution, which includes transporting and stocking products for consumption. For

example, our plastic bag will be shipped from the manufacturer to a grocer.

4. Use/reuse, which is the stage where products perform a useful service to the consumer. In

our case, the plastic bag will carry our groceries home. Some consumers might also reuse

the bag for additional shopping trips or as a garbage can liner, which is why we often include

reuse in the use phase as well.

5. Stage 5 is the end of life stage, where products enter the waste management system.

Depending on local waste management practices, the plastic bag might be recycled,

landfilled, or incinerated to generate energy.

So what is meant by “relevant impacts?” As you’ll learn in this course, an environmental impact is a

consequence associated with inputs and outputs of energy and mass across the product life cycle.

For example, the combustion of diesel fuel in the trucks that transport plastic bags to the grocer

releases carbon dioxide, which leads to global warming. When conducting an LCA, we strive to

include all non-negligible impacts so that informed decisions can be made and any tradeoffs

between impacts are made explicit.

Consider again this plastic bag. Many jurisdictions have banned plastic

bags at grocery stores in an effort to reduce litter. However, several

LCA studies have shown that if consumers shift to paper bags, more

diesel trucking might be required. Why is that? It’s because a paper

bag takes up more space than a plastic bag, and therefore more trucks

might be required to bring the same number bags to the grocer. So in

this case, one tradeoff of a shift from plastic to paper grocery bags

might be that plastic litter is reduced but diesel fuel use and emissions

are increased.

This case teaches us two important lessons. First, an LCA can reveal that, while we think we’re

making a green choice, environmental impacts may shift based on the consumption choices we

make. That’s why it’s important to consider all relevant impacts in an LCA; otherwise such shifts in

impacts might be missed when we’re evaluating our options. Second, consideration of all life cycle

stages allowed for identification of unintended consequences. That is, a reduction in plastic litter in

the end of life stage might come at the cost of increased diesel fuel use in the distribution stage. If

we just focused on non-biodegradable litter, surely paper bags would look greener than plastic. It’s

only by looking at all life cycle stages did we see that paper bags might make things worse in the

distribution stage. So you see that even the simple case of plastic versus paper bags involves

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environmental tradeoffs. With proper application of the LCA method, however, these tradeoffs are

made visible so we can make the most informed decisions.

You may be wondering how LCA is used in the real world, or, more directly, how you might use LCA

after completing this course. If you’re an engineer, LCA can help you choose materials and design

features that lead to greener products and technologies. If you’re a policy maker, LCA can help you

design public policies and incentives that improve sustainability without simply shifting

environmental problems from one type of impact to another. If you’re a consumer, LCA can arm you

with data and results that guide you to greener purchasing decisions. And no matter what you do,

LCA can give you a healthy degree of skepticism of the environmental claims that are so often made

without hard data and through analysis to back them up.

Let’s wrap up with an overview of what you can expect. Each lecture will

introduce a new concept, which will be reinforced through online quizzes,

homework, and the course notes. I believe LCA is best learned by

jumping in hands on, so in this course you’ll build an LCA model of a

simple product that you should all be familiar with … a bottled soft drink.

No special LCA software packages will be required; all that is needed is a

spreadsheet.

Each week you’ll be developing a new section of the model that relates to

that week’s lecture material, so by the end of the course you’ll have built

a complete bottled soda LCA. While the product is fairly simple, by

building the model across all life cycle stages and impacts, you’ll acquire the skills and perspectives

that should allow you to move on to more complex products after you complete this course.

Lastly, we’ll also occasionally offer separate videos describing real-world LCA studies that highlight

key material, so you can easily see how the theory relates to practice in real time.

I’m looking forward to this experience together. See you next time!

Additional notes

Correction: In the lecture video, I say “Compile an inventory of energy and material inputs and

environmental outputs across all relevant life cycle stages” when I really should have said “Compile

an inventory of energy and mass inputs and outputs across all relevant life cycle stages.” The goal of

LCA is to include all relevant mass flows, whether they are materials, resources (such as water or

biomass), pollutants to the environment, or products to society.

Correction: As you’ll see in Homework 1, natural gas must be extracted and processed before it can

be used in industrial systems. Processing is aimed at improving natural gas quality by removing

impurities. In the lecture video, I say “… raw materials acquisition would include extracting natural

gas and transporting it to a chemicals plant” when I really should have said “…raw materials

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acquisition would include extracting and processing natural gas and transporting it to a chemicals

plant.”

Correction: In the lecture video, I say “…an environmental impact is an adverse consequence

associated with inputs of resources and outputs of pollutants across the product life cycle” when I

really should have said “…an environmental impact is a consequence associated with inputs and

outputs of energy and mass across the product life cycle.” In reality, not all impacts arising from life-

cycle systems are negative. For example, a biomass system may sequester carbon dioxide from the

air and a remediation technology may remove hazardous pollutants from soil to make it safe again.

By quantifying all flows of mass and energy across a life-cycle system (and not just resource and

pollutant flows), LCA enables us to explore both adverse and positive impacts associated with these

flows. While we’ll focus exclusively on adverse impacts in this course, it is helpful to keep in mind

that LCA can just as easily quantify positive impacts.

Starting in week 3, you’ll begin building your very own LCA model of a bottled soft drink packaged in

plastic. See the “Course Project” section of the course website for more details. (The “Course

Project” section can be accessed by clicking on “Start Here!” or “Course Information” in the left

hand navigation pane on the course website.) Note also that I say “bottle of soda” in the lecture

video, which is a term used commonly in North America to refer to bottled soft drink.

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Lecture 1 Supplement Transcript

Welcome to our first lecture video supplement. Supplements such as this one have been added to

improve the course content and to provide additional discussions and examples to help you better

understand the topics covered in our core lecture videos.

In this first supplement, I’d like to give you a better idea of what to expect in this course as well as

some tips for success based on past course offerings.

First, I highly encourage you to review all of the material provided on the “Start Here!” section of

the website, which includes important information on policies, our course schedule, and further

details on the project.

Let’s take a look at the course schedule, which lists the topics we’ll cover in this course. The first

two weeks of this course will cover core skills that are necessary for sound LCA, such as constructing

unit process inventories, conducting energy and mass balances, and understanding data

conventions. These are the essential building blocks of an LCA. In Week 3, we’ll begin applying

these building blocks to learn the LCA methodology and to start constructing our very own LCA

models.

For more information on the LCA models, let’s take a look at the “Project” section of the website,

which describes the scope and intent of the course project. You’ll be exposed to two different LCA

models, both of which will be developed in spreadsheets.

The first is an LCA model for a plastic grocery bag that has been developed by the course staff. The

spreadsheet consists of different tabs that contain the various elements of the LCA model, which

we'll reveal in week by week fashion as we learn each step of the LCA methodology. Think of our

plastic bag LCA model as an example of how your bottled soft drink LCA model should be

constructed and how it should function, and refer to it often for inspiration and guidance.

The second is the LCA model for a bottled soft drink, which you’ll be developing yourself. Starting

in Week 3, you’ll be given tasks to construct your model based on recent lecture topics.

Furthermore, some of the homework assignments will contain exercises that help you build specific

portions of your model. By following the development of our plastic bag LCA model, and by

completing the homework and modeling tasks to construct your own bottled soft drink LCA model,

you’ll gain valuable “hands on” experience. The course staff will also post regular “solutions” for the

bottled soft drink model, which you can use to check the accuracy of your spreadsheet.

I’d also like to draw your attention to the discussion forums. If you’ve taken Coursera courses in the

past, you’ll know that the discussion forums can be a great way to enhance your learning

experience, but that they can also become unwieldy to navigate over time. To minimize “forum

fatigue,” we’ve established specific sub-forums for different types of posts. For example, there is an

“Assignments” sub-forum that you can use for posts related to specific homework assignments.

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There is also a “Lectures” sub-form for posts related to the lectures each week. Please review the

available sub-forums and be sure to choose the most logical sub-forum first before you make a post.

If we all do this, the discussion forums should be much more useful and manageable for everyone.

You’ll also notice that I’ll be suggesting discussion topics each week. These questions should be fun

to explore together, and will help us all think about how LCA relates to our own lives and the

sustainability problems we’d like to solve. While participation isn’t mandatory, I highly encourage

you to join in or review the posts whenever you can. The topics have been selected from some of

the most interesting and thought-provoking discussions in past offerings, so I’m sure you’ll enjoy

them.

Finally, here are some quick tips for getting the most out of this course and earning a high grade:

First, if you need to improve your spreadsheet skills, please use the first two weeks of this course to

do so. We’ve provided a specific discussion sub-forum that students can use to share spreadsheet

tips and tricks. Once we introduce the LCA models in Week 3, you may find it difficult to keep up if

you’re not comfortable with spreadsheets.

Second, while the first two weeks of this course are somewhat basic, the level of difficulty and

required effort will increase in Weeks 3 – 9 when we move into the LCA method and modeling.

Therefore, you should plan for a greater time commitment in the last 7 weeks of the course.

Third, please take full advantage of the discussion forums for seeking out help and providing help to

others. In past offerings, many questions related to homework assignments, project tasks, and LCA

concepts were collectively answered by students through ongoing discussion. And you may find

that assisting others deepens your own understanding of the course material.

Fourth, while I encourage students to exchange ideas, please try to complete the assignments and

project tasks on your own before seeking out answers online. Learning through “trial and error” is

important for any course, and especially for the LCA methodology given its many details and

nuances.

Fifth, and finally, try to review some of the additional resources that are indicated in the lecture

notes. This course only covers basic LCA concepts, but the additional resources we mention provide

a wealth of information that can bring you closer to LCA proficiency if you have the time to review

them.

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Lecture 2: Understanding unit processes Transcript

Welcome back! Today we’ll begin learning about the data structure of an LCA, starting with LCA’s

most fundamental building block: the unit process model. But first let’s quickly review what we

learned yesterday.

The product life cycle can be divided into five major stages: raw materials acquisition,

manufacturing, distribution, use, and end of life. In our plastic bag example, we learned that raw

materials acquisition covers the extraction, processing, and transportation of natural gas, which is

then converted into ethylene. Ethylene is converted into HDPE and formed into a bag in the

manufacturing stage. Next, the bag is distributed to retail stores, where it is filled with groceries to

transport food home during the use stage. Lastly, at the end of life stage, the bag is either recycled,

landfilled, or incinerated to generate energy.

We also learned that a key step in all LCAs is to compile an inventory of energy and mass inputs and

outputs across all relevant life cycle stages. So how do we compile such inventories? We do so by

modeling the product life cycle as a series of unit processes. The ISO 14040 standard for LCA

defines a unit process as the “smallest portion of a product system for which data are collected

when performing a life-cycle assessment.”

This is a picture of a generic unit process. On the left we have inputs of energy and mass required to

generate a useful product output. On the right we have the outputs of environmental emissions

and co-products that are associated with the process, along with the product output itself. From

now on, we’ll refer to the inputs and outputs associated with a unit process as the unit process

inventory, which is a term commonly used by LCA practitioners.

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To visualize how we use unit processes, let’s look more closely at the manufacturing stage of our

plastic bag. The first step is to convert processed natural gas into ethylene, which we’ll represent by

this first unit process model.

The second step is to convert ethylene into HDPE pellets, which we’ll represent with this second unit

process.

The third step is to melt the HDPE pellets, extrude a film, and form the bags in the bag production

process.

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As you’ve probably guessed, to construct a complete LCA model for the plastic bag, we’d need to

develop and apply unit process models to capture all unit processes at each life cycle stage. We can

then sum all the unit process inventories to quantify the total environmental footprint of the bag life

cycle. You’ll learn how to do this later; for now, you may be asking yourself how such unit process

inventories and life-cycle models can be developed without detailed engineering knowledge.

Fortunately, we have we have databases and literature sources to help us in this regard.

For example, a unit process inventory I obtained from the literature for converting ethylene to HDPE

pellets looks like this. If this level of detail seems a bit daunting, don’t worry … you’ll learn how to

work confidently with unit process inventory data in this course.

Fortunately, the LCA community has adopted a number of conventions for organizing unit process

inventories to make our lives easier. These conventions help ensure that inventories are intuitive

and use the same data structure for easy transfer between researchers and databases. So while the

unit process inventory for HDPE pellets may look complicated, thanks to this structured organization

of data it is actually simpler than it looks.

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First, many unit process inventories refer to inputs and outputs as “flows” or “exchanges.” In this

course, we’ll use the word flows. Unit process inventories are essentially comprised of flow

information listed in rows.

In many LCI databases, flows are further characterized as flows to or from nature or to or from the

technosphere. In this course, we’ll adopt this convention and organize our inventories into the

following four types of flows:

1. Inputs from nature,

2. Inputs from the technosphere,

3. Outputs to nature, and

4. Outputs to the technosphere

Inputs from nature are probably pretty obvious: they include flows such as crude oil extracted from

the ground or corn harvested from a field. Conversely, outputs to nature include pollutants and

wastes that are released back into the environment. Inputs from and outputs to the technosphere

refer to any flow of energy or mass that originates from a man-made process. For example, diesel

fuel is produced from crude oil in a petroleum refinery, but we don’t find diesel fuel occurring

naturally in the environment.

For our plastic bag, the extraction of natural gas describes a flow from nature. After extraction,

natural gas must be processed to remove impurities. In the next unit process, that processed

natural gas is converted into ethylene. Here, because the natural gas came from a pipe and not the

ground, it is considered an input from the technosphere. Because ethylene is an intermediate

product that is used by other unit processes, it is considered an output to the technosphere.

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Why do we need to distinguish between flows to and from nature and flows to and from the

technosphere? Besides helping us better visualize the origins and destinations of flows in our

inventory, identifying flows to and from nature allows us to quantify environmental impacts in the

life-cycle impact assessment step of an LCA. We’ll learn more about impact assessment later in the

course. For now, let’s get used to organizing our unit process inventories in this way.

Lastly, we’ll use SI units to describe all flows in our unit process inventories in this course. For

example, mass will be expressed in grams, energy in joules, and volume in liters. Some of you may

wish to review the SI system before proceeding with this course; further readings are provided in

this week’s course notes.

Additional notes

Correction: Here we’ve added in the processing step that was omitted in the lecture video. See

Note 1.2.

Correction: Here again I should have referred to “energy and mass inputs and outputs” instead of

“energy and materials inputs and environmental releases.” See Note 1.1.

The ISO 14040 series of standards are a set of “best practice” rules and guidelines for conducting

LCA that have been developed and revised by the international LCA expert community since the

1990s. We’ll be referring to these standards often throughout the course. We’ll use them to discuss

the “step by step” nature of an LCA and to reinforce best practices. Unfortunately, the actual

standards documents are not freely available to the public. However, you’ll get a basic

understanding of these standards through our class materials and through the additional readings

we’ll suggest and assign. There is no need to purchase the standards to benefit from the content of

this course. For those who would like to learn more about the formal standards, please visit the

International Organization for Standardization (ISO) website at:

http://www.iso.org/iso/home/store/catalogue_tc/catalogue_tc_browse.htm?commid=54854

Correction: Here we’ve changed “materials and energy” to the more general and correct “energy

and mass.” See Note 1.1.

For clarity, we’ve specified that it is processed natural gas that is converted into ethylene.

Processed natural as is a flow from the technosphere. This change was necessary to reduce

confusion in past course offerings as to whether natural gas from nature or natural gas from the

technosphere is used in ethylene production. See the Lecture 2 supplement video for more

information.

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http://www.iso.org/iso/home/store/catalogue_tc/catalogue_tc_browse.htm?commid=54854

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To give you a sense of the detail contained in a typical life-cycle inventory (LCI), and the

documentation that explains and supports such inventories, take a peek at the following report.

You’ll use some of these data in this course to build you spreadsheet LCA model of a bottled soft

drink. There is no need to carefully read this report now, or to understand its contents. But looking

it over will give you an idea of the types of information sources that we rely on when constructing

LCA models.

Franklin Associates (2009). Life Cycle Inventory of Three Single-Serving Soft Drink Containers:

Revised Peer Reviewed Final Report. Prepared for the PET Resin Association. Eastern

Research Group. Prairie Village, KS. http://www.container-recycling.org/assets/pdfs/LCA-

SodaContainers2009.pdf

Similar to the reasons for Note 2.5, here we’ve added “After extraction, natural gas must be

processed to remove impurities. In the next unit process, that processed natural gas is converted

into ethylene.” See the Lecture 2 supplement video for more information.

There are many useful resources online for reviewing conversions from Imperial and US Customary

units into International System (SI) units. While we’ll use SI units in this course, you are likely to

encounter data sources in your project – and in your LCA careers – that are expressed in Imperial

and US Customary units. Here are some conversion resources that the course staff recommends.

International System of Units from NIST. Essentials of SI units, background, and

bibliography. http://physics.nist.gov/cuu/Units/

A concise summary of the International System of Units from BIPM.

http://www.bipm.org/utils/common/pdf/si_summary_en.pdf

OnlineConversion.com Convert just about anything to anything else.

http://www.onlineconversion.com/

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http://www.container-recycling.org/assets/pdfs/LCA-SodaContainers2009.pdf

http://www.container-recycling.org/assets/pdfs/LCA-SodaContainers2009.pdf

http://physics.nist.gov/cuu/Units/

http://www.bipm.org/utils/common/pdf/si_summary_en.pdf

http://www.onlineconversion.com/

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To ensure that you understand the concept of a unit process and the distinctions between inputs

from nature, inputs from the technosphere, outputs to nature, and outputs to the technosphere,

let’s step through the plastic grocery bag example in a bit more detail. Furthermore, let’s try

working backwards in the life cycle so that the different types of flows are clear.

Let’s first consider the factory that makes plastic grocery bags. The production of plastic bags

involves melting HDPE pellets, extruding the melted plastic into a film, and cutting the film into the

shape of a bag. For simplicity, we’ll include these steps in one unit process that we’ll label “HDPE

Bag Manufacturing.” The output of this unit process is an HDPE grocery bag. Since this bag will be

shipped to a grocer for use by consumers, we’ll label this flow as an output to the technosphere.

To manufacture the plastic bag, the bag factory requires inputs of HDPE pellets, which are a man-

made product. Therefore, we’ll label this flow as an input from the technosphere. Of course, there

are many other flows associated with the bag factory, such as inputs of energy to power processing

equipment and outputs of mass, including emissions of air and water pollutants. For now, we’ll

ignore these flows to keep things simple.

The production of HDPE pellets occurs at a chemical factory, which converts ethylene—another

man-made product—into HDPE resin. Let’s label this unit process as “HDPE Resin Manufacturing,”

and denote the flow of ethylene into the factory as an input from the technosphere.

Ethylene is manufactured from processed natural gas at an olefins plant, which we’ll label as

“Ethylene Manufacturing” in our simple example. Remember that processed natural gas does not

come directly from nature; rather, it is made by removing impurities from raw natural gas. Hence,

we’ll label this flow as an input from the technosphere.

To produce processed natural gas, another unit process is required that we’ll call “Natural Gas

Processing.” This unit process requires extracted natural gas, which is yet another technosphere

product that we get as an output from natural gas drilling operations.

Finally, let’s label the natural gas drilling unit process as “Natural Gas Extraction.” The input to this

unit process is natural gas from the ground, which is an input from nature. Observing the entire

system, it’s now clear that to manufacture the HDPE grocery bag, a series of different unit processes

are required. These unit processes are linked by technosphere flows that can eventually be traced

back to an original exchange with nature.

Moving forward, you’ll be developing more detailed inventories of energy and mass flows across

unit process systems. For example, we could further include the input of processed natural gas to

be combusted for heat in HDPE resin manufacturing as well as the smokestack emissions of carbon

dioxide and other air pollutants that arise from natural gas combustion. Here, emissions of carbon

dioxide would be labeled as a flow to nature.

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As you’ll come to learn in future lectures, specifying and tracking types of flows in unit process

systems is critical from an accounting perspective, because the environmental impacts of a system

are related to its flows to and from nature. In our case, you can probably imagine that the sources

of impact in our system so far are related to the resources we extract from the ground and to the

pollutants we reject into the air.

You’ll get more practice with labeling flows in Homework 1.

