Monitoring ESG data in the future

February 20, 2023
Piotr Biernacki
Sustainability Managing Partner
A week ago, we were considering how the ESG data monitoring process could be improved today. It is impossible to implement it without the personal involvement of many people responsible for providing source data in all companies within a capital group. What will the future bring?

The main drivers of change will be the CSRD directive and ESRS standards. A fully standardized scope of information that must be included in a sustainability report will make it possible to develop a complete and ready-to-use IT system to support data monitoring. The broad scope of the directive will prompt many software providers to compete for this market. After all, there are 49,000 companies across the European Union, nearly five times more than under the current NFRD directive, which, moreover, did not enforce standardization. This will cause some chaos, and many solutions will emerge, but after a few years, the two or three most user-friendly and affordable ones will probably remain on the market.

The detailed nature of the ESRS standards will contribute to data being identified and collected at an earlier stage within organizations. Since reporting in accordance with ESRS E1, under disclosure requirement E1-6, paragraph 41(a), a, i.e., reporting gross greenhouse gas emissions in scope 1, I need, among other things, data on the fuel purchased by the organization, and I know that this data point in the report will be tagged with a specific marker, then there is nothing to prevent the company's financial and accounting system from appropriately marking the type and quantity of fuel purchased when recording the invoice. The relevant information can then be provided to the ESG data monitoring system on an ongoing basis. We eliminate a significant amount of work on an annual basis and errors that can be made by a person transcribing data from invoices to cells in a spreadsheet.

The digitization of reports in the era of the CSRD will also enable the automation of data collection from the value chain. All sustainability reports will be available in a central EU repository, the European Single Access Point (ESAP). If I need information for my report, e.g., on my supplier's greenhouse gas emissions, I will not have to ask them for this information, but my data monitoring system will retrieve it automatically. Of course, this will not solve all problems, because only data from reporting companies will be available in ESAP, and I will have to obtain information from most contractors outside the EU and from many small and medium-sized enterprises as before. If I report in March and some of my contractors report in April, the data from the previous year will be available when I prepare my report. We do not yet know when ESAP will be fully operational, but we can expect it to be in the second half of the 2020s.

Technology enthusiasts predict that artificial intelligence systems will soon solve the problem of obtaining any ESG data. I approach these predictions with great caution, not because I lack optimism, but out of prudence. Tools such as ChatGPT and DALL-E have become successful precisely because they have been „fed” with a huge amount of source data. Today, when ESG data from companies is lacking, and what is available is usually incomplete and unreliable, such systems would have no material on which to generate relevant results. However, once the data is available (after a few years of CSRD and ESAP operation), the problem of the reliability of information generated by artificial intelligence systems will remain. Auditors will be the first to face this problem; I eagerly await the first audit opinion in which a certified auditor confirms or refuses to confirm a report containing data provided by AI.

We have at least a few interesting years ahead of us, which will gradually bring about important changes in how we monitor ESG data. The solutions we will be using at the end of the 2020s not only do not yet exist, but have not even been designed yet. In this turbulent period, I believe it is worth improving the solutions we use today and testing new ones, but it is too early to conclude that we can solve the problem of high-quality data in one ideal and definitive way.

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