How to Satisfy EU Taxonomy Regulation Requirements with Limited Data

Clarity AI’s EU Taxonomy solution helps investors address sustainability data coverage gaps

There is an obvious disconnect occurring in the market, between what investors need to report as it relates to the EU Taxonomy and what type of data is available to inform that reporting. The lingering question for investors is, how do we broaden the coverage of the data we have while maintaining quality and satisfying the regulatory requirements. 

Clarity AI uses models to take the information that is available and create our best effort analysis to inform the reporting requirement. To bring this modeling to life we have created an example of how we evaluate EU taxonomy activity (See illustration below). We will be using an example of the technical criteria to determine if your investment is making a substantial contribution.

The example is regarding climate change mitigation, specifically, the transmission and distribution of electricity. There are 3 different criteria to evaluate whether the activity is sustainable or not. The first criteria in the regulation is whether the system is an interconnected European system, determining what areas they are selling and what systems they are operating in. If the answer is no, they are selling beyond the interconnected European system then we move to the second criteria in the regulatory text, emission factors. We analyze the average emission for the country in which the company is operating, then we review whether that is below 100gCO2 per KWh. If the average emission factor is not below this figure then we would move to the third criteria, generation capacity. We look at a five year rolling period, per the regulation, and we assess what capacity has been installed during this period, does it meet the technical criteria of below 100gCO2 per KWh. If it does meet the criteria then you comply, if not then it does not contribute to the Taxonomy.

This is a strong real life example of how you can use the limited data available in a systematic and methodical way to achieve results. We pulled together different pieces of information to make an assessment, activity by activity, leveraging the data that is available. Utilizing advanced technology like artificial intelligence, machine learning and Natural Language Processing makes this possible with many different specific regulations when evaluating whether a company meets or does not meet the regulation requirements.