Disclosure focus
The practical point is to make the measurement basis understandable and comparable, not just to present a final emissions total. An organisation should be clear about the scope of the approach across its operations, and whether the same method is used consistently or only for certain sites, business units or emission sources. The aim is to help users judge how robust and complete the reported emissions information is.
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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Year-on-year changes
Sustainability reporting / FinanceA clear note of what changed in the reported figure compared with the previous reporting year, including which parts of the calculation or coverage were updated.
Evidence Prior-year submission, current-year working papers, and a change log showing the revised basis.
Watch for The current figure is compared to the wrong prior-year basis, so a restatement or methodology update is missed and the trend is not like-for-like.
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Boundary and consolidation basis
Finance / Sustainability reportingThe rule used to decide which entities, sites, or activities are included in the reported total, and how that inclusion rule is applied in practice.
Evidence Group reporting policy, consolidation memo, organisational chart, and boundary mapping to the source data.
Watch for The reported total does not reconcile to the chosen consolidation basis because entities or operations are included on a different rule set than the one documented.
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Calculation factors used
Sustainability reporting / Data analyticsThe conversion or calculation factors applied to turn activity data into the reported result, including the source and version of each factor set.
Evidence Factor library, supplier or database extracts, calculation workbook, and version control records.
Watch for The result is built with a factor set that does not match the calculation file or reporting period, so the output cannot be reproduced from the source model.
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Key inputs and assumptions
Sustainability reporting / Operational data ownersThe main source inputs, estimates, and judgement calls used in the calculation, with enough detail to show what was measured, estimated, or assumed.
Evidence Source extracts, estimation notes, assumptions register, and calculation workbook comments.
Watch for A material assumption is left undocumented or the input basis is mixed up, so the reported number cannot be traced back to the underlying source data.
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Drivers of change
Finance / Sustainability reportingThe specific operational or methodological reasons why the reported figure moved from the prior year, separated into real activity changes and calculation changes.
Evidence Variance analysis, management commentary, and supporting operational records for the main drivers.
Watch for A change caused by methodology is explained as an operational shift, or vice versa, so the movement is attributed to the wrong driver.
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Scope 3 quality notes
Sustainability reporting / Supply chainAny short note on the reliability of the value chain data where it matters, including known gaps, estimates, or weak source data that affect confidence in the result.
Evidence Supplier data quality review, estimation log, gap analysis, and source-data assessment.
Watch for A weak sub-population or low-quality source is treated as if it were robust primary data, so the quality note does not match the actual coverage and reliability of the value chain data.
Before you start
- Have you fixed which parts of the business sit inside the carbon-counting boundary, and whether that boundary is based on control or another operational split?
- Are you clear which reporting period the figures cover, and whether any year-on-year changes need to be explained before you draft?
- Can every number be traced back to a source record, model, or calculation file, with the key inputs and assumptions documented?
- Have you chosen the emissions factors and any other conversion basis you will use, and checked they match the activity data you are pairing them with?
- If the figures have moved since last year, do you know the operational reason for the change and whether any data-quality notes are needed for outsourced or upstream/downstream items?
How to prepare it
Request the data
Request the emissions calculation pack from the carbon accounting owner
Translate the disclosure into an internal business question — then adapt it to your organisation's own language.
What calculation approach, inputs, assumptions, and year-on-year changes were used to produce the greenhouse gas figures for this reporting period?
Use your organisation’s own names for the team, systems, and calculation packs first, then map them to the reporting disclosure. Keep the ask in everyday internal language rather than framework wording, and check the source material before sign-off.
Weak request
Please provide the IFRS S2 GHG measurement approach, inputs and assumptions evidence for S2-29(a)(iii).
Why it fails: It uses framework language that many operational teams will not recognise, so the owner may not know which files or calculations to send. It also does not point to the practical artefacts needed to explain the numbers, such as the boundary, factors, assumptions, and year-on-year changes.
Better request
Please send the latest emissions calculation pack for [reporting period], including the boundary used, the source file or system extract, the factor set and version, the main inputs and assumptions, any changes since [prior period], and the reason for those changes. If you track indirect-emissions data quality, include those notes too.
