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IFRS S2: Climate-related Disclosures·Paragraphs 29–a–vi–1
Scope 3 categories
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Standard
IFRS S2: Climate-related Disclosures
Paragraphs 29–a–vi–1 · 2024
Zuletzt geprüft
—
LRA-Lehrmaterial · Nicht von IFRS herausgegeben oder gebilligt
Für diese Offenlegung
Kern der Offenlegung
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.
Dieses LRA-Lehrmaterial unterstützt die Erstellung der Offenlegung. Für die genauen Anforderungen ziehen Sie stets die offizielle IFRS-Quelle heran.
Bevor Sie beginnen
Bevor Sie beginnen
Eine kurze Checkliste, bevor Sie diese Offenlegung erstellen — haken Sie jeden Punkt ab, sobald er geklärt ist.
Vorbereitung
Wichtige vorzubereitende Angaben
| Vorbereitungsfeld | Was zu erfassen ist | Nachweishinweis | Verantwortlich |
|---|---|---|---|
| Excluded categories | List the emissions categories left out of the calculation, and give the reason each one was left out. | Working papers showing the boundary decision, category list, and the documented reason for each exclusion. | Sustainability reporting |
| Impracticability note | Record whether a formal note was prepared to explain why a full calculation could not be done, and keep the wording used. | Signed methodology memo or disclosure draft showing the impracticability explanation and approval trail. | Sustainability reporting |
| Included Scope 3 list | Capture which upstream and downstream emissions categories are included in the reported Scope 3 set. | Scope mapping, category register, and consolidation workbook showing the final included categories. | Sustainability reporting |
| Calculation basis | Set out the calculation approach, key assumptions, and any estimation rules used to build the figure. | Method statement, calculation model, and assumption log with version control and sign-off. | Sustainability reporting |
| Data source mix | Show how much of the result comes from direct source data versus estimated or third-party data. | Source register and calculation workbook with fields tagged by data origin and aggregation method. | Sustainability reporting |
| Value chain boundary | Describe which parts of the upstream and downstream chain are inside the reporting boundary and which are not. | Boundary memo, supplier/customer coverage list, and process map showing the included stages. | Sustainability reporting |
| Verified data share | Calculate the share of the reported figure that has been independently checked, using the same total as the main disclosure. | Assurance report or verification log linked to the reported total and the verified subset. | Sustainability reporting |
＋ Teilelemente von s2-29-a-vi-1 anzeigen (LRA-Arbeitscheckliste)
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.
So bereiten Sie es vor
Set 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.
Decide, 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.
Gather the support behind each figure or statement, including source records, working papers, and any checks that show how the numbers or narrative were built.
Prepare 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.
If 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.
Before 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.
Daten anfordern
Request the Scope 3 category evidence pack
Übersetzen Sie die Offenlegung in eine interne Geschäftsfrage — und passen Sie diese dann an die Sprache Ihrer Organisation an.
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.
Schwache Anfrage
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.
Warum sie scheitert: 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.
Bessere Anfrage
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.
Vorlage für eine formelle E-Mail
Subject: Request for value-chain emissions category evidence pack Hi [name], I’m pulling together the reporting pack for [period] and need your help with the value-chain emissions section. Please could you send over the information for the areas your team owns, using your normal internal labels first and then, if helpful, mapping them to the reporting categories? I’m looking for: - which value-chain areas are included in this cycle - which areas are left out, and why - the method and main assumptions used - the split between supplier/activity data and estimated data - the scope of the value-chain covered - the share of data that has been checked or verified A simple table plus any supporting notes, files, or links would be ideal. Please include the period covered, the source system or file, and the person who prepared it. If anything is unclear, I’m happy to talk it through. Please also check the official source before sign-off. Thanks, [preparer name]
Kurzfassung für Teams / Slack
Hi [name] — could you share the value-chain emissions evidence for [period] from your team’s normal files/systems? I need: included areas, excluded areas and why, method/assumptions, primary vs estimated data split, coverage, and any checked/verified share. A table plus notes is fine. Please use your own internal labels first, then map if needed. Thanks — [preparer name]
Branchenbeispiele
Manufacturing
Kontext. A plant team tracks purchased materials, inbound freight, waste, and outsourced processing in separate operational files.
Angepasste Anfrage. Please share the emissions evidence pack for [period] from your plant reporting files. Use your own labels for materials, transport, waste, and outsourced work first, then map them if needed. Include what was covered, what was left out and why, the calculation method, the assumptions used, the split between supplier data and estimates, the value-chain areas covered, and the share of data that was checked.
