Robust ESG data for biodiversityProject
Financial institutions are increasingly aware of and interested in biodiversity- and nature- risks and opportunities, but such attempts have often been hindered by incomplete, incomparable and unreliable environment, social and governance (ESG) disclosure and scores.
Our AI team, in collaboration with the Oxford University Sustainable Finance group, the UNEP World Conservation Monitoring Centre, the Centre for the Environment and Hydrology (Wallingford) and the Satellite Applications Catapult are exploring innovative solutions to overcome existing challenges of ESG disclosure and analytics for biodiversity by tapping into the space of ‘discoverable data’ (e.g. satellite data, disclosures, traditional and social media, Internet of Things, in situ sensors).
Our work explores how machine learning can deliver a data solution to comparably, comprehensibly and credibly measure and present granular, asset-level environmental risks and impact data, as well as co-benefits, to complement or fill gaps in existing nature disclosure datasets. This work will reduce investment risks faced by the finance sector, a key ask of our finance industry collaborators (inc. Barclays, Lombard-Odier) as well as scale up financial flow towards nature recovery.
Please note, this is a NERC-funded project.
