Disclosure focus
The practical point is comparability and completeness. A reader should be able to see whether the reported Scope 3 number reflects all relevant categories across the business, or only a selected set such as the most material ones, flagship operations, or categories where data are available. If coverage is partial, that should be clear from the disclosure.
This LRA educational guidance supports disclosure preparation. For the exact requirements, always refer to the official IFRS source.
Prepare
What to collect before drafting
Tick each field as the evidence lands. Owners are the teams that usually hold the source files.
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Excluded categories
Sustainability reportingList the emissions categories left out of the calculation, and give the reason each one was left out.
Evidence Working papers showing the boundary decision, category list, and the documented reason for each exclusion.
Watch for The excluded set does not reconcile to the category inventory, so a category is left out without a documented reason.
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Impracticability note
Sustainability reportingRecord whether a formal note was prepared to explain why a full calculation could not be done, and keep the wording used.
Evidence Signed methodology memo or disclosure draft showing the impracticability explanation and approval trail.
Watch for The note is used without a supporting memo, so the explanation is not tied to the actual data limitation or reporting period.
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Included Scope 3 list
Sustainability reportingCapture which upstream and downstream emissions categories are included in the reported Scope 3 set.
Evidence Scope mapping, category register, and consolidation workbook showing the final included categories.
Watch for The included list does not match the category mapping, so one or more relevant categories are omitted or double-counted.
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Calculation basis
Sustainability reportingSet out the calculation approach, key assumptions, and any estimation rules used to build the figure.
Evidence Method statement, calculation model, and assumption log with version control and sign-off.
Watch for The assumptions are not aligned to the model inputs, so the calculation basis changes between files or reporting cycles.
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Data source mix
Sustainability reportingShow how much of the result comes from direct source data versus estimated or third-party data.
Evidence Source register and calculation workbook with fields tagged by data origin and aggregation method.
Watch for Primary and secondary inputs are mixed without a clear split, so the reported mix cannot be traced back to the source files.
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Value chain boundary
Sustainability reportingDescribe which parts of the upstream and downstream chain are inside the reporting boundary and which are not.
Evidence Boundary memo, supplier/customer coverage list, and process map showing the included stages.
Watch for The boundary is set at the wrong stage, so the reported scope does not match the defined value-chain coverage.
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Verified data share
Sustainability reportingCalculate the share of the reported figure that has been independently checked, using the same total as the main disclosure.
Evidence Assurance report or verification log linked to the reported total and the verified subset.
Watch for The verified portion is calculated against a different total, so the percentage does not reconcile to the disclosed figure.
Before you start
- Which parts of the value chain are you treating as in scope, and which are you leaving out?
- For any category you are not covering, have you settled the reason and whether an impracticability note will be used?
- Are you clear on the method and assumptions that will sit behind the figures, including how you define the categories you are using?
- Will the numbers come mainly from your own records, from external sources, or a mix of both?
- Do the figures relate to the reporting period you are using, and can you trace them back to source evidence?
- If you plan to show a verified-data share, have you agreed what counts as verified and how that share will be calculated?
How to prepare it
Request the data
Request the Scope 3 category evidence pack
Translate the disclosure into an internal business question — then adapt it to your organisation's own language.
Which value-chain emissions categories are included in our reporting, which ones are left out, and what support do we have for the choices and calculation approach?
Use your organisation’s own labels for value-chain emissions, spend, logistics, purchased goods, travel, waste, and similar internal groupings first; then map them to the reporting categories only when you prepare the disclosure pack. Keep the request in the language the data owner already uses, and check the official source before sign-off.
Weak request
Please provide the Scope 3 category disclosure inputs for the reporting period, including excluded categories, methodology, data mix, value-chain scope, and verified-data share.
Why it fails: This uses framework language that many internal owners will not recognise, so it is harder to action and easier to answer incompletely. It also does not tell the owner what their own files, labels, or evidence should look like, so the response may not be usable for the reporting pack.
