Disclosure focus
The practical focus is therefore on coverage and consistency across the organisation’s relevant activities, not just a few prominent sites or operations. An organisation should think about whether its reporting captures the full scope it is meant to cover, uses a consistent approach across locations and business units, and can be backed up if reviewed or challenged.
This LRA educational guidance supports disclosure preparation. For the exact requirements, always refer to the official MOCCAE 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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Assurance provider
Finance / Sustainability reportingRecord who carried out the assurance work for the reported information, using the name of the firm or individual actually engaged.
Evidence Engagement letter, signed assurance statement, or contract naming the provider.
Watch for The named provider does not match the signed assurance engagement on file.
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Validation checks
Data management / Reporting controlsDescribe the checks used to test the reported data before submission, including the main review steps and any automated or manual controls applied.
Evidence Data quality checklist, control log, review sign-off, or validation workflow output.
Watch for The stated process does not reconcile to the actual validation workflow used in the source system.
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Verification status
Finance / Sustainability reportingState whether the reported information was checked only inside the organisation or also by an outside party, using the status applied in the reporting pack.
Evidence Assurance report, internal review memo, or reporting status sheet.
Watch for Internal review and external assurance are mixed up, so the status does not match the evidence trail.
Before you start
- Have we settled which emissions figures sit in scope for this check, and which ones stay out?
- Are we using the same time period and cut-off point across the numbers we plan to present?
- Can each figure be traced back to a source record or working file that someone else could follow?
- Have we agreed how the figures were checked or cleaned before reporting, and is that process documented?
- Do we know whether the numbers were reviewed only inside the business or also by an outside reviewer?
How to prepare it
Request the data
Request emissions data checks and verification evidence
Translate the disclosure into an internal business question — then adapt it to your organisation's own language.
How were the emissions figures checked, who reviewed them, and what evidence shows whether they were reviewed internally or by an outside party?
Use your organisation’s own names for the emissions dataset, checking steps, review sign-off and assurance arrangements first, then map them to this request. Keep the wording in your internal language rather than using framework terms unless that is how your team already speaks.
Weak request
Please provide the emissions data verification evidence for the disclosure.
Why it fails: This uses framework language only and does not tell the owner which file, checks, reviewer, period, or boundary to pull. It is too vague to trace the figures back to the source records or to show how the data was checked.
Better request
Please send the [period] emissions dataset for [site / business unit], the checks performed on it, the reviewer or approver, and whether the figures were reviewed in-house or by an outside party. Include the file name, source system, review notes, and any corrections or exceptions so we can trace the numbers back to source records. This is a possible LRA training template only; please adapt it to your organisation and check the official source before sign-off.
Formal email template
Subject: Request for emissions data checks and review evidence for [period] Hi [name], We are preparing the emissions reporting pack for [period] and need the supporting evidence for the figures from [site / business unit / dataset name]. Please send: - the dataset or file used for the reported emissions figures; - the checks or review steps carried out on the data; - who reviewed or signed off the figures; - whether the figures were reviewed in-house or by an outside party; - any supporting notes, audit trail, or review record that shows how the figures were checked; - any corrections, estimates, or unresolved issues that affect the numbers. Please use your team’s own terms and file names where helpful, and include enough detail for us to trace the figures back to source records. This is a possible LRA training template only; please adapt it to your organisation and check the official source before sign-off. Thanks, [preparer name]
Short Teams / Slack version
Hi [name] — could you send the emissions file for [period] plus the check/review evidence, reviewer name, and whether it was reviewed in-house or by an outside party? Please include any notes on corrections or exceptions. Use your team’s own file names/terms. This is a possible LRA training template only; please adapt it to your organisation and check the official source before sign-off.
Industry examples
Manufacturing
Context. A plant team maintains monthly fuel and process emissions files, with review by the site EHS lead and finance controller.
Adapted request. Please send the [period] plant emissions workbook, the checking steps used on meter and fuel data, the reviewer sign-off, and whether the figures were reviewed internally or by an outside party. Include any exception log, correction note, and file reference. This is a possible LRA training template only; please adapt it to your organisation and check the official source before sign-off.
Example response. Dataset: Plant emissions workbook v4; Source system: meter portal and fuel invoices; Checks: monthly reconciliation and spot check against invoices; Reviewer: Site EHS lead; Review status: reviewed in-house; Evidence: sign-off note and exception log; Notes: one estimated meter reading later replaced with actual data.
Financial services
Context. A corporate services team compiles office energy and travel emissions from central systems, with review by sustainability and an external reviewer.
Adapted request. Please provide the [period] emissions extract for offices and travel, the validation steps used, the internal reviewer, and whether any outside party reviewed the figures. Include the source system, review record, and any adjustments. This is a possible LRA training template only; please adapt it to your organisation and check the official source before sign-off.
Example response. Dataset: Corporate emissions extract; Source system: utility portal, travel booking system, and finance ledger; Checks: duplicate removal and cross-check to invoices; Reviewer: Sustainability manager; Review status: reviewed by an outside party; Evidence: review memo and assurance report reference; Notes: one travel booking correction applied before finalisation.
