Response to consultation on Implementing Technical Standards on amended disclosure requirements for ESG risks, equity exposures and aggregate exposure to shadow banking entities

1. Do you have any comments on the proposed set of information for Large institutions?

Overall, we support: 

• The tiered and proportionate approach requiring large institutions to provide information for the full EBA Pillar 3 disclosure template.
• Granular, NACE Rev 2.1 sectoral breakdown of activities
• Harmonization of templates 6-10 with the Taxonomy Regulation – extending the templates to include all six environmental objectives.
• The introduction of new GHG emissions reporting metrics under Template 1 including a breakdown of Scope 1: Direct emissions from owned or controlled sources (e.g. fuel combustion on-site) and Scope 2: Indirect emissions from the generation of purchased electricity, steam, heating, and cooling. This provides clarity to banks and data providers on key data requirements to ensure regulatory compliance.
• The new metric in Template 1: Coverage of portfolio with use of proxies (according to Partnership for Carbon Accounting Financials (PCAF) methodology) (in %). This ensures alignment and interoperability with other standards and disclosure frameworks (PCAF, TCFD, ISSB, BCBS)

Taxonomy Simplification Measure (July 2025)

In parallel, the European Commission has adopted the recent Taxonomy Simplification measure (July 2025) to ‘cut red tape’ around sustainability reporting across impacted institutions. While the intention behind this initiative is to streamline obligations for corporates, the introduction of a 10% materiality threshold for activities related to CapEx, OpEx and turnover may significantly hinder the granularity and consistency of taxonomy reporting. ESG Book analysis suggests that over 85% of activities fall below 3% of the relevant KPI value. This creates room for cherry-picking and strategic exclusions —as companies may omit activities with low overall alignment, thus reducing data granularity. As a result, banks may face disruptions in the availability of comparable, standardized data on taxonomy-aligned exposures. In addition, non-financial corporates are provided with flexibility to either continue reporting in line with the pre-existing EU Taxonomy data templates in 2026, or to already adopt the newly released templates. This is undesirable from an end-user perspective, as financial institutions would need to account for the differences between the templates and standardize them before conducting further GAR/portfolio-level analysis. 

In this context, suspending or making GAR-related disclosures voluntary until the end of 2026 appears justified—as a pragmatic step to account for the lack of company-based disclosures. 

We strongly recommend that while the GAR-related disclosures are paused, key regulatory bodies work collaboratively and proactively to provide enhanced guidance, develop robust methodologies for the use of proxies, and ensure consistency—so that the final GAR-related disclosures accurately reflect the new Taxonomy materiality landscape. 

The expansion of ESG disclosures to all large institutions (listed/non-listed) is a necessary step toward market transparency. We support the tiered and proportionate approach requiring large institutions to provide information on all ten EBA Pillar 3 disclosure templates.

However, we note that the proportionality mechanism lacks operational clarity. While large institutions can omit immaterial data (Art. 432(1) CRR), the absence of standardized materiality thresholds creates compliance uncertainty. This complicates the delivery of consistent input metrics (e.g., GHG emissions, transition risk scores). Banks will require highly customizable data feeds to align with their internal materiality assessments, potentially increasing costs and fragmentation. Therefore, we recommend that EBA define quantitative materiality triggers to ensure interoperability between external data and banks’ internal models.

2. Do you have any comments on the simplified set of information for Other listed institutions and Large subsidiaries?

Overall, we support the principle of proportionality and the simplified disclosure requirements for other listed institutions and large subsidiaries. 

3. Do you have any comments on the simplified set of information proposed for SNCI and other non-listed institutions?

The simplified templates are justified, particularly the reduced reporting frequency and a proportionate, gradual approach for listed subsidiaries, SNCI and non-listed institutions. This ensures proportionality of compliance burden depending on size and complexity of institutions. 

4. Do you have any comments on the proposed approach based on materiality principle to reduce the frequency (from semi-annual to annual) of specific templates (qualitative, template 3, and templates 6-10) for large listed institutions?

We have reservations regarding the proposed approach to reduce the reporting frequency of selected templates based on the materiality principle. While we acknowledge the intent to reduce reporting burden where risks are non-material, we would like to highlight that the current proposal lacks clarity on how materiality should be assessed and who is involved in the process.

