ESRS 2 — Metrics in Relation to Material Sustainability…

Q: How do I use the GDR-M page to draft the disclosure from scratch?

Start with the plain-language explainer, then work through the step-by-step preparation section and the datapoints to prepare. The page is designed to help you turn source data into a draft disclosure, not to replace your internal reporting process.

Q: What data do I need to gather for GDR-M before I start writing the disclosure?

The page points you to reported figure, measurement unit, calculation approach, input sources, estimation approach, key assumptions, known constraints, reporting context, period-on-period movement and target progress. Use that list as your collection checklist so you can build the disclosure from complete inputs.

Q: How should I decide the scope and calculation approach for GDR-M?

Use the page’s preparation steps and the datapoints list to define what is being reported, how it is measured and how it is calculated. The page also prompts you to capture the reporting context, assumptions and known constraints so the scope is clear in the draft.

Q: Who should own the GDR-M data and sign-off process?

The page does not assign roles for you, but it is set up for a sustainability/ESG manager, HR or data owner, and assurance reviewer to use together. In practice, you would assign ownership for the reported figure, the calculation method, the evidence pack and the final review.

Q: What should I include in the evidence pack for GDR-M assurance?

The page includes an evidence pack with five items to support assurance readiness, alongside six assurance claims to verify. Use those sections to assemble the documents and checks that show the figure, method and assumptions are supported.

Q: What are the common mistakes to avoid when preparing GDR-M?

The page lists common reporting gaps and mistakes so you can check for missing inputs, weak explanations or unsupported figures before drafting. It is best used as a pre-submission quality check rather than as a substitute for your own controls.

Q: How do I use the GDR-M workbook download to prepare the disclosure?

The Download Centre includes a Prep & Assurance workbook in .xlsx format and a printable Library Card in .pdf. Use the workbook to organise the datapoints, evidence and assurance checks, and use the PDF as a quick reference while drafting.

Q: Can I use the synthetic example disclosure on the GDR-M page as a template?

Yes, but only as a worked illustration. The page says the example is synthetic, so use it to understand structure, data presentation and narrative style rather than copying it as a real disclosure.

Q: How do I turn GDR-M data into a draft narrative and content index line?

The draft-output section gives visualisation ideas, narrative starters and a content-index line to help you convert the data into report-ready text. Use those prompts to explain the figure, movement, assumptions and context in a concise way.

Q: Where can I find real company examples for GDR-M reporting?

The page includes a 'From company reports' table that links to real published reports at the pages where the topic is disclosed. Use it to see how others present similar information, but keep your own disclosure based on your organisation’s data and context.

Disclosure focus

The practical focus is on coverage and comparability: readers should be able to see whether the metrics cover the whole organisation, specific business units, sites or value-chain activities, and whether any important parts are excluded. If the organisation uses different measures for different operations or locations, it should explain the scope and any limitations so users can understand how complete the picture is.

This LRA educational guidance supports disclosure preparation. For the exact requirements, always refer to the official European Commission source.

Request the data

Request the metric pack from Finance

Translate the disclosure into an internal business question — then adapt it to your organisation's own language.

What figures, methods and supporting notes do we need to explain each material sustainability metric clearly and consistently?

Use your organisation’s own names for measures, reports and systems first, then map them to the reporting disclosure. Ask for the business terms people already use, not the framework label.

Weak request

Please provide the ESRS 2:GDR-M metrics, including the metric value, unit of measurement, calculation methodology, sources/inputs used, estimation method, assumptions, limitations, contextual information, change vs prior period, and progress toward targets.

Why it fails: It uses framework language that many internal owners will not recognise, so it is harder to action. It also reads like a compliance checklist rather than a practical request for the team’s own report, system output, and explanatory notes.

Better request

Please send the latest performance figures and the notes your team already uses to explain them for [period] and [scope]. Include the value, unit, how it was built, the source report or system, any estimates, assumptions, limits, context, movement since last period, and progress against target. Use your normal business terms first, then we will map them to the reporting pack.

Formal email template

Subject: Request for metric data and supporting notes for [reporting period]

Hi [name/team],

We are preparing the sustainability reporting pack and need the latest figures and supporting notes for the measures your team owns.