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 basis used to prepare the figures, including who reviewed them and what checks were applied before the draft was assembled.
Context note
Explain what the review and validation details tell the reader about the reliability of the reported information and how far it has been tested.
Fluctuation statement
If the checking approach changed from one period to the next, note whether that was due to a different reviewer, a revised validation process, or a shift between internal and external review.
Content index entry
Art.6(3) Accuracy and verifiability of emissions data — [location / page] / [notes]Download Centre
Preparation tools & forms
Download preparation tools for Art.6(3) with free verified email access for 24 hours. Source-report links remain public.
Check
For each claim, check the evidence
I checked the coverage figure against the underlying records and made sure the boundary used for the calculation matched the disclosed operations.
Risk An assurer may test whether the figure includes the right entities, sites, periods, and exclusions, and whether any boundary choices could change the result.
Evidence to check Boundary memo; entity/site list used in the calculation; consolidation or inclusion/exclusion rationale; source records showing which operations were counted; sign-off on the final boundary.
I used the same cut-off dates and source set throughout the calculation, so the figure was built from a consistent reporting period.
Risk An assurer may probe whether timing differences, late entries, or mixed periods could distort the figure.
Evidence to check Reporting timetable; data extraction dates; version history of source files; evidence of any late adjustments; reconciliation between source period and reported period.
I validated the underlying data before publication by checking it back to primary records and resolving any mismatches.
Risk An assurer may look for weak controls over completeness, accuracy, and traceability from source to reported figure.
Evidence to check Validation checklist; sample trace-back from reported numbers to invoices, logs, meters, or registers; exception log; evidence of corrections and approvals.
I kept a clear audit trail showing how the figure was assembled, reviewed, and finalised.
Risk An assurer may test whether the organisation can explain each step from raw data to published output and whether changes are fully documented.
Evidence to check Working papers; calculation file with version control; reviewer comments; approval records; change log showing edits, reasons, and dates.
I identified whether the figure had been checked internally or by an outside reviewer and retained the evidence for that status.
Risk An assurer may verify that the stated review status is accurate and that the named reviewer or assurance provider really performed the work claimed.
Evidence to check Internal review note or external assurance statement; engagement letter; scope of review; reviewer credentials or appointment record; final report or sign-off.
Before release, I carried out a final quality check to confirm the disclosed figure was complete, internally consistent, and ready for publication.
Risk An assurer may examine whether pre-publication checks were sufficient to catch omissions, arithmetic errors, inconsistent labels, or unsupported statements.
Evidence to check Pre-publication checklist; arithmetic checks; cross-footing or reconciliation evidence; management review sign-off; published version compared with the approved draft.
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 ownerChasing the wrong team for the emissions file means the person who knows the numbers, checks, or review route never gets asked.
- Framework language instead of site termsAsking for the data using disclosure labels rather than the organisation’s own process names leaves staff unsure which report, tracker, or control sheet you mean.
- No boundary setStarting collection without saying which sites, activities, or business units are in scope leads to a mixed dataset that cannot be traced back cleanly.
- Wrong period basisPulling figures for the wrong month, quarter, or year creates a mismatch between the numbers gathered and the reporting window being used.
- Mixed counting basisCombining figures built on different counting methods, such as one team’s operational totals and another team’s adjusted totals, makes the dataset internally inconsistent.
- Source labels lostCopying values into a new file without keeping the original file name, system tag, or record label breaks the link to where the figure came from.
- Separate groups mergedPutting together populations that should stay apart, such as different sites or different data streams, hides differences that matter for later checking.
- No evidence trailCollecting numbers without saving the supporting notes, screenshots, or file history leaves no clear trail for later review or challenge.
- No sign-off recordFinishing the collection step without a named reviewer and date-stamped approval means nobody can show who checked the data before it moved on.
Judgement needed
- Acquisition or disposal during the reporting periodSet a clear cut-off for when sites or activities enter or leave the emissions boundary, then explain the date used and how the change affected the figures.
- Different country rules for the same activityWhere local definitions or measurement practices vary, use one organisation-wide approach for reporting and note any country-level differences that were translated into that approach.
- Operations close to the boundaryDecide in advance how to treat shared services, leased assets, contractors, or other borderline activities, and disclose the rule used to include or exclude them.
- Choosing the reporting basisIf more than one reasonable timing or consolidation basis is available, pick one consistently for the period and explain why it gives the clearest picture of the emissions data.
- Measured figures versus estimatesUse direct readings where available, rely on estimates only where needed, and state which parts of the dataset were estimated and what method was used to fill the gaps.
- Rounding and presentationApply one rounding approach across the dataset so totals still reconcile, and note the rounding rule if it could affect small differences in the published numbers.
- Protecting sensitive location or people dataAggregate or mask detail where needed to avoid exposing personal or commercially sensitive information, while still keeping the emissions figures traceable and explain the level of aggregation used.