There is no explicit requirement to involve key stakeholders—such as investors, shareholders, or even corporate counterparties—in the materiality assessment. This raises concerns about the transparency and consistency of decisions that may ultimately limit the availability of timely, comparable data, specifically related to transition and physical risks.

Moreover, reducing the frequency of disclosures for templates such as Template 3 (transition risk exposures) and Templates 6–10 (which includes the GAR and BTAR) risks creating a broken chain of information between corporates, banks, and investors. This undermines the original purpose of the Pillar 3 ESG framework: to enable effective market transparency and channel capital towards sustainable and transition-aligned activities. More importantly, reporting frequency must be designed to capture point-in-time risk and resilience, to reflect the dynamic nature of bank lending portfolios in the face of climate change-related events.

We recommend that any reduction in frequency of disclosures be accompanied by:

• Clear supervisory expectations on the governance and documentation of materiality assessments
• Minimum disclosure standards to ensure that the decision to reduce frequency does not compromise comparability
• Requirements to ensure that stakeholder engagement—especially investor and counterparty input—is factored into materiality assessment. Alternatively, regulatory authorities could issue complementary resources – such as industry-level guidance for materiality assessment and FAQs – to promote greater standardization and consistency.
• Consistent point-in-time measure of loss-absorption capacity for the banking lending book into Pillar 3 disclosure framework, alongside longer-term indicators of risk and resilience. This would improve transparency on ESG-linked shocks and provide more decision-useful information to supervisors and market participants. 

5. Do you have any comments on the transitional provisions and on the overall content of section 3.5 of the consultation paper?

We support the transitional provisions and interim guidance in Section 3.5, which recommends competent authorities to refrain from requesting additional disclosure data during the period in which institutions are expected to comply with the amended ITS. This approach helps to minimize compliance and administrative burden. This section also clarifies that all in-scope institutions including large and listed institutions, other listed institutions, large subsidiaries, small and non-complex institutions (SNCIs), and other non-listed institutions will be required to apply the amended ITS starting from 31^st December 2026.

However, it must be noted that the suspension of key metrics (e.g., GAR, Templates 6–10) risks creating a market information gap until 2026. To address this without undermining proportionality, we recommend permitting institutions to report voluntarily using interim standardized metrics that could leverage metrics for which data is already collected for other frameworks, minimizing additional burden while preserving market confidence. Crucially, having this approach optional would allow institutions facing resource constraints to defer entirely until 2026. By enabling early adopters to share forward-looking climate risk snapshots, the EBA would balance its dual goals of reducing short-term burdens and maintaining long-term transparency.

6. Do you have any comments on the proposed amendments to Table 1 and Table 3?

N/A, as a quantitative-focused information provider, ESG Book is able to opine mainly in relation to the Template-based disclosures that require quantitative data inputs. 

7. Do you have any further suggestions on Table 1A?

N/A, as a quantitative-focused information provider, ESG Book is able to opine mainly in relation to the Template-based disclosures that require quantitative data inputs. 

8. Do you have any comments on the proposed additions and deletions to the sector breakdown?

We believe that the proposed additions and deletions are justified. The increased granularity in the NACE sector breakdown for the fossil fuel sector is appropriate, given the emphasis on transition activities and the materiality of these exposures for all in-scope institutions. The amended ITS’s approach to tailored disclosures based on institution size requires small and non-complex institutions (SNCIs) to report only essential ESG information (e.g., fossil fuel sector exposures), thereby reinforcing the relevance of sectoral activity breakdowns where they are most material. 

Similar to the fossil fuel sector, we suggest the breakdown for the agricultural sector could potentially be refined further. 

We also agree with the removal of Accommodation and Food Services sector from high climate-impact sectors, as it is not listed in Commission Delegated Regulation (EU) 2020/1818 that list of sectors considered to significantly contribute to climate change. 

9. Do you have any views with regards to the update of the templates to NACE 2.1?

We agree with the incorporation of NACE Rev 2.1 across templates as it is the official and most widely used classification system for EU economic activities. 

