what-is-impact-assessment

version	0.2
name	what-is-impact-assessment
author	Junghyun Choi (elenachoi1)
description	Teaching skill for the LCA concept of life cycle impact assessment (LCIA) — the step that converts the raw inventory of emissions and resource use (kg of CO2, kg of methane, kg of ammonia, litres of water) into a small set of comparable impact category scores, using conversion numbers called characterization factors. Invoke as /what-is-impact-assessment followed by a case study name, for example /what-is-impact-assessment wool_yarn or /what-is-impact-assessment cotton_fiber. The skill reads the product graph and lca_results.md for that case study and teaches the concept using a currency-exchange analogy and fashion or retail business context. Designed for FIT students with no science or coding background. Builds directly on the life-cycle-inventory skill. Four to five exchanges to complete.

What this skill does

This skill teaches life cycle impact assessment (LCIA) — the step in LCA that turns the raw inventory into a small set of meaningful scores.

Students who have completed the inventory skill know that the inventory is a list: so many kilograms of CO₂, so many kilograms of methane, so many litres of water, all crossing the boundary between the human-made world and nature to deliver one unit of product. But that list has a problem — you cannot add litres of water to kilograms of CO₂, and you cannot say that 1 kg of methane is "the same size" of problem as 1 kg of CO₂, because methane traps far more heat in the atmosphere.

LCIA solves this. It uses scientifically-derived conversion numbers called characterization factors to convert every flow into a common unit within its own type of damage — for example, every greenhouse gas gets converted into "kg of CO₂ equivalent" so they can be added into one climate change score. Other factors convert flows into "kg of SO₂ equivalent" for acidification, or "kg of PM 2.5 equivalent" for particle pollution. The result is a short profile of scores — one number per type of environmental damage — instead of one giant, meaningless list of mismatched units.

A student who completes this skill will be able to:

• Explain what a characterization factor is and why it is needed
• Multiply an inventory flow by its characterization factor to get its contribution to an impact score
• Understand why a single substance (like ammonia) can contribute to more than one impact category at once
• Explain why LCIA reports several separate scores rather than one combined "total damage" number

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 (destination)

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 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 four to five short exchanges — not a lecture.

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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 business. Use this or something very close to it:

"Imagine you run a retail chain with stores in the US, the UK, and Japan. At the end of the month, your US stores report $40,000 in sales, your UK stores report £25,000, and your Japan stores report ¥3,000,000.

Here is the question: can you find your total global sales just by adding 40,000 + 25,000 + 3,000,000? Why or why not — and what would you need to do first before those three numbers could be added together?"

Wait for the student to answer before moving on.

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Step 2 — Validate their answer and introduce impact assessment

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

• If they said you need to convert to one currency first: "Exactly — you'd use the day's exchange rate to convert pounds and yen into dollars, and only then could you add the three numbers together into one meaningful total."
• If they said the numbers can't simply be added: "You've spotted the key problem — dollars, pounds, and yen are different units, and adding them directly would produce a meaningless number."
• If they were unsure: "The issue is that $40,000, £25,000, and ¥3,000,000 are in three different currencies. Before you can add them, you have to convert each one into the same currency using that day's exchange rate."

Then introduce the concept:

"Life cycle assessment has exactly this problem. The inventory you've already learned about lists emissions in their own original units — kg of CO₂, kg of methane, kg of ammonia, litres of water. You cannot add a kilogram of methane to a kilogram of CO₂ any more than you can add pounds to yen — they cause different kinds of environmental damage at different intensities.

The step that solves this is called life cycle impact assessment, or LCIA. It uses conversion numbers — called characterization factors — that work just like exchange rates. Each factor tells you how much damage one kilogram of a substance causes, relative to a reference substance, for a specific type of damage. For climate change, the reference substance is CO₂, so every other greenhouse gas gets converted into 'kg of CO₂ equivalent.' Once everything is in the same unit, you can finally add it up into one score."

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Step 3 — Walk through the worked example for the case study

Introduce the product:

"Let's convert the inventory for [name] into impact scores together."

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.

For wool_yarn:

"Here is the compiled inventory you already built in the last skill:

Flow	Inventory total
Carbon dioxide	2.55 kg
Methane	0.44 kg
Water	30 L

For the Global warming impact category, the characterization factors (the 'exchange rates') are:

• Carbon dioxide → 1 kg CO₂-eq per kg (CO₂ is the reference substance, so its own factor is always 1)
• Methane → 25 kg CO₂-eq per kg (methane traps far more heat than CO₂ per kilogram, which is why this factor is so much bigger than 1)

I've already converted the CO₂: 2.55 kg × 1 = 2.55 kg CO₂-eq. Can you convert the methane the same way — multiply 0.44 kg by its factor of 25 — and then add the two converted numbers together to get the total Global warming score?"

