Aligned Green, Social, Sustainability, Sustainability-Linked and Transition (ESG bonds or GSS+) finance volumes passed the $4trillion mark in H1 2023, according to the Climate Bonds Initiative (CBI). But how green is green? And how confident are fund and asset managers about the quality and integrity of the data required to support Environmental, Social and Governance (ESG) fund management decisions?
Under the latest release of the European Union’s Sustainable Finance Disclosure Regulation (SFDR), which imposes mandatory ESG disclosure obligations for financial markets participants, fund and asset managers are required to provide more detailed disclosures to justify the categorisation of their light green Article 8 (environmental and/or social characteristics) and dark green Article 9 (environmental and/or social objectives) funds. Yet a lack of confidence in data, combined with the use of multiple different standards, raises fears about non-compliance leading some to rebrand dark green funds as light green simply to mitigate the risk of punishment – a response that risks to fundamentally undermine the visions of ESG-led investment.
With a better approach now vital, Maria Vigliotti, CEO at Fidata and D2 Legal Technology’s Akber Datoo and Elliot Curtis discuss the power – indeed, necessity – of technologies which enable financial market participants to have a common ESG understanding and support greener investment decisions. Is this the moment for AI?
ESG Imperative
Analysis from McKinsey estimates that the investment in new infrastructure and systems needed to meet international climate goals could be $9.2 trillion a year annually through 2050 – a figure estimated to be at least $3.5 trillion more a year than the current level of investment in both low-carbon and fossil-fuel infrastructure and changes in land use.
The EU is leading the world in its commitment to achieving change and the financial services market is recognised as a vital element in driving the fight against climate change. The SFDR is key to achieving the level of investment required to support innovation and compel companies to evolve towards more sustainable operations. It is fast-becoming the pre-eminent financial regulation in respect to combatting greenwashing, building on the work already done at the retail/product level to hold companies to account for all their activities.
However, the lack of trusted data remains a massive concern that is without a doubt constraining the pace of ESG-led change. This is a transition which cannot be achieved without the correct technology enablers. If the investment industry is not confident in the quality of data or, critically, able to make valid comparisons between business ESG performance, global opportunities to deliver on environmental strategies will be lost.
Inconsistent Data and Multiple Standards
A lack of consistent data is somewhat inevitable given the immaturity of the ESG market. However, adding to the problem is the arrival of multiple, incompatible standards and frameworks that are making it incredibly difficult for fund and asset managers to confidently compare performance. Are companies measuring their performance based on the Climate Bond Initiative’s (CBI) Climate Bonds Taxonomy, the EU’s taxonomy for sustainable activities or the International Capital Market Association’s (ICMA) Green Bond Principles?
Each of these – and there are many others evolving across the world – sets out a framework for disclosing and reporting on ESG bonds, but each has a different set of requirements which make it very difficult, time-consuming, and expensive to truly understand comparative performance.
Incompatible Comparative Performance
This problem affects every part of the hugely complex and diverse ESG-led investment concept. Take the area of buildings renovation to reduce carbon emissions, for example. The CBI requirements will demand different approaches depending on whether the building is new or old, commercial or residential, and where it is located across the world. This requires a significant amount of work to assess different impacts in France, for example, compared to Australia.
An alternative approach could be to adhere to the ICMA model, which simply considers the site and the carbon reduction level. Meanwhile, the EU taxonomy demands adherence to the relevant national standard legislation. In some cases, this will allow the creation of an Environmental Performance Certificate (EPC) without the need to measure carbon reduction. While each approach is legitimate, they are significantly different. So how are fund and asset managers to confidently make comparisons?
In theory, the problem could be resolved if funds reported to all three standards, but the cost would be prohibitive given both the lack of skilled resources in this nascent area and difficulties in securing relevant, consistent data. Essentially, while the thinking behind each taxonomy and model is laudable, the existence of more than one so-called standard is creating a significant and unnecessary barrier in a vital area of investment.
Engagement, Methodology and Technology
Consistency is essential if the ESG bond market is to fulfil its potential. Fund and asset managers require an effective methodology to enable true comparison of performance. Using the right taxonomy, it is possible to overcome the fragmentation and provide valid information that not only supports fund management decisions, but also compliance with regulations such as the SFDR.
Today, fund and asset managers are rightly nervous. They are concerned about the reputational risk and the real prospect of fines for SFDR non-compliance and, as a result, there are increasing reports of billion-dollar downgrades, where dark green (Article 9) funds are being downgraded to light green (Article 8) as part of a risk mitigation exercise. But this is hugely damaging the objectives of sustainable investment. It undermines the value of ESG activity and limits the investment opportunities.
Rather than worrying about SFDR fines or undervaluing genuine ESG investment, the onus is on fund and asset managers to find better ways to measure and compare performance. This is the time for systems which will be able to algorithmically traverse across these differing standards and frameworks, assessing, cleansing, standardising, and only then utilising the data to make the right decisions. Doing this at scale necessitates complex algorithms combined with a deep understanding of this area of ESG. A number of AI approaches (Natural Language Processing (NLP) and ESG machine learning classifiers for accurate date structures) are already delivering results in this area. Deploying AI tech to detect greenwashing is an innovation which is valued highly by the Financial Conduct Authority (FCA), as illustrated by their recent announcement of the winner of the Global Financial Innovation Network (GFIN) Greenwashing TechSprint. The regulator’s recognition of the benefits of a technology-led approach in this area provides a strong signal to the market.
Conclusion
The EU is at the vanguard of sustainable investment and, as such, the world is watching. Greater international consensus is clearly required to accelerate investment in innovative ESG activity and that will only be achieved when fund and asset managers globally have the data, methodology and systems required to confidently discharge their responsibilities.
Regulation and market maturity go hand in hand. Over the next 12-months, fines and cases of reputational damage will no doubt hit the market, unless there is a cautious downgrading of funds. Now must be the time to implement the right methodologies and systems, or fines will begin to roll as regulators call out greenwashing. But now is not the time to dial back. It is essential to move the dial on sustainably-driven activity and the market opportunity is enormous. Fund and asset managers that proactively embrace a methodology that brings together different standards and data sources will not only safeguard SFDR compliance, but also achieve new levels of investment clarity.