XBRL Tagging – what do we really win with this?

On the 30th of August, EFRAG published the XBRL taxonomy for ESRS that companies have to comply to under CSRD. 

Wow,  I just dumped 4 acronyms in one sentence, I might be able to top that later in this post. 

To further elaborate:

• EFRAG is the European Financial Reporting Advisory Group, basically the organisation tasked with setting up drafts of the sustainability reporting standards;
• CSRD is the Corporate Sustainability Reporting Directive, the legislation which requires companies to report on sustainability standards;
• ESRS is the European Sustainability Reporting Standard drafted by EFRAG, it defines what companies that fall under CSRD have to report on;
• XBRL (eXtensible Business Reporting Language)  is a global standard for digital reporting. It is used by financial institutions to ensure that they have a common language when exchanging documents.

The publication was expected and a part of the plan for ESRS, to make sure all the data points in ESRS from customers have a tag making the statement ‘machine readable’. XBRL has been a standard for a number of years and has a number of so-called taxonomies, which are basically dictionaries. They define which fields should be tagged and what kind of data can then be expected. This has done wonders for financial statements and is mandated by governments across the world. 

So it is not a nice to have, companies have to tag their financial statements. 

The good thing about financial statements is that they have a predictable nature and predictable datafields. The other thing with financial statements is that they are primarily about numbers. Sustainability statements do not have such a clear, well defined setup and are also mostly text.

I have four main reservations with this blanket approach:

• Sustainability statements have more freedom when it comes to their structure compared to financial statements. Information can be spread across chapters, paragraphs etc which makes tagging a difficult undertaking..
• 85% of all data points are text-based or narrative. Even though we’re getting better at interpreting large pieces of texts, it’s still not a science to machine-read all these answers and conclude things. Comparing results is problematic as well, because unless you classify the answers it is hard to make an apple to apple comparison.
• The cost to tag all data points is fully on the preparers and they are not the group benefitting from this and thus this is seen as an additional cost they will have to bear.
• Finally, who is really benefiting from all this machine-readable data? A recent survey from EFRAG asked 49 stakeholders: 

“Q2: Do you agree that the Draft ESRS XBRL Taxonomy as currently designed meets the needs of users (analysts, data providers, financial institutions, investors, regulators, etc.)? If not, what could be improved?” 

50% of respondents answered with a hard “no”. 

Interestingly, all nine preparers (companies) were in the “no’ group. 

What is the alternative? I propose to use a more pragmatic approach. Instead of tagging all data points, let’s start with selecting a top 25 of valuable data points that everyone understands and are easy to consolidate and report on. I’m obviously referring to quantitative data points such as emissions and ask all preparers to tag those in a simple data table. This significantly reduces the costs and effort involved for preparers, it limits the scope for stakeholders when they start reporting a wealth of data and helps with comparing apples to apples. Finally, it will force stakeholders to be explicit why they want to have more data points machine readable.