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Lecture 3: Constructing unit process inventories: Part 1 Transcript

Welcome back. In today’s lecture, we’ll dive deeper into how unit process inventories are

structured for ease of interpretation and ease of transfer between researchers and databases. Last

time I introduced the four types of flows we’ll use in our inventories:

1. Inputs from nature,

2. Inputs from the technosphere,

3. Outputs to nature, and

4. Outputs to the technosphere

Let’s take a closer look at the complete unit process inventory for converting ethylene to HDPE

pellets. I’ve created this inventory in a spreadsheet in the same way that you’ll be creating unit

process inventories in your spreadsheets. As we discussed last time, flow data appear in rows of the

inventory table, and they are organized into our four types of flows. In this course, the first column

in the inventory will always contain the flow type, starting with inputs from nature, followed by

outputs to nature, inputs from the technosphere, and outputs to the technosphere.

The second column will always contain the name of the flow, which, by convention, uses standard

names for products (e.g., diesel fuel), pollutants (e.g., carbon dioxide), and resources (e.g., water).

In many cases, the name of the flow will be taken directly from the LCI database from which we get

the flow data. It is critically important to use standard flow names and to use them consistently so

we can link up unit process inventories correctly when creating our LCA model.

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The third column contains additional information on the origins and destinations of flows to and

from nature, which we’ll refer to in this course as the flow “category.” Inputs from nature will

always be denoted as “resources” in the category column, while outputs to nature will be denoted

by the medium to which they are released. There are three media we’ll denote: air, water, and land.

The fourth column is reserved for subcategories of the third column. For example, the

subcategories for outputs to air include emissions to areas with low population density and

emissions to areas with high population density. And the subcategories for resources include

resources extracted from in the ground (like coal), from water (like drinking water), or from the

biosphere (like wood). In this course, we’ll use a standard set of subcategories to describe inventory

flows. I’ve provided the list of subcategories we’ll use in the lecture notes because there are too

many to mention here.

Why do we need information on flow categories and subcategories? The main reason is that this

information helps us better quantify the environmental impacts caused by flows to and from nature

in the life-cycle impact assessment step of an LCA. For example, you might easily imagine that a

pollutant emitted in a high population density area will have a higher human health impact than if it

were emitted in a low population density area where there are fewer persons exposed. We’ll learn

more about impact assessment later in the course.

I also want to mention that in many LCI databases, flows to and from nature are referred to as

“elementary flows.” So you aren’t confused by this, moving forward we’ll also use this label for our

flow types in unit process inventories.

By convention, we’ll always use the category “product” for flows to and from the technosphere.

This makes sense when we consider that once a resource enters the technosphere, it is converted

into different forms of products for further use by industry and society.

The fifth column in our inventory table will always contain a numerical value and our sixth column

will always contain the unit in which that value is expressed. Where do these values come from?

Typically through some combination of direct measurement, engineering estimation, or literature

sourcing. Knowing where the data come from and how to determine their quality is a critical step in

any credible LCA, and one which we’ll discuss later in this course. For now, just assume that all data

in our inventory come from reliable sources.

The numerical value expresses the amount of each flow that corresponds to the units of product

output listed in the inventory. For example, our product output is one kg of HDPE pellets, and the

emissions of CO2 to air associated with the production of one kg of HDPE pellets is 100 g CO2.

Here the product output is expressed in units of mass; however, the product output in a unit process

inventory can be expressed in many different units depending on what goods or services are

provided. The unit process of pellet production logically has product outputs expressed in units of

kg, which corresponds to physical production. However, a unit process for a diesel freight truck

might have product output expressed in units of kilogram-kilometers, which corresponds to the

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useful service provided by trucking. Or a unit process for electricity production might specify kWh of

electricity produced, which is the useful output of that process. You’ll get exposed to all of these

types of outputs and more moving forward.

Lastly, our simple example inventory focused on single unit process, but you’ll often encounter unit

process inventories that combine several unit processes into one aggregated inventory. For

example, rather than finding every unit process step in the manufacture of the bag – which would

include natural gas extraction, transportation, conversion to pellets, and bag forming – you might

just find a single inventory for all of these processing steps combined. This aggregated inventory

would contain the sum of all included unit process flows to and from nature.

Aggregated inventories are quite common in practice, because they can simplify a complex chain of

processes for general use. Aggregated inventories also protect private entities who may not want to

release detailed unit process data on each step in their production chain. The downside is that one

loses visibility on which of the aggregated processes might be “hot spots” and often the ability to

recreate the inventory using process-level knowledge.

How can you tell if you have an aggregated inventory? Good databases will tell you this in their

inventory documentation. You’ll notice terms like “cradle to gate,” which refers to flows from

nature to a certain point in the technosphere, or “gate to gate,” which refers to flows between

points in the technosphere. All unit processes included in the aggregated inventory should be listed

explicitly.

Additional notes

When you gain access to the spreadsheet LCA models in Week 3, the structure and contents of this

unit process inventory will make more sense. For now, just concentrate on following the logic for

each column, and how that information will be useful when you link together many different unit

process inventories to construct a systems model.

In the models we’ll use in the current offering of this course, the order of flows has been updated as

follows “In this course, the first column in the inventory will always contain the flow type, starting

with inputs from nature, followed by outputs to nature, inputs from the technosphere, and outputs

to the technosphere.” The updated order is reflected in the spreadsheet figure as well.

In our plastic bag and bottled soft drink LCA models, we’ll use a simplified set of categories and

subcategories for all flows. As discussed in the lecture video, we’ll adopt the convention of using

the category “Product” for all flows to and from the technosphere. Product flows will not be further

divided into subcategories.

Inputs from and outputs to nature – that is, elementary flows – will be labeled using the following

simplified set of categories and subcategories in our inventories.

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Elementary flow type Category Subcategory

Inputs from nature Resource Biotic (from biosphere)

In air

In ground

In water

Outputs to nature Air High population density

Low population density

Land Unspecified

Water Unspecified

There are several important caveats to our simplified selection of elementary flow categories and

subcategories.

First, because this is a basic introductory course, the course staff has chosen to keep our flow

conventions simple. Once you get in the habit of labeling flow categories and subcategories at a

basic level, you’ll be well equipped to use more detailed protocols for labeling of flow categories and

subcategories in the future. To get an idea of the level of detail that many LCA practitioners use

when conducting LCAs and working with LCA databases, take a look at the following reports:

Overview and methodology: Data quality guideline for the ecoinvent database version 3

(2013), Weidema B P, Bauer C, Hischier R, Mutel C, Nemecek T, Reinhard J, Vadenbo CO,

and Wernet G.

http://www.ecoinvent.org/fileadmin/documents/en/Data_Quality_Guidelines/01_DataQual

ityGuideline_v3_Final.pdf

The ecoinvent database is used widely by LCA practitioners and within various LCA software

packages. Take a look at Table 9.1, page 63, which lists the compartments and sub-

compartments (i.e., categories and subcategories) used for elementary exchanges (i.e.,

flows) in the ecoinvent database. You’ll notice that many more subcategories are available

for defining flows with greater precision in practice.

U.S. LCI Database Project –User’s Guide, National Renewable Energy Laboratory (2004).

http://www.nrel.gov/lci/pdfs/users_guide.pdf.

The U.S. LCI data contains publicly-available life-cycle inventory (LCI) data that are reported

using a standardized unit process inventory structure. We’ll make use of some of the data

from the U.S. LCI database in this course. Take a look at the table on page 16. You’ll notice

many categories and subcategories that are similar to those in the ecoinvent database, but

also some differences. Again, the subcategories listed allow for greater precision when

documenting flows.

Second, even though the categories and subcategories included in many LCA databases can be quite

detailed, in practice many LCI data sources do not include such detail in their reporting. For

example, one may find that pollutant outputs to water are reported, but that this flow is not further

specified as an output to a lake, ocean, or river. Thus, in many LCI data sources, the most common

http://www.ecoinvent.org/fileadmin/documents/en/Data_Quality_Guidelines/01_DataQualityGuideline_v3_Final.pdf

http://www.ecoinvent.org/fileadmin/documents/en/Data_Quality_Guidelines/01_DataQualityGuideline_v3_Final.pdf

http://www.nrel.gov/lci/pdfs/users_guide.pdf

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subcategory you’ll encounter is “unspecified.” The publicly-available data sources we’ll use in our

course projects do not contain such detailed specification of subcategories, either. This is another

reason we’ll keep our labeling of flow categories and subcategories simple in this course!

Third, as discussed in the lecture video, the primary benefit of identifying categories and

subcategories for elementary flows is that it can enable more sophisticated estimation of life-cycle

impacts. In your course project, the labeling of air emission flows with the subcategories “high

population density” and “low population density” can enable the estimation of human health

impacts to both types of demographic areas. We’ll discuss impact analysis later in this course.

In the spreadsheet models, and throughout this course, numbers will be expressed using the U.S.

numeric convention where commas separate thousands and the dot (or “decimal point”) is the

decimal separator. For example, the number one thousand two hundred and one-tenth is written

1,200.1 in the US numeric convention. However, when working with spreadsheets in this course,

you can change the numeric format in which data are displayed in your spreadsheet software to

match your local numeric convention.

We’ve added in the term “to and from nature” here, because the process of aggregation eliminates

intermediate flows to and from the technosphere in the system. See the Lecture 3 supplement video

for a simple example of unit process inventory aggregation.

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In this video supplement, we’ll use the simplified system of unit processes for HDPE grocery bags

that we discussed earlier. Below the figure I’ve added in an inventory table that contains a

simplified list of flows for each unit process. In this example, we’ll only track a few flows to illustrate

how inventory aggregation works. However, you’ll practice aggregating much more complicated

inventories later in this course.

Let’s start with the unit process inventory for HDPE Bag Manufacturing. In this simplified inventory,

its only input is 1.02 kilograms (kg) of HDPE pellets and its only outputs are 1 kg of HDPE grocery

bags and 0.5 kg of carbon dioxide (CO2) emissions to air. By convention, the flows of HDPE pellets

and HDPE grocery bags are labeled as product flows from and to the technosphere, respectively.

Also by convention, the flow of CO2 is labeled as a flow to nature, or elementary flow, and to air.

Now let’s take a closer look at the Natural Gas Extraction process. Its only input is 1.08 kg of in-

ground natural gas, which is a resource flow from nature. Its only outputs are 1.05 kg of extracted

natural gas and 0.02 kg of CO2 emissions to air. You’ll notice that the next unit process, Natural Gas

Processing, requires 1.05 kg of extracted natural gas as a product input. If you look carefully at the

rest of the unit process inventories, you’ll also notice that the product mass output of each unit

process matches exactly the product mass input that is required by the next unit process.

This means that my unit process inventory data have all been properly scaled to produce the mass

flows necessary to ultimately manufacture 1 kg of HDPE grocery bags. You’ll learn how to scale unit

process inventories later in this course. For now, you just need to understand that since the product

mass flows have been balanced across all unit processes, we can simply add up the flows of CO2 to

arrive at a total CO2 emissions footprint for the system.

In this example, to ultimately produce 1 kg of HDPE grocery bags, the unit processes in the system

will collectively emit 2.02 total kg of CO2 to the air. One can also scan the inventory data to

determine which unit processes account for the greatest share of CO2 emissions; namely, HDPE Bag

Manufacturing, HDPE Resin Manufacturing, and Ethylene Manufacturing.

In a similar fashion, I could also add up all resource inputs from nature in the system, which, in this

case, would amount to 1.08 kg of in-ground natural gas required to ultimately produce 1 kg of HDPE

grocery bags.

In fact, using these totals I could create a single inventory for the entire system, which would just

contain the inputs from nature, the outputs to nature, and the product output of the system. Such

an inventory is known as an aggregated unit process inventory, because it represents the sum totals

of flows to and from nature associated with all unit processes within its system boundaries. These

flows are expressed relative to the mass quantity of the final product output from the system, in our

case, 1 kg of HDPE grocery bags.

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Another way to think of aggregation is that I’ve drawn a boundary around the entire system and I’ve

only counted the flows that cross this boundary in my aggregated inventory; namely, flows from and

to nature and flows of the final product to the technosphere. All of the intermediate product flows

in the system do not cross this boundary, and are therefore not counted. This makes sense when

you observe that all of these flows will simply cancel out; for example, the ethylene output from the

Ethylene Manufacturing unit process will subsequently be consumed as a product input by the HDPE

Resin Manufacturing unit process.

As you gain more practice with LCA, you’ll notice that many data sources contain aggregated unit

process inventories. Aggregation can be done as a matter of convenience, since it can be quite time

consuming to work with inventories for all intermediate unit processes in a product system, even for

simple products. Aggregation is also often done for confidentiality reasons, so that data on

individual factories or processing steps within a system are not revealed to the public. For example,

assume that you have obtained only the aggregated inventory for 1 kg of HDPE grocery bags. While

you would know the total CO2 emissions to air from the “cradle to gate” system, you would have no

way of identifying HDPE Bag Manufacturing, HDPE Resin Manufacturing, and Ethylene

Manufacturing as the largest contributors to this CO2 footprint.

In our spreadsheet models for our plastic bag and bottled soft drink, we’ll make use of aggregated

inventories as a matter of practicality and convenience. However, we’ll be sure to carefully

document the system boundaries associated with the aggregated inventories we use, so that we and

others can understand which intermediate unit processes have been included therein.

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Lecture 4: Constructing unit process inventories: Part 2 Transcript

In this course, we’ll mostly be using data from available databases and literature sources that have

already been neatly organized into structured unit process inventories. In practice, however, LCA

analysts must often construct new unit process inventories by gathering data from various sources.

Today we’ll practice constructing our own unit process inventories to help you gain proficiency in

data compilation and analysis. We’ll also learn an important convention for ensuring we can scale

our unit process inventories for use in different LCA models.

Let’s suppose we are conducting an LCA of a residential hot water heater that is fueled by natural

gas. In today’s example, we’ll be constructing the unit process inventory for the use stage of the

water heater, which refers to its operation. I’ve gathered some data on the average natural gas

consumption and hot water generation of US residential hot water heaters from the U.S.

Department of Energy and the U.S. Environmental Protection Agency.

The average U.S. residential hot water heater consumes 27 gigajoules (GJ) of natural gas per

year

The average U.S. residential home uses 64,000 liters of hot water per year

As you gain more experience with LCA, you’ll probably notice that there are typically more data

available on the energy consumption of different processes and products than there are for other

flows such as water pollutant releases and solid waste generation. The reason for this is quite

simple: energy use is easy to track because it is something we pay for and monitor closely.

Moreover, many regional governments track energy supplies and demands as part of energy policy

planning. When we have energy data, it is often fairly easy to derive air emissions data as well

based on combustion emission factors for various fuels, which are readily available.

For example, since I know our residential water heater uses natural gas, it was fairly easy to find the

following air pollutant emission factors for natural gas combustion in residential appliances. These

came from the US Environmental Protection Agency’s AP-42 emission factor reports:

56,000 grams of carbon dioxide (CO2) per GJ of natural gas combusted

44 grams of nitrogen oxides (NOx) per GJ of natural gas combusted

19 grams of carbon monoxide (CO) per GJ of natural gas combusted

4 grams of particulate matter (PM) per GJ of natural gas combusted

The lesson here is that generating a unit process inventory that contains data on energy flows and

energy-related air emissions flows is often possible when we can’t find existing unit process

inventories in LCI databases or literature sources. Unfortunately, data on flows of water pollutants,

solid waste generation, and other elementary flows that are not related to a unit process’s energy

use are typically much harder to come by outside of LCA databases. The reason for this is also quite

simple: these flows are harder to monitor and record in practice, and many firms do not release

such data publicly.

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So now I have all the data in hand to construct a simple unit process inventory for hot water heater

operation. First, I determine the annual air emissions associated with the natural gas combustion by

simple multiplication. When I have these data, I can now create a simple inventory using the

structure we’ve discussed. Processed natural gas is a flow from the technosphere, and the air

emissions are flows to nature. Lastly, my product is 64,000 liters of hot water.

While this inventory is reasonable for the average U.S. home, is it highly useful in its current form?

In other words, can I easily use it in other analyses, such as to analyze hot water generation for an

LCA of a home dishwasher? For example, if a dishwasher uses less than 64,000 liters of hot water, I

can’t directly apply this inventory. Luckily, one useful convention for unit processes inventories with

single product outputs is that such outputs are expressed as multipliers of 1, for example, 1 liter of

hot water or 1 kWh of electricity.

Having a multiplier of 1 in our denominator makes for much easier scaling of unit processes to

different product output quantities. In the hot water example, let’s say I want to calculate the CO2

emissions associated with generating only 5,000 liters of hot water.

First I divide all inputs and outputs in my unit process inventory by the product output to get flows

per liter. Next, I recreate the inventory on this basis. Finally, I multiply by 5,000 liters to get the unit

process inventory for producing 5,000 liters of hot water.

You’ve just learned the simple but powerful concept of using multiples of 1 as single product

outputs to allow for easy scaling of unit process inventories in an LCA. Trust me, you’ll get much

experience with scaling inventories since rarely do we analyze neat units of 1 product output in real-

world systems.

But what if you have more than one product output in the inventory, for example, a process with

multiple co-products? The fact is we encounter unit process inventories with more than one

product output quite often in LCA because many real-world plants manufacture more than one

product at a time. Take for example the unit process inventory for 1 kg of general output from

petroleum refining, a process that converts crude oil into multiple product outputs such as gasoline,

diesel fuel, kerosene, and refinery gas.

Because this inventory contains flow information for more than one product output, we need some

way of assigning a portion of the inventory to each product flow. This process is so important in LCA

that it has its own name: allocation. In this particular inventory, the author indicates that allocation

of flows to individual product outputs can be based on the percent by mass indicated for each

product output. However, as you’ll learn later in this course, there are other ways to allocate flows

to multiple products in a system, such as assigning portions of the inventory to each product output

based on their economic value. Each allocation method has potential drawbacks, which we’ll

discuss in future lectures. For now, just be aware that you will encounter inventories with multiple

product outputs in practice, but that you’ll also learn to work with them effectively in this course.

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Additional notes

For those who may be interested in operational energy data for a wide variety of appliances and

devices, check out the U.S. Building Energy Data Book, which is where we obtained the average

natural gas use of U.S. residential hot water heaters (Table 2.1.17). Similar data are compiled by

other countries and regions in the world, and can be helpful for estimating unit process inventories

for the operation of common appliances and devices. In fact, we’ll use U.S. Building Energy Data

Book data for residential refrigerators to build our unit process inventory for the use phase (i.e.,

beverage chilling) in our bottled soft drink LCA model. http://buildingsdatabook.eren.doe.gov/

The U.S. Environmental Protection Agency’s AP-42 compendium of emission factors is an exhaustive

resource that can be used to estimate the air emissions from a wide range of combustion sources in

the absence of primary or secondary inventory data on unit processes with combustion. In our

residential hot water heater example, we used emission factors for natural gas combustion from

Chapter 1: External Combustion Sources, Section 1.4. While we won’t make further use of this data

source in this course, you may find it useful in the future for estimating the air emissions associated

with burning fuels in common processes across the residential, commercial, industrial, and transport

sectors. http://www.epa.gov/ttnchie1/ap42/

Correction: As in previous lectures, here I should have said “Processed natural gas is a flow from the

technosphere …” to be more precise. Also, note that the inventory you’re seeing in the lecture

video is very simplified, as it only contains a few flows to and from nature. We’ll work with a much

more comprehensive list of flows to and from nature in the standard unit process inventory that

we’ll use in our plastic bag and bottled soft drink LCA model.

To view an example of expressing product outputs in multiples of 1 in a unit process inventory for

ease of scaling, take a look at the unit process inventory for “corrugated product” in the U.S. LCI

database. Follow the steps below. Can you identify other unit process inventories that follow this

convention?

1. Go to http://www.nrel.gov/lci/

2. Click on the “Database” link in the left side navigation box

3. Select the checkbox for “Paper manufacturing” within the “Category” list

4. Select the checkbox for “Converted Paper Product Manufacturing”

5. Click on “Corrugated Product,” which appears in the list at right

6. Click on the “Exchanges” tab, and look for the “Corrugated Product” output

Correction: In the lecture video, I should have said “… for example, a process with multiple co-

products” instead of “… for example, a product with multiple co-products.”