Formal email template
Subject: Request for the emissions calculation pack for [reporting period]\n\nHi [name/team],\n\nWe are preparing the sustainability reporting pack and need the calculation support behind the greenhouse gas figures for [reporting period].\n\nPlease send the latest version of your emissions calculation pack, including:\n- the boundary used for the inventory and any exclusions\n- the source file or system extract used to calculate the figures\n- the emission factor set and version/date\n- the main inputs and assumptions used in the calculations\n- any changes made since [prior period]\n- the reason for those changes\n- any notes on data quality for indirect emissions, where relevant\n\nIf it is easier, you can return this in your usual template or workbook. Please also include the period covered, the owner of the file, and the date it was last updated.\n\nThis is a possible LRA training template only; please adapt it to your organisation’s own terms and check the source material before sign-off.\n\nThanks,\n[preparer name]
Short Teams / Slack version
Hi [name/team] — could you share the latest emissions calculation pack for [reporting period]? Please include the boundary, source file/system, factor set/version, key inputs and assumptions, any changes since [prior period], the reason for those changes, and any data-quality notes for indirect emissions if relevant. Use your usual format if easier. Thanks.
Industry examples
Manufacturing
Context. A plant-based group with fuel, electricity, refrigerants, and freight data held across site logs and a carbon workbook.
Adapted request. Please share the latest carbon workbook for [reporting period] covering the plant boundary, site list, fuel and power inputs, refrigerant assumptions, freight estimates, factor library version, and any changes since [prior period]. Include the reason for any restatements or methodology updates.
Example response. Attached: carbon workbook v4.2; boundary note; site list; factor library v2025.1; assumptions tab; change log showing one new warehouse added and one freight proxy replaced; reason: improved meter coverage and updated logistics data.
Financial services
Context. A services group with office energy, business travel, purchased goods, and supplier estimates managed by sustainability and finance.
Adapted request. Please send the emissions calculation pack for [reporting period] used for the office and travel inventory, including the group boundary, source extracts, factor set, assumptions for supplier and travel estimates, and any changes from [prior period]. Add notes on any indirect-emissions data gaps or proxies.
Example response. Attached: inventory model; boundary memo; travel extract; office energy file; factor register v3; assumptions log; change note showing a switch from estimated to booked travel data for one region; data-quality note: supplier spend-based estimates still used for one category.
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
Explain the basis used to prepare the figures, including how the reporting boundary was defined, which inputs and assumptions were applied, and what conversion factors were used.
Context note
Describe what the numbers represent in practice, including the effect of the chosen boundary, the main calculation inputs, and any quality limits that shape how the figures should be read.
Fluctuation statement
Set out the main reasons the figures changed from the previous year, distinguishing between real operational movement, changes in the reporting boundary, and updates to data or assumptions.
Content index entry
s2-29-a-iii GHG measurement approach, inputs and assumptions — [location / page] / [notes]Download Centre
Preparation tools & forms
Download preparation tools for s2-29-a-iii with free verified email access for 24 hours. Source-report links remain public.
Check
For each claim, check the evidence
We kept the coverage figure on the same basis as last year, and where we changed the basis we explained the effect clearly.
Risk Assurer may test whether the year-on-year comparison is genuinely like-for-like, or whether a basis change has been hidden in the narrative.
Evidence to check Prior-year calculation file; current-year methodology note; reconciliation showing the effect of any basis change; draft-to-final review comments; sign-off confirming the comparison basis used.
We set out the practical method we used to build the figure, including the boundary we applied to the disclosed operations.
Risk Assurer may probe whether the reported perimeter matches the entity’s chosen reporting boundary and whether any exclusions were deliberate and consistent.
Evidence to check Boundary memo; consolidation or operational control papers; list of included and excluded entities/sites; methodology paper; management approval of the boundary choice.
We explained why we used this approach, rather than another available method, for the disclosed figure.
Risk Assurer may challenge whether the chosen approach was selected for convenience or to improve the result, rather than because it was the most suitable basis.
Evidence to check Method selection paper; options appraisal; internal emails or committee papers showing the rationale; approval records; consistency check against prior reporting decisions.
We described the data inputs and assumptions that fed into the calculation, so the reader can see what drove the result.
Risk Assurer may look for missing assumptions, undocumented estimates, or inputs that were applied inconsistently across parts of the calculation.
Evidence to check Calculation workbook; assumption log; source data extracts; data dictionary; review notes showing each key input was traced and approved.
Where we changed the calculation basis, we explained what changed and why the update was made.
Risk Assurer may test whether the explanation is complete, whether the change was intentional, and whether it has been applied consistently across the reporting period.
Evidence to check Change log; version history; revised methodology note; approval of the revised basis; comparison of old and new outputs showing the impact of the change.
For the supply-chain part of the figure, we noted the quality of the underlying data and how much came from detailed activity records or independently checked sources.
Risk Assurer may question whether lower-quality estimates were presented as stronger evidence, or whether the mix of data types was properly disclosed.