For cotton_fiber:

"Here is the compiled inventory you already built in the last skill:

Flow	Inventory total
Carbon dioxide	1.5 kg
Nitrous oxide	0.015 kg
Ammonia	0.01 kg
Water	8,000 L

For the Global warming impact category, the characterization factors are:

• Carbon dioxide → 1 kg CO₂-eq per kg
• Nitrous oxide → 298 kg CO₂-eq per kg (nitrous oxide is an extremely potent greenhouse gas — almost 300 times more powerful than CO₂, kilogram for kilogram)

I've already converted the CO₂: 1.5 kg × 1 = 1.5 kg CO₂-eq. Can you convert the nitrous oxide — multiply 0.015 kg by its factor of 298 — and then add the two together to get the total Global warming score?"

For polyester_tshirt:

"Here is the compiled inventory you already built in the last skill:

Flow	Inventory total
Carbon dioxide	2.16 kg
Methane	0.015 kg

For the Global warming impact category, the characterization factors are:

• Carbon dioxide → 1 kg CO₂-eq per kg
• Methane → 25 kg CO₂-eq per kg

I've already converted the CO₂: 2.16 kg × 1 = 2.16 kg CO₂-eq. Can you convert the methane — multiply 0.015 kg by its factor of 25 — and then add the two together to get the total Global warming score?"

Wait for the student's answer before moving on.

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Step 4 — Reveal the full impact profile and ask a what-if question

Validate the student's calculation, then show the full TRACI 2.2 profile for the case study — including the categories that scored zero — and ask a what-if question connecting to a business decision.

For wool_yarn (expected answer: 0.44 × 25 = 11.0, plus 2.55 = 13.55 kg CO₂-eq):

"Exactly — 0.44 × 25 = 11.0, plus the 2.55 from CO₂ = 13.55 kg CO₂-eq. That matches the real openLCA result. Here is the full impact profile — all eight TRACI 2.2 categories for one kg of wool yarn:

Impact category	Score	Unit
Global warming	13.55	kg CO₂-eq
Smog formation	0.0063	kg O₃-eq
Acidification	0	kg SO₂-eq
Human health — particulate matter	0	PM 2.5-eq
Eutrophication (freshwater)	0	kg P-eq
Human health — cancer	0	CTUcancer
Human health — non-cancer	0	CTUnoncancer
Ozone depletion	0	kg CFC-11-eq

Notice that six categories are exactly zero. That's not a mistake — it means none of the flows in this particular supply chain (just CO₂, methane, and water) happen to match the substances that drive those other kinds of damage. A zero score never means 'no data' — it means 'nothing here causes this type of harm.'

Here's the what-if: methane makes up only 0.44 kg out of the 2.99 kg of raw emissions (CO₂ + methane combined) — about 15% of the raw weight. But after characterization, it makes up 11.0 out of 13.55 kg CO₂-eq — about 81% of the climate score. Why does methane's share grow so much once you apply the characterization factor?"

For cotton_fiber (expected answer: 0.015 × 298 = 4.47, plus 1.5 = 5.97 kg CO₂-eq):

"Exactly — 0.015 × 298 = 4.47, plus the 1.5 from CO₂ = 5.97 kg CO₂-eq. That matches the real openLCA result. Here is the full impact profile — all eight TRACI 2.2 categories for one kg of cotton fiber:

Impact category	Score	Unit
Global warming	5.97	kg CO₂-eq
Acidification	0.0188	kg SO₂-eq
Human health — particulate matter	0.000667	PM 2.5-eq
Smog formation	0	kg O₃-eq
Eutrophication (freshwater)	0	kg P-eq
Human health — cancer	0	CTUcancer
Human health — non-cancer	0	CTUnoncancer
Ozone depletion	0	kg CFC-11-eq

Notice something interesting: the 0.01 kg of ammonia from the cotton farm shows up in two categories at once — Acidification (0.01 × 1.88 = 0.0188 kg SO₂-eq) and particulate matter (0.01 × 0.0667 = 0.000667 PM 2.5-eq). One substance, two completely different kinds of damage, two separate scores.

Here's the what-if: a cotton brand is told their fertilizer use is the source of nearly all the acidification and particulate matter scores — not the climate score, which is dominated by CO₂ and N₂O instead. If a sustainability manager only tracked Global warming, would they have any idea their farms were contributing to acid rain and air-quality damage?"

For polyester_tshirt (expected answer: 0.015 × 25 = 0.375, plus 2.16 = 2.535 kg CO₂-eq):

"Exactly — 0.015 × 25 = 0.375, plus the 2.16 from CO₂ = 2.535 kg CO₂-eq. That matches the real openLCA result. Here is the full impact profile — all eight TRACI 2.2 categories for one polyester T-shirt:

Impact category	Score	Unit
Global warming	2.535	kg CO₂-eq
Smog formation	0.000216	kg O₃-eq
Acidification	0	kg SO₂-eq
Human health — particulate matter	0	PM 2.5-eq
Eutrophication (freshwater)	0	kg P-eq
Human health — cancer	0	CTUcancer
Human health — non-cancer	0	CTUnoncancer
Ozone depletion	0	kg CFC-11-eq

Here's the what-if: almost all of this shirt's Global warming score comes from the polyester fiber and assembly steps, not the methane leaking from the oil well two steps upstream. If a brand switched to recycled polyester and eliminated the oil extraction step entirely, would the Global warming score drop a lot, a little, or barely at all? (Hint: look back at how much of the 2.535 kg CO₂-eq came from oil extraction's methane versus the other two steps.)"

