In attendance at the October forum were representatives from a number of financial services and data analytics companies. The discussion exposed the challenges that many companies face as they navigate the ESG data dilemma, stemming from the complexity and volume of reporting that many companies are required to produce. Sadly, there is often a perception that this reporting is done for the sake of compliance, rather than to drive meaningful progress in sustainability. A stark statistic was shared to set the tone for the following conversation: only 10% of European countries believe that the ESG data produced is truly reliable.* This highlights a lack of trust in the data being reported, compounded by the complexity of data collection and validation. Indeed, one participant commented that ESG data is often less about “collection” than it is about “creation”.
ESG REPORTING
The conversation began with a discussion about the various global reporting frameworks and standards, particularly focusing on the disjointed regulatory landscape. The EU’s Sustainable Finance Disclosure Regulation (SFRD) and Corporate Sustainability Reporting Directive (CSRD) were cited as primary examples of complex frameworks that companies struggle to navigate. Companies operating in multiple regions such as the UK, EU and US must grapple with varying sustainability reporting requirements. To add to the confusion, each of these regions have their own evolving standards, and with the impending American election on the horizon, the US commitment to sustainability is uncertain. However, the panel suggested that global companies should at least comply with EU regulations because they establish what is currently considered the “gold standard” in sustainability reporting.
Benchmarking was highlighted as a tool that can be very useful in reporting, as it allows companies to compare their performance against others within their industry, identify areas for improvement, and enhance their transparency of data. However, comparisons are only meaningful at the sector level and the lack of reliable data at that level is a challenge. Undoubtedly, there is a clear need for both greater harmonization across member states and better sector-level data to better deal with a fragmented reporting landscape that is confusing and costly.
In recent years, there has been a noticeable shift in focus beyond the E element of ESG to a more holistic understanding of sustainability, particularly extending to supply chains and social factors. However, this has also introduced the challenge of greenwashing, where companies may make ambitious sustainability claims that they cannot fully support. Companies are encouraged to be transparent with their data, but the risk of overpromising and underdelivering is real. Public scrutiny is intense, and if businesses fail to back up their claims with robust data, their credibility and reputation can be put at risk.
ESG METRICS
The second theme discussed was the challenges of ESG metrics and data. The quality of data is a recurring concern -- with businesses striving to provide accurate, reliable information. The concept of “double materiality” is increasingly gaining attention, evaluating both risks and opportunities from a sustainability perspective. Having metrics that capture both dimensions is key. However, the collection and validation of this data is often complex and requires significant resources. And while some areas of ESG data, such as climate-related information are in high demand, metrics related to biodiversity and nature often go overlooked.
There is also an increasing demand for third-party assurance to validate ESG data, which can be critical for increasing trust. However, the quality of the input will ultimately affect the quality of the audit that is done, so cleaner and more transparent data is better for the provider. Despite this, there is no universally accepted standard for measuring non-financial factors like social value -- some companies have opted for a narrative style approach, rather than providing numbers, in order to provide better visibility into their efforts.
The cost of collecting and validating ESG data can be significant. SMEs face significant barriers in meeting these growing demands. The costs associated with adhering to complex ESG standards can be prohibitive, leading to a steep learning curve for many businesses. This has created a clear divide between companies with the resources to proactively comply with ESG requirements and those that are scrambling to meet basic standards. It can be difficult to compete with multinational corporations that have the capacity to hire climate scientists and other specialists on their teams. Additionally, while the push for more detailed ESG data is accelerating, there is a recognition that some standards, like those set by the Science-Based Targets initiative (SBTi), continue to be overly complicated, constantly changing, and expensive to implement. There is a balance to be struck here because the more sophisticated the measures, the more reliable the data; however, the more complex the data collection, the less people will adhere to it. Although, the panel concluded that proactive preparation and adhering to standards early can be a strategic move, if you have the cost to do it.
TECHNOLOGY TOOLS FOR ESG DATA COLLECTION AND VALIDATION
The third theme focused on was technology’s role in ESG data management. While artificial intelligence (AI) and other advanced technologies hold promise in improving the collection and analysis of sustainability data, there remains a degree of scepticism about their current capabilities. AI, with its predictive analytics and ability to handle large volumes of data could be a game changer for ESG reporting because solutions are often slow and quite costly. As made previously evident, the sustainability workforce is often restrained by capacity, so large language models could allow smaller companies to distil mass amounts of data. However, it is still out of reach for many smaller companies due to the high costs involved. Many believe that AI will get cheaper over time, but at present, tools like Excel remain the most widely used for ESG data management.
Despite the optimism around AI’s future role, there are valid concerns about its environmental impact. For example, the water consumption required to power AI models, and the carbon emissions generated by the vast computing resources needed, pose sustainability challenges in their own right. Therefore, while AI has the potential to revolutionize ESG data reporting, it also raises important questions about its own sustainability and its impacts for the future.
IN SUMMARY
To conclude, there is a paradox facing companies today. Businesses are expected to be transparent and ambitious in their sustainability goals, but they also face the risk of overextending themselves and being held accountable if they fail to meet those goals. There is a delicate balance to be struck between being aspirational and realistic.
As the industry continues to evolve, ESG metrics will remain a moving target, with increasing demands for transparency and accuracy. Ultimately, businesses will need to carefully navigate this evolving landscape and ensure that they can deliver on the promises they make to their stakeholders, while also ensuring that they are driving a positive impact. And while AI has the potential to aid in sourcing data, improving its reliability and improving efficiency, most companies have yet to realise those benefits and continue to rely on unsophisticated tools like Excel. Key to getting the full advantages of AI will be higher levels of data standardisation across jurisdictions.
*Reuters IMPACT Sustainability Survey, April 2024
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