4.1

4.2

4.4

4.5

4.3

http://buildingsdatabook.eren.doe.gov/

http://www.epa.gov/ttnchie1/ap42/

http://www.nrel.gov/lci/

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Check out these inventory data for yourself in the U.S. LCI database, and note how many flow data

are provided. Petroleum refining is a complicated process, with many co-products and emissions to

account for in an inventory. As you gain proficiency working with unit process inventory data, you’ll

be well equipped to understand and apply even the most complicated inventory data.



3. Select the checkbox for “Petroleum and Coal Products Manufacturing” within the

“Category” list

4. Click on “Diesel, at refinery (Petroleum refining, at refinery),” which appears in the list at

right

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In this video supplement, we’ll further explore the concept of unit process inventory scaling in an

LCA. Furthermore, we’ll use the simplified unit process system for manufacturing of HDPE grocery

bags from previous supplemental videos to illustrate this concept.

Recall that each unit process in the system has a unit process inventory, which documents its flows

to and from nature and to and from the technosphere. You may be wondering how we obtain such

flow data to construct a unit process inventory in practice. Typically, such data are compiled from

real-world facilities and operations, and can be based on direct process measurements, engineering

estimation, or annual facility reporting.

Take for example the HDPE bag manufacturing plant. It would typically be straightforward to gather

data on the total tons of HDPE grocery bags manufactured at this plant in a year, since any business

should know this quantity. It can also be straightforward to gather data on some other annual flow

quantities, such as the amounts of natural gas, electricity, HDPE pellets, water, and other production

inputs that are purchased by the plant. Through process-level measurements and/or engineering

estimation, it can also be possible to determine the plant’s annual flows of air, water, and land

emissions and solid waste.

In this example, we’re showing data gathered for the annual raw material inputs, CO2 emissions

outputs, and manufactured product outputs for an example HDPE bag manufacturing plant. Of

course, in a real LCA we would account for many other flows in our unit process inventories, but to

keep things simple, we’ll focus on just these three flows for now. Let’s also display these data using

our standard unit process inventory structure.

Now let’s revisit our simplified unit process system for manufacturing HDPE grocery bags. Assume

that we’ve gathered similar flow data on the annual raw material inputs, CO2 emissions outputs, and

manufactured product outputs for each plant in our system. As you see here, we’ve listed annual

flow data for each plant in our system using our standard unit process inventory structure.

Recall from the hot water heater example in Lecture 4 that it is most convenient to express unit

process inventories on the basis of one unit of product output whenever possible. We do this

because it makes unit process scaling in a system much easier, as you’ll see next. To do this for

HDPE bag manufacturing, we’d divide all flows by the total manufactured product output as shown

in this table. This calculation produces an inventory in which all flows are expressed on the basis of

one unit of product output; in our case, 1 kg of HDPE grocery bags.

In this table, we’ve normalized the inventories to one unit of product output for all other plants in

the system using the same procedure.

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Now it’s time to connect our unit processes into a simple system model in which mass and energy

requirements are balanced. A straightforward way to do this is to start with a given quantity of final

product output, and to work our way backward to calculate the quantities of inputs required from

each proceeding unit process.

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Let’s assume we want to produce 1 kg of HDPE grocery bags. Based on the unit process inventory

for HDPE bag manufacturing, we see that manufacturing 1 kg of HDPE grocery bags requires 1.02 kg

of HDPE pellets. Therefore, the HDPE resin manufacturing plant must produce 1.02 kg of HDPE

pellets to meet the mass input requirements of the HDPE bag manufacturing plant. So we must

scale up all flows in our unit process inventory for HDPE resin manufacturing by a factor of 1.02 to

meet this level of production output.

This procedure reveals to us that to produce 1.02 kg of HDPE pellets, 1.02 kg of ethylene is required

at the HDPE resin manufacturing plant. Now we must scale up all flows in our unit process inventory

for ethylene manufacturing by multiplying by a factor of 1.02. Doing so shows us that to produce

1.02 kg of ethylene, 1.04 kg of processed natural gas is needed at the ethylene manufacturing plant.

Next, we need to scale up all flows in our unit process inventory for natural gas processing by a

factor of 1.04, which reveals that, to produce 1.04 kg of processed natural gas, 1.05 kg of kg of

extracted natural gas are required by the natural gas processing plant.

Lastly, this means we must scale up all flows in our unit process inventory for natural gas extraction

by a factor of 1.05. Doing so indicates that 1.08 kg of in-ground natural gas is required as an input

from nature into the natural gas extraction process.

You’ve just witnessed a simple example of normalizing plant-level flow data into unit process

inventories expressed on the basis of one unit of product output, and then how those unit processes

can be related and scaled into a simple unit process system model.

Note that the final inventory table I’ve generated is the same one that allowed us to construct an

aggregated inventory of all of these unit processes in the Lecture 3 Supplement video. In that video,

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I mentioned that each unit process had been properly scaled to represent the mass flows required

by the system to ultimately produce 1 kg of HDPE grocery bags. I hope that statement is clearer to

you now, as is the need to properly scale unit process inventory data before we can aggregate them.

You’ll gain more practice with normalizing, relating, and scaling unit process inventories in

Homework 2.

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Lecture 5: Energy flow basics Transcript

Today we’re going to discuss nomenclature and conventions for flows of energy in unit processes

inventories. Energy flows are common to nearly every type of unit process, and for many products,

the emissions related to energy flows account for a significant fraction of total life-cycle impacts.

Therefore, careful consideration of energy flows is critical for credible LCAs.

Let’s first distinguish between two different types of energy flows: energy as a fuel and energy in

materials. Just as it sounds, energy as a fuel refers to the energy that performs useful work in a

process. Typical fuels include diesel fuel, gasoline, electricity, and natural gas. In this course, we’ll

typically document flows of energy used as a fuel in physical units, such as the liters of gasoline or

the cubic meters of natural gas consumed in a unit process. One major exception is electricity,

which we’ll always document using kilowatt-hours.

Energy in materials refers to the inherent energy value of materials used to create products. For

example, in the United States, our plastic bag contains HDPE that was derived from natural gas. As

such, the bag itself could be used as a fuel after it is discarded, and it often is in waste to energy

incinerators. By convention, unit process inventories account for the energy content of such

materials and denote this as “feedstock energy.” We’ll follow that convention in this course as well,

by making a note in our unit process inventories for any energy flow that should be treated as a

feedstock. In fact, you’ll do this yourself when you build your LCA model of bottled soda.

When it comes to energy as fuels, you also need to understand the difference between primary and

converted forms of energy. In most energy statistics, primary energy refers to the calorific value of

fuels found in nature, which includes coal, natural gas, uranium, crude oil, wind, sunlight, and

biomass. Converted forms of energy are not found in nature, but rather are created by converting

primary energy sources into more convenient or useful forms. For example, to generate electricity

we might convert the thermal energy in coal into electricity in a power plant. Or to generate steam,

we might convert the thermal energy in natural gas into steam in a boiler. Converted forms of

energy are also commonly called “energy carriers.” For ease of reference, a list of primary energy

sources and common energy carriers has been provided in the lecture notes.

In an LCA, it’s critically important to account for all energy losses that occur when converting

primary energy sources into energy carriers. Let’s use the example of electricity generation to

illustrate.

First, the thermal energy in the input fuel is converted into mechanical work in a turbine, which is

then converted into electricity in a generator. During the conversion processes, a significant fraction

of the thermal energy in the input fuel is lost as waste heat to the environment. Some of the

electricity generated is used in the power plant itself, resulting in additional energy losses. Lastly,

there are also energy losses in the systems that transmit and distribute electricity from the power

plant to the consumer. As a result of all these losses, only a fraction of the thermal energy that was

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contained in the input fuel remains in the electricity that is delivered to the customer. For example,

in the United States, on average only around 1/3 of the energy that goes into a fossil-fuel-fired

power plant is contained in the electricity that obtained at the wall plug.

Why is it important to account for such conversion losses? Let’s use a simple example to illustrate.

Assume we can bake a loaf of bread in a natural gas oven or electric oven. Further assume that the

energy required to bake the bread is the same in both ovens, say, 5 MJ per loaf. Note that 5 MJ is

equivalent to 1.4 kWh of electricity. It might seem that both ovens use the same amount of energy,

and are therefore comparable from an energy use perspective. But let’s not forget about the energy

losses associated with generating and transmitting the electricity used by the electric oven. If we

assume that the electricity comes from a natural gas-fired power plant, and that the power grid is

33% efficient, it means that 15 MJ of natural gas are required to provide 5 MJ of electricity to the

electric oven. In other words, in this particular example the electric oven requires 3 times the

natural gas to bake a loaf of bread as the natural gas oven.

What we’ve just done is to convert an energy carrier (i.e., electricity) back into its original primary

energy form (i.e., natural gas) in order to facilitate a fair comparison between the two oven options.

In LCA, we’ll always compare the life-cycle energy use of different products on a primary energy

basis. In this course, such calculations will be enabled by including all unit processes associated with

converting primary energy sources into the energy carriers that are ultimately consumed in the life

cycle system. Or, in other words, we’ll apply life-cycle thinking by considering not just the direct

energy use of a unit process, but also the cradle-to-gate systems that supply the energy forms used

the unit process.

5.4

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We’ll do this by clearly labeling all flows of energy carriers as product inputs from the technosphere

in our unit process inventories. By doing so, we’ll be forced to trace all energy carriers in the system

back to the original elementary flows of energy from nature. See the lecture notes for some

examples of this approach.

By following this approach, we’ll minimize our risk of mistakenly adding primary energy values and

energy carrier values to each other when summing up energy flows across unit processes, which

would invalidate our results. Good data sources will always make the distinction between primary

energy data and energy carrier data in their unit process inventories explicit, but don’t be surprised

if you come across data sources where this distinction is not made. Unfortunately, this is a common

omission than can render a data source useless.

Lastly, note that conversion losses vary greatly by input fuel type, energy carrier type, and

conversion technology type, and all of these can vary greatly by location. For example, a coal-fired

power grid in China will have different conversion losses than a natural gas power grid in the United

States. So if our electric oven were in China, a different amount of primary energy would be

required to bake the bread than if that electric oven were in the United States. As you gain more

experience with LCA, you’ll become accustomed to choosing the right unit processes inventories to

accurately capture conversion losses in different geographical regions.

Additional notes

The concept of feedstock energy is most commonly applied in LCA to materials that are derived

from fossil fuels, including plastics, chemicals, paints, synthetic rubber, and bitumen, to name a few.

However, feedstock energy is technically relevant to any material that has energetic value, including

biogenic materials such as wood. In this course, we’ll only denote feedstock energy for plastics and

paper products, because these two products are the only relevant materials used in our simplified

grocery bag and bottled soft drink life cycles. In practice, however, you’ll encounter other product

life-cycle systems and LCA data sources that track feedstock energy for a much broader range of

materials.

In LCA, we also need to be aware that the calorific energy value of fuels can be reported on either a

higher heating value (HHV) or a lower heating value (LHV) basis in energy statistics. The HHV of a

fuel, which is also known as its gross calorific value, includes the latent heat of vaporization of water

in the combustion process. The LHV of a fuel, which is also known as its net calorific value, does not

include the latent heat of vaporization of water. Therefore, a fuel’s HHV is higher than its LHV. The

difference between HHV and LHV depends on the fuel. Ideally, in an LCA one should establish

whether HHV or LHV bases are used in life cycle inventory data and consistently use only one basis

throughout the analysis.

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5.5

5.2

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For a helpful primer on basic energy units and concepts, see the following reference:

Food and Agriculture Organization of the United Nations, 1991, Energy for sustainable rural

development projects - Vol.1: A reader: Chapter 1 - Basic energy concepts. Rome.

http://www.fao.org/docrep/u2246e/u2246e02.htm

In practice, you may encounter slight differences in the definition of primary energy across the

various agencies and institutions that compile energy statistics or create regional energy balances.

In this course, we’ll define primary energy as the energy content or calorific value of fuels found in

nature prior to any significant conversion or transformation. Energy carriers are defined as more

convenient forms of energy that are created through conversion or transformation processes from

primary energy sources. The following table contains the major primary energy sources and energy

carriers in use in many societies. In the data one uses to compile unit process inventories, one may

sometimes encounter energy inputs expressed in units of energy carriers, such as kWh or electricity

or MJ of steam. The important point to remember is that we must consider the primary energy that

was used to generate each energy carrier, otherwise the “true” energy cost of a system might be

undercounted!

Primary energy sources Common energy carriers

Biomass Compressed air Coal Conditioned air Crude oil Conditioned water Geothermal heat Electricity Natural gas Mechanical work Running or falling water Refined fuels (gasoline, diesel, kerosene, etc.) Solar energy Steam Tidal energy Uranium

Wind

In fact, the average system efficiency of electricity generation, transmission, and distribution in the

United States has been getting higher in recent years due to technological improvements and a shift

away from coal and toward natural gas in the electricity grid. As you’ll learn in Homework 2, the

efficiency of electricity generation in the United States is closely tied to the type of fossil fuels used

in its power plants.

See the Lecture 5 Supplementary video for an example of primary energy versus energy carriers for

a coal-fired electricity production system.

5.4

5.3

5.5

http://www.fao.org/docrep/u2246e/u2246e02.htm

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To better understand the difference between primary energy and energy carriers, let’s consider

again the example of electricity generation in the United States. This simplified figure depicts the

major unit processes within a coal-fired electricity system, starting with the coal mine and ending

with a residential home that consumes the electricity.

We’ll use 100 megajoules (MJ) of coal input so you can track energy flows and losses easily through

the system. Furthermore, we’ll just consider energy flows related to coal and its conversion to

electricity in this system to keep things simple. In reality, there are many other flows of mass and

energy associated with these unit processes, which we would normally track in a full life cycle

assessment.

First, in-ground coal is extracted from nature and transported by rail to a power plant. Given that

coal is a raw fuel from nature with minimal processing before it is combusted in the power plant, it

is considered a form of primary energy. The coal is then combusted in the power plant’s boiler to

generate steam, which is an energy carrier.

Typical conversion efficiencies for U.S. power plant boilers are around 88%, which means 12 MJ of

the energy in the coal is lost as waste heat to the atmosphere. The steam is then run through a

steam turbine generator to produce electricity, which is another energy carrier.

Typical conversion efficiencies for steam turbine generators are around 45%, which means that 48

MJ of the energy in the steam is lost as waste heat to the atmosphere and only 40 MJ of electricity is

generated. Additionally, some of the generated electricity is used by the power plant itself to power

its own operations (typically 5-7%). Thus, we assume that 37 MJ of electricity leaves the power

plant and is distributed to consumers.

At this point in the system, we can calculate what is known as the net power plant efficiency, which

expresses the efficiency of converting input fuels into the net power exported from the plant.

Net power plant efficiency = [Net electricity generation (MJ)]/[Power plant fuel input (MJ)]

= 37 MJ/100 MJ = 0.37 = 37%.

Lastly, the transmission and distribution system will also incur heat losses (typically around 8% in the

United States), which means that only 34 MJ of electricity ultimately reaches the consumer for use

in the home.

Now at this point in the system, we can calculate what is known as the total system efficiency, which

expresses the efficiency of converting input fuels into the power that is ultimately provided to

consumers at the final point of use.

Total system efficiency = [Delivered electricity (MJ)]/[Power plant fuel input (MJ)]

= 34 MJ/100 MJ = 0.34 = 34%.

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In the diagram, flows from nature are indicated with a green arrow and flows to and from the

technosphere (i.e., product flows) are indicated with dashed black arrows. Heat losses are indicated

with red arrows. Note that heat losses are not often listed as an output to nature in many LCA

databases, but thorough, well balanced unit process inventories can include flows of waste heat.

You can use this example to understand the distinction between primary energy and energy carriers

in unit process systems, and to visualize how we can track conversion losses that occur between

primary energy sources and energy carriers through the unit process modeling approach in an LCA.

You’ll get more practice with these concepts in your course project.

Lastly, note also that in this simple example, we’ve neglected the fuel inputs that are necessary to

power the coal mining and coal transport unit processes for the purposes of illustration. However, in

any real-world LCA (including in our plastic bag and bottled soft drink spreadsheet models), we must

account for these and other fuel inputs. If we did so here, those additional fuel inputs would add to

the primary energy used by the system by around 5%.

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Lecture 6: Mass balances Transcript

Today is the last lecture in which we’ll cover the structural basics of an LCA. Moving forward, we’ll

begin discussing the four major steps in the LCA process. In future lectures, you’ll also begin

“learning by doing” by applying your knowledge to build an LCA model of bottled soft drink.

However, before we move on I’d like you to gain some proficiency in mass balancing across life-cycle

systems. If you’ve had a basic physics class, you’ll probably recall the law of mass conservation. We

strive to apply the same principle in LCA; that is, all mass that goes into a life cycle system must be

accounted for as either a product flow within or out of the system or an elementary flow out of the

system.

Let’s again consider the life cycle of a plastic bag, and let’s focus on the end of life stage.

Suppose we want to analyze the CO2 emissions of different end of life scenarios for plastic grocery

bags in an urban region in an effort to inform policy makers. In this example, we’ll consider two end

of life processes for waste plastic bags collected in our urban region: landfill and recycling. Let’s

designate the variable m as the total mass of plastic bags collected from consumers in our region

each year. To help us visualize, assume that this stack of blocks represents our total mass m.

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Now let’s designate the variables ac, al, as, and ar to represent emissions to air of carbon dioxide

(CO2) per unit mass processed in collection, landfill, sorting, and recycling, respectively (kg CO2/kg).

Finally, let’s assume that currently all collected mass is being sent to landfill. In this case, the CO2

emissions of the end of life stage would be calculated as:

mac + mal = m(ac + al)

Now suppose we wanted to evaluate changing this system by recovering some portion of the

collected mass for recycling. Let’s represent the fraction of collected mass sent to sorting for

recycling by the variable x. Therefore the mass quantity to sorting is represented by mx and the

mass quantity to landfill is represented as m(1-x).

The sorting process will also generate waste due to contaminants and inefficiencies. We’ll represent

the fraction of sorted mass that gets sent to the plastic recycling plant as y. Therefore, the mass

quantity from sorting to recycling can be represented as mxy and the mass quantity generated from

the sorting process as waste to landfill can be represented as mx(1-y).

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Lastly, we need to consider that the recycling process will also generate waste. Let’s represent the

mass fraction that is recycled into pellets as z. Therefore, the mass quantity of recycled pellets is

mxyz and the mass quantity of waste generated by the recycling process is mxy(1-z), which we’ll

send to the landfill.

Now let’s verify that we’ve conserved mass across all flows in our system by summing up the mass

flows that terminate in the landfill process or as recycled pellets. That is:

mxyz + mxy(1-z) + mx(1-y) + m(1-x) = m

mxyz + mxy – mxyz + mx – mxy + m - mx = m

m = m

which we verify by collecting our blocks.

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Great, now that mass is conserved, let’s calculate the CO2 emissions associated with this new

scenario for the end of life stage. You’ll note in this expression that I’ve included a new

CO2emissions factor c, which allocates possible CO2 emissions savings associated with plastic bag

recycling to the pellets themselves (kg CO2/kg recycled). We’ll cover allocation in more detail in

future lectures.

For now, we’ll use this factor to solve for the value of c that will ensure that the alternative recycling

scenario reduces the CO2 emissions of the end of life stage compared to existing landfill scenario.

Here’s the math:

[mac + mal] – [mac + [m(1-x) + mx(1-y) + mxy(1-z)]al + mxas + mxyar + mxyzc]> 0

mac + mal - mac -[m(1-x) + mx(1-y) + mxy(1-z)]al - mxas - mxyar - mxyzc > 0

mac + mal - mac - mal + mxal - mxal + mxyal - mxyal + mxyzal - mxas - mxyar - mxyzc > 0

(mac - mac) + (mal - mal) + (mxal - mxal) + (mxyal - mxyal) + mxyzal - mxas - mxyar - mxyzc > 0

mxyzal - mxas - mxyar > mxyzc

c < al - as/yz - ar/z

which shows us that for our recycling scenario to reduce CO2 emissions at the end of life stage, the

value of c must be less than the value of the expression on the right-hand side of this relation.