10. Do you have any views with regards to NACE code K – Telecommunication, computer programming, consulting, computing infrastructure and other information service activities, and in particular K 63 - Computing infrastructure, data processing, hosting and other information service activities, whether these sectors should be rather allocated in the template under section Exposures towards sectors that highly contribute to climate change?

We recommend including NACE K.63 (Computing infrastructure, data processing, hosting and other information service activities) under ‘Exposures towards sectors that highly contribute to climate change’ in Template 1. The argument is based on the three parameters:

• Emissions Materiality: EU data confirms the sector’s rapidly growing carbon footprint (e.g., data centers consume 2.7% of EU electricity; projected to reach 3.2% by 2025 per EEA 2023). The sector’s energy intensity directly drives Scope 2 emissions, aligning with the EU Taxonomy’s substantial contribution to climate mitigation criteria (Annex I, Climate Delegated Act);
• Regulatory Precedents: The Corporate Sustainability Reporting Directive (CSRD) already designates digital infrastructure as high-risk, while the ECB’s climate risk guidelines explicitly flag ‘information technology’ as transition vulnerable;
• Forward-Looking Risk Exposure: AI/cloud expansion under Europe’s ‘Digital Decade’ agenda will amplify K.63’s emissions trajectory. Banks financing this sector face stranded asset risks if projects rely on non-renewable energy.

We recommend a phased in approach with proportionality built in, so that thresholds apply only to institutions above a certain size and maturity to avoid burdening smaller reporting entities.

11. Do you have any comments on the inclusion of row “Coverage of portfolio with use of proxies (according to PCAF)”?

We support the introduction of the new metric in Template 1: Coverage of portfolio with use of proxies (according to Partnership for Carbon Accounting Financials (PCAF) methodology) (in %) as this supports alignment and interoperability with international standards – PCAF and TCFD. 

12. Do you have any further comments on Template 1?

Templates 1 and 4 require disclosures to be split by “environmentally sustainable (CCM)” (column c). Therefore, it is important to clarify whether these EU Taxonomy-aligned disclosure datapoints also benefit from the planned reporting exemption applicable for templates 6-8 regarding GAR reporting by banks. Leaving the CCM aligned reporting requirement in Template 1 and Template 4, while suspending Taxonomy reporting obligations under the remaining reporting templates would result in an inconsistent application of Taxonomy-focused disclosures and is also prone to lead to reporting errors, in light of the flexibility afforded to non-financial corporates for their own Taxonomy-based disclosures throughout 2026. Therefore, for consistency reasons, we recommend making such disclosures voluntary for institutions who have been able to collect the relevant data in time, or suspending the reporting requirement in column c, Template 1 and Template 4 until 31 December 2026. 

13. Do you have any comments or alternative suggestions for SNCIs and other institutions that are not listed, regarding the sector breakdown?

We do not have any further comments on the question above.

14. Do you have any additional suggestions how to adjust Template 1A for SNCIs and other institutions that are not listed?

It would be advisable to provide further clarity on the methodology breakdown by Geography. Is the template intended to cover the same scope of geographic regions as Template 5?

15. Do you have any further comments on Template 1A?

The breakdown by residual maturity bucket could follow the same structure as template 5, as this is information that is internally available for banks and it is an important component that offers much needed granularity regarding the time horizon exposures of physical risks. 

16. Should Template 2 in addition include separate information on EPC labels estimated and about the share of EPC labels that can be estimated?

Yes, EPC data is one of the most challenging for banks to source. Therefore, further clarity regarding the use of estimated data as well as the proportion of assets that enable estimations in the first place would be highly valuable. 

17. Should rows 2, 3 and 4 and 7, 8 and 9 for the EP score continue to include estimates or should it only include actual information on energy consumption, akin to the same rows for EPC labels?

We recommend a standardized and robust methodology for the use of proxies and estimations for EPC data in covered bonds, where data availability currently remains limited. The framework’s supporting materials should include a clear supervisory approach that acknowledges the limitations of proxies as comparable and reliable data during the interim disclosure period. This approach should provide an appropriate timeframe to bridge the gap between current data limitations and supervisory expectations, while emphasizing the need for continuous data quality improvement. 