Please send, for [reporting period] and [boundary/scope]:
- the metric value in your usual business terms
- the unit used
- how the figure was calculated
- the source files or systems used
- any estimation approach, assumptions, and known limits
- a short note on how the result compares with [prior period]
- a short note on progress against any relevant target

If you already have this in a dashboard, workbook, or management report, please share that and confirm the key fields above.

Please use your team’s own terminology first, then we will map it to the reporting disclosure. Please also flag anything that needs review before sign-off.

Thanks,
[preparer name]
[role]
[contact details]

Short Teams / Slack version

Hi [name] — could you send the latest metric pack for [period] / [scope]? We need the value, unit, method, inputs, assumptions, limits, prior-period change and target progress. Please use your usual business terms and share the source file/report if you have it. Thanks.

Industry examples

Manufacturing

Context. A plant team tracks water use, scrap, and energy performance through site dashboards and monthly operations reviews.

Adapted request. Hi [site manager] — please share the latest site performance pack for [period] / [site]. We need the measure value, unit, how it was calculated, the meter or log source, any estimated figures, assumptions, known gaps, the change since last period, and progress against target. Please use the site’s own measure names and send the dashboard or workbook if available.

Example response. Metric name internal: electricity intensity; value: 0.42; unit: kWh per unit produced; period: FY2025; scope: Plant A only; method summary: total site electricity divided by finished units; inputs used: meter reads and production counts; estimation flag: no; assumptions summary: none; limitations summary: one late meter read for March; context summary: output mix shifted to higher-volume lines; prior period value: 0.45; change vs prior period: down 0.03; target value: 0.40; progress to target: close but not yet met; source reference: monthly operations dashboard v3.

Retail

Context. A retail business monitors store-level waste, returns, and customer service measures through a central reporting tool and regional summaries.

Adapted request. Hi [regional operations lead] — could you send the latest regional performance summary for [period] / [region]? Please include the measure value, unit, how the number was built, the source report or system, any estimates, assumptions, limits, context, movement versus the last period, and progress against the current target. Use the team’s usual labels for the measures and attach the source file if possible.

Example response. Metric name internal: customer complaint rate; value: 1.8; unit: complaints per 1,000 transactions; period: Q4 FY2025; scope: Region West; method summary: complaints logged divided by transaction count; inputs used: CRM cases and POS sales data; estimation flag: no; assumptions summary: none; limitations summary: two stores submitted late data; context summary: promotion period increased footfall; prior period value: 1.5; change vs prior period: up 0.3; target value: 1.6; progress to target: above target and under review; source reference: regional service dashboard export.

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

Describe the basis used to produce the figure, including the calculation approach, the data sources or inputs relied on, and any estimation method applied.

Context note

Explain what the figure represents in practice by adding the relevant unit, the surrounding circumstances, and any other information needed to interpret it properly.

Fluctuation statement

If the figure moved versus the prior period, set out the main reasons for the change and note whether progress toward the target improved or slowed.

Content index entry

GDR-M Metrics in Relation to Material Sustainability Matters — [location / page] / [notes]

Download Centre

Preparation tools & forms

Download preparation tools for GDR-M with free verified email access for 24 hours. Source-report links remain public.

Free · Email access

01XLSPrep & Assurance workbookDatapoints, risks, evidence to check, owner and evidence pack.Get it free ↧ 02XLSData-request packRequest email, message template, response fields and sign-off structure.Get it free ↧ 03PDFLibrary CardPrintable one-page summary of this disclosure card.Get it free ↧