6.3

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In fact, you’ll analyze a similar expression yourself as part of the next homework assignment, using

numerical values for the variables.

Besides giving us practice with mass balancing, tracking mass flows in this fashion has another

benefit. Doing so allows us to easily assess different end of life scenarios simply by changing the

values of the mass fractions in an LCA model.

In fact, I’ve found that it’s good practice to balance mass in this way in all of my spreadsheet LCA

models. For example, in my LCA model of the plastic bag life cycle, I’ve specified mass flows with

mass fractions indicated right on my life cycle system diagram to make all mass flows explicit and

intuitive.

Now, if you’re working with a commercial LCA software package you’ll find that it tracks and displays

mass flows automatically, which is a big help, especially for complicated life cycle systems!

However, you’ll be balancing mass flows manually in your spreadsheet model of the bottled soda

life-cycle. Even if mass balancing is fairly trivial in your class project, getting in the habit of carefully

tracking mass flows is important for visualizing the system you are modeling, ensuring mass

conservation, and analyzing “what if” scenarios analytically as you’ll do in the homework

assignment.

In practice, we might be able to neglect small mass flows in the system if doing so doesn’t

substantially affect our results. Neglecting small mass flows is something we’ll cover when

discussing “cut off rules” for life-cycle inventories a bit later in this course. For now, I want you to

assume that all mass flows are important to track until you later determine otherwise.

Additional notes

This lecture is designed to provide a basic understanding of mass balancing across unit process

systems for those without experience conducting mass and energy balances. For those of you who

already have such experience, the most important information to take away from this chapter is

that manual mass and energy balances are often necessary in an LCA, especially when compiling

one’s own unit process inventory data. It will also be necessary to understand the notion of “cut

off” rules, which refer to criteria for excluding certain mass and energy flows from an LCA on the

basis of insignificance or acceptable uncertainties. We’ll cover cut off rules in more detail later in

this course.

Correction: Mass entering a system can leave the system as an elementary flow or as a product flow.

Additionally, mass can be converted to energy within a system, such as when materials or their by-

products are combusted for their energy value. For example, a biorefinery may combust biomass

feedstock byproducts to generate heat and/or electricity for use onsite. We’ll only focus on simple

mass and energy balances in this course, with the goal of ensuring that we account for inputs and

outputs of all flows in our systems. However, in practice, the balancing of mass and energy across

systems can become complicated, especially when mass-to-energy conversions occur within a

6.1

6.2

2

6.5

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system. Fortunately, most commercial LCA software packages will ensure balancing of mass and

energy flows as part of the model building process.

As you’ll learn later in this course when we discuss allocation and partitioning, emissions savings

assigned to recycled materials are known as “avoided burdens” in an LCA. When we recycle

materials, environmental impacts can be avoided in two ways. First, when we don’t send those

materials to the landfill, we avoid the emissions that would have otherwise been associated with

hauling that waste to the landfill and compacting it there. Second, by recycling materials, we avoid

demand for virgin materials in the marketplace and the emissions that would have otherwise been

associated with manufacturing those virgin materials. The amount of avoided emissions can be

estimated analytically, as you’ll see in the Lecture 6 Supplement video. Such avoided burdens have

a negative value, which indicates environmental impact savings at the societal level. For a classic

explanation of impacts avoided by recycling, read the following white paper by Ian Boustead, one of

the LCA field’s longstanding experts. Note that there are other views on allocation and partitioning

within the LCA community (Boustead’s discussion is in the context of plastics recycling). You’ll also

notice that our mass balancing approach is similar to that discussed by Boustead.

Boustead (2001). Who gets the credit?

http://www.plasticseurope.org/Documents/Document/20100312112214-

WHOGETSTHECREDITS-20050701-005-EN-v1.pdf

To help you understand this example, we’ve added the Lecture 6 Supplement video. Please be sure

to watch it, especially if the equations presented and the concept of “avoided emissions” due to

recycling are not clear.

Now that we’ve added the Lecture 6 Supplement video, you will not solve this expression in the next

homework assignment. Therefore, please just ignore this statement. However, you will get further

experience with mass and energy balances and “avoided impacts” later in this course.

6.3

6.4

6.5

http://www.plasticseurope.org/Documents/Document/20100312112214-WHOGETSTHECREDITS-20050701-005-EN-v1.pdf

http://www.plasticseurope.org/Documents/Document/20100312112214-WHOGETSTHECREDITS-20050701-005-EN-v1.pdf

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Let’s work through the solution to the equations presented in Lecture 6, which quantified the

avoided emissions necessary for recycling to reduce greenhouse gas emissions compared to

landfilling at the end of life stage. Again, let’s “zoom in” on just the end of life stage of our HDPE

plastic bag, because here is where mass flows between unit processes would change if we were to

recycle bags instead of sending them to the landfill.

So you can better visualize the solution, we’ll define two distinct scenarios and calculate the CO2

emissions associated with each. In Scenario 1, we’ll send all mass discarded by consumers to

collection and then to the landfill. Recall that landfill operations result in an elementary flow of solid

waste to nature, which is depicted in this figure. Using the mass flow and unit process emissions

notation from Lecture 6, the CO2 emissions at the end of life stage in this scenario would be

expressed as follows:

Now let’s define Scenario 2, in which we’ll send all mass discarded by consumers to sorting for

recycling. Again, using the mass flow and unit process emissions notation from Lecture 6, our unit

process system would look like this. Importantly, note that we’ve added in the variable C, which

represents the quantity of CO2 avoided when our recycled pellets displace virgin plastics in the

marketplace. As you’ll learn later in this course, this quantity is commonly known as an “avoided

burden” in LCA. It’s a convenient way for analysts to include the net societal benefits that would

occur due to recycling materials or reusing products from a unit process system when conducting an

LCA of that system itself.

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Using the notation in the figure, the CO2 emissions at the end of life stage in this scenario would be

expressed as follows:

Clearly, we’d like for the recycling scenario to generate lower CO2 emissions than the landfill

scenario. This condition is necessary in order for recycling to deliver greenhouse gas emissions

benefits to society. Mathematically, we can express this condition as follows:

Based on this mathematical expression, we can now solve for the value of C that would be required

for Scenario 2 to have lower CO2 emissions than Scenario 1 using simple algebra. First, notice that

the variable m appears on all terms on both sides of the equation, so we can cancel it out. Next,

let’s move ac and al to the left side of the equation so we can consolidate our terms, which results in

this expression. Finally, let’s solve for C by moving the other terms back to the right and dividing by

xy. Now we see that, in order for the recycling scenario to generate lower CO2 emissions than the

landfill scenario, the value of C must be less than the value of the term to right of the “less than”

sign.

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If you’re having trouble following the math, try to work through the algebraic equations yourself.

You’ll see that the solution is pretty straightforward.

Now let’s plug in some arbitrary values for unit process emissions and mass recovery fractions and

see what happens. Using these values, and solving for C, indicates that for recycling to result in

lower CO2 emissions than landfilling, the avoided emissions associated with recycled pellets must be

less than negative 1.23 kg of CO2 per kg of recycled pellet. Why do we have a negative value? It’s

because, in our example, recycling would lead to a net reduction in CO2 emissions at the societal

level. Often we call such net reductions “avoided burdens” in an LCA.

For an example of avoided burdens in a real-world LCA, let’s take a look at some of the results from

a carrier bag LCA report published by the UK’s Environment Agency1. This report is also the subject

of this week’s case study on paper versus plastic grocery bags. Note in this figure how the red

portion of the results graph for several carrier bags is labeled “avoided products and recycling,”

which the report defines as “the avoidance of virgin materials through secondary reuse or

recycling.” Note also how the red portion of the graph has a negative value, which indicates

greenhouse gas emissions reductions at the societal level due to avoidance of virgin materials.

These examples should give you some familiarity with the concept of avoided burdens, their

negative values, and how one can calculate avoided burden thresholds using mass balancing and

unit process inventory data. As mentioned earlier, you’ll learn more about avoided burdens later in

this course, but at least for now you’ll understand the concept of avoided burdens in case you come

across them in the LCA literature.

1 Source: Environment Agency (2011). Life Cycle Assessment of Supermarket Carrier Bags. Bristol, UK. Report:

SC030148. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/291023/scho0711buan-e-e.pdf

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/291023/scho0711buan-e-e.pdf

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Lecture 7: Goal definition Transcript

Today we’ll begin discussion of the formal LCA process, which, as you’ll recall, is comprised of four

major steps: goal and scope definition, inventory analysis, impact assessment, and interpretation.

Thus far we’ve been focused on familiarizing ourselves with unit process inventories, understanding

flows, and learning important data conventions. This

knowledge will serve you well as we move forward

through the four steps in an LCA in the weeks ahead.

We’ll cover each step one at a time, although it’s

important to recognize that these steps are often

conducted in parallel and iterative fashion in practice.

For example, the interpretation step is best conducted

throughout an LCA to verify that the assessment is

meeting its goals, while the impact assessment step

can often indicate the need to go back and refine the

data compiled in the inventory.

Moving forward, you’ll hear me make mention of the

ISO 14040 series of standards when explaining key LCA concepts. These are international standards

that have been created by the LCA community to ensure methodological consistency and

transparency in LCA studies. In fact, this figure represents the basic ISO 14040 view of the LCA

process.

Gaining proficiency with each step in the LCA process takes time and lots of practice. Since we can

only cover a few key concepts in each lecture, you’ll need to study the lecture notes, complete and

understand the course assignments, and engage with your classmates in the discussion forum to get

the most out of this course. For those of you who are looking for a deeper understanding of the

course topics, I’ve provided recommendations for advanced readings on many topics in the lecture

notes.

This week you’ll also begin building your LCA model of a bottled soft drink, so you can learn each

step of the LCA process in hands-on fashion. To assist you in this effort, I’ll be constructing my own

LCA model of this plastic grocery bag alongside you.

Our focus this week is on goal and scope definition. In this step, we specify the purpose of the

study, what systems are to be analyzed, how the LCA will be carried out, the intended audience, and

the quality of the data. On its face, this step might seem like a formality, but nothing could be

further from the truth. In fact, it is the goal and scope definition step that provides the necessary

detail and transparency that make an LCA study useful to its intended audience. In fact, when the

goal and scope are poorly constructed or documented, an LCA study is often very difficult to

interpret and validate, which can render its results practically useless.

7.1

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Today we’ll limit our discussion to defining the goals of an LCA. We’ll cover scope definition in the

next few lectures.

When defining the goals of an LCA, we want to explicitly state:

The intended applications of the study

The purpose of the study

The target audience

Method, assumption, and impact limitations

If it contains product comparisons intended for public disclosure, and

The initiator of the study

Let’s step through each of these one by one:

The intended applications of the study describe how the study’s results are to be used, stated as

clearly and precisely as possible. There are many different uses of LCA results. Some of the more

common uses are listed here:

Identify environmental “hot spots” in a product’s life cycle

Guide corporate product or process development (e.g., inform green design decisions)

Benchmark against similar products

Compare different products or services

Support product eco-label certification

Support public policy decisions

The purpose of the study describes the drivers and motivations of the LCA, including the specific

decisions that the study is designed to support. For example, if the intended application of the

study is to compare different products, will the study support decisions regarding which specific

products to purchase or which suppliers to engage?

The target audience describes who will use the LCA results to support the stated decisions. There

can be many different audiences for an LCA, including internal staff and colleagues, external

customers, public policy makers, those with technical backgrounds and those without, or general

consumers.

At this point I’d like to stress that careful selection and clear communication of a study’s intended

application, purpose, and target audience is critical for a sound LCA. These three elements are

obviously interrelated, so their definitions must support each other. Moreover, how these three

elements are defined has profound implications on the scope, data quality, complexity, analytical

rigor, and documentation requirements of an LCA study.

For example, if my intended application is to identify major environmental “hot spots” of the plastic

bag life cycle, which will be used by internal staff to decide on which hot spots warrant further

study, I can probably accomplish this by using literature data and a fairly simple analysis. However,

7.2

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if my intended application is to support public policy decisions, I’ll need a study with high data

quality, rigorous uncertainty analysis, extensive documentation, and external peer review.

Method, assumption, and impact limitations describe any limitations to the study that arise from

your chosen methods for inventory analysis and impact assessment, your key analysis assumptions,

or any impacts you’ve excluded. Essentially, this element indicates the general types of decisions or

conclusions that your LCA results are not intended to support. For example, if the study compared

two products only on the basis of life-cycle energy use, the study’s results should not be used to

conclude which product is superior from an overall environmental perspective.

If the study contains product comparisons intended for public disclosure, it’s critical to state this

explicitly because the study would then need to comply with ISO 14040 series standards for study

design, documentation, and external peer review.

Lastly, stating clearly the initiator of the study is important so that the target audience is

knowledgeable of the study’s source and are free to take into account any special interests or biases

they may perceive.

OK, that was a lot of talking to digest for one lecture! However, the importance of proper goal

definition cannot be overstated. When the intended application, purpose, target audience,

limitations, and initiator of an LCA study aren’t made crystal clear, it can make the study’s results

difficult to interpret, difficult to use, and, frankly, difficult to trust. And, when that happens, an LCA

study is far less useful than it could be.

To ensure that doesn’t happen in your bottled soft drink LCA, you’ll include your goal and scope

definition on the very first tab of your spreadsheet model. We’ll work on this step together this

week, using my LCA model for plastic bags as an example.

Additional notes

In the remainder of this course, we’ll refer to the ISO 14040 series of standards often because these

standards provide specific guidance on structured content and procedures within the four major

steps of the LCA process. It also bears mentioning that these standards are used widely by the LCA

community as a standard of conformance to generally-accepted LCA implementation and reporting

practices. In practice, one is likely to encounter many studies that include a statement of

conformance with the ISO standards as a measure of study diligence and transparency. Although

the ISO 14040 series of standards must be purchased, one can find mentions of and references to

these standards in the following handbook, which is publicly-available at the link below. Note that

this handbook is available in PDF format for educational purposes, but it cannot be printed.

Guinée, J.B.; Gorrée, M.; Heijungs, R.; Huppes, G.; Kleijn, R.; Koning, A. de; Oers, L. van;

Wegener Sleeswijk, A.; Suh, S.; Udo de Haes, H.A.; Bruijn, H. de; Duin, R. van; Huijbregts,

M.A.J. Handbook on life cycle assessment. Operational guide to the ISO standards. I: LCA in

perspective. IIa: Guide. IIb: Operational annex. III: Scientific background. Kluwer Academic

7.1

7.3

7.4

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Publishers, ISBN 1-4020-0228-9, Dordrecht, 2002, 692 pp.

http://www.cml.leiden.edu/research/industrialecology/researchprojects/finished/new-

dutch-lca-guide.html

For more discussion on these elements of goal definition, see the following reports for some helpful

explanations and examples:







dutch-lca-guide.html (See Part 2a, Section 2)

B.P. Weidema, H. Wenzel, C. Petersen, and K. Hansen (2004). The product, functional unit

and reference flows in LCA. København: Danish Environmental Protection Agency.

(Environmental News 70). http://www.lca-center.dk/resources/777.pdf

The United States Environmental Protection Agency’s “Life Cycle Assessment: Principles and Practice”, 2006, Chapter 2, pages 7-8: http://www.epa.gov/nrmrl/std/lca/lca.html (Note this document is also commonly referred to as “LCA 101.”)

Correction: In the lecture video I said ““… the importance of proper goal definition cannot be understated” when I should have said “… the importance of proper goal definition cannot be overstated.” For an example of an LCA report that laid out several goal definition elements explicitly, and in accordance with the ISO 14040 standards, see the UK Environment Agency’s LCA of grocery carrier bags from last week’s case study. You’ll notice that the authors make the following elements very clear at the beginning of the study: the intended application, the reasons for carrying out the study, the intended audience, and whether the results are intended to be used in comparative assertions intended to be disclosed to the public. Before reading any LCA, you should first seek out such goal definition elements to understand the context of the study and to judge for yourself if the stated goals were adequately met in the LCA’s methods, results, and documentation.

Environment Agency (2011). Life Cycle Assessment of Supermarket Carrier Bags. Bristol, UK. Report: SC030148. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/291023/scho0711buan-e-e.pdf

7.2

7.3

7.4

http://www.cml.leiden.edu/research/industrialecology/researchprojects/finished/new-dutch-lca-guide.html




http://www.lca-center.dk/resources/777.pdf

http://www.epa.gov/nrmrl/std/lca/lca.html



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Lecture 8: Scope definition: functional units Transcript

Now that we understand the basics of goal definition, let’s move on to scope definition. During

scope definition, we must determine the methodology, included life cycle stages and impacts, data

quality, documentation, and reviewing requirements of the LCA study. As mentioned in the last

lecture, the scope of the LCA study must directly support the goals defined for the LCA study to

ensure that these goals are met. Therefore, the scope of an LCA study must be carefully designed

with this aim in mind.

In this course, we’ll follow guidance from the ISO 14040 standard for defining the scope of an LCA

study. More specifically, we’ll include the following:

Function, functional unit, and reference flow(s)

Initial system boundaries

Description of data categories

Criteria for inclusion of inputs and outputs

Methods for handling multi-functionality and allocation

Methods for modeling life-cycle impacts

Data quality requirements

Key assumptions and limitations

Critical review, and

The type and format of any required report

We’ll discuss all of these ISO 14040 scope definition elements in this course.

Today we’ll cover definition of the function, functional unit, and reference flow, which are very

important concepts. Let’s do this using the plastic grocery bag as an example, and watch as I build

my spreadsheet-based LCA model. Before we can begin defining the scope of my plastic bag LCA,

however, we first need to explicitly define the goals of my study. Here they are, as seen in the first

tab of my spreadsheet:

The intended applications of the study: identify environmental “hot spots” in the life cycle of

a plastic grocery bag

The purpose of the study: determine stages/impacts for further examination

The target audience: Internal staff/internal knowledge generation

Method, assumption, and impact limitations: ?

No product comparisons intended for public disclosure

The initiator of the study: Eric Masanet

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You’ll create a similar tab in your bottled soft drink LCA spreadsheet. Now that my goals have been

defined, I can proceed to defining the function, functional unit, and reference flow of my LCA. So

what is meant by these terms?

As you might imagine, the term “function” refers to the useful service provided by the product or

process under study. For example, the function of paint is to color and protect a surface. The

function of an automobile is to transport goods and persons. And a reasonable definition of

function for my plastic bag might be the containment and transport of groceries for one trip. Note

that it’s quite common for a product to serve more than one function; if I were to reuse my plastic

grocery bag as a home garbage can liner, the functions of my plastic bag would be to contain and

transport groceries for one trip and to contain and transport one load of household waste.

The purpose of the functional unit is to quantify the identified functions in a more precise way that

facilitates mathematical analysis. For example, a functional unit for paint might be to “cover 10

square meters for 10 years.” It is important for the functional unit to be both precise and

measurable, because it serves as the reference to which the inputs and outputs of our life-cycle

system are normalized. The functional unit also allows for credible comparisons of different product

options on the basis of providing an equivalent service.

These points will become clearer when you understand the reference flow, which is the amount of a

product required to fulfill the function. For example, to cover 10 square meters for 10 years might

require one liter of high quality paint. In other words, the reference flow for high quality paint is

one liter per functional unit. Without the reference flow, we wouldn’t know how much paint makes

sense to analyze in our LCA. But now that we have a logical reference flow, when we construct a

life-cycle inventory for high quality paint, all system inputs and outputs would be based on providing

one liter of paint in the product use stage.

Now let’s consider a low quality paint. Let’s say, to cover 10 square meters for 10 years requires

two liters of low-quality paint because it needs extensive touch-ups each year. In this case, the

reference flow for low quality paint is two liters per functional unit.

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As I mentioned earlier, the functional unit allows us to compare different products on the basis of an

equivalent service. Thus, if we were to conduct an LCA study that compared the high quality paint

to the low quality paint, we’d do so by comparing the reference flow of each paint required to meet

the same functional unit. That is, we’d compare one liter of high quality paint to two liters of low

quality paint because those are the quantities of each paint required to cover 10 square meters for

10 years.

