- ESG agencies can assign very different assessments to the same company because scores depend on methodological choices, selected indicators, weighting systems, and analytical objectives.
- Comparing ESG trajectories between companies is even harder than comparing financial metrics, as similar-looking strategies may rely on very different methodologies and data quality standards.
- Two companies reporting similar carbon emissions may face vastly different economic realities, including differences in energy mix, regulatory constraints, and transition investment capacities.
- European regulatory frameworks such as CSRD, ESRS, the EU Taxonomy, and SFDR are gradually improving transparency and comparability, but standardisation remains a work in progress.
- Understanding the limitations of ESG data is becoming as important as reading the data itself, making critical interpretation a genuine strategic skill for finance professionals.
ESG data is now everywhere.
Scores, ratings, rankings, reporting, climate pathways, transition indicators: companies are producing and publishing a growing amount of extra-financial information.
And yet, two ESG agencies can sometimes assign very different assessments to the same company.
Behind the apparent precision of ESG data lies a much more complex reality.
An abundance of data, but not always consistency
The development of sustainable finance has considerably accelerated the production of data: carbon emissions, social policies, governance practices, taxonomy alignment, climate objectives and transition indicators.
This evolution is gradually improving transparency.
But it does not automatically mean that analyses are becoming simple or homogeneous.
Methodologies remain numerous.
Scopes differ.
The indicators selected are not always the same.
Time horizons vary.
And calculation assumptions can lead to very different interpretations.
As a result, two actors may analyse the same company and reach very different conclusions.
Why ESG scores sometimes diverge significantly
This is one of the aspects that often surprises professionals discovering sustainable finance: the same company can receive very different ESG assessments depending on the agencies or methodologies used.
In Horizon & Beyond’s Sustainable Finance MOOC, Emilie Beral, Founder and CEO of Systemik ESG, explains that ESG data is never entirely “neutral”.
Results always depend on methodological choices, selected indicators, weighting systems and the objectives pursued by the analysis.
In other words, numbers never speak entirely for themselves. They always require context and interpretation.
The challenge of comparability
Comparing two companies based on financial criteria is already complex.
Comparing two ESG trajectories is even more challenging.
Two funds may display similar ESG strategies while relying on very different methodologies: sector exclusions, carbon weighting, governance criteria, transition horizons or data quality standards.
On the surface, the approaches may seem comparable.
In practice, interpretations of risk and sustainability can differ substantially.
Emilie Beral also explains that the quality of ESG analysis depends heavily on the quality and robustness of the available data. And on this point, companies do not yet all share the same level of maturity.
For example, two industrial companies may report similar carbon emissions while operating under very different realities: energy mix, resource dependencies, local regulatory constraints or investment capacities for transition.
The numbers may appear comparable.
The economic realities are not always the same.
Regulation is gradually improving the situation
European frameworks such as the CSRD, ESRS, the EU Taxonomy and SFDR are precisely intended to improve transparency, data quality, consistency and comparability.
The objective is to progressively build a common language for sustainable finance.
But this standardisation process is still under construction.
Methodologies are evolving rapidly.
Practices are becoming more professionalised.
And market usages are still stabilising.
The risk of oversimplified interpretations
Faced with complexity, the temptation is strong to reduce ESG analysis to a score or a regulatory category.
But these shortcuts can hide implicit assumptions, methodological differences or much more nuanced economic realities.
Understanding the limits of the data is therefore becoming almost as important as reading the data itself.
A normal evolution for a still-young field
This situation does not mean ESG data is useless or unreliable.
It mainly reflects a field that is still evolving.
As often happens when a new economic language emerges:
- standards evolve,
- data quality improves,
- practices progressively converge,
- and analytical tools become more sophisticated.
A strategic skill becoming essential
As ESG data gains importance in financial decisions, the ability to interpret it correctly is becoming a genuine strategic capability.
Because the challenge is no longer simply to access data.
It is to understand what the data truly says, and what it still does not say.
Going further
👉 Join Horizon & Beyond’s Complete MOOC, developed in partnership with Institut Louis Bachelier and Institut de la Finance Durable, to gain clear insights into double materiality and the transformations shaping sustainable finance.
👉 Join Horizon & Beyond’s Essential MOOC to acquire the fundamentals of sustainable finance in less than two hours.
Frequently Asked Questions
ESG ratings diverge because agencies use different methodological choices, selected indicators, weighting systems, and analytical objectives. As a result, ESG data is never entirely neutral — numbers always require context and interpretation to be meaningful.
Comparing ESG scores is challenging because companies operate under very different realities — such as energy mix, regulatory constraints, and investment capacities — even when they report similar metrics. Additionally, funds and analysts may apply varying methodologies including sector exclusions, carbon weighting, and data quality standards, making surface-level comparisons misleading.
Frameworks such as the CSRD, ESRS, EU Taxonomy, and SFDR are designed to improve transparency, data quality, consistency, and comparability by building a common language for sustainable finance. However, this standardisation process is still under construction, with methodologies and market practices continuing to evolve.
Reducing ESG analysis to a single score or regulatory category can hide implicit assumptions, methodological differences, and nuanced economic realities. Understanding the limitations of the data is becoming almost as important as reading the data itself to avoid misleading conclusions.
As ESG data increasingly influences financial decisions, correctly interpreting it has become a genuine strategic capability. The challenge is no longer just accessing data but understanding what it truly reveals — and recognizing what it still does not capture.
