Integrating digital Earth Observation environmental data into financial services decision making

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This application seeks to address the lack of consistent, scalable, science-based and timely climate and environmental data for farming and land management businesses ("FLMs") and those financial services companies ("FSCs") that support them. New data that captures detailed knowledge of land use, biodiversity and carbon is rapidly becoming essential for risk decision-making and reporting in bank lending and insurance as the UK transitions to new land management subsidy regimes and net zero.Technology SolutionThe proposed innovative solution will deliver consistent, scalable and science-based data on the climate and environmental credentials of FLMs for multi-faceted uses across FSCs, supporting the changing land management practices that will deliver carbon capture and improved biodiversity.The technology integrates Earth Observation data streams with geographically accurate land parcels and translates this into timely, accessible and relevant data to FSCs, for the purpose of understanding, assessing and acting on the financial risks and opportunities from climate and environmental change.Key FSC use cases (none of which are possible today) include:* enhanced credit risk descisioning, for the c.£20bn market in bank lending to FLMs, that takes account of the shift in land management practices towards habitat restoration and environmentally sustainable land management (and the monetisation potential arising from this)* reliable, science-based quantification and valuation of changes in Natural Capital (e.g. carbon and biodiversity) to underpin insurance and lending security calculations* consistent, accurate disclosure of scope 3 financed emissions and broader net environmental impact from lending to the agricultural and land management sector.Scalability and readinessFLMs are beginning to use drone technology to measure their activities -- crop management, forestry surveys etc. This same technology is used to collect the data necessary to accurately measure carbon and monitor biodiversity, making the digital environmental data output entirely scalable.The foundations of the solution have been developed, including relevant machine learning algorithms for forestry and woodland, a map-based technology platform and a user interface. Work is ongoing to extend capabilities to other habitats. This grant is therefore focused on marrying the foundation technology with financial services use cases that will revolutionise environmental risk management.This application brings together two highly motivated and relevant companies to support the delivery of this proposed solution across what is expected to be the whole of the rural land management enterprise sector and provides the opportunity to create an FLM and FSC environment that together can deliver substantial positive climate and environmental impact.