Fundamental ESG research analyst blue sustainAX

SFDR – Different types of ESG* data and why you will always need the fundamental ESG* analyst

The debate about ESG, the search for best practice and the search for ESG data are still hot topics and it will be for some time.

In many recent meetings with asset managers, we have had many interesting discussions about ESG data. Asset managers need ESG data to be able to align with EU Sustainable Finance Disclosure Regulation (SFDR) requirements and they need data vendors. There are two types of ESG data vendors in the market, Corporate ESG Data (reported and estimated data) and ESG Research Data. Very tempting to call them CESGD and ESGRD, but we have enough acronyms by now.

And here it is important to stop of for a second and think. What is the difference?

In the following, we do not distinguish between reported and estimated Corporate ESG Data, as the EU Corporate Sustainability Reporting Directive (CSRD) likely will remove the need for estimated Corporate ESG Data for most companies. There is also a third type of data, that is very relevant nowadays, the estimated contribution to environmental and/or social objectives as a Sustainable Investment qualification. Many do this through revenue-based UN SDGs (UN Sustainable Development Goals) contribution estimates, see below under the SFDR Article 9 discussion. A fourth type of data that we do not discuss here is ESG “sentiment” data based on word frequency/combinations and web/text library scrapping. The latter is a type of data that is not useful in the SFDR context, neither for ESG risk integration or reporting, but it may have value for some specific investment strategies.

The difference: Corporate ESG Data versus ESG Research Data

In these discussions with asset managers, we have touched upon the importance of understanding the difference between these two types of ESG data.

The first type of data is Corporate ESG Data. This is data that is published by companies, or at least is supposed to be and will be as it will to a large extent be required by the CSRD and it will be widely available in planned EU data hubs. The best estimate today for the relevant required CSRD data points is the suggestion by EFRAG and it contains a lot, more here: This is basic historic data that contain no assessments.

The second type of data is ESG Research Data. This is data generated by ESG research analysts based on reporting and communication from the companies and other sources. It is private and will only be available through licence agreements. This is for the most data that contain assessments by the fundamental ESG research analyst of the residual ESG risk after mitigation efforts. For a certain number of the data points, it will also be assessments on issues that indicates how well the companies are equipped to deal with future events and how well they are equipped to succeed reducing their negative impact on the world around them.

Let us exemplify this to make it tangible.

Example 1 – The company claim they have a policy for anti-corruption.

Corporate ESG Data: Yes (alternative is No) – Binary

ESG research Data: How good do we assess it to be to mitigate ESG risks? Possible scale 0 -100, where some points on the scale can be: 0 – The company claims they have an anti-corruption policy, but not public and not described on the web; 25 – The company claims they have an anti-corruption policy, not public, but described on the web, 100 – The company has an anti-corruption policy, it is public and the ESG research analyst consider it very good (ESG analysts know what the latter requires)

Example 2 – The company claim they have an Environmental Management System, but not certified

Corporate ESG Data: Yes (alternative is No) – Binary

ESG research Data: How good do we assess it to be to mitigate ESG risks? Possible scale 0 -100, where some points on the scale can be: 0 – The company claim they have an Environmental Management System, but not described on the web or in documents, none of the required components are in place; 50 – The company claim they have an Environmental Management System, described on the web or in documents, but only half of the required elements are included , 100 – The company has an Environmental Management System, it is well described on the web or in documents and all required elements are included (ESG analysts know what the latter requires)

The use: Only ESG Research Data can be used for complete ESG risk assessments

An important difference between these two types of ESG data, is that only one of them is suitable to be used for a complete residual ESG risk assessment of a company. Only the ESG Research Data will contain risk assessment for each data point that can be aggregated up to a total ESG risk assessment score for a company. This should be done by the aggregator in line with its ESG risk materiality matrix, i.e. sector dependent. This can be done by the ESG Research Data producers, like Sustainalytics, MSCI, sustainAX, etc. or by an asset managers having own materiality understanding. We see a growing population of asset managers making their own ESG risk score based on raw indicator ESG Research Data, mainly the larger ones for the moment.

The use: Corporate ESG data is good for parts of the SFDR required reporting

As stated by EFRAG in their suggestion for the CSRD technical details, all SFDR PAI data points should be included and hence Corporate ESG data will cover this. And this data type can also eventually cover some SFDR binding elements if defined that way. But it cannot be used alone for ESG risk assessment.

The specific product level SFDR requirements and the corresponding ESG data to use
Product level SFDR Article 6

Product level SFDR Article 6 is about ESG risk integration in investment decisions. Here the portfolio managers really need fundamental ESG risk reports made by fundamental ESG risk analysts. These can be accompanied by an ESG risk score built on the earlier described ESG Research Data. But let us be clear here, the ESG score (aka ESG rating) is just like a fever indicator. A doctor will look for the underlying cause for the risks if the indicator is not good, as the portfolio manager should read the fundamental ESG research report to understand what kind of ESG risks there are leading to a bad ESG score. This is the private playground of ESG Research Data. Many investors publish the % ESG risk research coverage of their portfolios as a proxy for proving they integrate ESG risk in investment decisions.