Better request
Please send the value-chain emissions pack for [period] from your team’s own systems and working files. Use your normal internal category names first, then map them to the reporting categories if needed. Include what you covered, what you left out and why, the method and assumptions, the split between supplier/activity data and estimates, the coverage of the value-chain, and the share of data that has been checked or verified.
Formal email template
Subject: Request for value-chain emissions category evidence pack Hi [name], I’m pulling together the reporting pack for [period] and need your help with the value-chain emissions section. Please could you send over the information for the areas your team owns, using your normal internal labels first and then, if helpful, mapping them to the reporting categories? I’m looking for: - which value-chain areas are included in this cycle - which areas are left out, and why - the method and main assumptions used - the split between supplier/activity data and estimated data - the scope of the value-chain covered - the share of data that has been checked or verified A simple table plus any supporting notes, files, or links would be ideal. Please include the period covered, the source system or file, and the person who prepared it. If anything is unclear, I’m happy to talk it through. Please also check the official source before sign-off. Thanks, [preparer name]
Short Teams / Slack version
Hi [name] — could you share the value-chain emissions evidence for [period] from your team’s normal files/systems? I need: included areas, excluded areas and why, method/assumptions, primary vs estimated data split, coverage, and any checked/verified share. A table plus notes is fine. Please use your own internal labels first, then map if needed. Thanks — [preparer name]
Industry examples
Manufacturing
Context. A plant team tracks purchased materials, inbound freight, waste, and outsourced processing in separate operational files.
Adapted request. Please share the emissions evidence pack for [period] from your plant reporting files. Use your own labels for materials, transport, waste, and outsourced work first, then map them if needed. Include what was covered, what was left out and why, the calculation method, the assumptions used, the split between supplier data and estimates, the value-chain areas covered, and the share of data that was checked.
Example response. Included: purchased steel, inbound road freight, production waste, and third-party coating. Excluded: employee commuting, because it was not assessed in this cycle. Method: supplier activity data for steel and freight; spend-based estimates for waste and coating. Primary data share: 58%. Checked data share: 41%.
Retail
Context. A retail sustainability team holds data on bought-in products, upstream transport, packaging, store waste, and some customer-use assumptions.
Adapted request. Please send the value-chain emissions evidence for [period] from your retail reporting workbook and source files. Use your internal labels for product supply, logistics, packaging, store waste, and customer-related assumptions first, then map them if needed. Include included and excluded areas, reasons for any exclusions, the method and assumptions, the split between direct supplier data and estimates, the coverage of the value chain, and the share of data that has been checked or verified.
Example response. Included: bought-in products, inbound logistics, packaging, and store waste. Excluded: customer use of products, because the team has not modelled it this year. Method: supplier data for packaging and logistics where available; estimates for product categories with limited supplier detail. Primary data share: 46%. Verified data share: 29%.
Draft your disclosure
Notes that turn data into a disclosure
LRA training templates — adapt them to your organisation, and check the official source before sign-off.
Method note
Set out the calculation basis in plain terms, including the assumptions used, how the boundary was drawn, which categories were included or left out, and whether any exclusions were due to practical limits.
Context note
Explain what the figures represent in business terms, including how much of the value chain is covered, the mix of direct and indirect data, and the extent to which the underlying information has been checked.
Fluctuation statement
If the numbers move materially from one period to the next, point to the main drivers — such as a wider or narrower boundary, a different data mix, or changes in the share of checked information — and say whether the shift reflects a real change or a reporting-method change.
Content index entry
s2-29-a-vi-1 Scope 3 categories — [location / page] / [notes]Download Centre
Preparation tools & forms
Download preparation tools for s2-29-a-vi-1 with free verified email access for 24 hours. Source-report links remain public.
Check
For each claim, check the evidence
We documented which parts of the wider value chain we left out of the coverage figure, and why those parts were not brought in.
Risk An assurer will test whether the exclusions were chosen consistently, whether the reasons are specific rather than generic, and whether any omitted parts should have been included.