Note that a casual analyst may have well chosen to compare one liter of high quality paint to one

liter of low quality paint. However, his analysis would have been fatally flawed because he’d be

treating these two products as functionally equal when they are clearly not. Only by comparing

reference flows on the basis of a functional unit can we properly compare the impacts of different

products in an LCA.

Hopefully the simple example of paint has helped you visualize the purpose of carefully defining the

function, functional unit, and reference flow when defining the scope in an LCA. These are very

important concepts, so let’s turn back to the plastic bag for another example.

A reasonable functional unit for the functions I described might be to contain and carry 9 liters of

groceries for one trip and to contain and carry 9 liters of household waste. Note that since I’m

choosing a volumetric basis for my functional unit, I’m implicitly assuming that the bag can hold any

relevant types mass that can fit within 9 liters, which seems like a reasonable assumption. If we

assume that a single plastic grocery bag satisfies this functional unit (that is, no double bagging at

the grocery store), the reference flow would be one five gram HDPE grocery bag. Now, suppose we

wanted to compare this plastic bag to a paper bag … can we compare them directly?

If you’ve been paying close attention, you’ll know that the answer is “no!” We must compare them

based on reference flows that provide the same functional unit. For paper bags, we must consider

two key differences when defining reference flows. First, if we recognize that most paper grocery

bags are larger than plastic bags, we might find that 9 liters of groceries only fills two-thirds of a

paper bag. In other words, only two-thirds the mass of the paper bag is technically required to meet

the function of containing and carrying 9 liters of groceries for one trip. Second, if we assume that

paper bags cannot serve as garbage can liners, we’ll find that a dedicated garbage can bag is

required to meet the function of containing and carrying 9 liters of household waste.

So, when we compare the reference flows of our product options on the basis of the functional unit,

we must compare one plastic bag to two-thirds of a paper bag plus one garbage can bag. Or, if we

scale this comparison up to include only whole bags, which may seem more logical, we’d compare

three plastic bags to two paper bags plus three garbage can bags. Again, only by comparing

reference flows on the basis of a functional unit can we properly compare the impacts of different

products in an LCA. While it might seem tempting to directly compare one plastic bag to one paper

bag in an LCA, we wouldn’t be making a valid comparison based on equivalent service.

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If the concepts of function, functional unit, and reference flows are still a bit fuzzy, don’t worry. I’ve

provided recommendations in the lecture notes to give you more practice with these important

concepts.

Additional notes

For further information on these scope definition elements, see:








B.P. Weidema, H. Wenzel, C. Petersen, and K. Hansen (2004). The product, functional unit

and reference flows in LCA. København: Danish Environmental Protection Agency.

(Environmental News 70). http://www.lca-center.dk/resources/777.pdf

Chapter 2 of LCA 101 provides a more truncated overview of scope definition, which does

not list all the various elements specified in the ISO 14040 standards. You’ll get some basic

exposure to each of these scope definition elements in this course, but these extra readings

are recommended for a more thorough understanding of their purpose and form in LCA

practice.

o The United States Environmental Protection Agency’s “Life Cycle Assessment: Principles and Practice”, 2006: http://www.epa.gov/nrmrl/std/lca/lca.html (Note this document is also commonly referred to as “LCA 101.”)

Scope definition is described in detail in the following handbook, Chapter 6. Be aware that this

chapter provides an exhaustive discussion of these elements, and is most useful to the experienced

LCA practitioner.

European Commission - Joint Research Centre (JRC) - Institute for Environment and

Sustainability: International Reference Life Cycle Data System (ILCD) Handbook - General

guide for Life Cycle Assessment - Detailed guidance. First edition March 2010. EUR 24708

EN. Luxembourg. Publications Office of the European Union; 2010.

http://eplca.jrc.ec.europa.eu/uploads/ILCD-Handbook-General-guide-for-LCA-DETAILED-

GUIDANCE-12March2010-ISBN-fin-v1.0-EN.pdf

For additional examples of establishing functional units and reference flows, see the Lecture 8

Supplement video.

8.1

8.2



http://www.lca-center.dk/resources/777.pdf


http://eplca.jrc.ec.europa.eu/uploads/ILCD-Handbook-General-guide-for-LCA-DETAILED-GUIDANCE-12March2010-ISBN-fin-v1.0-EN.pdf


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Correction: In the lecture video, we illustrate the concept of functional equivalence between one

HDPE grocery bag and two-thirds of a paper bag plus one garbage bin liner. While this statement of

equivalence is correct for the functional unit of carrying 9 liters of groceries for one trip and

containing 9 liters of household waste, in an LCA one could also analyze whole bags instead of bag

fractions as a matter of convenience. To do so, one would scale up the functional unit such that

only whole bags are required as reference flows. In our case, we’d scale up by a factor of three; that

is, our functional unit would now be carrying 27 liters of groceries and containing 27 liters of

household waste. The reference flows required to meet this functional unit would then be 3 HDPE

grocery bags and 2 paper bags plus three garbage bin liners. Note that there is still only 2/3 of a

paper bag and one garbage bin liner for every HDPE grocery bag; we’ve just scaled up to arrive at

more convenient analysis quantities.

For an example of a functional unit and reference flows that are based on whole bags, and over a

convenient time period, take another look at the UK Environment Agency’s LCA of carrier bags

(which is the report we discussed in our “paper vs. plastic bag” case study). See Table 3.1, in which

the reference flow data are described on the basis of meeting a functional unit of “483 items of

shopping in one month.” Here you’ll see that the reference flow for the number of paper bags is

lower than that for plastic bags due to the larger size of the paper bags considered in their study,

similar to what we assumed in the Lecture 8 video. In practice, the functional unit can be scaled up

or down easily to make for a more convenient or logical analysis approach. As long as the reference

flows are scaled accordingly, the choice of functional unit scale will not affect the relative

comparison of options in an LCA.


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To better understand the concepts of functional units and reference flows, and the important

relationship between them, let’s consider another example.

Let’s say we’d like to compare the environmental performance of two different household lighting

technologies using the LCA approach. Specifically, let’s compare a standard incandescent bulb with

a compact fluorescent bulb. Recall that there are three steps in the process of establishing

functional units and references flows: (1) defining the function; (2) defining the functional unit; and

(3) defining reference flows.

For household lighting, a reasonable function would be to provide interior illumination for general

household tasks.

Now let’s define a functional unit based on this function. Remember that the purpose of the

functional unit is to quantify the function in a more precise way that facilitates mathematical

analysis. In the case of lighting, we can measure the intensity of light in standard units of lumens.

For example, a standard 60-watt incandescent bulb provides 800 lumens of light output. So let’s

define our functional unit as 800 lumens of light for 8,000 hours. Why do we need to specify

operating hours as part of our functional unit? Whenever we compare durable products with a

service life, it’s critical to include operating hours in the functional unit, as you’ll realize when we

define reference flows next.

To establish reference flows, we need more information. First, let’s assume that we want to

compare a standard 60-watt incandescent bulb to a 15-watt compact fluorescent bulb, since both

provide 800 lumens of light. Let’s further assume that the lifespan of the 60-watt incandescent bulb

is 2,000 hours and that the lifespan of the 15-watt compact fluorescent bulb is 8,000 hours. These

assumptions are based on realistic service life data for each type of bulb, which indicate that, on

average, a compact fluorescent bulb should last much longer than an incandescent bulb.

Lastly, let’s define our reference flows. Recall that the reference flow is the amount of a product

required to fulfill the service expressed by the functional unit. Let’s start with the compact

fluorescent bulb. To provide 800 lumens of light for 8,000 hours, it’s clear from our data so far that

one 15-watt compact fluorescent bulb is required. Therefore, the reference flow for the compact

fluorescent technology in our analysis should be defined as one 15-watt compact fluorescent bulb.

Now, let’s consider the incandescent bulb. While a single 60-watt incandescent bulb is capable of

providing 800 lumens of light output, a single bulb only has a lifespan of 2,000 hours. Therefore, to

provide 800 lumens of light for 8,000 hours, a total of four 60-watt incandescent bulbs would be

required. Therefore, the reference flow for the incandescent technology in our analysis should be

defined as four 60-watt incandescent bulbs.

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By first defining the function, then the functional unit, and then the reference flows in our analysis,

we see that to compare these lighting technologies on the basis of an equivalent service, we must

compare one 15-watt compact fluorescent bulb to four 60-watt incandescent bulbs.

While it might seem logical at first glance to compare one incandescent bulb to one compact

fluorescent bulb because they both have the same lumen output, such an analysis would be

incorrect because these two bulbs do not have the same lifespans. Only by comparing reference

flows on the basis of functional equivalence can we properly compare these two lighting

technologies, which means that we must compare them on the basis of both lumen output and bulb

service life.

I hope this simple example helped you better understand functional units and reference flows, and

the importance of defining functional units that accurately capture the service being provided by a

product, which in the case of light bulbs, included both illumination and lifespan. We’ll brainstorm

additional examples of functional units and reference flows on the discussion forums this week.

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Lecture 9: Scope definition: initial system boundaries Transcript

Today we’ll discuss the next step in the scope definition process: defining the initial system

boundaries, which determine the specific unit processes that will be included in our life-cycle system

model.

As I stressed in the last lecture, all elements of an LCA

study’s scope must directly support the stated goals of

the study. That’s why I used the term initial system

boundaries, because, quite often in an LCA, we must

change and refine the system boundaries based on

what we learn in the inventory analysis and impact

assessment steps. Moreover, if the goals of the study

change based on what we learn in later steps, we must

often also adjust the system boundaries to support the

new goals. That’s why iterations between LCA steps

are so clearly depicted in our LCA figure.

So let’s discuss defining system boundaries using my

plastic bag LCA model as an example.

Recall that I’ve reserved the first tab in the spreadsheet for goal and scope definition. Here we see

the goals for my plastic bag LCA, which I defined in the last lecture. I’m now going to add the

functional unit and reference flow for my plastic bag LCA, given that these two definitions have a

strong influence on my system boundaries. To keep things simple at first, I’m going to assume that

my plastic grocery bag is only used once and only for the purposes of carrying groceries home.

Therefore, my functional unit is to contain and carry 9 liters of groceries for one trip and my

reference flow is a one 5 gram HDPE grocery bag.

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Now, to determine my initial system boundaries—that is, which unit processes are included in my

model of the plastic bag life cycle – I need to consider the goals of my study. My intended

application is to “identify environmental “hot spots” in the plastic bag life cycle,” and the purpose of

my study is to “determine stages/impacts for further examination” in future research. These goals

suggest that this LCA needs to be exploratory and inclusive in nature, which indicate to me that

when starting out I need to include all stages of the bag’s life cycle as well as all relevant

environmental impacts. I therefore need to choose a system boundary that includes the raw

materials acquisition, manufacturing, distribution, use, and end of life stages. Let’s take a look at

the unit processes I’ve selected to meet these goals.

But before we do so I want to quickly discuss the type of LCA we’ll be conducting in this course.

There are two major approaches to LCA: attributional and consequential. The aim of attributional

LCA is to describe the environmental impacts that can be attributed to a particular system or

product in retrospective fashion. In this course, we’ll be employing the attributional approach by

describing the impacts attributable to my plastic grocery bag and to your bottled soft drink by

collecting data on existing life-cycle systems for these products.

The aim of consequential LCA is a bit more complicated. Put simply, consequential LCA strives to

also describe the environmental impacts induced by a product in the greater economic system that

surrounds it. For example, in a consequential LCA of biofuels, one might consider how changes to

land use for biofuels affect regional and global food supply systems. Because this is an introductory

course, we’ll limit our focus to attributional LCA. However, some recommended readings on

consequential LCA will be provided in the course notes for those of you who are interested in

pursuing this more advanced topic later.

So let’s get back to my spreadsheet model. On the second tab of my worksheet, I’ve created a

drawing of my unit process system, which defines my system boundaries. You can see that I’ve

included a number of unit processes across the plastic bag life cycle.

First note how I’ve drawn my system diagram. Each unit process is represented by a box. Major

flows of inputs and outputs between the boxes, and from nature, are included in the diagram. The

goal is to graphically convey the structure of the life-cycle system I’m modeling and the

interdependencies that exist between unit processes, but to do so in the simplest and cleanest way

possible. As such, I’ve included only the major elementary and technosphere flows that govern the

economics of the real-world system, and omitted the many flows of emissions to nature that would

quickly make my diagram way too complex to interpret.

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9.1

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In practice, system boundaries should clearly define:

1. The boundaries between nature and the system under study;

2. The boundaries between included and excluded unit processes in the system under study;

and

3. The boundaries between the system under study and any related external systems that

might share flows.

Second, note that my system diagram only depicts disposal to landfill at the end of life stage. I’ve

done this to keep the model simple for now, but we’ll expand it to include other end of life options

later in the course.

The important point here is that when determining what unit processes to include within our system

boundaries, there are often different unit process systems we can select that still meet the stated

goals of our study. For example, my inclusion of only landfilling at the end of life stage will still allow

for identification of environmental hot spots in the plastic bag life cycle, but only for plastic bags

that are disposed of via landfill. While I’ve met the strict definition of my goals, I haven’t chosen a

system boundary that might be more representative of many real-world systems in which plastic

bags might also be recycled or used for waste energy recovery. For now, I’ll limit my model to only

consider landfilling, but later we’ll include other options.

However, I have identified a methodological limitation to my study; namely, that my life-cycle

system only considers landfilling at the end of life stage. Therefore, I’ll go back to my goal definition

and note this as a limitation of my current study design.

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Third, note that I’ve included a limited number of unit processes to model each life-cycle stage.

How did I know how many unit processes to include in my system, and specifically which unit

processes to include? Well, when defining system boundaries one must have some basic

understanding of the processes that comprise the life-cycle system. What this means is that some

background research is required. If you’re modeling a real world system that you can observe,

determining which unit processes to include often involves observing the actual systems and

understanding which components in the system make sense to model as unit processes. And which

unit processes make sense to model is often determined by the real-world processes and equipment

from which data can be collected directly.

As a quick note, in an LCA, data that are collected directly from the specific process we are modeling

are called primary data, while data for general processes reported by somebody else are called

secondary data. For example, data obtained from commercial LCA databases are inherently

secondary data, as are data obtained from literature sources.

In my case, because I’ll be exclusively using secondary data, I constructed the systems diagram

based on unit processes I identified from the literature, including previous LCAs of plastic grocery

bags. Importantly, my selection of unit processes was informed by the availability of life-cycle

inventory data for my product system. In practice, you’ll often find that your choice of system

boundaries is determined in part by which unit process data are available to model your system.

Moreover, when considering the inventory analysis step we also have to consider the impact

assessment step.

Why is that? As you’ll learn soon, assessing a given environmental impact, say, global warming,

requires that the life-cycle inventory contain very specific flow data related to that impact. For

global warming, this would include flows of carbon dioxide, methane, and nitrous oxide.

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Therefore, another important component of scope definition is to define the life-cycle impact

assessment methods and categories that will be included in the study. By defining impacts, we

define inventory requirements. And by defining inventory requirements, we can now select system

boundaries and included unit processes to meet these requirements. This process is another prime

example of the iterative nature of LCA.

We’ll learn more about the impact assessment step and the different impact categories one can

analyze in LCA later in this course. For now, I’ve listed the impact assessment method and impact

categories to be included in my plastic bag LCA on the second tab of my spreadsheet.

Note also that I’ve labeled a portion of my system diagram as “foreground processes” and another

portion as “background processes.” These are common terms in LCA, and their precise definitions

vary across the literature. In this course, we’ll treat foreground processes are those which are under

direct influence of the producers and consumers in a product system and background processes as

those which are typically not under such direct influence. For example, in my plastic bag life-cycle

system, how the bag is manufactured, distributed, used, and discarded is under the direct influence

of the manufacturers, distributers, and consumers.

You can think of background processes as those that supply fuels and materials that are more like

commodities, meaning they aren’t created exclusively for use by a particular product system.

Examples of this are the electricity and natural gas consumed in the manufacture of my plastic bag,

and perhaps the ink that is used to apply a logo to the plastic bag.

Defining foreground and background processes is important because it guides us on how specific

our unit process inventories need to be. For my plastic bag, to credibly model present day life-cycle

systems I need to know how natural gas is converted into ethylene, how ethylene is converted to

HDPE pellets, how these pellets are manufactured into bags, and how these bags are distributed,

used, and discarded. Thus, these are unit processes for which I’d like individual inventories.

For background processes, we can typically use aggregated “cradle to gate” inventories that

combine multiple process steps as a matter of convenience. For example, I’m fine with using one

aggregated unit process inventory for producing the diesel fuel that goes into the truck used during

the plastic bag distribution stage. Note that background process inventories must still align with the

geographical and temporal characteristics of foreground processes—for example, I don’t want to

use an electricity production inventory from Germany in 1995 if my plastic bag life cycle occurs in

the United States in the present day—but we’ll discuss that more in future lectures.

Lastly, how to we know just how broadly to extend our system boundaries? For example, I’ve

included key unit processes for manufacturing plastic bags, but do I also include the unit processes

for producing the capital equipment that manufactures those plastic bags? Or the steel that is

required to manufacture the capital equipment that manufactures the plastic bags? Often in LCA,

we need to apply what are known as “cut off” rules in our system boundaries to make the LCA

feasible from a time and resource perspective, since it’s impossible to trace back every flow.

Moreover, many of these flows have negligible effects on the results of our study, so we want to

9.6

9.7

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exclude them on the basis of mathematical insignificance as well. For example, while the

production of one piece of capital equipment itself might lead to high environmental burdens, when

we consider that that one piece of equipment might be used to manufacture millions of plastic bags,

the environmental impacts of that equipment per bag manufactured are negligible.

In practice, there are some “cut off” rules we follow as a matter of course in many LCAs, such as

excluding the flows associated with manufacturing capital equipment, transportation equipment, or

manufacturing plants. However, we might also apply “cut off” rules to more direct flows into our

product system if excluding them won’t compromise the goals of our study. There is also the matter

of allocation, which, if you recall from our discussion of unit process inventories, refers to the

process of choosing which flows to attribute to a product system when such flows are shared with

other product systems. For example, if my plastic bag gets recycled into plastic lumber at the end of

life stage, should the flows associated with HDPE pellet production be assigned to the plastic bag life

cycle, the plastic lumber life cycle, or both?

“Cut off rules” and allocation procedures are both very important topics, which we’ll discuss further

when covering the inventory analysis step. For now, just remember that any future decisions

related to cut off rules and allocation procedures may require us to come back and adjust our

system boundaries.

Starting today, you’ll be able to download the spreadsheet for your class project: an LCA model of a

bottled soft drink. You’ll notice that we’ve already filled out the goal definition and functional unit,

system boundaries, and impact assessment elements of the goal and scope definition on the first

tab. You’ll also notice that we’ve included an initial system diagram with included unit processes,

foreground processes, and background processes for the life-cycle of bottled soda, assuming for

now that all bottles are sent to landfill at the end of life stage. Over the coming weeks you’ll be

building out this spreadsheet model in tab by tab fashion, applying the concepts you learn in each

lecture and using my plastic bag spreadsheet model for guidance.

For now, please take a close look at the first two tabs. I’ll see you next time!

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Additional notes

In our first course offering, a leading LCA expert offered the following helpful suggestion for how

students can understand the differences between attributional and consequential LCA: “An

attributional product system is composed of the activities that have contributed to the production,

consumption and disposal of a product, that is, tracing the contributing activities backwards in time.

A consequential product system is composed of the activities that are expected to change when

producing, consuming and disposing of a product, that is, tracing the consequences forwards in

time."

