Reflets Magazine #152 | Sustainable Change and the Role of ESG Data

In Reflets #152, Cédric Baecher (E00), partner and co-leader of sustainability practice at Wavestone, along with Bettina Grabmayr, Head of Research and Methodology at EcoVadis, and Emmanuel de La Ville, founder of EthiFinance, explore the role to be played by ESG data in environmental transition. Here is a free online translation of an excerpt of the article… subscribe to get the next issues (in French)! 

The demand for ESG data is set to soar

The acceleration of sustainable change imparts an increasingly central role on ESG data (environmental, social and governance) in business, as a way to assess the global performance and longevity of companies. Like legal and financial data before it, the growth of ESG data fuels and objectifies discussions between businesses and their stakeholders, enabling investors and lenders to evaluate the risks and opportunities linked to energy, environmental and societal transition.

Demand for ESG data is set to soar in coming years, due to at least two factors. Firstly, the bringing into compliance inherent to new regulations (CSRD, European environmental taxonomy and SFDR). Secondly, the imperative need to adapt business models, given that ESG data will constitute an increasingly important aspect of sovereignty and business continuity, both for companies (identification of priority areas for transformation, effective allotment of resources, change management, etc.) and investors (directing capital towards activities more in keeping with the Paris Agreement, more durable and profitable in the long term).

The ESG data market will become specialised, with providers of advanced expertise and increasingly sophisticated measurement methods to deal with the complexity, multiplicity and interdependence of various issues (human, climatic and socio-economic, etc.). Some players will opt for imperfect, but rapid and massively available data (‘off the shelf’ ESG databases by sector or geography, etc.), with recourse to proxies and estimation models, at the risk of disconnecting themselves from on-the-ground realities. Others will prefer to prioritise the use of data which reflects reality as closely as possible (through sensors and other adapted tools, for example).

Sustainable change will become increasingly reliant on vast chains of ESG data. The collection, verification, processing and securing of this data will require ever more costly means, creating and deepening inequalities between small and large businesses.

Information systems (IS) will also have to follow suit, and the players who are already making headway with their digital transformation will gain a competitive edge in adapting to this new paradigm. The future role of the various players is yet to be defined, between EPM (Enterprise Performance Management) tools, ESG data platforms, and specialist systems such as CMS (Carbon Management Systems). Many companies plan to create ESG ‘data lakes’ (i.e., centralised repositories storing structured and non-structured data on various levels) and are beginning to work on the interconnection of their procurement systems and their suppliers’ IS, in an effort to secure the transmission of ESG data to their Scope 3 (greenhouse gas emissions indirectly linked to a product / service, both upstream and downstream in the lifecycle). Concentration movements will help us to achieve economies of scale along the entire value chain (producers, auditors, ESG data analysts, etc.).

Frameworks of trust are still in their early days

In addition to this quantitative growth in demand, investors, funders and insurers will become more attentive to the quality of ESG data provided by the market. Their priority will be to understand and compare in detail the relative performance of each company according to a set of criteria (business sector, size or geographical locations, etc.). In a competitive environment, the ability to produce reliable, comparable ESG data constitutes a key factor in attracting funding and capital. Markets will demand more audits and uniformity, to strengthen comparability (comparability which varies according to topic; while well advanced in terms of carbon, the question of biodiversity is more complex).

The gradual coming-into-effect of CSRD could tempt us to believe that the standardisation of ESG data will be a simple task. However, the frameworks of trust are still being structured and merged. Businesses face three major challenges:

1) Alignment and coherence: various managements and entities use calculation hypotheses and application scopes that are too diverse to be compared, yet the data is ultimately integrated in a single, consolidated report. 

2) Traceability and clarification: many companies underestimate the importance of systematically documenting and capitalising on their methodological choices, which form the fundamental baselines for the production of ESG data and continued improvement of the reporting process. 

3) Arbitration between transparency and security: ESG data reveals a great deal of information on the business model, and some sectors are concerned about the consequences for the security and protection of their customers’ interests.

These challenges raise major issues in terms of governance and organisation, and are even more difficult to manage in companies with highly-decentralised operations. The implementation of trust frameworks requires clarification of internal procedures and the services of independent auditors, agencies and reviewers. The latter act as third parties of trust in the securing of data quality, its coherence and the sincerity of calculation methods.

Major auditing companies will have an essential, but insufficient role to play. It appears vital to preserve a matrix of more modest, independent players, in order to contribute to the resilience of the whole system (e.g., to verify data produced by the SME which represent the majority of the economic fabric and supply chains). Several observers foresee a future rise in disputes and growing judicialization of ESG data, due to NGOs and investors who believe they have been deceived (e.g., by underestimation of the CAPEX level required to achieve a carbon neutral target).

Interpretation skills will make the difference

At the interface between ESG data quality and quantity, the question of its analysis and interpretation remains unresolved. A significant portion of the value of an item of ESG data resides in the ability to place it in a specific context (sector, geography, etc.), taking into account the error margins which must be clearly displayed (and in the future may be communicated systematically, thus becoming ESG ‘metadata’).

Rating agencies and investors will change their operating methods and call on the skills of increasingly specialised analysts, capable of putting company ESG data in perspective, shifting from quantitative scoring to more prospective, predictive approaches. Jobs involving the collection, verification, comparing and auditing of ESG data will speed up their specialisation.

In the early 2000s, practically all ESG data was qualitative. At present, the interpretation of our world has become increasingly complex, and we are forced to add more quantitative elements, which could lead to the (misguided) impression that they will be simpler to interpret. The challenge is considerable, even more so given that it draws on a reverse pattern to that which has prevailed in the financial data world, starting from unified quantitative data to be added later to qualitative data.

Technology will play an ever-greater role (artificial intelligence, blockchain, etc.) in producing, comparing (machine learning techniques enable the linking of vast amounts of parameters and information to establish the reliability of data relevant to a company’s specific situation), analysing and using ESG data, ensuring its traceability, and accessing it more rapidly and efficiently. Tools such as ChatGPT or Copilot will provide part of the solutions, even if their use has yet to be defined. 

AI nevertheless raises 5 key issues related to companies’ ESG performance measurement: the reliability of basic datasets, model transparency, adaptability to sectoral specificities, risk management (linked to decision-making processes) and interactions with human expertise (to ensure a balanced view and limit bias). 

In conclusion, ESG data forms one of the primary strategies in the quest for sustainable change, at the heart of a battle which has only just begun. It took a hundred years to stabilise financial data, but we have less time ahead of us to structure and organise the new reign of ESG data. We must be careful, however, not to create the illusion that everything will be controllable and quantifiable; life on Earth and human dignity will always remain more complex than a balance sheet.

Translation of an excerpt of an article published in Reflets Magazine #152. Get the next issues  (in French).

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