Evidence to check Boundary memo or methodology note; list of excluded categories; management rationale for each exclusion; review sign-off; any correspondence showing how the boundary decision was agreed.
Where we relied on an impracticability judgement, we recorded that conclusion and kept notes on how we are still handling the related emissions estimate in practice.
Risk An assurer will probe whether the impracticability call was made on a supportable basis, whether it was applied only where needed, and whether the related emissions were still addressed through an appropriate process.
Evidence to check Impracticability assessment; internal approvals; working papers showing the estimation approach used instead; action plan or controls for ongoing handling; evidence of periodic reassessment.
We set out which value-chain categories were brought into the disclosed figure, and the list matches the scope we used in the working papers.
Risk An assurer will check completeness and consistency: whether all intended categories were included, whether any category was accidentally left out, and whether the published list matches the calculation file.
Evidence to check Category mapping schedule; calculation workbook; consolidation or boundary checklist; draft-to-final comparison; sign-off from the preparer and reviewer.
Our method note explains how we measured the figure, including the main inputs we used and the assumptions we applied.
Risk An assurer will test whether the method is described clearly enough to reproduce the result, whether the inputs are appropriate, and whether the assumptions are reasonable and consistently applied.
Evidence to check Methodology paper; calculation model; assumption log; source data extracts; version history showing changes and approvals.
We can show how much of the figure came from direct activity information from within the value chain, rather than from broader estimates alone.
Risk An assurer will examine whether the stated mix of direct and indirect inputs is accurate, whether the direct activity data is sufficiently relevant, and whether the mix was calculated on a consistent basis.
Evidence to check Data source register; activity-level data files; allocation logic; calculation of the direct-input share; evidence of review over source selection.
Our boundary note makes clear which upstream and downstream parts were counted, and which related categories were left out.
Risk An assurer will probe whether the boundary description is complete, whether the omitted categories were intentionally excluded, and whether the published wording matches the internal scope decision.
Evidence to check Boundary statement; category inclusion/exclusion matrix; internal scope papers; management review notes; final disclosure cross-check.
Evidence pack to prepare
Common reporting gaps
- Figures are stated without the supporting narrative, or narrative without figures.
- Scope is inconsistent between the text and the numbers.
- The reporting boundary is left undefined.
- Material changes since the previous period are not disclosed.
- Estimates and measured values are not distinguished.
- Source records for the figures are not identified.
Where drafts go wrong
Mistakes to avoid and where judgement is needed
The left column is what preparers get wrong while collecting; the right column is where you will have to make and explain a judgement.
Mistakes to avoid
- Unclear ownerThe request goes to the wrong team or person, so the data is chased in framework language instead of the organisation’s own operational terms.
- No boundary setPeople start collecting figures before agreeing which parts of the business and value chain are in scope, so different teams work to different cut-offs.
- Wrong reporting periodThe data pull uses a different time basis from the rest of the pack, which leaves the category set out of step with the reporting cycle.
- Mixed counting basisSome teams send direct records while others send estimates without flagging the difference, so the final mix is not built on one consistent basis.
- Source labels lostOriginal file names, system tags, or category names are stripped out during consolidation, making it hard to trace each figure back to its source.
- Populations merged too soonSeparate groups that should stay distinct are rolled together early, which hides which category each number came from.
- Evidence details missingThe pack arrives without the supporting notes, dates, or file references needed to show where the figures came from and how they were checked.
- No approval trailThere is no record of who reviewed and cleared the data, so the team cannot show that the numbers were signed off before drafting.
- Scope list incompleteOne or more relevant category lines are never requested from the business, so the collection misses items that should have been captured or explained.
- Assumptions not capturedThe team records the numbers but not the method behind them, so later reviewers cannot see what judgement calls were used to build the dataset.
Judgement needed
- Boundary shifts after buying or selling a businessSet out whether the reporting perimeter was updated for the year’s acquisitions or disposals, and explain which value-chain activities were brought in or left out as a result.
