Disclosure focus
This asks the organisation to explain which Scope 3 categories are included in the emissions information it reports. In practice, the focus is on making clear the boundary of the Scope 3 figure: which upstream and downstream categories are covered, and whether any categories are left out or treated separately.
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.
Before you start
A quick mental checklist before you prepare this disclosure — tick each as you settle it.
Preparation
Key datapoints to prepare
＋ Show s2-29-a-vi-1 sub-elements (LRA working checklist)
List any categories you left out, and explain why each one was left out.
If you rely on an impracticability explanation, include it only where it is actually used.
State which Scope 3 categories are included in the calculation.
Describe the method used and any key assumptions made.
Show how much of the figure comes from direct data versus estimated or third-party data.
Set out which parts of the value chain are covered.
State the proportion of the data that has been independently checked.
How to prepare it
1Set the boundary for the value chain work first, so you know which parts of upstream and downstream activity are in scope for this disclosure and which are not.
2Decide, in plain business terms, what will count for each required field: the included Scope 3 categories, the mix of direct and indirect information, the share of data that has been checked, and any methodology or assumptions you will rely on.
3Gather the support behind each figure or statement, including source records, working papers, and any checks that show how the numbers or narrative were built.
4Prepare the disclosure content itself, making sure you can state the categories left out and why, explain the value-chain coverage, describe the data mix, and set out the methods and assumptions used.
5If you rely on an impracticability explanation, draft it clearly and keep it linked to the specific item it supports, rather than treating it as a general note.
6Before finalising, compare your draft with the official source to confirm nothing has been missed, any exclusions are explained, and any changes from prior workings are documented consistently.
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.
Industry examples
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
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Assurance readiness
For each claim, check the evidence
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.
Common gaps
Mistakes to avoid when collecting the data
Unclear owner
The 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 set
People 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 period
The 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 basis
Some 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 lost
Original 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 soon
Separate groups that should stay distinct are rolled together early, which hides which category each number came from.
Evidence details missing
The 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 trail
There 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 incomplete
One 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 captured
The team records the numbers but not the method behind them, so later reviewers cannot see what judgement calls were used to build the dataset.
Where judgement is often needed
Boundary shifts after buying or selling a business
Set 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 activity
Where 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 chain
For 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 year
If 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 figures
Describe 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 chain
If 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 amounts
State 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 information
Where 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 categories
If 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
Illustrative examples
Synthetic, written by LRA — not from a company report, not text from any standard.
Illustrative (synthetic) example — Manufacturing
:* 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.
Illustrative (synthetic) example — Retail
:* 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.
How companies report S2-29-a-vi-1 in practice
Real reports where this topic is disclosed. These are report practice, not exact disclosure templates to copy.
Iberdrola, S.A.
Electric Utilities / IPP / Energy Traders · Spain · 2025
Iberdrola’s 2025 Sustainability Report includes a disclosure on the mix of primary versus secondary data used for emissions calculations, noting on page 50 that categories 1 and 7 use secondary data while all others rely on primary data. The report also references verification of the Group’s inventory of Scope 1, 2, and 3 gross greenhouse gas emissions on page 46, though it does not clearly specify the share of verified data or fully detail the included Scope 3 categories. Notably, the report lacks explicit information on excluded categories and their rationale, any impracticability statements, methodology assumptions, and the overall value chain scope.
Lite-On Technology Corporation
Technology Hardware and Equipment · Taiwan · 2025
Lite-On Technology Corporation’s 2025 Annual Report includes some related context on methodology assumptions for climate scenario analysis, mentioning the use of RCP2.6, but does not clearly disclose specific assumptions or analysis factors (p.75). There is no evidence found regarding excluded categories and rationale, impracticability statements, included scope 3 categories, primary versus secondary data mix, value chain scope, or the share of verified data. Overall, the report provides limited and unclear information on the detailed methodology and scope related to this disclosure.
