This is the second article in a three-part series on the Basel Committee’s voluntary framework for climate-related financial risk disclosures. The first article provides an overview of the framework, its background, and key changes from the 2023 consultation. This article examines each of the six tables and templates in detail.
The framework published in June 2025 contains two qualitative tables (CRFRA and CRFRB) and four quantitative templates (CRFR1 through CRFR4). Each component serves a specific purpose and requires different types of data, governance inputs, and methodological disclosures. Understanding the structure and requirements of each is essential for banks preparing to report under this framework — whether voluntarily or as part of future jurisdictional mandates.
Qualitative Disclosures: Tables CRFRA and CRFRB
The two qualitative tables form the narrative foundation of a bank’s climate risk disclosures. They are designed with a flexible format and are to be published annually. While they do not require numerical data, they demand a high level of specificity regarding how climate-related financial risks are governed, assessed, and integrated into business strategy.
Table CRFRA — Governance, Strategy and Risk Management
Table CRFRA is the most extensive qualitative disclosure requirement. It asks banks to describe how climate-related financial risks are governed at the board and management levels, how those risks affect business strategy and financial planning, and what processes are in place to identify, assess, and monitor them.
The table is structured around two core areas: governance and strategy with risk management. Under governance, banks must disclose the structure responsible for oversight, including board competencies, reporting frequency, and how climate risk targets are linked to remuneration policies. Under strategy and risk management, the requirements extend to scenario analysis, transition plans, time horizon definitions, and the financial effects of climate-related risks on the bank’s position and performance.
This level of disclosure goes beyond what many banks currently publish. It requires institutions to define how they distinguish between short-term, medium-term, and longer-term climate risks and to link those definitions to their strategic planning horizons. Banks must also disclose whether they use climate scenario analysis aligned with the latest international agreement on climate change — effectively asking whether the scenarios include a 1.5°C pathway.
Table CRFRB — Transition, Physical, and Concentration Risk
Table CRFRB complements CRFRA by requesting methodological detail on how a bank determines which exposures are subject to material transition risk, physical risk, and concentration risk. This is where banks explain the criteria, time horizons, and scenario approaches they use for each risk type.
For concentration risk, banks are expected to disclose how they identify concentrated exposures in specific sectors or geographies, the processes used to assess the likelihood and effects of such concentration, and how concentration risks influence strategy and decision-making. This is particularly relevant for institutions with significant exposure to carbon-intensive sectors or to geographies vulnerable to flooding, wildfires, or sea-level rise.
Quantitative Templates: CRFR1 to CRFR4
The four quantitative templates represent the data-intensive core of the framework. Each requires specific metrics, sectoral or geographical classification, and narrative commentary. Banks are expected to disclose them annually in a fixed-column format, with flexibility in the number of rows depending on their portfolio composition.
Template CRFR1 is the most comprehensive quantitative template. It requires banks to report 15 columns of data for each material sector, including gross carrying values, non-performing exposures, allowances, residual maturity buckets, and GHG financed emissions covering Scopes 1, 2, and 3.
Emissions must be measured in accordance with the GHG Protocol Corporate Standard and expressed in metric tonnes of CO₂ equivalent (MtCO₂e). Banks can use counterparty-reported data or proxy measures based on physical or economic activity. Where emissions data is not available for a specific sector, the bank must disclose plans to implement estimation methodologies, as part of Table CRFRB. This is a significant obligation, given that data quality and availability remain a persistent challenge across the banking industry.
Interactive CRFR1 Sample — Illustrative Sectoral Data
Click a sector row to highlight it and view its contribution to total financed emissions.
| Sector | GCV (€m) | % Total | Scope 1+2+3 (MtCO₂e) | Avg maturity (yrs) |
|---|---|---|---|---|
| Energy | 12,400 | 31.0% | 4.82 | 6.2 |
| Utilities | 8,600 | 21.5% | 2.79 | 8.4 |
| Transportation | 6,200 | 15.5% | 1.91 | 5.1 |
| Materials | 4,800 | 12.0% | 1.52 | 4.7 |
| Agriculture | 3,200 | 8.0% | 1.02 | 3.8 |
| Other sectors | 4,800 | 12.0% | 0.61 | 4.2 |
| Total | 40,000 | 100% | 12.67 | 5.4 |
The emission scope tags below illustrate the coverage expected:
Template CRFR2 requires banks to categorise their exposures into three groups: those in regions subject to physical risk, those in regions not subject to physical risk, and those where the bank is unable to make a determination. Within exposed regions, exposures are further split between corporate exposures and loans collateralised with immovable property.
