Understanding ESG Factor Performance Across SFDR Classifications
Christopher Lee, Consultant, Impact Cubed
Note: The views expressed on these pages are the opinions of their respective author(s) only and do not necessarily reflect the views and opinions of UKSIF.
This website should not be taken as financial or investment advice or seen as an endorsement or recommendation of any particular company, investment or individual. While we have sought to ensure information on this site is correct, we do not accept liability for any errors.
Understanding ESG Factor Performance Across SFDR Classifications
Christopher Lee, Consultant, Impact Cubed
The Sustainable Finance Disclosure Regulation (SFDR) aims to guide investors towards funds that match their values. However, the impact of these classifications on funds’ environmental and social performance raises essential considerations. This analysis explores how Article 6, 8, and 9 funds perform across key ESG factors, with a focus on tracking error implications.
Net Impact Distributions Across SFDR Articles
Data shows that Article 9 funds, mandated to pursue sustainable investment objectives, exhibit a notably positive net impact, with a mean of 65.12 BPS. Article 8 funds, which promote ESG characteristics without a sustainability mandate, show a lower average net impact of 18.97 BPS. Article 6 funds, which have minimal ESG focus, average -2.26 BPS.
Although Article 9 funds outperform on average, there is a broad distribution, underscoring performance variability even within similar classifications.
Environmental and Social Alignment and Misalignment
Article 9 funds show a higher percentage of revenue from environmentally and socially beneficial products, consistent with their high net impact scores. Nevertheless, Article 9 funds are not entirely free from activities with environmental or social drawbacks. This trend underlines the challenges in achieving complete ESG alignment, even within the strictest SFDR classifications. Article 8 funds, more variable in their alignment, exhibit a broad range of ESG integration approaches, while Article 6 funds predictably show more ESG misalignment.
Carbon, Waste, and Water Efficiency: A Deeper Look
Article 9 funds typically lead in carbon efficiency, with Article 8 funds outperforming Article 6 to a lesser extent. Yet, results differ in waste and water efficiency. Some Article 9 funds, especially those invested in water-intensive renewable sectors, demonstrate lower water efficiency compared to Article 8 funds. The emphasis on renewable energy within Article 9 funds, while reducing carbon emissions, may contribute to higher water usage due to sector-specific needs.
Slightly more technical – Tracking Error and Carbon Efficiency
Our first analysis focused on the relationship between tracking error and carbon efficiency across the three SFDR classifications.
Analysing tracking error’s influence on carbon efficiency reveals that Article 9 funds generally maintain lower emissions due to their stringent ESG mandates. However, at higher tracking error levels, Article 8 funds sometimes exhibit better carbon efficiency due to more flexible constituent requirements. Article 6 funds, in comparison, remain higher emitters across the spectrum. These findings highlight the importance of data-driven insights into real ESG impact, rather than relying solely on ratings.
Water and Waste Efficiency
Next, we examined water and waste efficiency. The relationship between tracking error and these two metrics provides further insights into the environmental impact of these funds.
Further examination of water and waste efficiency reveals that Article 9 funds use more water on average than their Article 6 and 8 counterparts. This may seem counterintuitive, given Article 9’s focus on sustainability, but can be partially explained by allocations to renewable energy, which, though beneficial in reducing emissions, often involve water-intensive processes like CSP (concentrated solar power).
Waste efficiency shows similar variability, with Article 9 funds exhibiting higher figures, likely due to investment in recycling and waste management sectors, where waste processing is significant.
Net Impact Analysis
Finally, we explored the net impact of these funds, which offers a more holistic view of their overall contribution to societal and environmental well-being.
When we remove +/- 200 net impact funds, we get a more refined view that’s easier to analyse.
When adjusting for outliers (e.g., net impact beyond +/- 200), the relationship between tracking error and net impact varies by SFDR classification. Article 6 funds show consistent but low net impact, reflecting minimal sustainability focus. Article 8 funds, representing a balanced approach, see some net impact increase with tracking error but lack extreme variability. Article 9 funds display more pronounced and variable net impact, revealing trade-offs associated with high sustainability targets.
Interpreting Water use in Article 9 Funds
Higher water usage in Article 9 funds might seem contradictory to their sustainability commitments. However, the inclusion of sectors like renewable energy, which are crucial for emission reductions, involves trade-offs, including increased water demands. CSP and certain bioenergy processes require significant water for cooling, illustrating the complex realities of ESG investing.
Broader Implications
While Article 9 funds align more closely with high sustainability standards, investors should remain aware of the trade-offs between ESG goals. For example, carbon reduction can conflict with water efficiency, emphasising the importance of transparency in ESG reporting. Fund managers must consider sectoral allocations carefully to manage environmental trade-offs and ensure that carbon-focused strategies do not overshadow water and waste management.
Methodology note
We took every Article 6, 8 and 9 fund on our platform (over 8,000 funds) and examined the below ESG factors:
• Carbon efficiency (measured in tonnes per \$1M revenue)
• Water efficiency (measured in 1000 cubic meters per \$1M revenue)
• Waste efficiency (measured in tonnes per \$1M revenue)
• Net impact (measured in basis points)
• Socially good/bad revenue
• Environmentally good/bad revenue
Where applicable, regression lines—linear and quadratic—were added to better understand trends. Outliers were filtered to ensure the data’s readability.