- Different country labels for the same activityWhere local records use different names or groupings, map them to one internal category basis and explain the mapping so the same activity is not counted twice or missed.
- Activities close to the edge of the value chainFor shared, outsourced or partially controlled activities, state the rule used to decide inclusion or exclusion and explain any cases treated differently from the main approach.
- Choosing the cut-off date for the yearIf the reporting period does not align neatly with operational or supplier data cycles, explain the date basis used and why it gives the fairest picture for the year.
- Measured figures versus estimated figuresDescribe which parts came from direct records and which were estimated, and explain the method used to fill gaps where actual data was not available.
- Using different data sources across the chainIf some categories rely on primary records and others on secondary sources, explain the mix, the reason for it, and any material differences in quality or coverage.
- Rounding and small residual amountsState the rounding rule used and explain how minor residual amounts were handled so the category totals still reconcile to the overall picture.
- Protecting sensitive supplier or employee informationWhere detailed source data cannot be shown without breaching privacy or confidentiality, explain the level of aggregation used and how it still supports the reported category split.
- Impracticability judgement for hard-to-trace categoriesIf a category could not be covered in the normal way, explain the practical barrier, the alternative approach used, and which categories were left out on that basis.
Examples
How it reads on the page
Synthetic, written by LRA — not from a company report, not text from any standard.
we set out the value-chain emissions areas we left out, and why, because some upstream and downstream activities could not be measured with enough confidence for this reporting cycle. We also note where we relied on estimates rather than direct records, the parts of the chain covered by our review, and the main calculation choices and assumptions used.
- We included purchased goods and services, capital items, fuel- and energy-related activities, upstream transport and distribution, waste from operations, business travel, employee commuting, leased assets, downstream transport and distribution, processing of sold products, use of sold products, end-of-life treatment of sold products, and downstream leased assets; we excluded investments, franchises, and downstream processing where we had no reliable activity data, and we treated those exclusions as immaterial to the overall result.
- Our figures combine 68% primary records and 32% secondary factors; 74% of the total is based on data we had checked or had independently assured, while the rest comes from modelled or supplier-estimated inputs. We covered our own operations plus upstream and downstream activities across the full value chain, and we used spend-based factors where supplier-specific data were not available, distance-based methods for freight, and product-use assumptions based on expected lifetime and energy intensity.
Synthetic illustration for practitioner review only; not legal or compliance advice.
we explain which emissions areas were left out, the reason for each omission, and the point at which we judged further estimation to be too uncertain for this year’s report. We also describe the parts of the chain we covered, the mix of direct records and external factors, and the main assumptions behind the calculations.
- We reported on purchased goods and services, upstream transport and distribution, waste from operations, business travel, employee commuting, leased assets, downstream transport and distribution, use of sold products, and end-of-life treatment of sold products; we left out capital items, fuel- and energy-related activities, processing of sold products, investments, and franchises because the available information was too patchy or the activity was not material for our business model.
- Our dataset was 41% direct supplier or internal records and 59% third-party or modelled inputs; 62% of the total emissions figure rests on data that had been checked by an external party or otherwise verified, with the remainder unverified. We covered upstream, our own operations, and selected downstream stages, and we used average emission factors for missing supplier data, route-based freight estimates, and product-use assumptions tied to average customer behaviour and product life.
Synthetic illustration for practitioner review only; not legal or compliance advice.
Quality ladder
- Basic
I included Scope 3 categories 1, 2 and 4, and I left out categories 3 and 5 because we could not gather reliable data for them.
- Better
I included categories 1, 2 and 4 across our value chain, excluded categories 3 and 5 with a short reason for each gap, and used a mix of direct records and estimates based on our stated method.
- Best
I reported categories 1, 2 and 4 for the full value chain in the year ended 31 December 2025, explained why categories 3 and 5 were left out, set out the method and assumptions, showed that 60% of the data was directly verified, and noted that the excluded items were omitted because the underlying records were not available in time.