Here are some additional public domain readings (both short and long) to better understand the

difference between attributional and consequential LCA:

Blog post by Craig Aumann, “Attributional versus Consequential LCA,” March 1, 2010, Eco-

Efficiency Action Project: http://eco-efficiency-action-

project.com/2010/03/01/attributional-versus-consequential-lca/

Ecometrica Press Technical Paper by M. Brander, R. Tipper, C. Hutchison, and G. Davis,

“Technical Paper | Consequential and Attributional Approaches to LCA: a Guide to Policy

Makers with Specific Reference to Greenhouse Gas LCA of Biofuels,” April, 2008:

http://www.globalbioenergy.org/uploads/media/0804_Ecometrica_-

_Consequential_and_attributional_approaches_to_LCA.pdf

Chapter 1 of B.P. Weidema, T. Ekvall, and R. Heijungs, “Guidelines for application of

deepened and broadened LCA,” Deliverable D18 of work package 5 of the CALCAS project,

July, 2009, http://www.leidenuniv.nl/cml/ssp/publications/calcas_report_d18.pdf

Correction: Since the first offering of the course, we’ve updated and simplified the unit process

system diagram in our HDPE grocery bag LCA spreadsheet model. The image in the lecture video is

no longer valid; see the new image contained here in the lecture notes. Importantly, only the major

unit processes in the system are depicted, with single arrows between them depicting major

product flows. This simplified view is the way many unit process systems models are depicted in

LCA reports, given the goal is to convey the system boundaries, included unit processes, and major

product flows intuitively and with as little confusion as possible. For an example of a simple but

complete unit process system diagram, see page 19 of the UK Environment Agency’s LCA of grocery

carrier bags:


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http://eco-efficiency-action-project.com/2010/03/01/attributional-versus-consequential-lca/

http://eco-efficiency-action-project.com/2010/03/01/attributional-versus-consequential-lca/

http://www.globalbioenergy.org/uploads/media/0804_Ecometrica_-_Consequential_and_attributional_approaches_to_LCA.pdf

http://www.globalbioenergy.org/uploads/media/0804_Ecometrica_-_Consequential_and_attributional_approaches_to_LCA.pdf

http://www.leidenuniv.nl/cml/ssp/publications/calcas_report_d18.pdf



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Note that there are no shared flows with external systems and no excluded unit processes to

indicate thus far in our unit process system diagram. However, you’ll encounter more complicated

systems diagrams that do depict such boundaries as you move forward with LCAs of more

sophisticated product systems.

Note that definitions of primary and secondary data can vary slightly across the LCA community. In

this course, we’ll use the definitions that appear here in the lecture notes. One finer point in our

definitions is that data that are collected directly from the specific real-world process(es) under

study are always considered primary data, even if they are collected and reported by somebody

else. For example, inventory data collected from the specific HDPE resin manufacturing plant in my

system would be considered primary data, even if I did not collect them myself. However, if I use

inventory data collected from one or more unknown HDPE resin manufacturing plant(s) (as occurs

when one uses general inventory data from a commercial LCI database) to represent the HDPE resin

manufacturing unit process in my system, then I would be using secondary data.

For another explanation of foreground and background data, see page 10 of the “LCA 101” report.

Correction: In the lecture video, I said “In any LCA, we need to apply what are known as “cut off”

rules …” However, as you’ll learn later in this course, the use of input-output or hybrid LCI methods

in an LCA can eliminate the need for “cut off” rules. Therefore, I should have said “Often in LCA, we

need to apply what are known as “cut off” rules …”

We’ll be releasing the spreadsheet models with instructions midway through Week 3 to give you

time to familiarize yourselves with the lecture material. Additionally, in this offering of the course,

you’ll be responsible for completing several of the features we’ve mentioned here, to make for a

more complete “learning by doing” experience than was possible last time.

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Lecture 10: Scope definition: requirements for data and data quality Transcript

Let’s quickly review where we stand in the goal and scope definition step of the LCA process. We’ve

learned that the first task is to define the intended applications, purpose, and target audience of the

analysis. Once we define these goals, we must then determine the function and functional unit of

the product or service we are analyzing. Based on the functional unit, we can define the reference

flows. And, based on the reference flows, we can construct the initial system boundaries based on

our chosen life-cycle inventory methods and life-cycle impact assessment methods.

We’ve completed these tasks in our current plastic bag and bottled soda LCA models. We’ve also

learned that we may need to revisit all of these decisions based on what we learn in the inventory

analysis, impact analysis, and interpretation steps of an LCA.

Today we’ll talk about the next steps in the scope definition process: determining the data

requirements and the data quality requirements necessary to meet the goals of our study. The term

data requirements refers to the types of flow data we need in our unit process inventories, while

the term data quality requirements refers to the characteristics those data must have in order for us

to have confidence in our results. Our discussion today will be based on guidance from the ISO

14040 series of standards for data and data quality requirements.

Let’s begin with determining data requirements. All data used in an LCA must directly support the

stated goals of the LCA, so let’s return to the goals I defined for my plastic bag as an example. The

intended application of my plastic bag LCA is to “identify environmental “hot spots” in the plastic

bag life cycle. This broad goal suggests I should strive to include all relevant environmental impacts

in my study, which requires that I include the relevant elementary flows for quantifying all such

impacts. In general, an LCA should strive to include the broadest range of flows possible, but, as

we’ve discussed, this can sometimes be difficult in practice due to time, resource, or data

constraints.

Let’s take a look at my LCA model, where I’ve listed the elementary data flows I’ve determined I

need to capture in order to include all relevant environmental impacts, and thereby meet the goals

of my study. Given the length of my list, I’ve included my data requirements on separate tab in my

spreadsheet model. We can see that I’ve documented the elementary flows of resources, emissions

to air, emissions to water, and emissions to land required in my unit process inventories. I’ve also

listed the specific name of each flow, using generally-accepted names for each type of mass. You’ll

do the same in our spreadsheet model for the bottled soda life cycle.

You may now be asking yourself, how do I arrive at such a detailed list of flow data before actually

compiling the inventory data for my LCA? For my plastic bag LCA, I’ve included the flow data that I

know are required for my chosen life-cycle impact assessment categories, as discussed in the last

lecture. We’ll discuss the impact assessment step more in future lectures, but for now let’s take a

quick peek ahead so you can better visualize what I’m talking about here.

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This is the tab I’ll use to convert emissions of greenhouse gases in my plastic bag life cycle inventory

into units of carbon dioxide equivalents, which allows me to calculate an impact metric called

“global warming potential.” As we can see, to calculate global warming potential I need inventory

data for any flows of carbon dioxide, methane, nitrous oxide, and other air pollutants from my unit

processes. Thus, my data requirements should include these types of air pollutant flows if I want to

calculate global warming potential. And, when we review my data requirements list, we can see

that I have indeed included flows of these key air pollutants.

If I find in my inventory step that data on these air pollutants is not available, I can remove them

from my data requirements. However, if I do so, I may also need to adjust the goals and scope

defined for my LCA, since I’d no longer be able to calculate global warming potential. Moreover, I’d

also have to state explicitly that a limitation to my study is that it does not consider global warming

potential.

Alternatively, if one knows which data sources one will use for the life-cycle inventory step, one can

also look ahead to those data to determine which data flows can and can’t be included in the list of

data requirements. The truth is, in many LCAs, the list of required flow data evolves with the

inventory and impact steps based on what data are available and the resources and time available

to the analyst. This is yet another example of the iterative nature of LCA.

Lastly, in the data requirements field of my scope definition, I want to indicate that all data used in

my study will come from secondary data sources.

This is because I don’t have direct access to any actual processes in the plastic bag life cycle and also

because I’m conducting an exploratory study with limited resources. In practice, you’ll find that

LCAs can be made up of data that are measured, estimated, or obtained from secondary sources,

and that LCAs often include mixtures of these data types.

So, now that I’ve identified what flow data I need in my unit process inventories, I need to think

about the quality of those data. In other words, what characteristics do the data need to have for

me to have confidence that my LCA results have met the goals of my study? After all, LCA is a

modeling technique, and the outputs of any model are only as good as its inputs.

So how do we define the fuzzy concept of data quality? Fortunately, the ISO 14040 standards have

defined the different dimensions of data quality that we should be careful to address in any LCA.

Here is the list, which I’ll go through one by one:

Time-related coverage, which refers to the desired age of the data; for example, the results

of my plastic bag LCA will be far less relevant if I’m using data from 20 years ago than if I’m

using data from last year. When collecting primary data, time-related coverage can also

refer to the minimum length of time over which the data should be collected;

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Geographical coverage, which refers to the geographical area from which data for unit

processes should be collected to satisfy the goals of the study. For example, one can gather

data on plastic bag manufacturing in a given state, for a given nation, or at the global

average level. In my study, I’ll be analyzing the plastic bag life cycle in the United States, so

it wouldn’t be ideal for me to use data obtained for plastic bag systems in Asia;

Technology coverage, which refers to the technology mix for a given unit processes. The

technology used in any unit process can vary greatly based on the type of technology, the

vintage of the technology, or the efficiency of the technology. For example, there may be

short haul single unit trucks or long haul tractor-trailer trucks that distribute my plastic bag

to the grocery store. Furthermore, within the single unit truck category, there may be

trucks with high efficiency engines and standard efficiency engines. Thus, in my unit process

for trucking, I need to know what mix of truck types and performance was used to generate

the inventory so I can be sure the technology mix reflects the real-world mix for distributing

my plastic bag.

In practice, time-related coverage, geographical coverage, and technology coverage can often be

ascertained from well-documented data sources, including many commercial LCA databases. As

such, you should get in the habit of always understanding and documenting these data quality

indicators in your LCA studies. In fact, you’ll do this is by assessing the:

Representativeness of the data, which is a qualitative assessment of the degree to which

the data set reflects the true population of interest with respect to geographical coverage,

time period, technology coverage. The more representative your data are, the closer they

are to describing your actual system of study.

Two other critical indicators of data quality are consistency and reproducibility. In fact, these are so

important that many LCA experts recommend consistency and reproducibility be used as

fundamental guiding principles throughout an LCA, rather than a retrospective measure of quality. I

share this view, so we’ll stress these as follows:

Consistency is a qualitative assessment of how uniformly the study methodology is applied

to across the entirety of study steps and components. For example, for consistency we

want to use the same list of flows across unit processes, the same cut-off criteria for

excluded unit processes, data sources of similar quality, and data with the same level of

temporal, geographical, and technological coverage in all unit process inventories. We’ll

strive for consistency in all of these respects in our plastic bag and soda bottle LCA models.

Reproducibility is a qualitative assessment of the extent to which the documentation of

methods and data values allows independent reproduction of the results in the study. The

hallmark of good science is that it can be reproduced by independent experts. In our LCAs,

this means that we’ll be documenting all data sources, calculations, estimation methods,

and modeling approaches so that they are crystal clear to your audience. In that way,

independent experts can understand your methods and validate or critique your results.

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Unfortunately, I’ve seen many LCA studies that fail with respect to reproducibility of results. When

results are presented that I can’t replicate, input variables are not documented explicitly, or

calculations aren’t clearly explained, I simply don’t trust a study’s results. And when the audience or

scientific community doesn’t trust a study’s results, there is no point in conducting the study in the

first place. That’s why you’ll develop the habit of thorough documentation in this course.

Two final measures of data quality recommended in the ISO 14040 guidelines are;

Precision, which is the measure of variability of the data values for each data category

(variance)

Completeness, which refers to the percentage of locations reporting primary data out of the

potential number in existence for each data category in a unit process;

I’d like to close with a few key points. First, when a study contains a comparative assertion made to

the public, the ISO 14040 guidelines require that all of the aforementioned data quality elements be

documented explicitly. This is done to ensure full transparency for any public claims. Second, due

to time restrictions I had to review these data quality elements quickly, but you need to understand

them very well. Thus, I’ve provided some readings recommendations in the lecture notes. But don’t

worry, you’ll gain familiarity with these data elements as we move forward into the inventory step.

Third, as with all other aspects of the LCA process, in practice one must iterate on data quality

requirements depending on study goals, scope, and inventory data and how all these steps change

over time. And fourth, note that as part of the LCA process we’ll come back to all of these data

quality elements later and score how well we did as the study nears completion.

Additional notes

See the “Data Requirements” tab of the Week 4 version of the HDPE grocery bag LCA spreadsheet

file, which contains the list of flow data that are included in the plastic bag LCA model. Note that

this tab has a slightly different appearance than the screenshot shown in the lecture video due to

recent changes to the spreadsheet. These flow data were chosen on the basis of: (1) representing

all major environmental impact categories in the impact characterization model chosen for this

analysis (the U.S. EPA's TRACI, which we’ll discuss in Week 8); and (2) their availability in the

publicly-available life-cycle inventory data sources selected for this study. The publicly-available

data sources we used for the inventories are listed in each unit process inventory tab. In practice,

data requirements are typically determined by the availability of data to model the system, the

environmental impacts one includes in the analysis, the flow data required by the impact

characterization model to analyze the included environmental impacts, and the data quality

requirements specified for the study (e.g., geographical, temporal, and technology coverage). Take

a look at the data requirements section of the Environment Agency’s LCA of carrier bags for one

example of how data requirements are documented in practice.

This tab will be made available in the Week 7/8 version of the HDPE grocery bag LCA spreadsheet,

which is when we’ll be discussing the life-cycle impact assessment (LCIA) step of an LCA. For now,

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just understand that in order to quantify a given environmental impact in an LCA, we need to ensure

that the mass flows that are relevant to that impact are included in our life-cycle inventory data.

Take a look at the “Data Quality Requirements” tab of the Week 4 version of the HDPE grocery bag

LCA spreadsheet file, which contains descriptions of the desired geographical, temporal, and

technology coverage of the LCA in order to meet the stated goals of the study. Because we are

interested in identifying environmental “hot spots” in our example HDPE grocery bag LCA, the

statements of geographical, temporal, and technology coverage are fairly broad. For example, the

desired temporal coverage is data from within the past 10 years, given that older data are likely to

be sufficient for the identifying major “hot spots” in the plastic bag life cycle. Take a look at the

data requirements section of the Environment Agency’s LCA of carrier bags for one example of how

data quality requirements are documented in practice.

In Week 6, we’ll discuss assessments of representativeness and other data quality indicators in more

depth. You’ll also perform a representativeness check on your bottled soft drink LCA toward the

end of this course. For now, just understand the basic concept of representativeness and think

about the representativeness of your analysis as you build out your bottled soft drink LCA model.

Furthermore, note that in our spreadsheet LCA models, temporal, geographical, and technology

coverage are listed at the very top of each unit process inventory tab so that we consciously track

each data quality element for every inventory in our model. This careful attention documenting

data quality dimensions across an LCA helps one constantly check for consistency when compiling

data and makes assessment of overall study data quality much easier later on.

We’ll discuss a structured approach for checking the consistency of an LCA in Week 9, when you’ll

also perform a consistency check on your bottled soft drink LCA. For now, just understand the basic

concept of consistency as defined above and think about the consistency of your analysis as you

build out your bottled soft drink LCA model. Furthermore, you’ll ensure reproducibility of your

bottled soft drink LCA model by documenting all data sources and modeling assumptions and

capturing all calculations in your spreadsheet model. Note that we’ve carefully documented all data

sources within the HDPE grocery bag and bottled soft drink spreadsheet files.

For a detailed discussion on identifying and specifying data requirements and data quality

requirements in an LCA, see Sections 6.8 and 6.9 of the following report. The detailed guidance

provided in these sections underscores the importance of, and the nuances associated with,

establishment of important data and data quality requirements. In this course, we are taking a very

basic approach to identifying data requirements and data quality requirements in the interest of

time. In practice, however, these steps require much thought and careful planning. You’ll

appreciate just how complicated and important these steps are in practice when reading Sections

6.8 and 6.9!




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Lecture 11: Scope definition: review and reporting Transcript

Welcome back! Today we’ll wrap up our discussions of goal and scope definition. By now, it should

be clear to you just how important proper definitions of goals and scope are for a credible and

transparent LCA. We have just two more topics before we move on to collecting and working with

data in the inventory analysis step.

The first topic is critical review. As I mentioned in the previous lecture, one hallmark of good science

is that it can be reproduced by others. Another hallmark of good science is that it has stood up to

critical review by one’s peers. In a critical review, independent experts will evaluate an LCA to

ensure that its stated goals are supported by its scope and methods, its data sources are

appropriate and credible, its methods are of sufficient rigor and transparency while also being free

of major errors, and that its results are properly interpreted and communicated. In short, critical

review provides a valuable external quality check by knowledgeable experts.

There are several reasons one might want a critical review. First and foremost, if an LCA makes

comparative assertions to be disclosed to the public, the ISO 14040 standards require that the study

undergo critical review by a panel of independent experts before being made available to the public.

Second, external peer review can provide added credibility to a study, because the audience knows

that knowledgeable experts have performed an important quality check. Third, I’ve always found

that external peer review only strengthens a study, because independent experts can often offer

helpful advice on better data sources, better methods, and better ways of communicating the

study’s contents.

A critical review can either occur at the end of a study, or concurrently with it. Concurrent review

means that reviewers provide feedback on the goal and scope definitions as they are formed, and

on preliminary results from the inventory analysis and impact assessment steps. One advantage to

concurrent review is that it provides the analyst with early feedback so that, when changes are

necessary, they can be made before getting too far ahead when changes become more difficult to

implement.

The extent and type of critical review should be specified in the scope of a study. Similarly, who the

reviewers are and their level of expertise should be stated as well. Now, it’s very important that the

reviewers are independent experts, meaning they have not been involved in the study and don’t

have a vested interest in its outcomes. Moreover, there are guidelines for setting acceptable

qualifications for reviewers, which I’ve referred you to in the lecture notes. Lastly, a review

statement and author’s responses to reviewer comments are typically included in the final LCA

report for full transparency.

Let’s now move on to our second topic: the reporting format. In this course, you won’t be writing a

formal report for your bottled soft drink LCA study. However, it is important that you understand

the elements of a proper report for when you do issue such reports in the future. Also, note that

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nearly all of the elements of a proper report will be included in our spreadsheet models in the

interests of documentation and transparency.

The ISO 14040 standards state that reports should ensure that:

Results and conclusions are reported completely and accurately, and without bias to the

intended audience,

All results, data, methods, assumptions, and limitations are reported transparently, and

Results and interpretation are used in a matter that is consistent with the goals of the study.

To accomplish these goals, many practitioners follow a reporting template for LCA studies, which

has also been laid out by the ISO 14040 standards. There are seven major sections to this template,

which I’ll explain briefly one by one:

The first section contains general aspects of the LCA study, including the study commissioner, the

date of the report, and, when required, a statement that the study has been conducted in

accordance with ISO 14040 guidelines.

The second section describes the goals defined for the study, including the purpose, intended

applications, the target audience, who conducted the study, and whether comparative assertions

will be disclosed to the public.

Recall that we’ve documented all of these goal definition elements on the first tab of our

spreadsheets.

The third section describes the scope defined for the study, including the function, functional unit,

system boundaries, data and data quality requirements, the impact assessment methodology,

allocation methods, cut-off criteria, and critical review requirements.

Recall that we’ve documented most of these elements on the first tab of our spreadsheet as well.

Moreover, before we complete our study, we’ll go back and document allocation methods and cut-

off criteria, too.

The fourth section describes in detail the inventory analysis step, including the types of data, the

sources of data, descriptions of unit processes, and calculation methods. This section can also

include the results of any sensitivity analyses that were performed to refine the system boundaries,

which we’ll learn how to do later in this course. As you build your bottled soft drink LCA model,

you’ll include most of these reporting elements as part of your modeling structure. For example,

each unit process inventory will be contained on a separate tab for ease of review.

The fifth section covers the life-cycle impact assessment step. Ideally, this section describes the

impact assessment method that was chosen, how the impact assessment method supports the goals

of the study, and other details that we’ll discuss when we cover impact assessment later in this

course.

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With respect to your spreadsheet model, your impact assessment methods and calculations will be

contained on dedicated tabs, which will serve as your documentation.

The sixth section covers the interpretation step, including your results, your data quality

assessment, and any assumptions and limitations that would affect how your results should be

interpreted.

Each of these elements will also occupy separate tabs in your spreadsheet model.

The seventh and final section describes the critical review, including the type of review, the review

report, and responses to comments. Obviously, this section is only required for reports that

undergo critical review.

I’ve found that having this reporting structure in mind as I conduct an LCA study helps me better

document and organize my information along the way, which makes report writing much easier

when the study is complete.