Product level SFDR Article 8

Product level SFDR Article 8 contains several different requirements.

E and/or S characteristics – What type of data required will depend on what E and/or S characteristics are specified and how their sustainability indicators and their binding elements are defined. Here potentially both ESG Research Data and Corporate ESG Data can be used.

Consideration of PAI – The regulation requires only a narrative, but in practice most asset managers will also report on the PAI indicators on a product level, certainly those taking PAI into account on an Entity level, as they are required to have the data anyway. Here Corporate ESG Data will cover it. We need CSRD to be implemented to see the full scope available on a large scale, 2024 for large companies already subject to the NFRD (Non-Financial Reporting Directive to be replaced by CSRD) and 2027 for small caps with an option to extend to 2028.

Good Governance – Here a process description is required, but most asset managers wish to tie this to ESG Research Data showing that the assessment is already done by the ESG risk analyst. Some Corporate ESG Data can come into play here, but it will likely not cover a full assessment alone of the Good Governance aspect. ESG Research Data will cover this fully, with focus on the G part, and potentially including sub-elements of the G factor.

We have in-between the SFDR article 8 and article 9, the SFDR article 8 with a Sustainable Investment “pocket”. Make your own mix of the previous and following sections.

Product level SFDR Article 9

Product level SFDR Article 9 contains several specific requirements, hereunder we deal with the main points that adds versus SFDR Article 8.

The sustainable investment objective, its sustainability indicators and the binding elements will contain all the same categories as above for AFDR Article 8, but also specific elements for the Sustainable Investments. Without opening for the discussion on what Sustainable Investments really mean as per SFDR 2(17) here, this requires firstly a definition of Sustainable Investments by the asset manager and then a definition of the sustainability indicators and binding elements relative to the Sustainable Investments.  Here many types of ESG data can come into play. One is EU Taxonomy data that is Corporate ESG Data and another one is revenue-based SDG impact estimation that is delivered by specialist ESG data vendors. The latter is a type of quant/modelled ESG Research Data, but with lesser company specific research depth. Other ESG Research Data may also be used by some investors here. This is a mine field and that also explains why over 100bnUSD assets have seen their sustainability ambitions being lowered, leading to a change from regulation by SFDR Article 9 to SFDR Article 8.

In addition to this, narratives and ESG data is required to prove DNSH (Do No Siginficant Harm) and here investors can lean on the PAI approach as described above.


Different types of ESG data is required for different use and to answer to different parts of the regulation, hereabove described through the SFDR that is relevant in Europe. And asset managers will need to work with different ESG data types and different ESG data vendors.

Corporate ESG Data will never replace ESG Research Data. This is true, irrespective of how much you alter it through modelling and calculations. You will need the fundamental ESG analyst also in the future at least if you are seeking to understand ESG risk of your investments.

This discussion about ESG data should not blind us for what this is about.
  • All must integrate ESG risks in their investment decisions with target to deliver better expected risk adjusted returns over time, this is required to respect fiduciary duty. This can only happen by understanding the ESG risks through reading fundamental ESG research reports. Staring at an ESG score will not help!
  • ESG data, both ESG Research Data and Corporate ESG Data, is required to show you deliver what you have promised in the precontractual documentation, or with other words; to justify you are not #greenwashing or #socialwashing!

Fundamental ESG research providers do not consider themselves as data vendors, but rather as independent research providers. As asset managers unfortunately still focus mainly on the ESG score data than on the fundamental ESG research reports, they define us as data vendors by mistake. DS.

*Environmental, social and Governance factors

How do asset managers practically differentiate between Corporate ESG Data and ESG Research Data when making investment decisions?

Asset managers differentiate between Corporate ESG Data and ESG Research Data by assessing the source and depth of the information. Corporate ESG Data comes directly from the companies and may vary in reliability and scope, whereas ESG Research Data is derived from third-party analyses, offering a more comprehensive and potentially objective view of a company’s ESG performance. This distinction guides investment decisions, with research data often providing a deeper insight into ESG risks and opportunities.

What specific challenges do asset managers face in aligning with SFDR requirements using both types of ESG data?

Asset managers face challenges in aligning with SFDR requirements due to the varying quality, completeness, and standardization of ESG data. Corporate ESG data may lack uniformity, making comparisons difficult, while ESG Research Data can vary in analytical depth and methodology. The main challenge lies in integrating these diverse data types into a coherent investment strategy that complies with SFDR’s rigorous transparency and sustainability criteria.

Emerging trends and technologies, such as artificial intelligence and blockchain, could enhance the utility of Corporate ESG Data by improving data accuracy, standardization, and comparability. These technologies can automate the collection and analysis of ESG data, making it more reliable for asset managers. Additionally, they can facilitate the creation of a more transparent and traceable ESG reporting ecosystem, potentially making Corporate ESG Data more insightful, but it will never replace ESG Research Data for investment decision-making.