ESG Data Guide 2023

Accelerating progress with AI

Clarity AI outlines how the firm seeks to facilitate meaningful data collection and analysis, and accelerate progress toward a sustainable future.

Environmental Finance: Can you outline your approach ESG data collection? How does it differ from others?

Clarity AI employs a diverse and extensive range of sources to build a comprehensive database. In addition, we utilise proprietary estimation models to expand our coverage by incorporating information that organisations may not disclose.

We leverage technology to streamline the data and ensure quality. For example, when dealing with unstructured data, including text, pictures, and tables, typically presented in a PDF format, we use AI to extract information rapidly and at scale.

Machine learning and AI play a crucial role in enhancing our data coverage. We employ modelling techniques to capture metrics that companies may not report. By correlating a company's actions with its emissions, we can significantly improve coverage for specific metrics. The growing pool of reporting companies allows us to continually enhance the accuracy of our models.

We also integrate satellite imagery techniques. This allows us to gather more precise and up-to-date data, such as identifying power plants and cement factories that contribute to emissions, thereby enhancing the accuracy of our prediction models.

Finally, our team of experts not only includes data scientists; we also have a team of sustainability experts who create the methodologies and make sure the data is understood in the right context.

EF: Investors are increasingly concerned about biodiversity impacts and dependencies. What work have you done in this area?

In collaboration with GIST Impact, a prominent impact data and intelligence provider, we have launched a biodiversity impact assessment for investors. This partnership combines our unique tech capabilities and ecosystem of sustainability solutions with GIST Impact's biodiversity impact methodology and data. Together, we aim to offer investors access to reliable and comprehensive biodiversity data that is company-specific, geographically accurate, and encompasses a holistic range of biodiversity impact drivers. This dataset plays a critical role in effective risk management and supports efforts to mitigate further loss of natural ecosystems.

We leverage technology to ensure quality once again. Using reliability algorithms and models, we can challenge data points and consider factors such as historical trends, industry benchmarks, and other relevant indicators. This helps identify outliers and supports engagement efforts with companies to improve their disclosure and data.

Our technology also facilitates data aggregation from the asset level to the portfolio level. This automation seeks to allow users to manage their portfolios and make well-informed decisions regarding the inclusion or exclusion of companies based on their biodiversity impact and risk profiles.

EF: You’ve announced a series of strategic partnerships. What is the thinking behind this?

Our collaborative approach is driven by our mission to bring societal impact to markets. The challenges surrounding data, methodologies, and tools in the sustainability space require a combination of resources and capabilities. As such, we’ve integrated our technological infrastructure with various platforms and systems, including Aladdin by BlackRock and SimCorp, thereby enhancing the accessibility and usability of our data and methodologies. Our partnership with LSEG, focuses on expanding the reach of our Sustainable Finance Disclosure Regulation (SFDR) data coverage and reporting capabilities.

In the case of our partnership with CDP, their expertise lies in collecting and refining raw climate data. This partnership allows us to leverage their specialised knowledge to ensure the quality and accuracy of climate-related data. We want to ensure we are providing the best solutions possible to address any sustainability need.

EF: Your study on sustainable funds found that very few are likely to meet sustainable investing rules designed to address greenwashing. Was what that based on, and what are the implications?

As different regulatory frameworks for sustainable investment funds - besides SFDR in Europe - are developed we wanted to quantify the impact for funds being marketed as sustainable. Our Research team examined these proposals, drawing on data from over 18,000 funds across Europe.

The first finding is that most funds with sustainability or ESG terms in their names currently do not comply with any of the proposed naming rules in the regulations. The second finding is around the heterogeneity of the regulations. We found that a high share of funds with sustainability terms in their names will not be able to market themselves in a consistent manner across the – the US, UK and Europe – but funds with ESG terms in their names will not face the same problems.

This lack of alignment between the three jurisdictions could lead to difficulties for asset managers and fund distributors who are attempting to market the same ESG fund across different jurisdictions. This may mean that the level of sustainability of a given financial product may depend more on where it is located, than its characteristics.

This represents an added cost in terms of compliance and underscores how different actors – in this case, regulators – are interpreting ESG and sustainability.

We need stronger regulatory alignment across borders. Misalignment will only contribute to confusion in the market and potentially result in exactly what these regulations aim to fight, greenwashing.

EF: What are the next challenges in ESG data collection and analysis?

In a rapidly evolving world, timely data is essential. And with the emergence of new regulations, more data will come. The ability to efficiently handle and interpret large volumes of data will be vital for accurate analysis and decision-making. Transparency also plays a crucial role in overcoming confusion and promoting effective decision-making.

Interoperability poses another challenge. At Clarity AI, we recognise the importance of leveraging advanced technology to tackle these challenges. We believe that technology provides the necessary flexibility and accuracy at scale to keep up with the pace of change in the ESG landscape. And we prioritise transparency by offering an unbiased view of portfolio sustainability. By doing so, we can overcome barriers, facilitate meaningful data collection and analysis, and accelerate progress toward a sustainable future.

Guide entries by Clarity AI