The EBA should encourage reporting entities to refrain from using internal assumptions where this may result in misleading disclosures. Additionally, given that the use of both reported and estimated data in row 2/3/4 or row 7/8/9 might result in double counting of the relevant values, we recommend that the template clearly distinguishes between estimated and non-estimated values by splitting them into different columns or sections. 

18. Do you have any comments on the inclusion of information on covered bonds?

We do not have any further comments on the question above.

19. Do you have any comments on the breakdown included in columns b to g on the levels of energy performance?

We do not have any further comments on the question above.

20. Do you have any further comments on Template 2?

We do not have any further comments on the question above.

21. Do you have any comments on Template 3?

We support the addition of the baseline year and 2030 target for the value of the intensity metric as this aligns with the target year for most large banks. We also invite the EBA to consider adding 2050 as an additional target year for a more long-term view. Additionally, it would beneficial if the respective target metrics were published annually by the EBA. In the interest of comparability, publishing a defined sample list of sector-specific intensity metrics that banks can choose from would be advantageous. These metrics should align with the available EBA targets. We further recommend a higher level of interoperability with the NZBA and BCBS frameworks. In terms of sectoral classification, there is a gap in the classification under the EBA Pillar III proposal and NZBA framework. While a complete adoption of the NZBA sector classification might increase the reporting burden on banks, better alignment of sectoral classification will ensure comparability of data and result in easier reporting practices for banks. Additionally, we recommend enhanced clarification on sectoral classification that elaborate on critical details such the relevance of differentiation on country/region level.

Our final recommendation for this template would be to review and extend the reporting requirements beyond just intensity metrics. This is important because other metrics could be better suited to different sectors. For this, we reiterate our recommendation of better interoperability with the NZBA framework that provides sector-specific indicators per NZBA Target-Setting Protocol. This captures asset-level transition pathways where carbon intensity alone is insufficient (e.g., gas-to-coal phaseouts).

22. Do you have any comments with the proposals on Template 4 and the instructions?

Establish a single reference list for identifying exposures to top emitting firms to ensure consistency across institutions. This needs to be done in a considered manner, keeping in mind the limitations of current methodologies, e.g. Carbon Majors below:

The heavy reliance of the Carbon Majors database on company-reported production data highlights a key challenge: the accuracy and transparency of emissions related to flaring, venting, and company boundary definitions remain highly uncertain and controversial. Although the Carbon Majors methodology applies emission factors to production volumes, it still depends on the integrity of the underlying production data, which can be subject to manipulation or inconsistent reporting boundaries.

 This underscores the need to broaden the scope beyond just the top 20 emitters in the oil and gas and cement industries. Exploring the top 10 emitters within each high emitting sector would provide a more comprehensive view of climate risks and exposures. Different sectors have distinct emission profiles and challenges. 

For example, industrial manufacturing, transportation, agriculture, and power generation all contribute significantly but are often overlooked in aggregated Carbon Majors lists. Segmenting the analysis by industry would improve the granularity and relevance of ESG risk assessments for banks and investors. It would also highlight sector-specific nuances in emissions reporting, such as fugitive emissions in mining or methane leaks in agriculture. Furthermore, this approach would help financial institutions better align with increasingly sector-sensitive regulatory expectations.

In short, expanding the focus to top emitters by industry and critically evaluating the data sources and assumptions behind emissions estimates will strengthen the robustness and credibility of sustainability reporting by banks. While complex, this challenge presents an opportunity to lead in disclosing more transparent, nuanced, and actionable climate data.

23. Do you have any views on whether this template could be improved with some more granular information in the rows, by requesting e.g. split by sector of counterparty or other?

Yes, please see our comment to question 22 above. 

24. Do you have any further comments on Template 4?

See our comment to question 12 above. We strongly recommend aligning the Taxonomy-focused reporting datapoints within all templates to the updated reporting timeline, effectively exempting banks from Taxonomy reporting until 31 December 2025. 

Additionally, specifically for Template 4, given the vast majority of top 20 emitting entities would be non-EU domiciled entities and thus – out of scope of EU Taxonomy reporting to begin with, we recommend the introduction of an estimations-based CCM-alignment disclosure, which would account for these existing disclosure gaps. As with any estimation-based inputs, there should be emphasis on methodological and data transparency, as well as the requirement for portfolio engagement as a primary data source. This would ensure there is greater integrity in the underlying data and enable constructive dialogue between banks and their counterparties when it comes to fueling the low-carbon transition. 