- Validation checks and review trailDescribe the checks used to test the numbers, who reviewed them, and whether the figures were reviewed internally or by an outside party.
- Assurance provider selectionIf an external reviewer was used, identify the provider and explain the scope of their work; if not, state that the review was internal and describe the control steps applied.
Examples
How it reads on the page
Synthetic, written by LRA — not from a company report, not text from any standard.
We completed an external review of our reported climate figures for the year, using an independent assurance provider to test the underlying records and calculations. - Our data checks combined automated validation, manual review of source files, and follow-up queries where figures did not reconcile on first pass. - The final status was external verification, with the reviewer confirming the dataset was suitable for publication after resolving the exceptions we identified.
Illustrates how a company can describe who reviewed the information, how the numbers were checked, and whether the review was done internally or by an outside party. The wording is intentionally generic and synthetic.
We carried out an internal verification cycle for our sustainability metrics before release, with our own control team acting as the assurance provider for this round. - The validation process included cross-checking ledger extracts against source systems, sampling supporting documents, and escalating mismatches for correction before sign-off. - The verification outcome was internal, meaning the company relied on its in-house review rather than an external reviewer for this reporting cycle.
Illustrates the same three datapoints from a different sector, using an in-house review rather than an outside one. The example is fictional and designed only for training.
Quality ladder
- Basic
Our emissions figure for the year is 12,400 tCO2e.
- Better
We checked our 12,400 tCO2e emissions figure using monthly meter reconciliations and an internal review.
- Best
We reported 12,400 tCO2e for the year, validated it through monthly meter checks and an internal review, and the 5% rise from last year mainly reflects higher production volumes.
Check your understanding
Scenarios to work through
A group finance team has compiled the year-end emissions figures from three sites. One site used a spreadsheet check by the local sustainability lead, while the other two were reviewed by an outside verifier; the draft note only says the numbers were 'checked'.
A manufacturing business has emissions data from meters, invoices and estimates. The team has a reconciliation file, but one plant’s estimate was not linked back to source records, and the draft disclosure still presents all figures as fully verified.
A logistics company has had its emissions figures reviewed by an external specialist, but the draft report only names the specialist and gives no explanation of what was tested. The internal team also ran a data-quality review, but that is not mentioned.
A retail group has updated its emissions figures after finding an error in one store’s activity data. The revised numbers are correct, but the draft note does not explain that the figures were reworked after validation and still does not say whether the final set was internally reviewed or externally checked.
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.
Abu Dhabi Commercial Bank (ADCB)
First Abu Dhabi Bank (FAB)
Aldar Properties
| Field | Abu Dhabi Commercial Bank (ADCB) | First Abu Dhabi Bank (FAB) | Aldar Properties |
|---|---|---|---|
| Assurance provider | Not found | Not found | Not found |
| Validation checks | Partial | Partial | Not found |
| Verification status | Not found | Partial | Not found |
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Peer comparison uses examples on this card, not prevalence in the report corpus.
Advice, not assurance
Framework references
Relevant UAE requirements and related disclosures
UAE
Art.6(3)
within UAE Federal Decree-Law 11/2024: Federal Decree-Law No. (11) of 2024 on the Reduction of Climate Change Effects
Related & explore
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FAQ
Questions this page answers
The page says to prepare three datapoints: the assurance provider, the validation checks, and the verification status. Use the step-by-step preparation section to organise those inputs before drafting.
The page includes a step-by-step 'how to prepare' section plus draft-output support such as narrative starters, visualisation ideas and a content-index line. Use those pieces to turn collected data into a draft disclosure.
The page provides an evidence pack with five items to support assurance readiness. It is meant to help you assemble the backing material before review, rather than leaving evidence scattered across teams.
The page says there are six assurance claims to verify, each framed around claim, risk and evidence. Use them as a checklist to test whether the disclosure is supportable and whether the evidence pack is complete.
The page lists common reporting gaps and mistakes to help you spot weak points before submission. Use that section alongside the evidence pack and validation checks to reduce avoidable errors.
The page is designed for sustainability/ESG managers, HR or data owners, and assurance reviewers, so ownership should be set around who can supply and validate each datapoint. In practice, assign the assurance provider, validation checks and verification status to the people who can evidence them.
The Download Centre includes a Prep & Assurance workbook in .xlsx format and a printable Library Card in .pdf. The workbook is there to help you organise preparation and assurance inputs in a working format.
The printable Library Card is part of the Download Centre and is intended as a quick reference alongside the workbook. It can help teams keep the disclosure requirements, evidence and draft-output prompts in view during preparation.
The page includes draft-output support in the form of visualisation ideas, narrative starters and a content-index line. That makes it easier to move from source data to a report-ready draft.
Yes. The page is built around assurance readiness, with six claims to verify, an evidence pack, validation checks and common mistakes to avoid. It is meant to help you test the draft before it goes to review.
The page notes ESRS E1 (Climate Change) as the closest correspondence. You can treat that as a useful cross-reference and reuse data where it fits, but the page does not say the requirements are identical.
More questions this page can help with