CJ Cheiljedang Corporation
Food Production — Agricultural · South Korea · 2024
CJ Cheiljedang Corporation’s Sustainability Report 2024 provides some context on Scope 3 emissions categories and efforts to improve activity data and calculation methodologies, mentioning photovoltaic facilities added in 2024 (p.24). The report includes data on Scope 3 emissions by category for 2022 to 2024, such as waste generated in operations and downstream transportation and distribution, but does not clearly disclose the included Scope 3 categories or the share of verified data (p.101). There is no evidence found regarding excluded categories and rationale, impracticability statements, methodology assumptions, primary versus secondary data mix, or value chain scope.
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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.
QHow should the team handle the omitted categories in the disclosure, and what extra explanation is needed if the omission is due to practical limits on data collection?
Reveal model answer →Hide answer ↑The note should clearly identify which categories were left out and explain why they were left out. If the omission is being justified on the basis that collecting the information was not practical, that point should be stated separately and the explanation should make clear why the team could not reasonably obtain the data.
Why this matters · When some value-chain categories are not reported, the reader needs both the list of exclusions and the reason for leaving them out, including any practical-data limitation explanation if that is the basis used.
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.
QShould the disclosure show only the categories counted, or also make clear which other categories were excluded from the scope of the calculation?
Reveal model answer →Hide answer ↑It should do both: show the categories that were included and also make clear which other categories were left out, together with the reason for those exclusions. That way the reader can see the boundary of the calculation rather than only the final subset.
Why this matters · A clear boundary statement needs both sides of the picture: what was counted and what was left out.
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.
QWhat should be brought into the disclosure so users can understand how the value-chain categories were compiled?
Reveal model answer →Hide answer ↑The disclosure should summarise the approach used and the key assumptions that shaped the result, not just the final numbers. If the calculation relied on estimates, proxies, or simplifications, those should be described in plain language so the basis of the category selection and measurement is understandable.
Why this matters · Users need enough method detail to judge how the category set was built and how much estimation sits behind it.
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.
QWhat should the preparer clarify about the data sources and the part of the value chain covered by the calculation?
Reveal model answer →Hide answer ↑The note should explain the balance between direct information and estimated or external data, and it should state the part of the value chain that the calculation covers. If some categories are supported by stronger evidence than others, that mix should be described so readers can see where the numbers are more or less grounded in source data.
Why this matters · A useful disclosure shows both the reach of the value-chain boundary and the blend of source data used to populate it.
Framework references
Related framework references
How this disclosure maps across the major reporting frameworks.
Primary
Related & explore
FAQ
Questions this page answers
For s2-29-a-vi-1, what should I gather before I start drafting the disclosure?+−
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.
How do I decide the value chain boundary for s2-29-a-vi-1 using this page?+−
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.
What does the page mean by excluded categories and how do I record them for s2-29-a-vi-1?+−
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.
What evidence should I keep for assurance on s2-29-a-vi-1?+−
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.
How do I use the six assurance claims on the s2-29-a-vi-1 page?+−
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.
What are the most common mistakes to avoid when drafting s2-29-a-vi-1?+−
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.
How do I use the Prep & Assurance workbook for s2-29-a-vi-1?+−
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.
What can I take from the synthetic example disclosure for s2-29-a-vi-1?+−
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.
How do I turn the s2-29-a-vi-1 data into a draft narrative and content index line?+−
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.
Can I reuse data from an ESRS E1 climate disclosure for s2-29-a-vi-1?+−
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
s2-29-a-vi-1 disclosure checklist for sustainability managers2-29-a-vi-1 evidence pack what to include for assurances2-29-a-vi-1 excluded categories how to document thems2-29-a-vi-1 calculation basis and data source mix guidances2-29-a-vi-1 verified data share how to present its2-29-a-vi-1 common reporting gaps and mistakes to avoids2-29-a-vi-1 workbook download how to use the xlsxs2-29-a-vi-1 printable library card pdf downloads2-29-a-vi-1 synthetic example disclosure tables2-29-a-vi-1 draft output narrative starters and content indexs2-29-a-vi-1 assurance claims claim risk evidences2-29-a-vi-1 ESRS E1 data reuse cross framework
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