Company: BCS
Filing Date: 2025-02-13
Form Type: 20-F
Source: 0000312069-25-000114
Chunk: 85

Company: BARCLAYS PLC
Filing Date: 2025-02-13
Form: 20-F
Chunk 85
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 on Data quality score please refer 'Data sourcing and data quality' section on page 38. 2 Emissions excluding land-use, land-use change and forestry. Strategy Shareholder information Climate and sustainability report Governance Risk review Financial review Financial statements Barclays PLC 2024 Annual Report on Form 20-F 37 Implementing our Climate Strategy (continued) TCFD Strategy Recommendation B | Strategic Pillar 1

Data sourcing and data quality There are data quality challenges inherent in the calculation of financed emissions. Climate data, models and methodologies are evolving &#8211; and are not yet at the same standard as more traditional financial metrics. Our financed emissions calculations rely on externally sourced data mapped to internal customer and client identifiers. The externally sourced data has various limitations for each sector, including lack of coverage, low resolution, consistency and transparency of company-reported data, as well as the time lag for external sources to report estimates or actuals. Time lags in our external data could be as much as two years for data such as company value, company revenue share, emissions, production capacity and capacity factors. As a result, our financed emissions metrics are at best an estimate of our clients' activities on a given date, using the external data available at that point in time. We have scored the quality of the data we have used to estimate our financed emissions using PCAF's Global GHG Accounting and Reporting Standard. In the Standard, data quality score DQ1 and 2 relates to high quality from company disclosures, DQ3 to emissions estimated using physical activity data and DQ4 and DQ5 to deriving emissions estimated using revenue or asset-based emission factors. We disclose DQ score at an activity level. For activities where we have set targets (and for our UK Housing convergence point), DQ is mostly concentrated across DQ1/2 and DQ3 signifying that most of the relevant estimated financed emissions are either company-reported or calculated using the company&#8217;s physical activity data. For activities where we have not set targets, activities have DQ score spread across the scale which signifies we have used a combination of company reported emissions and revenue/asset fallbacks to calculate financed emissions. Our data vendor does not provide a split for reported emissions between DQ1 and DQ2. Hence, where we have relied on reported emissions sourced from the data vendor, we have conservatively used DQ2 for calculating DQ scores at an activity level. This indicates that we need to consider the current estimate of financed emissions for these activities as highly