Beispielantwort. Included: purchased steel, inbound road freight, production waste, and third-party coating. Excluded: employee commuting, because it was not assessed in this cycle. Method: supplier activity data for steel and freight; spend-based estimates for waste and coating. Primary data share: 58%. Checked data share: 41%.
Retail
Kontext. A retail sustainability team holds data on bought-in products, upstream transport, packaging, store waste, and some customer-use assumptions.
Angepasste Anfrage. Please send the value-chain emissions evidence for [period] from your retail reporting workbook and source files. Use your internal labels for product supply, logistics, packaging, store waste, and customer-related assumptions first, then map them if needed. Include included and excluded areas, reasons for any exclusions, the method and assumptions, the split between direct supplier data and estimates, the coverage of the value chain, and the share of data that has been checked or verified.
Beispielantwort. Included: bought-in products, inbound logistics, packaging, and store waste. Excluded: customer use of products, because the team has not modelled it this year. Method: supplier data for packaging and logistics where available; estimates for product categories with limited supplier detail. Primary data share: 46%. Verified data share: 29%.
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Methodenhinweis
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.
Kontexthinweis
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.
Erläuterung zu Schwankungen
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.
Eintrag im Inhaltsindex
s2-29-a-vi-1 Scope 3 categories — [location / page] / [notes]Download-Center
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Prüfungsbereitschaft
Prüfen Sie zu jeder Aussage die Nachweise
| Aussage | Risiko | Zu prüfende Nachweise |
|---|---|---|
| We documented which parts of the wider value chain we left out of the coverage figure, and why those parts were not brought in. | An assurer will test whether the exclusions were chosen consistently, whether the reasons are specific rather than generic, and whether any omitted parts should have been included. | Boundary memo or methodology note; list of excluded categories; management rationale for each exclusion; review sign-off; any correspondence showing how the boundary decision was agreed. |
| Where we relied on an impracticability judgement, we recorded that conclusion and kept notes on how we are still handling the related emissions estimate in practice. | An assurer will probe whether the impracticability call was made on a supportable basis, whether it was applied only where needed, and whether the related emissions were still addressed through an appropriate process. | Impracticability assessment; internal approvals; working papers showing the estimation approach used instead; action plan or controls for ongoing handling; evidence of periodic reassessment. |
| We set out which value-chain categories were brought into the disclosed figure, and the list matches the scope we used in the working papers. | An assurer will check completeness and consistency: whether all intended categories were included, whether any category was accidentally left out, and whether the published list matches the calculation file. | Category mapping schedule; calculation workbook; consolidation or boundary checklist; draft-to-final comparison; sign-off from the preparer and reviewer. |
| Our method note explains how we measured the figure, including the main inputs we used and the assumptions we applied. | An assurer will test whether the method is described clearly enough to reproduce the result, whether the inputs are appropriate, and whether the assumptions are reasonable and consistently applied. | Methodology paper; calculation model; assumption log; source data extracts; version history showing changes and approvals. |
| We can show how much of the figure came from direct activity information from within the value chain, rather than from broader estimates alone. | An assurer will examine whether the stated mix of direct and indirect inputs is accurate, whether the direct activity data is sufficiently relevant, and whether the mix was calculated on a consistent basis. | Data source register; activity-level data files; allocation logic; calculation of the direct-input share; evidence of review over source selection. |
| Our boundary note makes clear which upstream and downstream parts were counted, and which related categories were left out. | An assurer will probe whether the boundary description is complete, whether the omitted categories were intentionally excluded, and whether the published wording matches the internal scope decision. | Boundary statement; category inclusion/exclusion matrix; internal scope papers; management review notes; final disclosure cross-check. |
Vorzubereitendes Nachweispaket
Häufige Lücken in der Berichterstattung
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.
Häufige Lücken
Fehler, die Sie bei der Datenerhebung vermeiden sollten
Unclear ownerThe request goes to the wrong team or person, so the data is chased in framework language instead of the organisation’s own operational terms.
No boundary setPeople start collecting figures before agreeing which parts of the business and value chain are in scope, so different teams work to different cut-offs.
Wrong reporting periodThe data pull uses a different time basis from the rest of the pack, which leaves the category set out of step with the reporting cycle.
Mixed counting basisSome teams send direct records while others send estimates without flagging the difference, so the final mix is not built on one consistent basis.
Source labels lostOriginal file names, system tags, or category names are stripped out during consolidation, making it hard to trace each figure back to its source.
