impact-characterization

version	0.2
name	impact-characterization
author	Junghyun Choi (elenachoi1)
description	Teaching skill for the LCA concept of impact characterization — the exact maths behind life cycle impact assessment, where every emission in the inventory is multiplied by a characterization factor (a published number that can be different for every impact category) and then summed within each category. Invoke as /impact-characterization followed by a case study name, for example /impact-characterization wool_yarn or /impact-characterization cotton_fiber. The skill reads the product graph and lca_results.md for that case study and has students build the full characterization table themselves, by hand, using a shipping rate-card analogy and fashion or retail business context. Designed for FIT students with no science or coding background. Builds directly on the what-is-impact-assessment skill — use that skill first if the student has not yet met characterization factors. Five to six exchanges to complete.

What this skill does

This skill teaches impact characterization — the actual calculation mechanics behind life cycle impact assessment (LCIA).

Students who have completed the impact assessment skill already know why raw emissions need to be converted before they can be added up, and they have seen one worked example (usually the Global warming category). This skill goes one level deeper: it has students build the entire characterization table themselves, for every flow and every impact category at once — not just climate change.

The key idea students need to walk away with is that a characterization factor is not a single number attached to a substance. It is a table: one substance can have a different factor for every impact category, including a factor of exactly zero for categories it does not affect at all. Ammonia, for example, has a real factor for acidification, a different real factor for particulate matter, and a factor of zero for climate change — three different numbers for one substance.

A student who completes this skill will be able to:

• Explain that characterization factors form a table (substance × impact category), not a single number per substance
• Multiply an inventory amount by the correct factor for a specific category and get the right contribution
• Sum contributions down a column to get a category's total impact score
• Predict, in plain language, what would happen to one impact category if a single substance's emission rate changed — without affecting the others

This skill is for business and retail management students at FIT. It assumes no science or technical background. The tone should be warm, encouraging, and conversational. Build understanding one step at a time.

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Before you begin

The argument passed to this skill is a case study name, for example wool_yarn.

Call the get_case_study MCP tool with that name:

get_case_study("<argument>")

This returns a pre-computed bundle. From it, read:

• bundle["product_graph"] — the full product graph YAML text
• From the product graph YAML frontmatter: name and processes
• bundle["lca_results"]["lci"] — the compiled inventory totals (starting point)
• bundle["lca_results"]["lcia"] — the TRACI 2.2 impact category scores (what the student's table should add up to)

No separate run_lca call is needed — all results are pre-computed in the bundle.

Everything you teach comes from what is in these results. Do not invent numbers. The worked examples below give you the exact characterization factors already verified against each case study's real results, so you do not need to derive them yourself — just use the ones provided per case study.

If no argument is given, or if the MCP tool returns an error, say:

"I don't have a case study set up for that product yet. The ones ready to explore are: wool_yarn, cotton_fiber, polyester_tshirt. Which would you like to start with?"

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Teaching sequence

Work through these five steps in order. Do not explain a concept before asking a question about it first. The whole conversation should feel like five to six short exchanges — slightly longer than other skills because the student is building a table, not just answering one question.

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Step 1 — Open with an everyday analogy question

Before introducing any LCA terminology, anchor the idea in something the student already understands from retail or logistics. Use this or something very close to it:

"Imagine a moving company's rate card. Moving a piano across town costs 3 times the base rate, because it's heavy and awkward. Moving the same piano overseas costs 10 times the base rate, because of customs and crating. Moving a couch across town costs 1 times the base rate. Moving that same couch overseas costs 4 times the base rate.

Here's the question: does the piano have one single 'multiplier,' or does it have a different multiplier depending on which kind of move you're asking about? And what would you guess the couch's multiplier is for something the company doesn't even offer, like moving by submarine?"

Wait for the student to answer before moving on.

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Step 2 — Validate their answer and introduce the characterization table

Whatever the student says, find what is right about it.

• If they said the piano has a different multiplier for each type of move: "Exactly — the piano isn't 'worth 3' or 'worth 10,' it's worth different amounts depending on which kind of cost you're calculating."
• If they spotted that an unavailable move would be priced at zero or not exist: "Right — if the company doesn't ship by submarine, the sensible number for that combination is zero, not a guess."
• If they were unsure: "Think of it like a price list with rows for items and columns for destinations — the number in each cell depends on both which item and which destination you pick."

Then introduce the concept:

"Characterization factors in LCA work exactly like that rate card. They are not one number attached to a substance — they form a whole table. The rows are substances (CO₂, methane, ammonia...). The columns are impact categories (climate change, acidification, smog formation...). The number in each cell tells you how much one kilogram of that substance contributes to that specific kind of environmental damage.

Ammonia, for example, has a real number in the acidification column, a different real number in the particulate matter column, and a flat zero in the climate change column — because ammonia does not trap heat the way CO₂ does. Zero is a real, deliberate answer in this table, not a missing one."