25. Do you have any comments on the proposal using NUTS level 3 breakdown for Large institutions and NUTS level 2 for Other listed institutions and Large subsidiaries? Would NUTS level 2 breakdown be sufficient for Large institutions as well?

Classifying climate-related physical risks in Template 5 solely by physical medium (temperature, wind, water, solid mass) risks oversimplifying climate hazard dynamics. Acute and chronic event classification offers a clearer temporal distinction that captures all of these hazards, while enabling both temporal and geographical prioritization of risk. By framing hazards as either acute (sudden-onset, short-duration events) or chronic (slow-onset, long-duration changes), banks can better identify urgency, forecast impacts, and allocate resources efficiently—something physical medium categories alone cannot achieve.

Key limitations and challenges of the newly proposed approach:

1. Oversimplification of Hazard Processes – Reduces complex, multi-factor hazards to one attribute, ignoring dynamic interactions (e.g., volcanic eruptions involve heat, gases, and solids). Origin and process-based classifications provide richer causal understanding.

2. Ambiguity and Overlap – Many hazards span multiple media (e.g., cyclones generate both wind and water hazards). Medium-based classification struggles with compound or cascading risks.

3. Incompleteness and Gaps – Excludes intangible, biological, chemical, and anthropogenic hazards, limiting comprehensiveness.

4. Lack of Alignment with Standard Frameworks – Regulatory and academic systems classify hazards by origin or process (meteorological, hydrological, geological), not just medium. This approach aligns with risk assessment methodologies and mitigation strategies.

5. Limited Usefulness for Risk Management – Effective control measures generally rely on understanding hazard origin, triggers, and temporal profile—not merely the medium involved.

In short, a temporal lens (acute vs chronic) that spans all hazard types provides a more strategic, prioritization-oriented framework, especially when combined with geographic granularity like NUTS Level 3. It better aligns with established hazard taxonomies and supports decision-making for resilience planning.

While NUTS 2 and NUTS 3 regions provide useful regional-level granularity for climate risk disclosure, their spatial aggregation inherently misses critical local-scale variability and hazard hotspots. Effective climate risk assessment and targeted resilience require complementing these with finer, hazard-specific, and locally explicit spatial data.

1. Spatial Scale of NUTS Regions

• The NUTS classification system offers a hierarchical regional spatial scale for the EU:
• NUTS 2 regions: Basic regions with populations around 800,000 to 3 million, often comprising provinces or large administrative areas.
• NUTS 3 regions: Smaller regions with populations between 150,000 and 800,000, equivalent to districts or counties, providing finer but still aggregated regional granularity.
• These regional units are primarily designed for statistical reporting and socio-economic analysis, not for localized physical hazard identification.

2. Climate Hazards and Risks Are Intrinsically Localized

Physical climate risks such as flooding, heat stress, landslides, or storm surge exhibit high spatial variability at much finer scales than covered by NUTS 2 or NUTS 3. Local factors like topography, urban land use, water drainage, infrastructure, and microclimates result in significant variability within NUTS regions, often spanning tens to a few hundred meters to a few kilometers spatially. This local heterogeneity is crucial for precise hazard assessment and risk identification hotspots which are often lost in aggregations to larger regional averages.

3. Mismatch and Risk Blind Spots

Aggregating exposures and hazards to NUTS 2 or NUTS 3 averages can mask high-risk local hotspots, causing:

1. Underestimation of risk in vulnerable neighborhoods or zones with specific hazard drivers.
2. Dilution of hazard intensity when averaged over broad areas with mixed exposures and vulnerabilities.

This spatial mismatch occurs especially for acute hazards like flash floods or landslides and urban heat islands, which operate at sub-regional scales, not reflected in broader NUTS units.

