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## Overview

Our SFDR Principal Adverse Impact (PAI) Indicators Solution has been designed by ESG Book to enable clients to meet the reporting requirements of the EU Sustainable Finance Disclosure Regulation (SFDR), which entered into force on 10 March 2021. ESG Book’s SFDR PAI Solution is a granular tool that empowers clients to ingest the company-level data indicators needed for SFDR reporting based on our in-house SFDR raw data calculation methodology.

The SFDR PAI Solution is based on a mapping of ESG Book’s universe of over 400 raw data metrics to the 47 company-level SFDR Principal Adverse Impact (PAI) indicators, as stipulated in the draft Annex of the Regulation (indicators applicable to investee companies). This resulted in 100 raw data indicators that have been mapped to the mandatory and opt-in SFDR PAIs, aggregated and delivered at the entity level.

The ESG Book PAI SFDR indicators can be easily integrated into client systems, allowing full customisation and flexibility when it comes to portfolio-level reporting.

Importantly, we cover 100% of the Mandatory SFDR PAI metrics and over 80% of the Opt-in SFDR PAIs.

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## Data Sources

The SFDR PAI Solution is powered by underlying raw data values across our proprietary sustainability raw data module. This dataset includes coverage of Environment, Social, and Governance (ESG) related entity-level metrics, emissions and scope 1, 2 and 3 data. The tool utilises data from our ESG exclusionary screening solution called the Business Involvement Filter (BIF), along with data from the ESG Book Temperature Score. Our non-financial raw data is diligently collected by subject-matter experts and undergoes thorough data integrity checks.

The data in these modules is collected at the entity level using the following disclosure documents:

• Annual reports

• Corporate Sustainability Reports (CSR)

• Investor relation presentations/reports

• ESG reports

• Company website

All our scores and data metrics are based on actually disclosed raw ESG data values. We do not deploy data estimations or modelling techniques into our analysis.

In the instances where we provide data proxies, we clearly indicate where a proxy data point has been used in place of a direct data match.

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## Data Collection

Data for each indicator is collected from the aforementioned data sources through a set of Standard Operating Procedures (SOPs). These SOPs define each indicator in detail, direct the analyst to all relevant data sources, and instruct them on the acceptability and unacceptability of information as evidence for each indicator.

The SOPs have been developed with the objective to facilitate consistency and error minimisation when capturing ESG information.

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## Data Quality

After data collection, all data goes through a series of data validation tests to ensure data accuracy. Our technical validation tests look at 8 dimensions of data quality: Completeness, Conformity, Validity, Accuracy, Consistency, Uniqueness, Reasonableness, and Timelines. Our technical validation tests are run on the entire dataset of collected data with the aim to identify any inconsistencies in the data. Examples of the technical validation tests include:

• Input error tests - Checking if the input field has been correctly filled (e.g., checking that the analyst has recorded the correct year).

• Inconsistency tests - Checking if the data is consistent in consecutive years (e.g., checking that data present in one year is also present in the subsequent year).

• Related questions tests - Checking the input for two metrics is reasonable and/or expected based on the established relationship between the two metrics (e.g., if GHG emissions data disclosure is NULL, the quantitative GHG emissions metric should also be NULL).

Companies with identified errors will be flagged for further manual comprehensive data integrity validation by our ESG analysts.

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## Metric Mapping

ESG Book’s regulatory experts have mapped each of our raw sustainability metrics to the SFDR PAIs. This mapping has resulted in two types of metric matching:

1. Match - Exact match between Adverse sustainability indicator and ESG Book metric.

2. Proxy (T) - Thematic metric overlap, which allows an approximation of the requested indicator.

3. Proxy (M) - Missing information in metric. The level of overlap allows an approximation of the requested indicator.

Please request access to the underlying calculation methodology for each PAI metric.

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## Metadata

Meta Data | Information

---------------|----------------

Update Frequency | Weekly

Data Source(s) | ESG Book collected disclosures

Geographic coverage | Global

Time period coverage | 2016-present

Is historical data “point-in-time” | YES

Raw or scraped data | All report-based data is collected from CSR, annual reports, GHG assurance statements, and corporate websites.

Number of companies covered | ~9,000

Standard entity identifiers | Ticker Exchange

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## Pricing Information

Pricing is determined on a use-case basis, thus please contact for more information.

When requesting please include the following information:

* Organization Name

* Position (non-mandatory)

* Business Email Address or Telephone Number

* Country

* Use-case

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## Regulatory and Compliance Information

This product is allowed for internal use only, users are not allowed to distribute the data externally.

If you're interested in a re-distribution of data use case, please contact us.

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## Need Help?

* If you have questions about our products, contact us at support@esgbook.com

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## About Your Company

* [ESG Book Website](https://www.esgbook.com/)

* [ESG Book Platform](https://app.esgbook.com/dashboard)