Making sense of ESG Data: How to Quantify SDG Contribution

Analysis and interpretation– ESG, SDGs and data providers
Due to recent reporting regulations, we can expect the quality, quantity and breadth of data to improve. Historically, ESG data had negative implications for analysis, but new frameworks such as the SDGs demand more forward-looking data. Some of the available ESG data is raw data, for example from annual reports, while other data is estimated, aggregated or sector-adjusted. In turn, the methodology employed by providers in collecting and preparing the data has a significant impact on the outcome, and the methodology often differs in practice. Decision-making can therefore be affected by various frameworks providing different contexts to the actual sustainability level of a company.

As an example, let’s look at a hydropower plant. The company’s overall ESG rating is AA and both MSCI and ISS give the company good SDG scores, while the score for environment doesn’t look so good. The company’s CO2 footprint is high and its water usage categorised as very high. This example shows that ESG ratings and metrics for one company can express opposing views and that ratings may sometimes differ significantly.

Looking forward: SDGs as a framework for impact in liquid assets
Typically, ESG ratings present the current management of ESG risk of a company, and ESG metrics are usually rather backward-looking, e.g. by providing annual CO2 emissions. Therefore, these frameworks are frequently used to exclude less-desirable companies from the investment universe. SDGs however can provide a framework for measuring investments in terms of their positive ecological and social contribution to future goals. They provide a forward-looking essence to ESG investing which makes a new examination of SDG- relevant data necessary.

Multidimensional SDGs–the quantity makes the difference
At Quoniam, we use a total of 53 SDG-relevant data sets from various external providers to look at  a company and its contribution to the SDGs from numerous angles and to bring different dimensions into focus. Within our SDG strategy, information on products, patents, operating processes, environmental costs, corporate governance and controver- sies can be used to measure alignment to the SDGs.

Traditionally, data on product and service categories has predominately been used to calculate the SDG contribution of companies. It is questionable though whether the existence of a product or service justifies a positive rating. Therefore, operational processes, controversies and geographical aspects are increasingly considered in the assessments. The data we receive on this, as well as on external environmental costs, provides information on a company’s current status regarding its contribution to the SDGs and can be described as more backward-looking.

With respect to fulfilling the SDGs by 2030, we also consider forward-looking data such as patents and management commitment.

Patent information is also a useful dimension to sustainable sales data from an SDG perspective as it takes into account future products in addition to existing ones. Companies often patent new technologies and functionalities before a product is ready for market. This can create a competitive advantage or generate a monetary return through licensing. If such a patent also contributes to one of the SDGs, there is an attractive opportunity for impact-oriented investors: a potential return on investment with simultaneous alignment with the SDGs. For example, a company researching fuel cell technology currently generates only marginal revenues, but on the other hand it owns promising patents that in the future will contribute towards SDG 7 “Affordable and Clean Energy” and 13 “Climate Action”. Conversely, a wind power operator offers a variety of SDG-compatible services, but typically owns relatively few patents. Additionally, we believe that a management commitment towards SDGs also impacts future alignment with SDGs and can foster sustainable business models.

As quantitative asset managers we can calculate an overall SDG score utilising statistical methods to merge these various data types while considering the context of the data. For example, patents generally have a positive impact, while external costs are considered solely as negative impact. During portfolio construction it is possible to increase the exposure to the SDG score as part of bottom-up stock selection.

Mastering the data
As mentioned earlier, we see the biggest challenge in processing large and complex amounts of data and extracting the relevant information efficiently for individual frameworks. Research has shown that due to the complexity of ESG, it is necessary to include all aspects and perspectives. This ensures that significant divergences will not impact the ESG objectives of client portfolios.

At Quoniam, we believe that we can capture the complexity of sustainability by utilising vast amounts of data simultaneously. Therefore, we have focused our research on broadening our knowledge of ESG data and understanding its interdependencies. By analysing different types of data, we have calculated our own ESG composite as well as SDG score.

In an era of increasing data on ESG, quantitative asset managers have an advantage. They are experienced in the collection, analysis, interpretation and transformation of vast amounts of data into useful information. They can guide their clients and help them make the right decisions for their portfolios.

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