Check

For each claim, check the evidence

We present the figure using a sensible scale, including metric prefixes where that makes the number easier to read.
Risk The assurer may check whether the scale choice is consistent, whether the prefix is used correctly, and whether the same basis is applied across the table or chart.
Evidence to check Working papers showing the raw number, the scaled presentation, the unit chosen, and the review sign-off that confirms the display format was checked before publication.
We included the values for the metrics we judged relevant to the material issues, including any company-specific measures we use internally.
Risk The assurer may probe whether any relevant metric was left out, whether the selection matches the material topics identified elsewhere, and whether internal measures were included only where they genuinely relate to those matters.
Evidence to check The metric inventory, the materiality assessment, mapping from material topics to reported measures, and evidence of the internal performance measures used to track actions or outcomes.
For each metric, we recorded the unit, the calculation method, and the source data or inputs used.
Risk The assurer may test whether the unit is clear, whether the method is reproducible, and whether the inputs are complete, current and traceable to source records.
Evidence to check Method notes, calculation sheets, source-system extracts, data dictionaries, and a control trail showing who prepared and reviewed each metric.
Where a metric needed estimation, we documented how the estimate was built, the main assumptions, and the known limits of the result.
Risk The assurer may challenge whether the estimation approach is reasonable, whether key assumptions are supported, and whether the limitations are disclosed clearly enough for users to understand the uncertainty.
Evidence to check Estimation methodology papers, assumption logs, sensitivity checks, and evidence of management review of the main judgement calls.
For measures based on value-chain information, we noted where we relied on indirect data or proxies and what we plan to improve next.
Risk The assurer may ask whether the use of proxy or secondary information is justified, whether the gaps are clearly identified, and whether the improvement plan is specific and credible.
Evidence to check Data-quality assessments, supplier or other third-party data records, proxy rationale, and the documented plan for improving completeness, accuracy or timeliness.
We added context so readers can understand the figure, including why we chose that approach and what assumptions sit behind it.
Risk The assurer may check whether the context is sufficient to interpret the number, whether the chosen approach is explained in plain terms, and whether the assumptions are consistent with the underlying data.
Evidence to check Draft narrative, calculation support, assumption register, and internal review comments showing the context was checked against the final number.

Evidence pack to prepare

The governing policy or written commitment behind this disclosureA methodology / definition note setting out how the disclosure was scoped and preparedSource-system exports the figures or facts were drawn fromThe internal approval / sign-off record for the disclosure before publicationMinutes or records evidencing the relevant engagement or consultation

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 ownerThe team asks a policy lead or report drafter for the numbers, when the actual source sits with the operational manager who runs the process and can explain how the figures were built.
Framework language firstThe request is sent using disclosure jargon instead of the business’s own terms, so the person holding the data cannot tell which internal report, register, or system extract is being asked for.
No boundary setPeople pull figures from an undefined mix of entities, sites, or activities, so the dataset does not clearly show what is in scope and what has been left out.
Wrong time basisThe data is gathered for a different reporting window or cut-off than the one being used for the period under review, which makes the figures impossible to compare cleanly.
Mixed counting basisOne file combines values built on different measurement bases, such as headcount, full-time equivalent, or another internal unit, so the final total is not like-for-like.
Source labels lostDuring consolidation, the original names from the source system or spreadsheet are stripped away, so no one can trace each figure back to the exact input it came from.
Separate groups mergedDistinct populations that should stay apart are rolled into one dataset, which hides differences that matter for later explanation and review.
No evidence trailThe collector saves the number but not the supporting notes, assumptions, or file references, so there is no clear record of how the figure was assembled or checked.
No sign-off recordThe draft data is passed along without a named reviewer confirming it, leaving no audit trail showing who checked the numbers before they moved into reporting.

