News & Events

The Biodiversity Data Paradox for Finance

October 25, 2021

At the recent Climate Finance Week in Ireland, a new report presented by Irish Funds highlights that despite a high level of availability of climate-related data for the Sustainable Finance Disclosure Regulation’s (SFDR) Regulated Technical Standards, Financial Institutions (FIs) are left wanting on biodiversity, where availability from data vendors is far lower. These findings mirror a survey by Credit Suisse which found that 70% of investors believe a lack of available data is a key barrier to making investments supporting biodiversity.

However, both findings appear as a paradox in an era defined by explosive growth in volumes of Earth observation data and the continuous launch of remote sensing satellites with ever-greater image resolution, with the number of biodiversity databases currently available exceeding 250 according to UN WCMC.

The biodiversity data requirements coming out of the new SFDR rules are difficult to address without changes to today’s mainstream ESG data practices, which are currently defined by company questionnaires and web-scraping of large amounts of unstructured and self-disclosed data. It calls for inclusion of data harvested from the real economy via observations of company behaviors and/or self-reported data at the asset level. However, the data foundation needs to be available for adoption of such multidata layer approaches by financial service institutions.

Digital data moving to center stage in Disclosures

The SFDR signals a shift in how regulators look at data away from only defining and requiring disclosure of metrics, towards a demand of access to greater transparency on the types of data leveraged for such disclosure. Article 40 of the SFDR text stipulates that disclosures need to describe the data sources used, measures to ensure data quality, how data is processed, and the proportion of data estimated. Whether article 40 will end up incentivising FIs to gradually shift towards using more asset level data sources, rather than modelled data based on sector averages, remains to be seen. For the biodiversity-related mandatory disclosures under SFDR, this could end up becoming the case as biodiversity risks are much more location specific than climate risks.

Now the question is how FIs can start to develop such a multilayer data strategy composed of modelled data available today and adding in more asset level data as it becomes available.

Data vendors have patchy coverage of principal adverse indicators for biodiversity, water emission, hazardous waste compared to GHG emissions, as this data vendor survey from Irish Funds shows.

Source: Irish Funds – Principal Adverse Impacts Reporting Table1.

Different asset classes, different biodiversity data readiness

A lack of biodiversity data availability isn’t the problem; the greatest challenge lies in the fact that these data sets are often difficult to link to the geolocation of company activities. This is the reason for the paradox of large amounts of data available on the one hand while data vendors and FIs struggle to access investor-relevant biodiversity risk data on the other hand.

Linking biodiversity datasets to company activities is more difficult for some asset classes than for others. Data is easiest to come by for listed equities based on companies operating physical assets such as shipping or real-estate companies, or for companies in licensed industries such as renewable energy. For these assets the data foundation available is thicker than for listed equities in other sectors.

One reason for this is that for licensed industries, such as renewables, asset geolocation is available in public registries. Another reason is that regulation is increasingly making physical assets intelligent via integration of IoT, and sensor devises into the physical asset which gives these assets self-reporting capabilities. Lastly, the current increase in open-source earth observed data sets is making it possible to verify reports and track impacts over time of assets operating close to or within biodiversity protected or sensitive areas.

One such data repository is Copernicus, containing satellite data from the European Space Agency. This observed data could be used by FIs in addition to modelling under article 40 of SFDR and other disclosure requirements.

The data linking challenge and opportunity

Currently one of the draft mandatory Regulated Technical Standards on biodiversity requires disclosure of the share of investments in investee companies with sites/operations located in or near to biodiversity sensitive areas, where activities have a negative impact. This requires the linking of three datasets:

  1. Asset geolocation data of company activities
  2. Maps of protected and biodiversity sensitive areas
  3. Time series data set of how Redlist species has evolved in that area over time.

Open-source maps of biodiversity sensitive areas are widely available, so the data challenge is to get asset geolocation of company activities to overlay onto these maps. If the industry is based on assets that by regulation are required to become increasingly smart via remote tracking, then earth observation technology can be leveraged in combination with geolocation self-reported from the asset to link assets to biodiversity protected and sensitive areas.

Global Map of Shipping Captured by the European Space Agency’s ESAIL satellite using AIS data. The availability of this type of third party data is a potential game changer for FIs.

Source: European Space Agency

Marine Transport: a case study

Let’s look at the digital data for this exercise in the ocean environment and look at marine transportation as an example.

Regulation has required vessels to install technology called Automatic Identification System (AIS), which provides data on the geolocation of ships, and was originally intended only as an anti-collision tool. This dataset has been used to create accurate proxies on fuel consumption for a whole range of vessels to calculate emissions. This direct asset-level information, in combination with satellite data, can start to be leveraged also in an SFDR context for automated data analysis for monitoring activities in cargo and fishing within sensitive or marine protected areas. This analysis can be done by algorithms trained to search, download and classify images with respect to whether they contain vessels in protected areas and correlate this with AIS data.

Some FIs (mainly banks) have already been exposed to using AIS data in their practices. If they are in trade finance, then AIS is by some used by in due diligence to identify vessels seeking to evade sanctions. Several regulatory bodies have documented guidelines specifying that vessel tracking should form part of an FI’s approach to compliance in trade finance, including the Monetary Authority of Singapore, the United Kingdom Financial Conduct Authority, and the Hong Kong Association of Banks. That means some FIs can build on existing data practices to integrate biodiversity sensitive area maps into existing data for due diligence.

Navigating the Biodiversity Data Paradox

Linking datasets is the next big challenge for FIs in the context of SFDR and other biodiversity related risk frameworks. It can be integrated into an institutional digital ESG digital data strategy which assesses and defines priorities for asset-level disclosures based on self-reported data, while other parts of the portfolio will need to rely on modelled data.