Again, even though you won’t write a final report, I hope it’s clear how most of these required

sections will be captured automatically in your spreadsheet by the way you’ll build it out. My goal

is for you to document all aspects of your study within your spreadsheet so that, hopefully, report

writing will be a natural extension of what you’ve learned in this course.

That said, I want to stress that there are more details associated with each of these sections than

I’ve discussed here, so I’ve provided some recommended readings in the lecture notes that you can

consult you can learn more. You should definitely do so if you’re planning to write an LCA report

anytime soon. Lastly, so you can see some real-world examples of how good LCA reports are

structured, I’ve provided some links to recent publicly-available LCA reports in the lecture notes.

I’ll see you next time!

Additional notes

For some examples of critical review -- including the involvement of independent experts, their

comments, and the authors’ responses to their comments – see the LCA reports that are listed

under note 11.3 below.

As you’ll recognize toward the end of this course, the design of our HDPE grocery bag LCA and

bottled soft drink LCA models closely follows these major reporting elements and sections. In

practice, when conducting an LCA, careful planning of the analysis, data sources and structure, and

the model format can make writing a formal report much easier.

Here are a few recent studies that generally follow the ISO 14040 guidelines for reporting. Take a

look at them and see if you can identify the major sections we’ve discussed above. Note how this

reporting format is valuable for several reasons:

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It forces one to document clearly all major study design, data, analysis, and interpretation

decisions.

It provides a step-by-step overview of the entire LCA process, which makes for intuitive

reading.

It allows the audience to easily identify analysis assumptions and decisions of interest by

looking to the appropriate section; for example, one can easily find the definition of the

functional unit by jumping to that section (as opposed to hunting for it in a poorly formatted

report).

It facilitates critical review by explicitly documenting all major steps and assumptions in an

LCA study.

There are many other examples of well-formatted LCA reports in the public domain. See if you can

find some yourself! Also, you may also encounter poorly-formatted LCA reports in practice. Having

an idea of how useful a well-formatted report can be is a good reminder to follow best practice

reporting protocols in your own LCAs!

Environment Agency (2011). Life Cycle Assessment of Supermarket Carrier Bags. Bristol, UK.

Report: SC030148.

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/291023/s

cho0711buan-e-e.pdf

World Steel Association (2011). Methodology report: Life cycle inventory study for steel

products. Brussels, Belgium. ISBN 978-2-930069-66-1.

https://www.worldsteel.org/dms/internetDocumentList/bookshop/LCA-Methodology-

Report/document/LCA%20Methodology%20Report.pdf

PE Americas (2010). Life Cycle Impact Assessment of Aluminum Beverage Cans. Boston, MA.

http://www.container-recycling.org/assets/pdfs/aluminum/LCA-2010-AluminumAssoc.pdf

Bio Intelligence Service (2010). Nordic Life Cycle Assessment: Wine Packaging Study. Paris,

France.

http://www.tetrapak.com/se/DocumentBank/LCA%20Nordic%20Wine%20comparative_Au

gust_10_with%20final%20statement.pdf

Trisha Montalbo, Jeremy Gregory, and Randolph Kirchain (2011). Life Cycle Assessment of

Hand Drying Systems. Materials Systems Laboratory, Massachusetts Institute of Technology.

http://msl.mit.edu/publications/HandDryingLCA-Report.pdf



https://www.worldsteel.org/dms/internetDocumentList/bookshop/LCA-Methodology-Report/document/LCA%20Methodology%20Report.pdf

https://www.worldsteel.org/dms/internetDocumentList/bookshop/LCA-Methodology-Report/document/LCA%20Methodology%20Report.pdf

http://www.container-recycling.org/assets/pdfs/aluminum/LCA-2010-AluminumAssoc.pdf

http://www.tetrapak.com/se/DocumentBank/LCA%20Nordic%20Wine%20comparative_August_10_with%20final%20statement.pdf

http://www.tetrapak.com/se/DocumentBank/LCA%20Nordic%20Wine%20comparative_August_10_with%20final%20statement.pdf

http://msl.mit.edu/publications/HandDryingLCA-Report.pdf

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Lecture 12: Life cycle inventories: the basics Transcript

We’re now ready to move on to the inventory analysis

step in the LCA process.

I know you’re probably eager start collecting and

analyzing data for your bottled soda LCA, so today we’re

going to jump right into it with an overview of our

inventory modeling approach. We’ll use my plastic bag

LCA spreadsheet as an example, but you’ll be laying out

your bottled soda spreadsheet model in exactly the same

way.

But first, I wanted to remind you that we’ll be using only

secondary data in this course as a matter of practicality.

However, in the future you may need to compile primary

data directly from the systems you are studying, so let’s

quickly discuss how one actually gets primary data for unit

process inventories. In practice, there are many different ways of gathering primary data, but here

are some of the most common:

Direct process measurements over a period of time

Utility and fuel bills

Process monitoring and control software

Meters readings from equipment

Equipment and process specifications, and

Company data logs and records

If you’re interested, recommended readings for further guidance on collecting primary data have

been provided in the lecture notes. We’ll spend the rest of this lecture observing how I’ve laid out

my plastic bag LCA spreadsheet model in the inventory analysis step.

First, note that I’ve used a single tab to contain the inventory for each unit process in my life cycle

system. I’ve labeled the tab with a brief name that describes the unit process, making sure to use

the same unit process name I used in my system diagram. I’ve chosen to order my tabs roughly in

the order they appear in the life cycle of my plastic bag, but you can order them in any way that

makes sense to you, for example, alphabetically.

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Second, for ease of navigation around my model, I’ve made use of the hyperlink feature in my

spreadsheet software. At the top of each unit process inventory tab, I can jump back to the system

diagram. And I can click on a unit process box in my system diagram to jump to the inventory tab

for that unit process.

Third, I’ve laid out each unit process inventory tab in the exact same way. At the top, I list the unit

process name. In the following rows we’ll be tracking the important data quality characteristics we

discussed earlier: time-related coverage, geographical coverage, and technology coverage. We’ll do

this for each unit process inventory to get in the habit of paying close attention to data quality; this

will also make our data quality assessment later in the study much easier. Moreover, tracking data

quality for each unit process will help us ensure consistency throughout our LCA model.

Fourth, I’ve provided space for additional notes, including any allocation or scope details that help

me interpret my inventory as well as document key data assumptions. These notes are very

important for ensuring transparency and reproducibility of our modeling results.

Lastly, I’ve organized the actual inventory data into the four major categories we discussed earlier:

flows from nature, flows from the technosphere, flows to the technosphere, and flows to nature.

Under each category, I’ve listed the specific flow names that are captured across my unit process

inventories. Note-and this is very important—that I’ve made sure that all flows in each category

appear in the same row in every unit process inventory in my model.

For example, flows of carbon dioxide to nature always appear in this row, whether it be on my

ethylene to HDPE pellets unit process tab or my diesel truck unit process tab. Using the same layout

across my unit process inventory tabs is a matter of convenience; it allows me to easily sum up flow

data across all unit process models. I do this on a tab I’ve labeled life-cycle inventory summary,

where you can see I’ve simply summed up flows of carbon dioxide using a formula that refers to the

same row number in every unit process worksheet. You’ll do the same in your bottled soft drink LCA

spreadsheet.

Note also that we’ve added a seventh column to our standard unit process inventory structure,

which we’ll use for noting references for the data. We’ll use standard citation format for all

references, which you can review in the lecture notes. Carefully recording the sources of all data in

our unit process inventories is critical for ensuring full transparency and reproducibility. I’ve created

a master list of references on the last tab in my spreadsheet, which I’ve organized in alphabetical

order.

For those of you who’ll use commercial LCA software packages in practice, the good news is that you

don’t need to worry about such careful organization of data, rows, and tabs because the software

will track all data relationships for you. In our case, organizing your model in this way will help you

better visualize system flows and data relationships, which should improve your understanding of

the course material.

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To get you started, we’ve provided inventory data tabs for all background processes in your bottled

soft drink LCA model, which can be downloaded today. Specifically, we’ve provided “cradle to gate”

unit process inventories for transportation fuels like diesel fuel, industrial fuels like natural gas, and

for U.S. average electricity generation and distribution. We’ve also provided a life-cycle inventory

summary tab, which you’ll use to sum up flows across your life-cycle system.

In the next few lectures, we’ll discuss how the inventory analysis step works so you can begin

compiling and analyzing unit process inventory data for foreground processes in your bottled soft

drink LCA model. Before too long, you’ll have a complete life-cycle inventory in place, which will

give you a first glimpse of the environmental hot spots in the bottled soda life-cycle.

Additional notes

The following report contains very detailed guidance on the elements of LCI generation and

compilation, including some discussion of primary data collection considerations:







To better understand each of these tabs, refer to the Week 3 HDPE grocery bag LCA spreadsheet

file. Note that these tabs have slightly different appearances than the screenshots shown in the

lecture video due to recent changes to the spreadsheet. Try to follow along as I discuss each tab,

and take some time to understand the content of each tab as well as the calculations that occur to

generate the life-cycle inventory (LCI) results. Here are some additional points to consider:

- Observe how each unit process inventory has its product output expressed as a multiplier

of one for ease of scaling.

- Note that the order of flows has changed compared to the lecture videos. Specifically,

inputs to nature and outputs to nature appear first in each inventory so that these standard

flows appear on the same rows in each unit process inventory across the spreadsheet. This

allows for easy summation of LCI data on the “LCI Results” tab of the spreadsheet (which

lists flows in the same order and rows as the unit process inventories … observe this for

yourself).

- Please read the important notes on the “Read Me” tab of the spreadsheet, which contain

information on how to properly interpret the spreadsheet.

- This spreadsheet will be “built out” in week-by-week fashion over the duration of the

course, based on the topics covered each week. A similar process can be followed as you

develop your bottled soft drink LCA model.

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To keep the model intuitive and simple, all unit processes are represented by aggregated

inventories that contain:

1. only the major inputs and outputs to and from the technosphere that are related to the

physical transformation that occurs in the unit process itself;

2. flows from nature that account for the original resource inputs into the energy systems that

fuel the unit process;

3. flows to nature that arise from the energy systems that fuel the unit process; and

4. flows to nature due to direct emissions from the unit process itself.

For example, consider the aggregated unit process inventory for production of ethylene (see the

“Ethylene Mfg” tab), which is also depicted in the figure below:

1. Inputs from the technosphere include processed natural gas and refined petroleum

products, which are converted into ethylene; the output to the technosphere is ethylene.

2. The production of ethylene includes the consumption of electricity, natural gas, and other

fuels, which are denoted by dashed ovals in the figure. The system boundary for the

aggregated unit process inventory includes the extraction, conversion, and transport

processes necessary to produce these fuels from natural resources. For example, electricity

is produced by extracting and processing coal, natural gas, etc. and using these fuels to

generate electricity in a power plant. Thus, the system boundary includes the extraction/

conversion, transport, and generation processes to convert resources from nature into the

fuels consumed by the ethylene production process. As such, the inputs from nature refer

to the primary energy sources extracted from nature related to all fuels consumed in

ethylene production.

3. The extraction, processing, conversion, and transport of fuels consumed in the production

of ethylene result in emissions to air, water, and land. These emissions are included in the

outputs to nature leaving the aggregated unit process. See the processes denoted by boxes

in the figure; these are the processes from which the emissions to air, land, and water

leaving the system boundary arise.

4. The combustion of fuels in the production of ethylene, and other process-related emissions

(such as waste generation) from the ethylene production process are also included in the

outputs to nature. See the processes denoted by boxes in the figure; these are the

processes from which the emissions to air, land, and water leaving the system boundary

arise.

These aggregated inventories provide a more convenient form for the simple LCAs we’ll conduct in

this course, and allow us to include the life-cycle systems for production of fuels consumed in our

unit processes in an efficient manner. You’ll also work with aggregated unit process inventories in

Homework 4.

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The hyperlink feature has been eliminated from the spreadsheet files given that hyperlinks would

not function properly in all versions of MS Excel and OpenOffice Calc in past course offerings.

A very common reference for proper formatting of citations from different sources (e.g., academic

journals, websites, and technical reports) is:

The Chicago Manual of Style, 16th Edition, University of Chicago, Chicago, IL:

http://www.chicagomanualofstyle.org/tools_citationguide.html

While this statement was true in our first course offering, our current versions of the HDPE grocery

bag LCA spreadsheet and bottled soft drink LCA spreadsheet files will introduce these various unit

process inventory tabs in more gradual fashion so that you can have more “hands on” experience

building the spreadsheet model on your own.

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Lecture 13: Life cycle inventories: mass flows and cut off criteria Transcript

Today we’ll continue learning and practicing the life-cycle inventory analysis step. As you can

imagine, the inventory analysis step is profoundly important in an LCA, because the results of an LCA

are only as good as its input data. Given that this is a short, introductory course, we only have time

to cover the basic elements of inventory analysis. Similarly, given the time constraints, we’ll also be

providing you with many of your inventory data to help you construct your bottled soft drink LCA

model. However, I want to stress that becoming proficient at compiling consistent, representative,

and credible inventories takes a lot of time and practice, especially for complex product systems.

Imagine, for example, the complexity of compiling an inventory for an automobile or a computer,

which contain hundreds of different parts and materials!

My point is, I don’t want you to get the impression that inventory analysis is always so quick and

easy; for this reason, I’ve provided more discussion of inventory analysis in the lecture notes as well

as recommended readings for learning more about this important topic.

So let’s move forward with the inventory analysis of my plastic bag life-cycle.

Recall that in my scope definition, I identified the data requirements for my inventory analysis. With

this knowledge, I can conduct the following tasks to complete my inventory analysis:

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My first task is to collect inventory data for all unit processes in my systems diagram. Since I am

using secondary data, my sources will include public LCA databases, literature data, and perhaps

data I estimate myself. For your bottled soft drink life-cycle inventory, you’ll be compiling data from

sources we provide for you online.

Next, as I collect these data, it’s important to validate them to ensure they meet my defined data

quality requirements, for example, for time-related, geographical, and technology coverage.

Additional validation can include checking to ensure that mass is conserved across a unit process

inventory and looking for obvious errors in the data. Another quick check is to scale up unit process

data to see if they make sense at the regional level. For example, I could multiply my unit process

inventory for HDPE pellet manufacturing by the annual tons of HDPE pellets manufactured in the

United States each year. If doing so results in a total energy use that is greater than the energy use

of the U.S. resin manufacturing sector as a whole, I know there is something wrong with my unit

process inventory!

After collecting my unit process inventory data, I need to scale them up or down in my model to

match the mass flow quantities required to meet the reference flow of my system, which is one 5g

HDPE grocery bag. This scaling is necessary because many unit process inventories you’ll find are

based on product outputs with multiples of 1, as we discussed earlier. Let me show you how I do

this in my plastic bag LCA model.

Because the plastic bag life-cycle is a simple system with relatively few technosphere flows to track,

I’ve created a separate tab for specifying the mass flows necessary from each unit process in my

system to meet my reference flow requirements. In this way, I can make changes to my reference

flow or mass requirement relationships in one place in my model. Not only does this allow me to

easily adapt my model to different mass flow relationships or reference flows, but it also allows me

to keep all my unit process inventories based on product outputs that are multiples of 1. In other

words, I can easily scale my flows up or down without having to change my unit process inventory

data. As I mentioned earlier, I’ve also linked this information to my system flow diagram, which is

something I like to do to help me visualize the system.

Once my unit process inventories are scaled to meet my reference flow, the inventories can be

aggregated to calculate the inventory for the entire life-cycle system. As I mentioned last time, this

is done on my results summary tab, where you can see I’ve put in formulas to add up each flow

across all unit process models.

But before we leave mass flows, let’s talk briefly about “cut off” rules for which flows and unit

processes to exclude from an LCA. While the goal of LCA is to be as inclusive as possible, from a

practicality perspective it makes sense in most LCAs to leave out mass flows or unit processes that

have negligible impacts on the results. The ISO 14040 standards define “cut off” criteria as the

amount of material or energy flow or the level of environmental significance associated with unit

processes or product system to be excluded from a study.

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In practice there are three criteria we can use:

A mass criterion, in which we require that all inputs that cumulatively contribute more than

a defined percentage of the total system mass input must be included;

A energy criterion, in which, similar to mass, we require that all inputs that cumulatively

contribute more than a defined percentage of the total system energy input must be

included; and

An environmental significance criterion, which states that we should include any inputs

that contribute more than a defined amount of select environmental impacts because of

their environmental relevance.

This last criterion is important because we don’t want to exclude small mass flows, for example, if

they can still have a significant environmental impact. For example, very small amounts of mercury

still pose human and ecological health hazards when released into nature.

A good LCA study will state clearly what these cut off criteria are in the scope definition. For

example, one might state that if a mass or energy flow is less than 2% of the total, and these mass

and energy flows contribute less than 2% of environmental impacts, they will be left out of the

study. The specific thresholds are at the discretion of the analyst, but they must always be stated

clearly so that others are aware that the criteria were applied. As you can imagine, identifying mass

and energy flows that can be excluded is an iterative approach. One must first compile an inclusive

inventory, then assess the environmental impacts, and then determine if the cut off criteria have

been met. It’s good practice in LCA to report the specific mass and energy flows that were left out

due to cut off criteria.

In our plastic bag and bottled soda LCA studies, we’ll leave out capital equipment and facilities from

our system boundaries, but we won’t apply any further cut off rules to keep things simple.

However, you should be a aware that cut off rules are quite common in practice and can save a lot

of work when applied carefully in an LCA.

I’d also like to stress that, after any cut off rules are applied, one must go back and refine the system

boundary defintions and system flow diagram to reflect these changes. For example, since I’ll leave

out capital equipment and facilities from my plastic bag LCA system boundaries, I’ll go back and

document this on my scope definition tab.

Lastly, let’s close with another inventory data matter: dealing with missing data. In the next lecture

we’ll discuss the use of estimation as a means of generating one’s own unit process inventories

when such inventories can’t be obtained from primary or secondary data sources. In reality, we

often encounter missing data and it’s important to acknowledge this in the documentation of an LCA

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Additionally, it’s important to explain how you addressed the missing data. Specifically, the ISO

14040 standards advise that when you fill a data gap:

a non-zero data value is explained; for example, did you estimate it or use data from a

similar process?

a zero value is explained; foror example does this mean that a given flow is not present or

you assume that it’s negligible?

You can probably imagine how such documentation is critical for transparency and for

reproducability of your results. Moreover, to ensure your LCA study has consistency, the

explanations for filling data gaps should use similar logic and methods.

Additional notes

For more discussion on the life-cycle inventory (LCI) step in an LCA, and the details and protocols

associated with LCI compilation, the course staff recommends the following resources. Note that

the second resource contains many nuanced details for more advanced reading.

The United States Environmental Protection Agency’s “Life Cycle Assessment: Principles and Practice”, 2006, Chapter 3: http://www.epa.gov/nrmrl/std/lca/lca.html (Note this document is also commonly referred to as “LCA 101.”)














Note that this screenshot appears slightly different than the “Scaling” tab in the HDPE grocery bag

LCA model due to recent improvements to the model.

With the increasing use of input-output (IO) LCI methods (which you’ll learn about in Week 7), and

hybrid methods that combine traditional process-based LCI with IO LCI, the use of “cut off” rules is

becoming less common. However, we’ll cover “cut off” rules in this course so you have some

familiarity with them in case you encounter “cut off” rules in published LCA studies or LCI databases

in your career moving forward. You may be asking yourselves: If I have sufficient data to know

when one or more flows contribute less than 2% to the sum of all flows, why wouldn’t I simply leave

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those flows in my LCA (as opposed to cutting them out) since I’ve already compiled them? In fact, if

you have sound data that meet your stated data quality goals, you can simply leave them in your

analysis even if they represent a small fraction of total flows. However, if including multiple small

flows adds to the complexity or duration of your project, or the inventories for those small flows do

not meet your stated data quality goals (e.g., with respect to time-related, geographical, or

technology coverage), it is sometimes advantageous to exclude them on the basis of well-articulated

and logical “cut off” rules. You’ll get some practice with this approach in Homework 7, in which

you’ll examine the flows of ink in your bottled soft drink LCA project to determine if you can exclude

those flows using simple “cut off” rules.