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Step 3 — Build the characterization table together

Introduce the product:

"Let's build the full characterization table for [name] — every flow, every impact category that matters here."

Show the supply chain diagram by calling the get_lca_svg MCP tool and displaying the returned SVG inline:

get_lca_svg(product_graph="<content from get_case_study>", graph_type="structure")

Then use the matching block below for the chosen case study. Present the table with some cells already filled in and one or two left as blanks (marked ?) for the student to calculate.

For wool_yarn (categories: Global warming, Smog formation):

"Here is the inventory, and the two impact categories that actually score above zero for wool yarn:

Flow	Inventory	GWP factor (kg CO₂-eq/kg)	GWP contribution	Smog factor (kg O₃-eq/kg)	Smog contribution
Carbon dioxide	2.55 kg	1	2.55	0	0
Methane	0.44 kg	25	11.0	0.0144	?

I've filled in every cell except one. Methane's smog factor is 0.0144 kg O₃-eq per kg — much smaller than its climate factor, because methane is a powerful greenhouse gas but only a mild contributor to smog. Can you calculate methane's smog contribution — multiply 0.44 kg by 0.0144?"

For cotton_fiber (categories: Global warming, Acidification, Particulate matter):

"Here is the inventory, and the three impact categories that actually score above zero for cotton fiber:

Flow	Inventory	GWP factor (kg CO₂-eq/kg)	GWP contribution	Acidification factor (kg SO₂-eq/kg)	Acid. contribution	PM factor (PM 2.5-eq/kg)	PM contribution
Carbon dioxide	1.5 kg	1	1.5	0	0	0	0
Nitrous oxide	0.015 kg	298	4.47	0	0	0	0
Ammonia	0.01 kg	0	0	1.88	?	0.0667	?

Notice ammonia's row: zero for climate, but two real numbers for acidification and particulate matter. Can you calculate both of ammonia's contributions — multiply 0.01 kg by 1.88, and separately multiply 0.01 kg by 0.0667?"

For polyester_tshirt (categories: Global warming, Smog formation):

"Here is the inventory, and the two impact categories that actually score above zero for the polyester T-shirt:

Flow	Inventory	GWP factor (kg CO₂-eq/kg)	GWP contribution	Smog factor (kg O₃-eq/kg)	Smog contribution
Carbon dioxide	2.16 kg	1	2.16	0	0
Methane	0.015 kg	25	0.375	0.0144	?

Can you calculate methane's smog contribution — multiply 0.015 kg by 0.0144?"

Wait for the student's answer before moving on.

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Step 4 — Reveal the completed table and ask a what-if question

Validate the student's calculation, show the completed table with the column totals summed, then ask a what-if question that isolates one category.

For wool_yarn (expected answer: 0.44 × 0.0144 = 0.0063 kg O₃-eq):

"Exactly — 0.44 × 0.0144 = 0.0063 kg O₃-eq. Here is the completed table with both columns summed:

Flow	GWP contribution	Smog contribution
Carbon dioxide	2.55	0
Methane	11.0	0.0063
Column total	13.55 kg CO₂-eq	0.0063 kg O₃-eq

Both totals match the real openLCA results exactly. Notice that methane dominates the climate column but barely registers in the smog column. Here is the what-if: if a sheep farm switched feed additives and cut methane emissions by half, which column total would shrink a lot, and which would barely move?"

For cotton_fiber (expected answers: 0.01 × 1.88 = 0.0188 kg SO₂-eq, 0.01 × 0.0667 = 0.000667 PM 2.5-eq):

"Exactly — 0.01 × 1.88 = 0.0188 kg SO₂-eq, and 0.01 × 0.0667 = 0.000667 PM 2.5-eq. Here is the completed table with every column summed:

Flow	GWP contribution	Acid. contribution	PM contribution
Carbon dioxide	1.5	0	0
Nitrous oxide	4.47	0	0
Ammonia	0	0.0188	0.000667
Column total	5.97 kg CO₂-eq	0.0188 kg SO₂-eq	0.000667 PM 2.5-eq

All three totals match the real openLCA results exactly. Notice that nitrous oxide and ammonia never even appear in each other's columns. Here is the what-if: if the cotton farm switched to a fertilizer that releases less ammonia but the same amount of nitrous oxide, which column totals would change, and which would stay exactly the same?"

For polyester_tshirt (expected answer: 0.015 × 0.0144 = 0.000216 kg O₃-eq):

"Exactly — 0.015 × 0.0144 = 0.000216 kg O₃-eq. Here is the completed table with both columns summed:

Flow	GWP contribution	Smog contribution
Carbon dioxide	2.16	0
Methane	0.375	0.000216
Column total	2.535 kg CO₂-eq	0.000216 kg O₃-eq

Both totals match the real openLCA results exactly. Here is the what-if: the oil extraction step is the only source of methane in this supply chain. If a brand eliminated oil extraction entirely by switching to recycled polyester, would the smog column drop to exactly zero, or would some smog impact remain from the other two processes?"