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Judgement needed

Set the reporting perimeter after a buy-in or saleIf a business has been added or sold during the period, explain which parts of the year are counted, which operations are included in the figure, and whether the prior period has been restated so readers can compare like with like.
Choose one country rule where local definitions differWhere the same measure is defined differently across countries, pick one basis for the group figure, describe the local differences that were bridged, and state any country-specific exceptions that affect the total.
Decide how to treat people or assets near the boundaryFor workers, sites, customers, suppliers, or other populations that sit close to the inclusion line, state the practical rule used to include or exclude them and explain any threshold, test, or judgement applied.
Fix the time basis for the number you reportState whether the figure is taken at period end, averaged over the year, or built from another timing basis, and explain why that timing gives the most useful picture for the matter being measured.
Separate measured data from estimated dataWhen complete records are not available, identify which parts come from direct records and which parts are estimated, describe the method used for the estimate, and note the main limits on confidence in the result.
Explain the assumptions behind any estimateSet out the key assumptions that drive the calculation, including any proxy, scaling factor, or conversion used, so users can see how the number was built and how sensitive it may be to those choices.
State how missing or weak source data were handledIf source information is incomplete, inconsistent, or not independently checked, explain the fallback inputs used, the checks performed, and any areas where the figure is less robust than the rest of the metric.
Show how you rounded the numberTell readers the rounding rule used for the metric and make sure the rounded figure still matches the underlying calculation logic, especially where small changes could affect trend analysis or target tracking.
Aggregate carefully where privacy limits detailWhen individual-level disclosure is not possible, report the measure in grouped form, explain the aggregation rule used, and confirm that the grouping still gives a fair view without exposing personal data.
Explain any change from the last reporting cycleIf the method, scope, source data, or estimate approach has changed since the previous period, describe what changed, why it changed, and how that affects the current figure and any comparison with earlier years.

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Examples

How it reads on the page

Synthetic, written by LRA — not from a company report, not text from any standard.

Illustrative (synthetic) example — Manufacturing

We report a single group-wide figure for the year, expressed in tonnes of CO2e, and we derive it from metered fuel use, electricity bills, refrigerant logs and supplier activity data using our internal emissions model. For this period, the result is 18,400 tCO2e; the estimate relies on spend-based proxies for 12% of purchased goods where primary data were not yet available, assumes no material change in production mix during the final quarter, and is constrained by incomplete supplier coverage in two upstream categories.
- Compared with the prior year, the figure is 4% lower, mainly because of lower grid electricity use and better refrigerant leakage control.
- Against our reduction plan, we are 38% of the way to the 2030 target, based on the gap between the current result and the target pathway.

Illustrative only: shows how to present a quantified result, the unit, the calculation basis, the data sources, the estimation approach, the key assumptions, the main limits, the business context, the year-on-year movement and progress against a target in one narrative disclosure.

Illustrative (synthetic) example — Retail

We present one consolidated result for the reporting year in cubic metres, calculated from site meter readings, tanker delivery notes and wastewater invoices, then adjusted through our internal water-balance approach. The reported amount is 126,000 m3; where direct readings were missing, we estimated 9% of the total using seasonal occupancy patterns, assumed normal trading hours across the estate, and note that the figure is less precise for leased sites with shared utilities.
- The year-on-year change is a 7% reduction, helped by fixture upgrades and lower wash-down demand.
- This leaves us 52% of the way toward our 2028 water-intensity goal, measured against the remaining distance to the target.

Illustrative only: shows a plain-language narrative that covers the measured result, the unit, how the number was built, the inputs used, the estimation approach, the assumptions, the main constraints, the operating context, the movement from the prior period and the position against a target.

Quality ladder

Basic
I report 42 units for this material topic.
Better
I report 42 units for this material topic, measured in tonnes using our internal records and a simple allocation method.
Best
I report 42 tonnes for this material topic for 2025, based on our internal records and allocation approach; the figure is up from 38 tonnes last year because we expanded the covered sites, and it still leaves us short of our target path.

Check your understanding

Scenarios to work through

Your team has calculated water use for a material environmental topic using meter reads from owned sites, but the draft note only shows the final figure. A reviewer asks how the number was built and where the underlying data came from.

QWhat should you add so a reader can understand the figure and trace it back to its basis?

Reveal model answer →

For a greenhouse-gas estimate, part of the data was unavailable for one site, so the team used a proxy based on floor area and utility spend. The draft note mentions the estimate but does not explain the shortcut or its weaknesses.

QHow should you present the estimate so users can judge how much confidence to place in it?

Reveal model answer →

A social metric on training hours is reported this year in hours per employee, while last year it was shown as total hours. The team also has a target to raise average hours, but the draft only gives the current number.

QWhat extra explanation is needed before the disclosure is ready for review?

Reveal model answer →

A biodiversity indicator combines supplier data, internal site records and a modelled estimate for missing locations. The draft note gives the total but leaves out the fact that some locations were estimated and why the team believes the figure is still useful.

QWhat should the preparer disclose to make the metric understandable and balanced?