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 reporting teams:

  "When a brand publishes 'our product has a carbon footprint of X kg CO₂-eq,' that number is the output of exactly this process — every raw emission converted with a characterization factor and added up. If you ever see a footprint reported in 'kg of emissions' without saying CO₂-equivalent, ask what they actually measured — raw kilograms of mixed gases is not a real number."

• For category managers comparing suppliers:

  "Two suppliers might report the same Global warming score but very different Acidification or particulate matter scores, because those scores come from different substances entirely. A buyer who only checks one impact category can miss real differences in environmental harm between two seemingly similar products."

• For product developers:

  "Characterization factors are why 'natural' materials don't automatically mean 'low impact.' Cotton's water and nitrogen-based emissions score high on acidification and particulate matter even though no fossil fuel is burned — while a synthetic fiber might score worse on climate but better elsewhere. The impact profile, not a single number, tells the real story."

Then close with an invitation to continue:

"Impact assessment is the last step that turns raw inventory data into the scores you see in a finished LCA report. From here, the natural next question is: across this whole supply chain, which single process is responsible for most of the damage — and that is exactly what a hotspot analysis answers. Would you like to explore that, or is there anything about characterization factors or impact categories you'd like to look at more closely first?"

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LCIA 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	Take the compiled list of raw flows per functional unit	Inventory table
2. Pick an impact category	Choose a type of damage to measure, e.g. climate change	Category name
3. Apply characterization factors	Multiply each relevant flow by its factor for that category	Converted contributions
4. Sum within the category	Add the converted contributions together	One impact score
5. Repeat for each category	Different flows matter for different categories	Full impact profile

TRACI 2.2 — the eight categories used in this project

Category	Unit	Plain-English meaning
Global warming	kg CO₂-eq	Contribution to climate change
Acidification	kg SO₂-eq	Contribution to acid rain
Smog formation	kg O₃-eq	Contribution to ground-level smog
Human health — particulate matter	PM 2.5-eq	Fine particle health damage
Eutrophication (freshwater)	kg P-eq	Excess nutrients causing algal blooms
Human health — cancer	CTUcancer	Cancer risk from toxic substances
Human health — non-cancer	CTUnoncancer	Other toxic health impacts
Ozone depletion	kg CFC-11-eq	Damage to the stratospheric ozone layer

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Common student questions

"Why does Global warming for wool yarn add up to 13.55 when the raw emissions only weigh 2.99 kg?" Because characterization factors can be much bigger than 1. Methane's factor of 25 means each kilogram of methane counts as if it were 25 kilograms of CO₂ for climate purposes. The "equivalent" weight goes up even though the actual physical weight of gas released did not change — what changed is how much climate damage that weight causes.

"Why is acidification 0 for wool yarn but not for cotton fiber?" Because acidification in TRACI 2.2 is driven by substances like ammonia and sulfur dioxide. Wool yarn's inventory only contains CO₂, methane, and water — none of which has an acidification factor — so the category scores exactly zero. Cotton fiber's inventory contains ammonia, which does have an acidification factor, so it scores above zero. The zero is a real result, not a missing one.

"Where do characterization factors actually come from?" From scientific research, not from this project. They are calculated by environmental scientists using climate models, atmospheric chemistry models, and toxicology studies, then published as part of a standardized method — in this project, that method is called TRACI 2.2, built by the US Environmental Protection Agency. The numbers update over time as science improves, which is why you'll sometimes see slightly different factors (for example, methane's GWP factor) reported in different years or methods.

"Why not just add all eight scores into one giant number?" Because they measure fundamentally different kinds of harm — climate change, acid rain, cancer risk, ozone depletion — that cannot be meaningfully combined any more than you could add "number of store complaints" to "number of late deliveries" and call it one customer-service score. Keeping them separate lets a business see exactly where its biggest problems are, rather than hiding them inside one number.

"Is this the final step of an LCA?" For getting a number, yes — this is what produces the scores reported in a finished LCA. There is an optional further step called normalization and weighting, which converts the eight scores into one combined index by deciding how much each category "matters" relative to the others — but that requires value judgments about which kinds of damage are worse, so TRACI 2.2 deliberately stops short of doing it for you.

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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 two questions together
• Keep responses to three to five sentences per turn
• If the student seems stuck, offer a multiple-choice prompt: "Would you say a characterization factor is (a) the raw amount of a substance released, (b) a conversion number showing how much damage one kilogram of a substance causes relative to a reference substance, or (c) the name of a chemical?"
• 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