4. Need for Finer Spatial Units and Multi-Scale Assessment

More granular spatial units such as Local Administrative Units (LAUs), municipalities, or bespoke hazard maps using geospatial modelling offer more precise risk exposure identification. Incorporation of hazard-specific spatial data (e.g., floodplains, slope stability zones, urban heat islands) mapped at local scales can complement NUTS-level regional reporting. Multi-scale approaches that integrate regional socio-economic assessments with high-resolution local hazard mapping enable more informed risk management and resource allocation.

5. Implications for Climate Risk Disclosure and Management

1. Statistical harmonization and cross-regional benchmarking favor NUTS-level data but should be augmented with local-scale hazard information to avoid blind spots.
2. Disclosure frameworks for climate physical risk should enable reporting at multiple spatial scales, balancing broad comparability with local accuracy.
3. This improves banking and financial sector resilience planning by ensuring critical high-risk micro-locations are not overlooked.

While it is acknowledged that enhanced reporting requirements at NUTS 3 level bring additional complexity and reporting burden for large banks, it is crucial to assert that spatial analysis for climate-related physical risk assessment is indispensable to capture the full spectrum of local nuances and hazards. Climate risks are inherently spatial and exhibit significant variability even within small geographic areas. As such, only analyses with sufficient spatial granularity can reveal critical risk hotspots and heterogeneities that coarser regional aggregations like NUTS 3 might obscure.

Given this, banks must ensure they have the necessary resources, tools, and capabilities to conduct rigorous spatial risk mapping as part of their risk management frameworks. Importantly, recent advances in geospatial information technologies, Geographic Information Systems (GIS), improved geolocation data, and satellite-based remote sensing applications provide unprecedented capabilities to analyze fine-scale climatic hazards and exposures with high precision. Many global and regional datasets, including those from public entities and initiatives supported by central banks, are increasingly accessible for institutions to incorporate granular local climate hazard data into their risk assessments.

Moreover, various granular data sources such as loan-level geographies (e.g., ECB’s AnaCredit), satellite imagery, detailed hazard maps, and high-resolution climate models enable a holistic linkage of physical climate risks to exposures on a place-based basis. Integrating these spatial datasets facilitates more accurate forward-looking scenario analysis, stress testing, and resilience planning, which is vital for prudent risk management given evolving climate threats.

Therefore, rather than limiting NUTS 3 reporting initially or suggesting the majority banks may lack capacity, it is imperative that institutions are encouraged and supported to build these advanced spatial analytic competencies promptly. Regulatory and supervisory frameworks should promote investment in spatial data toolkits, geospatial expertise, and technology adoption to fully harness these capabilities. This will ensure that banks can effectively triangulate climate risks at the local scale, tailor mitigation strategies, and align their disclosures with evolving scientific and policy expectations.

In summary, spatial analysis is not an optional enhancement but a foundational pillar for robust climate risk assessment and management. With rapidly growing geospatial data availability, satellite applications, and analytical tools, banks have both the opportunity and responsibility to deploy these resources comprehensively, securing a granular understanding of climate physical risks down to highly localized contexts, ultimately strengthening financial sector resilience.

26. Do you have any comments on the instructions for the accompanying narrative and on whether they are comprehensive and clear?

We do not have any further comments on the question above.

27. Do you have any further comments on Template 5 and on its simplified version Template 5A?

We do not have any further comments on the question above.

28. Do you have any comments on the proposal to fully align templates on the GAR, that is, templates 7 and 8, with those under the Taxonomy delegated act by replacing the templates with a direct cross reference to the delegated act?

We welcome harmonization of templates 6-10 with the Taxonomy Regulation – extending the templates to include all six environmental objectives. By replacing static templates with a direct cross-reference to the applicable EU Taxonomy Delegated Acts – including both the Climate Delegated Act (Commission Delegated Regulation (EU) 2021/2139) and the Environmental Delegated Act (Commission Delegated Regulation (EU) 2023/2486) – any future amendments or updates to these legal acts are automatically reflected and linked in the disclosure template. This approach reduces the need for manual revisions, ensures consistency with the latest regulatory text, and strengthens alignment with the EU’s evolving sustainable finance framework.