Reveal model answer →

✓ Knowledge Hub AI Assistant · Human-in-the-loop

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Framework references

Relevant ESRS requirements and related disclosures

ESRS

GDR-M

within ESRS 2: General Disclosures

Open official source →

Guides that settle this question

ESRS 2 General Disclosures: basis of preparation, governance, strategy, business model, stakeholders, material IROs, content index and GDR →ESRS Data Governance and Internal Controls: From Datapoint Owners to Assurance Evidence →ESRS Entity-Specific Disclosures: When the Standards Do Not Cover a Material IRO →

Related & explore

General Basis for Preparation of Sustainability Statements → Card Disclosures in Relation to Specific Circumstances → Card Actions and Resources in Relation to Material Sustainability Matters → Card Policies Adopted to Manage Material Sustainability Matters → Card Targets in Relation to Material Sustainability Matters → Card Browse all disclosures →

More in ESRS 2 →Browse full catalogue →Search all disclosures →

FAQ

Questions this page answers

How do I use the GDR-M page to draft the disclosure from scratch?+

What data do I need to gather for GDR-M before I start writing the disclosure?+

How should I decide the scope and calculation approach for GDR-M?+

Who should own the GDR-M data and sign-off process?+

What should I include in the evidence pack for GDR-M assurance?+

What are the common mistakes to avoid when preparing GDR-M?+

How do I use the GDR-M workbook download to prepare the disclosure?+

Can I use the synthetic example disclosure on the GDR-M page as a template?+

How do I turn GDR-M data into a draft narrative and content index line?+

Where can I find real company examples for GDR-M reporting?+

	Report A	Report B	Report C
Datapoint 1	✓ p.213	unclear	✓ p.87
Datapoint 2	✓ p.214	—	✓ p.90

Metrics in Relation to Material Sustainability Matters

What to collect before drafting

Reported figure

Measurement unit

Calculation approach

Input sources

Estimation approach

Key assumptions

Known constraints

Reporting context

Period-on-period movement

Target progress

How to prepare it

Request the metric pack from Finance

Notes that turn data into a disclosure

Preparation tools & forms

For each claim, check the evidence

We present the figure using a sensible scale, including metric prefixes where that makes the number easier to read.

We included the values for the metrics we judged relevant to the material issues, including any company-specific measures we use internally.

For each metric, we recorded the unit, the calculation method, and the source data or inputs used.

Where a metric needed estimation, we documented how the estimate was built, the main assumptions, and the known limits of the result.

For measures based on value-chain information, we noted where we relied on indirect data or proxies and what we plan to improve next.

We added context so readers can understand the figure, including why we chose that approach and what assumptions sit behind it.

Mistakes to avoid and where judgement is needed

Mistakes to avoid

Judgement needed

How it reads on the page

Scenarios to work through

Ask the Knowledge Hub AI Assistant about this disclosure

Check your text

Relevant ESRS requirements and related disclosures

Questions this page answers

Secure your enrolment

Metrics in Relation to Material Sustainability Matters

What to collect before drafting

Reported figure

Measurement unit

Calculation approach

Input sources

Estimation approach

Key assumptions

Known constraints

Reporting context

Period-on-period movement

Target progress

How to prepare it

Request the metric pack from Finance

Notes that turn data into a disclosure

Preparation tools & forms

For each claim, check the evidence

We present the figure using a sensible scale, including metric prefixes where that makes the number easier to read.

We included the values for the metrics we judged relevant to the material issues, including any company-specific measures we use internally.

For each metric, we recorded the unit, the calculation method, and the source data or inputs used.

Where a metric needed estimation, we documented how the estimate was built, the main assumptions, and the known limits of the result.

For measures based on value-chain information, we noted where we relied on indirect data or proxies and what we plan to improve next.

We added context so readers can understand the figure, including why we chose that approach and what assumptions sit behind it.

Mistakes to avoid and where judgement is needed

Mistakes to avoid

Judgement needed

How it reads on the page

Scenarios to work through

Ask the Knowledge Hub AI Assistant about this disclosure

Check your text

Relevant ESRS requirements and related disclosures

Questions this page answers

Get in touch