Check your understanding
Scenarios to work through
A preparer has mapped 11 upstream and downstream emissions categories for the year, but two of them were left out because the team could not obtain reliable activity data in time. The draft note also says those two areas were not included in the totals.
A group has included only purchased goods, transport, waste, and business travel in its value-chain emissions work because those were the only areas with usable data this year. The sustainability team is unsure whether it should also describe the wider set of categories it considered but did not include.
A preparer has built the calculation using a mix of supplier invoices, internal records, and industry averages. The team has also applied a few simplifying assumptions where direct measurements were unavailable, but these choices are only documented in working papers.
A company has complete supplier data for some categories, partial internal data for others, and industry-average estimates for the rest. The draft note says only that the figures are ‘based on available information’ and does not explain the data mix or the coverage of the value chain.
Real reports
How 3 companies report it
Examples of full and partial reporting practice. These are evidence-led reviews, not exact disclosure templates to copy.
Iberdrola, S.A.
Partial
Lite-On Technology Corporation
Partial
CJ Cheiljedang Corporation
Partial| Field | Iberdrola, S.A. | Lite-On Technology Corporation | CJ Cheiljedang Corporation |
|---|---|---|---|
| Excluded categories | Not found | Not found | Not found |
| Impracticability note | Not found | Not found | Not found |
| Included Scope 3 list | Partial | Not found | Partial |
| Calculation basis | Not found | Partial | Not found |
| Data source mix | Not found | Not found | Not found |
| Value chain boundary | Not found | Not found | Not found |
| Verified data share | Partial | Not found | Partial |
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Advice, not assurance
Framework references
Relevant IFRS / ISSB requirements and related disclosures
IFRS / ISSB
s2-29-a-vi-1
within IFRS S2: Climate-related Disclosures
Guides that settle this question
Related & explore
More in IFRS S2 →Browse full catalogue →Search all disclosures →
FAQ
Questions this page answers
The page says to prepare a specific set of datapoints first: excluded categories, any impracticability note, the included Scope 3 list, calculation basis, data source mix, value chain boundary and verified data share. It also has a step-by-step preparation section you can use to turn those inputs into a draft.
Use the page’s preparation guidance to define the value chain boundary consistently before drafting, then keep that boundary aligned with the included Scope 3 list and the data source mix. The page is designed to help you document the boundary clearly rather than leaving it implicit.
The page tells you to prepare excluded categories as one of the core datapoints, so you should list what is left out and keep that visible in the draft. It also flags common reporting gaps, which is useful for checking that exclusions are explained rather than overlooked.
The page includes an evidence pack with five items and also sets out six assurance claims to verify, each with a claim, risk and evidence angle. That gives you a practical starting point for building an assurance-ready file before review.
Treat them as a checklist for what needs to be supported in the file: each claim is paired with the related risk and evidence to look for. The page is meant to help you test whether the draft is backed by enough documentation, not just whether the numbers are filled in.
The page has a section on common reporting gaps and mistakes, so it is worth using that as a final quality check before sign-off. In practice, it helps you spot missing boundary detail, weak methodology notes or unsupported figures early.
The Download Centre includes a Prep & Assurance workbook in .xlsx format, which is intended to support preparation and assurance readiness. Use it alongside the page’s datapoint list, evidence pack and draft-output section to organise the disclosure work.
The page includes synthetic illustrative examples, including a quantitative table, to show what a finished disclosure can look like. They are there to help you shape your own draft and check internal consistency, not to copy as a template.
The draft-output section gives visualisation ideas, narrative starters and a content-index line, so you can move from raw inputs to a readable disclosure. Use those prompts to explain the boundary, basis and data mix in plain language.
The page notes ESRS E1 (Climate Change) as the closest correspondence, so the same underlying data may be reusable where it fits your reporting setup. It does not say the requirements are identical, so you still need to check the page’s own datapoints and preparation steps.
More questions this page can help with