Populations merged too soonSeparate groups that should stay distinct are rolled together early, which hides which category each number came from.
Evidence details missingThe pack arrives without the supporting notes, dates, or file references needed to show where the figures came from and how they were checked.
No approval trailThere is no record of who reviewed and cleared the data, so the team cannot show that the numbers were signed off before drafting.
Scope list incompleteOne or more relevant category lines are never requested from the business, so the collection misses items that should have been captured or explained.
Assumptions not capturedThe team records the numbers but not the method behind them, so later reviewers cannot see what judgement calls were used to build the dataset.
Wo häufig eine Ermessensentscheidung nötig ist
Boundary shifts after buying or selling a businessSet out whether the reporting perimeter was updated for the year’s acquisitions or disposals, and explain which value-chain activities were brought in or left out as a result.
Different country labels for the same activityWhere local records use different names or groupings, map them to one internal category basis and explain the mapping so the same activity is not counted twice or missed.
Activities close to the edge of the value chainFor shared, outsourced or partially controlled activities, state the rule used to decide inclusion or exclusion and explain any cases treated differently from the main approach.
Choosing the cut-off date for the yearIf the reporting period does not align neatly with operational or supplier data cycles, explain the date basis used and why it gives the fairest picture for the year.
Measured figures versus estimated figuresDescribe which parts came from direct records and which were estimated, and explain the method used to fill gaps where actual data was not available.
Using different data sources across the chainIf some categories rely on primary records and others on secondary sources, explain the mix, the reason for it, and any material differences in quality or coverage.
Rounding and small residual amountsState the rounding rule used and explain how minor residual amounts were handled so the category totals still reconcile to the overall picture.
Protecting sensitive supplier or employee informationWhere detailed source data cannot be shown without breaching privacy or confidentiality, explain the level of aggregation used and how it still supports the reported category split.
Impracticability judgement for hard-to-trace categoriesIf a category could not be covered in the normal way, explain the practical barrier, the alternative approach used, and which categories were left out on that basis.
Beispiele
Veranschaulichende Beispiele
Synthetisch, von LRA verfasst — nicht aus einem Unternehmensbericht und kein Text aus einem 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.
Unternehmensberichte
Wie Unternehmen S2-29-a-vi-1 in der Praxis berichten
Beispiele für vollständige und teilweise Berichtspraxis. Es handelt sich um evidenzbasierte Analysen, nicht um Vorlagen zum Abschreiben.
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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Ausprobieren
Wie bereite ich s2-29-a-vi-1 vor?
Welche Daten muss ich erheben?
Wo finde ich ein Beispiel aus einem echten Bericht?
Welche Fehler sollte ich vermeiden?
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Szenarien zum Durcharbeiten
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.
FHow 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?
Musterantwort anzeigen →Antwort ausblenden ↑
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.
Warum das wichtig ist · 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.
FShould the disclosure show only the categories counted, or also make clear which other categories were excluded from the scope of the calculation?
Musterantwort anzeigen →Antwort ausblenden ↑
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.
Warum das wichtig ist · 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.
FWhat should be brought into the disclosure so users can understand how the value-chain categories were compiled?
Musterantwort anzeigen →Antwort ausblenden ↑
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.
Warum das wichtig ist · 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.
FWhat should the preparer clarify about the data sources and the part of the value chain covered by the calculation?
Musterantwort anzeigen →Antwort ausblenden ↑
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.
Warum das wichtig ist · A useful disclosure shows both the reach of the value-chain boundary and the blend of source data used to populate it.
Verweise auf das Rahmenwerk
Einschlägige IFRS / ISSB-Anforderungen und verwandte Offenlegungen
Verfügbare Verweise auf das Rahmenwerk und benachbarte Offenlegungen, die für diese Anforderung relevant sind.
IFRS / ISSB
s2-29-a-vi-1
innerhalb von IFRS S2: Climate-related Disclosures
Verwandtes & Entdecken
Climate-related risks and opportunities → KarteIdentification inputs and industry topics → KarteObjective →Business model and value chain impact → KarteStrategy response and transition plan → Karte
Alle Offenlegungen durchsuchen →
Mehr in IFRS S2 → Gesamten Katalog durchsuchen → Startseite der Disclosure-Bibliothek → Alle Offenlegungen durchsuchen →
FAQ
Fragen, die diese Seite beantwortet
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.
Weitere Fragen, bei denen diese Seite hilft
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
Tiefer einsteigen · s2-29-a-vi-1
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