The geographical regions subject to climate-related physical risks are defined at the jurisdictional level by national supervisors, not by the bank itself. This means the scope of CRFR2 will vary depending on where a bank operates and what its national regulator classifies as physically exposed. Events covered include flooding, coastal erosion, rising sea levels, hurricanes, wildfires, and other chronic or acute climate phenomena.
Energy efficiency is measured in kWh per square metre (kWh/m²) of the collateral. Exposures are bucketed into six efficiency bands, from highly efficient (≤100 kWh/m²) to least efficient (>500 kWh/m²), plus a category for properties without an efficiency measurement.
Illustrative Energy Efficiency Distribution (kWh/m²)
Hover or tap bars to see details. Use the slider to model different portfolio compositions.
This template is particularly important for European banks with large mortgage books, where energy performance certificates (EPCs) are increasingly available. For banks operating in jurisdictions without mandatory building energy assessments, the “without energy efficiency measurement” column will likely account for a significant share of exposures. The framework allows banks to use estimates and internal calculations where counterparty-level data is not available, but requires disclosure of whether the information was collected directly or modelled.
Where a loan is secured by multiple properties with different efficiency levels, the bank must allocate the gross carrying value proportionally based on collateral value — a calculation that requires granular collateral-level data typically managed through dedicated data management systems.
Banks report emission intensity using physical denominators — such as CO₂e per gigajoule, CO₂ per passenger kilometre, or CO₂ per tonne of product — weighted by loan size at portfolio level. The template also requires disclosure of GHG intensity targets for two reference years, along with a point-in-time distance formula measuring progress.
The illustrative sector metrics provided in the framework include:
The point-in-time distance formula is defined as: the difference between the current reporting year metric and the target, divided by the target, expressed as a percentage. This allows stakeholders to assess how far a bank’s portfolio is from its stated climate targets at any given reporting date. Banks that have not yet set intensity targets must disclose their plans to develop the necessary methodologies.
PiT Distance Calculator
Enter a current emission intensity metric and a target value to calculate the point-in-time distance.
Formula: PiT distance = 100 × (current − target) ÷ target
Key Data Challenges
Across all four quantitative templates, the recurring challenge is data availability. Financed emissions require counterparty-level GHG data that many borrowers, particularly smaller enterprises, do not yet produce. Energy efficiency data for real estate collateral is uneven across jurisdictions. Physical risk geographical classifications are not yet standardised globally.
These challenges make it essential for banks to invest in structured data collection infrastructure. Tools that support questionnaire-based data collection from counterparties, combined with robust framework interoperability, will be critical to producing disclosures that meet the granularity required by these templates.
The third article in this series addresses these implementation challenges in detail, covering data sourcing strategies, system architecture requirements, and how banks can align Basel disclosures with existing obligations under CSRD, ISSB, and EU Pillar 3.
Frequently Asked Questions
What emissions scopes are required in Template CRFR1?
Template CRFR1 requires disclosure of aggregated Scope 1, 2, and 3 financed emissions associated with a bank's lending and investment activities, with Scope 3 reported separately. Emissions must be measured in accordance with the GHG Protocol Corporate Standard and expressed in metric tonnes of CO₂ equivalent.
How is energy efficiency measured in Template CRFR3?
Energy efficiency is measured in kilowatt-hours per square metre (kWh/m²) of the underlying collateral. Exposures are allocated across six energy efficiency bands, ranging from 0–100 kWh/m² (most efficient) to above 500 kWh/m² (least efficient), plus a category for properties where measurement is not available.
What is the point-in-time distance formula in CRFR4?
The point-in-time (PiT) distance measures how far a bank's current emission intensity metric is from its stated target. It is calculated as: (current metric − target) ÷ target × 100. A positive value means the bank has not yet reached its target; a negative value indicates it has exceeded the target.
Can banks use estimated data in these templates?
Yes. The framework allows banks to use proxy measures and internal estimates for financed emissions and energy efficiency data. However, banks must disclose whether the information was collected directly from counterparties or estimated internally, and they must describe the methodology used for any estimates.