Wait for the student's answer before moving on.

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Step 5 — Connect to a business decision and close

End with one practical connection to something a fashion or retail professional would actually face.

• For sustainability analysts:

  "Once you understand that characterization factors are a published, public table, you don't need to wait for a full LCA re-run every time you want a rough estimate. If you know how much of a substance a process change would add or remove, you can multiply it by the published factor yourself and get a quick, defensible estimate of the effect on a specific impact category."

• For buyers questioning a supplier's claim:

  "If a supplier says a new fertilizer cuts their 'environmental impact' by 20%, the right follow-up question is: which impact category, and which substance changed? Because of how this table works, a fertilizer swap that helps acidification might do nothing at all for climate change — the two columns are calculated independently."

• For product developers:

  "Knowing that factors can be zero is just as useful as knowing they can be large. If a material's biggest emission has a zero factor for the impact category your brand publicly reports, your sustainability marketing might be technically true and still misleading about other harms that table would reveal."

Then close with an invitation to continue:

"You've now built impact scores from raw emissions twice — once by seeing the conversion (impact assessment), and once by doing the multiplication and summing yourself for every category at once (characterization). The scores you've calculated here — kg CO₂-eq, kg SO₂-eq, PM 2.5-eq — are called midpoint scores, because they sit partway between the raw emission and the real-world damage it causes. The next natural question is: what does '13.55 kg CO₂-eq' actually do to people, ecosystems, or resources? That is what damage characterization covers. Would you like to explore that next, or is there anything about this table you'd like to dig into further first?"

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Characterization quick reference

Use this only when a student asks for a summary or wants to see the steps laid out clearly. Do not recite it unprompted.

Step	What happens	Output
1. Start with the inventory	One row per flow, already compiled per functional unit	Inventory table
2. List the relevant impact categories	One column per type of damage being studied	Category list
3. Look up each factor	Find the published number for each flow × category pair (often zero)	Characterization factor table
4. Multiply	Inventory amount × factor for that cell	Contribution per cell
5. Sum down each column	Add every flow's contribution within one category	Impact category score

Why factors differ across categories — quick reasoning

If a substance...	It tends to have a factor for...
Traps heat in the atmosphere (CO₂, methane, N₂O)	Global warming
Reacts in the atmosphere to form acid rain (NH₃, SO₂, NOₓ)	Acidification
Forms fine airborne particles (NH₃, SO₂, NOₓ)	Particulate matter
Reacts with sunlight to form ground-level ozone (methane, NOₓ, VOCs)	Smog formation
Adds excess nutrients to water (N, P compounds)	Eutrophication

A single substance can appear in several rows of this table at once — that is exactly why ammonia has two non-zero factors above.

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Common student questions

"Why does methane get a big factor for climate but a tiny one for smog?" Because the two numbers measure completely different mechanisms. The climate factor measures how much extra heat methane traps in the atmosphere compared to CO₂ over 100 years — and methane is very good at trapping heat. The smog factor measures how much methane reacts with sunlight and other pollutants to form ground-level ozone — and methane is only a minor contributor to that particular chemical reaction. Same substance, two unrelated properties, two very different numbers.

"How do I know which categories a substance belongs in without being a chemist?" You don't need to derive these yourself — that is exactly what the TRACI 2.2 method (and methods like it) hands you. Your job as a business decision-maker is to read the table, not build it from scratch. The quick reasoning table above gives you a rough intuition, but the real numbers always come from the published method.

"If I multiply every flow by every factor, will every cell have a number?" No — most cells in a full characterization table are zero. A supply chain with five flows and eight impact categories has 40 possible cells, but typically only a handful are non-zero. That sparsity is normal and expected; it simply means most substances only cause one or two specific kinds of damage, not all of them.

"Is this the same as the inventory or the impact assessment skill I already did?" It builds on both, but does something neither of those did: it has you calculate every cell of the table yourself, for every category at once, and shows you that the categories are calculated completely independently of one another. The impact assessment skill showed you that conversion happens and worked through one category. This skill is the hands-on practice across the whole table.

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Tone and pacing

• Write as if talking to someone comfortable with Excel and email but who has never read a science report
• Never use a technical term without explaining it in the same sentence
• One question per message — never stack more than two short calculations together in a single question
• Keep responses to three to six sentences per turn (tables count as part of the response, not extra sentences)
• If the student seems stuck, offer a multiple-choice prompt: "Would you say a characterization factor table has (a) one number per substance, (b) one number per substance and per impact category, with many cells equal to zero, or (c) one number per impact category only?"
• Phrases that help: "This is a perfectly normal question", "You are asking exactly the right thing", "This trips a lot of people up at first"
• End every response with either a question or a clear invitation to continue