29. Do you have any comments on the proposal related the BTAR and to keep it voluntary?

We agree with the voluntary nature of BTAR disclosures, considering the broader context of the European Commission's recently adopted Taxonomy Simplification Measure. The calculation of BTAR largely relies on the use of estimations – procuring Taxonomy-alignment data and ratios on non-EU and non-CSRD counterparties. We suggest further guidance and clarification and robust methodologies in place in the interim. While the transition provisions in the EBA Consultation offer valuable relief to reporting entities, clear and effective guidance remains essential. Notably, the ECB’s recent opinion[1] underscores the critical need for robust, harmonized sustainability and climate data under the CSRD—specifically greenhouse gas emissions, transition plans, decarbonization targets, and biodiversity data—to support its monetary policy, financial stability, and statistical assessments.

Regulatory uncertainty, particularly stemming from the two-year extension of ESRS for large companies within the CSRD’s scope, further obscures the baseline for sustainability disclosures. To ensure that end users—including banks and investors—have access to the necessary data, regulators must clarify the baseline reporting requirements, including which data points remain mandatory during this transitional period. Given recent regulatory changes, actually reported EU Taxonomy data for GAR is likely to be significantly reduced in scope. Therefore, reporting indicators such as BTAR will be essential in filling in the remaining data gaps for non-reporting entities. 
 

[1] https://www.ecb.europa.eu/pub/pdf/legal/ecb.leg_con_2025_10.en.pdf

30. Do you have any comments regarding the adjustments to template 10?

Template 10 can capture mitigating activities outside of the Taxonomy-aligned list of activities, which is critical since the Taxonomy is still incomplete. This allows for the recognition of credible transition financing that may have not yet fully developed or defined technical screening criteria but still contributes significantly to sustainability-related goals. Template 10 provides regulators and investors with additional detail on investment and lending activities, outlining assets that contribute to environmental and climate objectives, and complementing the information disclosed through other Taxonomy KPIs. 

31. Do you have any further comments on the Consultation Paper Pillar 3 disclosures requirements on ESG risk?

We do not have any further comments on the question above.

32. Are the new template EU SB 1 and the related instructions clear to the respondents? If no, please motivate your response.

We support the EBA’s development of a new disclosure template for exposures to shadow banking entities under CRR 3, noting that the instructions provided are clear in regulatory terms and sufficiently outline the scope of shadow banking entities, definitions, criteria, and essential data points. The template reflects the updated CRR 3 definition of shadow banking entities with the RTS and provides clear criteria on identifying exposures to shadow banking entities. The new template builds on existing reporting on the top ten exposures and ensures consistency with the LE3 template.  The inclusion of information on original exposures (on- and off-balance sheet), as well as exposure values before and after exemptions and CRM techniques, is well-defined, with a logical mapping between disclosure and reporting frameworks – connecting the ITS disclosures to the LE3 reporting template seamlessly. This ensures regulatory consistency and interoperability and improves reporting efficiency. 

We also welcome the EBA’s decision to maintain a simplified approach at this stage given the ongoing CRR 3 policy developments, which may necessitate more granular breakdowns in the future. In this context, we support a simplified aggregate disclosure in the interim, as this strikes a proportionate balance between transparency, data availability, and the operational burden of producing highly granular breakdowns before the policy framework is finalized. 

33. Do the respondents agree that the new template EU SB 1 and the related instructions fit the purpose and meet the requirements set out in the underlying regulation?

See our response to Q32. 

34. Are the amended template EU CR 10.5 and the related instructions clear to the respondents? If no, please motivate your response.

We do not have any further comments on the question above.

35. Do the respondents agree that the amended template EU CR 10.5 and the related instructions fit the purpose and meet the requirements set out in the underlying regulation?

We do not have any further comments on the question above.

36. Do the respondents consider that the “mapping tool” appropriately reflects the mapping of the quantitative disclosure templates with supervisory reporting templates?

The ITS for template EU SB 1 is designed so that the disclosure cells map directly to existing reporting requirements (under CRR II/CRR III), particularly to the LE3 template for large exposures and the supervisory reporting of shadow banking entities. 

That mapping ensures consistency in disclosure data by matching what institutions are already reporting to supervisors, avoiding duplicate work and reducing the chance of inconsistencies. It also ensures that banks can easily pull from the same underlying datasets and systems rather than establishing parallel reporting flows, thereby making disclosure and reporting frameworks interoperable.

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