Evidence to check Supplier data pack; activity-based input schedule; third-party verification statements where available; data quality assessment; summary of estimated versus directly sourced inputs.
Evidence pack to prepare
Common reporting gaps
- The information is presented without a date or as-at point.
- The scope or boundary of the statement is left undefined.
- Key terms are used inconsistently across the report.
- Material changes since the previous period are not disclosed.
- Assertions are made without supporting detail or a source record.
- Boilerplate is used that does not actually answer what is asked.
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
- Wrong owner, wrong languageThe team asks a technical lead in framework terms instead of the person who actually runs the emissions data, so the answer comes back in the wrong business language and cannot be used cleanly.
- Boundary left undefinedPeople start collecting figures before agreeing which parts of the business sit inside the reporting perimeter, so different teams pull different populations and the dataset cannot be reconciled.
- Period basis not fixedOne team uses the latest month available while another uses the reporting year cut-off, so the inputs are built on different timing bases and do not line up.
- Counting basis gets mixedActivity data, estimates and converted values are gathered together without separating how each was counted, which makes the underlying method impossible to trace.
- Source labels are stripped outOriginal file names, system tags and version markers are lost during collection, so no one can tell which extract or calculation the numbers came from.
- Populations merged too earlyData for different business units, sites or asset groups is combined before the collection rules are agreed, even though those groups should stay separate for review.
- Evidence details not capturedThe team saves the number but not the supporting context, such as where it came from, who produced it and when it was taken, so the trail is incomplete.
- No sign-off trailDraft inputs move through email and spreadsheets without a clear approval record, so there is no reliable record of who checked the data before it was used.
- Change reasons not loggedYear-on-year movements are collected without recording what changed in the business or the method, so later reviewers cannot explain the difference.
- Quality notes for indirect data missedWhere third-party or estimated data needs a quality note, the collector skips it, leaving no record of known limitations or assumptions.
Judgement needed
- Acquisitions and disposals during the yearState whether you bring in or remove emissions from businesses bought or sold part-way through the period, and explain the cut-off date and any restatement used so the year-on-year comparison is understandable.
- Different country definitions for the same activityIf local records classify the same fuel, site, or workforce group differently across countries, choose one organisation-wide basis, explain the mapping rule, and note where local data had to be translated.
- People or sites close to the reporting lineFor assets, teams, or contractors that sit near your chosen boundary, explain the rule used to include or exclude them and why that rule gives the clearest picture of the business you are measuring.
- Using estimates where direct readings are missingWhere actual measurements are not available, disclose which figures are estimated, the method used to build them, and how you checked that the estimate is reasonable.
- Choosing the timing basis for the periodIf activity data, meter reads, invoices, or other source records arrive on different dates, explain the timing rule you applied and keep it consistent unless you can clearly justify a change.
- Selecting emission factors and updating themExplain which factor set you used, why that source was chosen over alternatives, and whether any mid-year update or country-specific factor changed the result.
- Rounding and aggregation of small amountsIf you round figures or combine small items before reporting, say how you did it and make sure the rounded totals still reconcile to the underlying figures.
- Protecting sensitive supplier or employee dataWhen detailed source data cannot be shown because of privacy or confidentiality limits, explain the level of aggregation used and how that still supports a fair view of the calculation.
- Explaining changes from the prior periodIf the current year differs from last year because of a method change, boundary shift, or better source data, describe the reason for the movement and separate it from genuine operational change where possible.
Examples
How it reads on the page
Synthetic, written by LRA — not from a company report, not text from any standard.
We have kept the same group perimeter as last year, with one small plant added after acquisition and no other boundary changes, so the year-on-year movement is mainly operational rather than structural.
- For the current year, we used the same calculation methods across the group and applied location-based electricity factors from our market data provider, plus supplier-specific factors where we had them for purchased materials and freight.
- The main shift came from higher output at our two largest sites and a fuller set of supplier data for upstream transport; our scope 3 figures remain partly estimate-based for categories where supplier coverage is still incomplete, so those numbers are less precise than our direct emissions data.
This example shows how to explain a stable reporting perimeter, the main drivers of movement, the factor sources used, the assumptions behind the numbers, and where indirect-emissions data still relies on estimates.
Our reporting boundary stayed unchanged this year and continues to follow the stores, warehouses and central functions we control, with franchise activity left outside the group totals.
- Compared with the prior period, the rise in our footprint was driven mainly by more customer deliveries and a larger electricity load in distribution centres; we used the same grid factors as last year, together with supplier data for leased vehicles where available.
