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 lead, in collaboration with the Oxford University Sustainable Finance group, the UNEP World Conservation Monitoring Centre, the Centre for the Ecology and Hydrology (Wallingford) and York University has been 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, road maps).
There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial institutions is challenging due to the large volume of heterogeneous data available on nature and the complexity of investment value chains and the various components’ relationship to nature. The dual problem of scaling data analytics and analysing complex systems can be addressed using Artificial Intelligence (AI). In their report they address issues such as plugging existing data gaps with discovered data, data estimation under uncertainty, time series analysis and (near) real-time updates.
Our report (linked below) presents potential AI solutions for models of two distinct use cases: (1) the Brazilian cattle farming use case is an example of greening finance – integrating nature-related considerations into mainstream financial decision-making to transition investments away from sectors with poor historical track records and unsustainable operations; (2) the deployment of nature-based solutions in the UK water utility use case is an example of financing green – driving investment to nature-positive outcomes. The two use cases also cover different sectors, geographies, financial assets and AI modelling techniques, providing an overview on how AI could be applied to different challenges relating to nature’s integration into finance.”
Download the report here
Please note, this is a NERC-funded project.
Project outputs
- Scale and Technology
Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks
There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial institutions is challenging due to the large volume of heterogeneous data available on nature and the complexity of investment value chains and the various components’ relationship to nature. The dual problem of scaling data analytics and analysing complex systems can be addressed using Artificial Intelligence (AI). We address issues such as plugging existing data gaps with discovered data, data estimation under uncertainty, time series analysis and (near) real-time updates. This report presents potential AI solutions for models of two distinct use cases