In this course, we’ll be providing you with many empty cell values in your inventories, which you can

treat as indicative of no or negligible flow quantities. In practice, however, missing or zero flow

values within inventories used to conduct an LCA should be clearly explained per these ISO 14040

guidelines.

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Lecture 14: Life cycle inventories: data estimation Transcript

So far you’ve been provided with all of the inventory data you’ve used in your bottled soft drink LCA,

which is helping you focus on learning the basics of inventory analysis and building out your model.

In practice, however, you’ll often encounter data gaps when compiling a unit process inventory. For

example, you may find that important flow data are missing from existing inventory sources, or,

even more commonly, that an inventory does not even exist for one or more of your unit processes.

What do you do when this happens? Of course, if you have direct access to the actual process, you

can try to collect primary data to fill your data gaps.

But in many LCA studies, we don’t have access to the real-world processes and so primary data

collection isn’t possible. And, even though today’s commercial and public LCA databases contain a

lot of great inventory data, they do not come close to being inclusive of many materials, processes,

and products you may like to study.

For example, perhaps you are studying a specialty material that is not used widely. Or perhaps you

are studying a new material or unit process for which data don’t yet exist. Or, as is most common,

you may simply be studying one of the many thousands of materials and unit processes that are in

use in the real world but haven’t been subjected to an LCA yet. As you’ve learned, a good life-cycle

inventory takes time, planning, and money to compile, so existing inventories are limited. So, if you

practice LCA, dealing with data gaps comes with the territory.

Today we’ll discuss using analysis-based estimation methods to generate inventory data. By

“analysis-based,” what I mean is that we use reasoned data analysis or engineering methods to

estimate inventory data. In other words, we don’t make estimates based on guesswork or intuition,

which can seriously reduce the credibility of an analysis.

Let me give you three quick examples of analysis-based estimation, one of which you’ll also practice

in your homework assignment. The first is an example of what some might call a “black box”

inventory, where we assemble consistent inputs and outputs without explicitly characterizing the

functional relationships between them. Such inventories can sometimes be constructed using

environmental statistics, such as those issued by regional or national government agencies.

In my case, I’ll construct a “black box” inventory for diesel-fueled light trucks in the United States

using national fleet, energy use, and emissions data. My data source is the U.S. Department of

Energy’s Transportation Energy Data Book, which is issued annually. The year of my data is 2005,

the geographical coverage is the United States, and the technology mix is the average of all light

diesel trucks in operation.

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Here are the data, which include total passenger-miles traveled, diesel fuel use, and emissions of

CO2, carbon monoxide, particulate matter, volatile organic compounds, nitrogen oxides, and sulfur

dioxide for the 2005 U.S. light truck fleet. To construct the inventory, I’ll choose a product output of

1 passenger-mile, and normalize all my input and output data on this basis. My estimated inventory

then looks like this:

Such an approach typically only works for widespread technologies or entire industries for which all

relevant flow data are consistently tracked. Note that when using this approach, you must be sure

that it is acceptable in light of the data quality requirements for your study.

For example, this sort of estimated inventory might be acceptable as a background process in an LCA

model. Note also that we don’t know the relationship between inputs and outputs; for example, if

we change the amount of fuel used per passenger-mile, we don’t know by how much emissions of

VOCs per passenger-mile should also change. Hence the name “black box” model. Another

important consideration is that one needs to establish that all relevant flows are captured. In this

case, I’m only capturing combustion-related air emissions so I need to document that any other

relevant flows, such as waste engine oil discarded per passenger-mile of travel, are not considered.

The second example is using verified air emission factors for combustion-related unit processes such

as furnaces, kilns, or boilers. Recall that I used this approach earlier in the course when I estimated

the unit process inventory for operating a residential hot water heater.

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I obtained air emission factors from the U.S. Environmental Protection Agency’s AP-42 Emission

Factor database, which contains emission factors for many different combustion technologies based

on test data and engineering methods.

One can also find emission factors from other environmental agencies and in the engineering

literature. Note that this approach typically only captures air emissions, which are the primary

outputs of most combustion processes. Other flows such as disposal of recovered ash or other

pollutant abatement flows would have to be estimated from other sources. Thus, like our “black

box” model, the emission factor approach applies to generic technology populations and is thus best

suited for background processes. However, the emission factor approach does capture the

functional relationship between fuel inputs and air emissions outputs—and sometimes the

relationship between product outputs, fuel inputs, and air emissions outputs—and thus it is more

flexible than a “black box” approach.

The third example is using an engineering approach to quantify the functional relationship between

unit process inputs and outputs using a process model. For example, let’s say I want to construct a

unit process inventory for an industrial blower used in a pneumatic conveyor. From basic energy

engineering, I know that the relationship between fan input power and fan output power is:

Where HP = fan horsepower, CFM =cubic feet per minute of airflow and PSI = pounds per square

inch of air pressure. If I have some understanding of the process I am analyzing, I can estimate the

CFM and PSI and use a typical fan efficiency for the application from the engineering literature. I

can then convert to kWh of electricity required to provide pressurized air at a given flow rate and

pressure for a given amount of time.

This is admittedly a simple example. In practice, engineering estimation can become more

complicated for more complex processes. The point is that, if one can characterize the underlying

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physics or chemistry of a process in a way that conserves mass and energy, it is often possible to

construct engineering based inventories when no other sources of data are available.

However, with this method it’s critically important that the engineering functions reflect the real-

world equipment characteristics, including efficiency losses. Thus, engineering estimates are best

left to engineers with process knowledge.

Lastly, I want to point out that all of these methods can also be helpful for checking unit process

inventory data throughout a study. As I mentioned previously, data validation is an important step

in the life-cycle inventory process. It is always a good idea to double check all inventory data using

logic, comparing to black box data, comparing to emission factors, or comparing to engineering

models.

For example, let’s check the CO2 flow in my unit process inventory for diesel fueled light trucks

using an air emission factor for diesel fuel combustion. According to the US EPA, on average a liter

of diesel fuel will release 2.7 kg of CO2 during combustion. If I apply that emission factor to the

diesel fuel and CO2 data in my “black box” model, I can see that the CO2 flow value seems

reasonable for the stated fuel input.

So we’ve now seen three ways we can address data gaps using analysis-based estimation

approaches. However, there are important caveats that I’ll stress again. First, all approaches to

addressing data gaps must be clearly documented and reproducible so your audience can form their

own opinions about the credibility of your methods. Second, compiling primary data is clearly

preferable to using estimation approaches whenever feasible. Third, any estimation method must

meet the minimum data quality requirements stated in the scope definition of an LCA study,

otherwise the goals of the study will not be met.

Despite these caveats, these methods can often be helpful in the absence of better data. In my

experience, using estimation to fill data gaps is especially helpful for generic background processes

and for determining if a given unit process or flow qualifies for exclusion from a study on the basis of

stated cut-off rules.

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Additional notes

Correction: Here I said “Let me give you three quick examples of analysis-based estimation, which

you’ll also practice in your homework assignment” when I should have said “Let me give you three

quick examples of analysis-based estimation, one of which you’ll also practice in your homework

assignment.”

The U.S. Department of Energy’s Transportation Energy Data Book is a good source of data on U.S.

national transport vehicle fleet characteristics, energy use, driving characteristics, and emissions. It

can be accessed at the link below. It is a good example of a data source that enables analysis-based

estimation of generic, “black box” inventories of select flows for national average technologies in

the United States based on data collected by government agencies.

Davis, S., Diegel, S.W., and R.G. Boundy (2014). Transportation Energy Data Book, Edition 33.

Oak Ridge National Laboratory, Oak Ridge, Tennessee. http://cta.ornl.gov/data/index.shtml

The U.S. Environmental Protection Agency’s AP-42 compendium of emission factors is an exhaustive

resource that can be used to estimate the air emissions from a wide range of combustion sources in

the absence of primary or secondary inventory data on unit processes with combustion. As

discussed in Lecture 4, you may find it useful in the future for estimating the air emissions

associated with burning fuels in common processes across the residential, commercial, industrial,

and transport sectors. http://www.epa.gov/ttnchie1/ap42/

You’ll practice constructing an analysis-based unit process inventory in Homework 5. Specifically,

you’ll estimate the kWh of electricity required for treating water and delivering it to the bottled soft

drink plant for inclusion in the bottled soft drink. You’ll then use unit process inventory data for

electricity to create one aggregated, analysis-based unit process inventory for water treatment and

delivery in your spreadsheet model.

For those of you who might be interested in estimating air pollutant emissions from cars, trucks,

motorcycles, and other vehicles (including non-road vehicles) under various conditions, check out

the U.S. EPA’s suite of mobile emissions modeling tools at the link below. While not recommended

for casual analysts, this software can be used by those who wish to conduct detailed and rigorous

assessments of vehicle emissions in transport-related LCA studies:

U.S. Environmental Protection Agency (2014). Modeling and Inventories. Washington, DC.

http://www.epa.gov/otaq/models.htm

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http://www.epa.gov/ttnchie1/ap42/

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Lecture 15: Life cycle inventories: multi-functionality Transcript

Today we’re going to discuss the issue of multi-functionality, which is a common challenge we face

when compiling life-cycle inventory data. Simply put, if a unit process provides more than one

function, it is known as a multi-functional process. Let’s illustrate multi-functionality with a simple

example.

Suppose we have a unit process inventory for a commercial baking facility, which was compiled

based on plant-level data provided by the baking company. The major technosphere inputs into this

facility include electricity and natural gas, and ingredients such as flour, yeast, and salt. The major

outputs to nature from this facility include emissions of CO2, NOx, and other air pollutants, as well

as wastewater. The major outputs to the technosphere are baked bread, which is sold to retailers,

and frozen dough, which is sold to restaurants that will bake their own bread later.

So, here we have a unit process that has two different product outputs: baked bread and frozen

dough. Let’s assume that we want to use these inventory data to conduct an LCA of only one of the

products, say, baked bread. To use these data, we must decide what fractions of the inputs should

be assigned to just the baked bread, and what fractions of the outputs to nature to assign to just the

baked bread. In other words, we have a multi-functionality problem to address in our inventory

data.

In practice, dealing with multi-functionality is a common challenge in LCA, because multi-functional

unit processes are everywhere.

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For example, many electric power plants generate both electricity and district steam. What

fractions of the fuel inputs and pollutant outputs should we assign to just the steam?

Or, consider an end of life pathway in which a waste-to-energy plant generates electricity and

steam from many different types of waste mixed together. How much of the electricity and steam

outputs do I assign to just my waste, which is part of that mixed waste stream?

There are many different types of multi-functionality problems that one encounters when

conducting LCAs. I’ll illustrate a few of them, using guidance from the ISO 14040 standards for how

to deal with multi-functionality in practice. The standards use the term “allocation,” which is a more

general way to refer to the process of assigning fractions of inventory flows to different inputs or

outputs in multi-functional processes.

The standards presents a 3-step approach for dealing with allocation, which I’ll go through one-by-

one:

Step 1: Wherever possible, allocation should be avoided by

a) dividing the unit process to be allocated into two or more sub-processes and collecting

data on these sub-processes, or

b) expanding the product system to include the additional functions related to the co-

products.

Let’s revisit our commercial bakery example to explore how step 1(a) would work. Since my unit

process inventory is based on the entire manufacturing plant, there are clearly sub-processes within

that plant that manufacture the baked bread and the frozen dough. To understand these sub-

processes, it’s necessary to observe more details of the plant’s operations.

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Here we see the sub-processes within the commercial bakery, where there are separate production

lines for baked bread and frozen dough. Furthermore, the thickness of the electricity and natural

gas input arrows correspond to the electricity and natural gas demands of each production line.

Thus, we could in theory collect data for just the sub-processes on the baked bread production line,

and thereby obtain an inventory applicable to just the baked bread. Doing so would reveal that

most of the electricity going into the plant is not attributable to baked bread production; rather,

most electricity is used for freezing of frozen dough. Conversely, we’d also determine that most of

the natural gas going into the plant goes to the ovens for baking the bread, and that very little

natural gas use is associated with frozen dough production. We could also determine how much of

the wastewater is attributable to pan washing for baked bread. In other words, by understanding

the sub-processes we can now determine credible input and emissions flows for just the baked

bread.

While dividing a unit process into subprocesses worked well in this simple example, in practice

subdividing may not solve the problem because many sub-processes can also be multi-functional in

nature.

Let’s now consider Step 1(b), which involves expanding the system boundary to include the

functions related to all co-products. In my bakery example, this means we must include the

functions of both the baked bread and frozen dough in our analysis system boundaries. While this

can help us avoid having to allocate fractions of flows to each co-product, it also means we must

now consider the function provided by frozen dough in addition to the function provided by baked

bread, which was our original interest.

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We’re going to discuss system boundary expansion in more depth in the next lecture. I’ll use my

plastic bag life-cycle as an example, considering the case of recycling the plastic bag into a secondary

product.

For now, let’s move on to Step 2, which states:

Step 2: Where allocation cannot be avoided, the inputs and outputs of the system should be

partitioned between its different products or functions in a way that reflects the underlying

physical relationships between them.

To visualize how partitioning based on physical relationships can work, let’s use another example of

food processing. Here is a unit process depiction for a tomato processing plant, which produces

both diced tomatoes and tomato paste.

If we divide this plant into some simplified sub-processes, we can see that our co-products are

related in that pulp and juice from the diced tomato production line are used for making tomato

paste. Both diced tomatoes and tomato paste undergo major thermal processing using steam. The

diced tomatoes are sterilized in their cans while the paste is made by evaporating copious amounts

of water out of the tomato pulp and juice. To allocate a fraction of the steam to diced tomatoes,

one could determine the heat gain dictated by the sterilization process. That is, how hot and for

how long the diced tomato cans need to be sterilized. Once could also determine the heat gain

required to remove enough water from juice and pulp to make paste. In this manner, one could

assign fractions of the plant’s total steam to each production line based on thermodynamic

requirements. Finally, one could apply those fractions to the natural gas inputs that are used to

make the steam. So, in this example, we’ve successfully used physical relationships between steam

and production processes to partition the plant’s natural gas use to the diced tomatoes and tomato

paste.

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In practice, partitioning based on physical relationships often requires fundamental engineering

knowledge of sub-processes, which isn’t always available. Moreover, for complex multi-functional

processes like petroleum refining, in which crude oil is turned into many different outputs that are

subject to interdependent processes, such partitioning can get very complex, and often somewhat

arbitrary.

So now let’s move on to the third and final step in the ISO 14040 hierarchy, which states that:

Step 3: Where physical relationships alone cannot be established or used as the basis for allocation,

the inputs should be allocated between the products and functions in a way that reflects other

relationships between them.

This guidance is somewhat vague, but in practice this step can involve partitioning flows

proportionally to the mass of each co-product output or to the economic value of each co-product

output. For example, you may recall that I showed you the unit process inventory for petroleum

refining earlier in this course, which we’re using in our plastic bag and bottled soft drink LCAs.

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Here the creators of this inventory have indicated that the flows can be partitioned between the

refinery’s outputs on the basis of mass, which is also indicated for each co-product.

Whichever approach we use for dealing with multi-functionality, there are some strict rules we must

follow when doing so in an LCA.

First, it’s absolutely critical to document any allocation methods clearly so that the target

audience can understand and scrutinize them. In many unit process inventories obtained

from LCA databases, any allocation methods that were applied are stated clearly in the

inventory notes. We’re doing the same in our plastic bag and bottled soda LCA

spreadsheets.

Second, when partitioning it’s important to ensure that all partitioned inputs and outputs

still sum up to the original unit process inventory totals. This is a basic check for mass and

energy conservation.

Third, it’s important to state clearly the multi-functionality approach in the scope definition

phase, because this will affect how you compile data in your inventory analysis step. In

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practice, one must often iterate between inventory analysis and goal and scope definition

steps before settling on a multi-functionality approach.

Fourth, care should be taken to apply the same solutions for multi-functionality across the

life-cycle inventory for maximum study consistency.

And, fifth, any chosen approach for dealing with multi-functionality must support the stated

goals of the study to ensure that your results are credible and useful to your target

audience.

For more discussion on multi-functionality, take a look at the course notes. I’ll see you next time!

Additional notes

For those of you who might be wondering how allocation procedures might be harmonized across a

class of products or an industry, take a look at the following document prepared by the International

Dairy Federation. Specifically, Section 6.3 lays out some very specific proposed allocation

procedures for assigning the CO2 emissions to co-products arising from the following common

processes:

Crop production (how much CO2 from crop production and processing gets assigned to cow

feed versus other food products?)

Cow husbandry (how much CO2 from a cow’s life gets assigned to its milk versus its meat?)

Milk processing (how much CO2 from a cow’s milk gets assigned to cheese versus whey?)

You’ll see that the methods vary, and include allocation based on economic and physical bases

depending on the processing stage and the co-products. While these are just recommendations,

they lay out a precise way of conducting allocation calculations that could, in theory, be replicated

across LCAs of different dairy products to promote greater methodological consistency. I leave it to

you to review these proposals and to decide for yourselves whether or not you agree with the

approach and recommended calculations!

Bulletin of the International Dairy Federation (2010). A common carbon footprint approach

for dairy: The IDF guide to standard life cycle assessment methodology for the dairy sector.

Bulletin 445/2010. Brussels, Belgium. http://idf-lca-guide.org/Files/media/Documents/445-

2010-A-common-carbon-footprint-approach-for-dairy.pdf

You’ll practice sub-dividing an inventory for this commercial bakery to assign energy use to just the

baked bread in Homework 5.

In this example, division into sub-processes revealed that there are sub-processes that are shared by

the two co-products. Namely, washing, peeling, and dicing generate mass flows that serve the

production lines for both canned diced tomatoes and canned tomato paste. Therefore, we cannot

simply assign each sub-process to one specific co-product as we could in the previous example, in

15.1

15.5

15.2

15.3

http://idf-lca-guide.org/Files/media/Documents/445-2010-A-common-carbon-footprint-approach-for-dairy.pdf

http://idf-lca-guide.org/Files/media/Documents/445-2010-A-common-carbon-footprint-approach-for-dairy.pdf

P a g e | 98


which the commercial bakery could indeed be divided into two distinct and separate production

lines for baked bread and frozen dough. As a result, in this example we use the thermodynamic

requirements of each unit process to assign energy use to mass flows for canned diced tomatoes

and mass flows for canned tomato paste. The procedure described for sterilization and evaporation

would be repeated for each unit process until all fuel use in the facility were assigned to each co-

product based on its thermodynamic or mechanical processing energy requirements. Clearly, such

an approach would require detailed engineering analysis and process data!

Check out these inventory data for yourself in the U.S. LCI database.



3. Select the checkbox for “Petroleum and Coal Products Manufacturing” within the

“Category” list

4. Click on “Diesel, at refinery (Petroleum refining, at refinery),” which appears in the list at

right

5. Read the description on the “Activity” page of this inventory to view their explanation of

how data are allocated to the many co-product outputs from a petroleum refinery.

Multi-functionality is a topic that is widely covered in LCA methodology guidebooks. For more

discussion on multi-functionality, and approaches for dealing with multi-functionality, see the

following resources:

The United States Environmental Protection Agency’s “Life Cycle Assessment: Principles and Practice”, 2006, Chapters 2 and 3: http://www.epa.gov/nrmrl/std/lca/lca.html (Note this document is also commonly referred to as “LCA 101.”)

Matthews, H.S., Hendrickson, C.T., and D.H. Matthews (2014). Life Cycle Assessment: Quantitative Approaches for Decisions That Matter. Chapter 6: Analyzing Multifunctional Product Systems. Carnegie Mellon University, Pittsburgh, Pennsylvania. http://www.lcatextbook.com/








Section 7.9 of European Commission - Joint Research Centre (JRC) - Institute for Environment and Sustainability: International Reference Life Cycle Data System (ILCD) Handbook - General guide for Life Cycle Assessment - Detailed guidance. http://eplca.jrc.ec.europa.eu/uploads/ILCD-Handbook-General-guide-for-LCA-DETAILED-GUIDANCE-12March2010-ISBN-fin-v1.0-EN.pdf

15.4

15.5



http://www.lcatextbook.com/





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