With the growth in ESG regulation within the European Union, businesses and data users are demanding more efficient ways to exchange ESG information and check ESG KPIs. In particular, as ESG reporting under regulations like the CSRD relies heavily on the disclosure of “qualitative” (or narrative) information, there is a growing need for easier ways to manage this flood of data.
Narrative data is textual information that articulates a company's sustainability journey, strategies, and impacts, and it’s a major part of ESG reporting under the CSRD. Currently, it comprises about 50% to 60% of the data reported.
Making narrative data digitally accessible and taggable would be a crucial step in enhancing the comprehensiveness and utility of CSRD reporting. To this end, bodies like EFRAG (the European Financial Reporting Advisory Group) are pushing for the digital tagging of narrative data reported under the CSRD.
EFRAG's dedication to digitization has led to the development of the Draft ESRS Set 1 XBRL Taxonomy, a framework for the digitization and “digital tagging” of the content of (ESRS) disclosures.
This initiative, slated for handover to the European Commission (EC) and the European Securities and Markets Authority (ESMA) in the summer of 2024, will represent a significant milestone in sustainability reporting in the European continent and beyond, as it will enable a more efficient exchange of sustainability information and data.
When implemented, the XBRL Taxonomy will have major implications for the way ESG reporting is currently approached and performed. This will also see substantial synergies with the rise of AI technology to streamline CSRD reporting. This synergy between digital taxonomy and AI not only promises to reduce the workload for sustainability analysts but also to elevate the precision and comprehensiveness of sustainability disclosures.
XBRL stands for “eXtensible Business Reporting Language”, and it’s a globally recognized framework for exchanging business information.
The Draft ESRS XBRL Taxonomy introduces a set of XBRL elements, or tags, designed for use to digitally tag and store information about ESRS disclosures.
These tags not only facilitate the identification, navigation, and extraction of digital disclosures but also incorporate dimensions that allow for the disaggregation of data, enhancing the depth and clarity of reported information.
According to EFRAG, following the digital tagging guidelines should not create an additional burden for companies dealing with CSRD reporting. In fact, inline XBRL will enable the creation of documents that are both human- and machine-readable, eliminating the need to create separate documents and helping centralize and streamline efforts
One of the objectives of EFRAG was to develop ESRS XBRL Taxonomy that offers a level of detail mirroring the ESRS standards themselves.
Every ESRS datapoint that belongs under a certain disclosure requirement has (at least) one corresponding element in the XBRL taxonomy. This correspondence will facilitate the digital tagging of ESRS disclosures that follow the ESRS guidelines and requirements closely, allowing for straightforward updates.
The XBRL taxonomy introduces a hierarchical system with three “nested” levels of tagging:
Level 1 Tagging addresses the whole disclosure requirement (DR), acting as a "parent" tag. Level 1 tagging can additionally serve as an automatically generated table of contents or index, that can be used to quickly identify sections of the disclosure corresponding to certain requirements.
Level 2 Tagging focuses on smaller narrative disclosures, targeting specific aspects of the disclosure requirement with dedicated "children" elements.
Level 3 Tagging zooms in on individual aspects within a larger disclosure, offering an even finer granularity of data representation.
This structured approach ensures that each piece of information is placed within a logical framework, enhancing both human readability and automatic processing of the data disclosed.
In some instances, the XBRL even introduces a “more granular” level of disclosure compared to ESRS requirements (where one requirement is transposed into more than one XBRL element): for example, the XBRL taxonomy provides for the disaggregation of data, such as GHG emissions, by category (e.g. CO2, CH4, N2O, etc). While carrying out digital tagging at this level of granularity is not mandatory for businesses, it can be handy for companies that want to be even more transparent and precise with their disclosures.
Standardization is a big topic in ESG. Nobody wants to deal with a plethora of data requests asking for the same information in a slightly different format.
This is why EFRAG wants to achieve digital interoperability among other sustainability reporting frameworks, such as the IFRS sustainability-related Disclosure Standards and the Global Reporting Initiative (GRI), EFRAG plans to simplify comparability and even automatic tagging across different standards through digital “concordance tables”, which will enable an automatic “translation” of digitally tagged information between different standards.
As of today, no concordance table has been developed or drafted yet, although this is currently being scoped out by EFRAG, as well as the ISSB and GRI. Part of the reason for this is that the feasibility of digital interoperability depends on the interoperability of the “human-readable” requirements themselves - and a high level of interoperability has already been achieved between the ESRS and the ISSB Taxonomy climate standards, according to EFRAG.
According to EFRAG, qualitative data (also called “narrative” in the ESRS) accounts for 50% - 60% of the total data points in sustainability statements.
XBRL Taxonomies will enable an easy tagging of this kind of textual information, so that it can be easily ingested and interpreted by AI systems and other forms of sustainability reporting automation technology. The taxonomy is additionally designed to handle “semi-narrative” data types, such as boolean (Yes/No) and enumeration (multiple choice or single-choice options).
Besides narrative, semi-narrative and non narrative (quantitative) XBRL elements, the XBRL taxonomy features “dimensions” that can be used to disaggregate digital disclosures further.
XBRL taxonomies distinguish between explicit dimensions (also called axis) or lists of elements that already exist (are pre-defined) within the XBRL taxonomy (e.g., country, gender, GHG type, etc.), and typed dimensions, which will be defined by users (e.g., geographical areas, policies, targets, operating segments) as they are not already covered within the XBRL.
In practice, EFRAG predicts that businesses will rely on two primary methodologies for digital tagging: a "content-first" and "taxonomy-centric" preparation process.
The former approach means that the users will complete a sustainability statement before mapping the information onto its digital counterpart, as defined by the XBRL taxonomy.
Conversely, the latter process involves identifying relevant XBRL elements prior to the creation of the report, where each element can be created separately, or “in chunks”, in compliance with ESRS requirements and the digital tagging system.
Aligning the statement with the ESRS structure before completing the reporting can simplify the tagging process, however EFRAG does not particularly recommend one approach over the other.
The approach you choose can depend on your circumstances, processes, team setup and tools used. For instance, AI tools like Briink enable users to identify chunks of relevant information to tag directly from an existing report, and in complete automation.
The finalization of the ESRS XBRL Taxonomy is especially important for early adopters of AI technology within their sustainability and ESG reporting.
Both innovations benefit heavily from one another, creating synergies that promise to revolutionize the current approach to sustainability reporting.
On the one hand, digital tagging of disclosures will enable AI systems to more easily and accurately identify and classify ESG data and information. On the other, AI will empower users to tap into raw and unstructured sustainability data, efficiently organizing them into compliant sustainability statements and disclosures that can also easily be tagged according to the XBRL taxonomy framework.
At Briink, we are already working to harness the impact of digital tagging of sustainability reporting and embed it into our AI tools for sustainability data extraction and verification. You can learn more about our solutions by booking a call with us here.
The quest to digitize sustainability reporting spearheaded by EFRAG will most surely not be over after the development of the adoption of the ESRS Set 1 XBRL Taxonomy and future digital taxonomies.
All sustainability frameworks and regulations will benefit from the standardization and ease of use that stems from making sustainability disclosures digital and machine-readable.
This big effort towards digitization will also have major implications for the adoption of AI-powered sustainability data extraction technology - with both approaches benefiting heavily from one another.
This will result in far less work for sustainability analysts, and a more seamless and objective exchange of critical ESG information and data across a broader ecosystem of stakeholders, investors, suppliers, buyers, and authorities.
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