- For indirect categories, we relied on spend-based estimates for a few upstream items and on activity data for logistics and waste; those areas are the least mature in our dataset, so we flag them as lower-quality inputs and expect future revisions as supplier coverage improves.
This example shows how to describe an unchanged boundary, explain the main reasons for movement, identify the factor set and input types used, and note where indirect-emissions data is still less robust.
Quality ladder
- Basic
Our emissions were 12,400 tCO2e.
- Better
We measured 12,400 tCO2e using our group boundary, standard emissions conversion values, and the main activity data and assumptions behind the calculation.
- Best
We reported 12,400 tCO2e for the year ended 31 December 2025 using our group boundary, current emissions conversion values, and the activity data and assumptions in our model; the 8% rise from 2024 mainly reflects higher purchased energy use and a fuller set of Scope 3 estimates, with a note that those third-party data were less complete than last year.
Check your understanding
Scenarios to work through
A group has changed from using a market-based electricity factor to a location-based one for part of its footprint, and the emissions team also updated the boundary to include a newly controlled subsidiary from 1 July. The draft note currently says the numbers moved because of 'method updates' but does not separate the effects.
A preparer has used a mix of supplier-specific activity data, spend-based estimates, and default factors for purchased goods and freight. The working papers are complete, but the draft disclosure only lists the final tonnes and does not explain the main data sources or the assumptions behind the estimates.
A company has changed its emissions factor set during the year because it moved to a newer database for stationary fuel and refrigerants. The draft note mentions the new database name, but it does not say whether the change affected the current-year result or whether the prior-year figure was restated.
For Scope 3, the team has estimated several categories using secondary data because supplier data were incomplete. The draft disclosure says only that 'data quality is acceptable', even though some categories rely on broad industry averages and others on more specific shipment records.
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.
SITC International Holdings Company Limited
Exact
ANZ Group Holdings
Partial
Aditya Birla Fashion and Retail Limited
Partial| Field | SITC International Holdings Company Limited | ANZ Group Holdings | Aditya Birla Fashion and Retail Limited |
|---|---|---|---|
| Year-on-year changes | Not found | Partial | Partial |
| Boundary and consolidation basis | Not found | Not found | Not found |
| Calculation factors used | Partial | Not found | Partial |
| Key inputs and assumptions | Not found | Partial | Not found |
| Drivers of change | Not found | Not found | Not found |
| Scope 3 quality notes | Not found | Partial | Not found |
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Advice, not assurance
Framework references
Relevant IFRS / ISSB requirements and related disclosures
IFRS / ISSB
s2-29-a-iii
within IFRS S2: Climate-related Disclosures
Guides that settle this question
Related & explore
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FAQ
Questions this page answers
Start with the plain-language explainer, then work through the step-by-step preparation section and the datapoints list. The page is set up to help you move from source data to a draft disclosure and an assurance-ready evidence pack.
The page points you to year-on-year changes, the boundary and consolidation basis, calculation factors, key inputs and assumptions, drivers of change, and any Scope 3 quality notes. Use those as your minimum data checklist before drafting.
The page flags boundary and consolidation basis as a required datapoint to prepare, so you should document the scope you are using and keep that consistent through the draft and evidence pack. The workbook is there to help you capture that information in a structured way.
The page tells you to capture calculation factors used, key inputs and assumptions, and the drivers of change. That gives reviewers enough context to understand how the numbers were built and what changed year on year.
The page does not assign roles, but it is designed for sustainability/ESG managers, HR or data owners, and assurance reviewers to use together. In practice, ownership should sit with the people who can explain the source data, assumptions, and evidence behind the disclosure.
The page includes an evidence pack with five items to support assurance readiness, alongside six assurance claims to verify. Use those materials to show the claim, the risk, and the evidence trail for each point you are relying on.
The page has a section on common reporting gaps and mistakes, so it is worth checking your draft against that before sign-off. A practical way to use it is to compare your numbers, boundary, assumptions, and narrative against the page’s preparation checklist.
The Download Centre includes a Prep & Assurance workbook in .xlsx format and a printable Library Card in .pdf format. Use the workbook to organise the disclosure inputs and evidence, and the library card as a quick reference while drafting or reviewing.
Yes, but only as a synthetic illustration of how a draft can be structured. The example is there to show the style of narrative, the kind of table that may be useful, and how the content-index line can be written.
The page notes ESRS E1 (Climate Change) as the closest correspondence, which can help you think about reuse of data and supporting material. It does not say the requirements are identical, so treat it as a cross-reference rather than a one-to-one mapping.
More questions this page can help with