Conference Agenda

The calculation of nature-related economic and financial risks should adhere to the conventional formula that characterises risk assessment. This requires the calculation of three key components: hazard, exposure and vulnerability. Earth observation (EO) represents a valuable source of critical information, capable of providing essential data for all of the aforementioned components. Specifically, on "hazard" by contributing to the mapping and assessment of relevant factors such as land cover, topography, proximity to waterways, weather patterns. On "exposure" by providing the geographical location of physical assets whose economic and financial value depends directly and/or indirectly on nature. On "vulnerability" by providing the critical variables to model where services from nature are needed but not provided. Such services refer in the short term to the provision of ecological inputs, the removal of pollution and the protection against physical and biological disasters; in the long term, they refer to overarching environmental targets such as climate change and halting biodiverosty loss. The presentation offers a conceptual framework for tracing the information flows required to assess "hazard", "exposure" and "vulnerability". It also identifies the specific contributions that Earth Observation (EO) can make at each stage of this process. The discussion is illustrated with a series of concrete examples, which provide a useful point of reference for further debate.

Financial institutions (FIs) are increasingly concerned about the nature-related financial risks of the companies they lend to and invest in. Before they can use earth observation and geospatial data to monitor and report on biodiversity impacts, FIs need data on where their clients are physically operating. This location-based approach is novel for most financial institutions but is embedded in the Taskforce for Nature-related Financial Disclosures' assessment and reporting framework. Asset location data – i.e. spatial data on the location and characteristics of capital-intensive assets like production facilities or plants - is, therefore, an essential building block for monitoring and reporting on biodiversity loss and risks. However, companies do not (consistently) disclose the specific locations and characteristics of their activities or associated nature-related impacts in those areas.

At the Oxford Sustainable Finance Group's Spatial Finance Initiative, we have been working with asset location data for nearly 10 years. We want to collaborate with the EO biodiversity data community and present two areas of our work:

1. An overview of data sources and methodologies for identifying the location and characteristics of companies’ assets. This includes (a) machine learning models to identify the location of cement and steel plants in China using EO data and (b) using EO-derived measurements to model the plant capacity and capital investments of Chinese cement and steel plants and of meatpacking plants in the US.
2. Case studies on how financial institutions then combine this asset location data with EO and other geospatial data layers to support different biodiversity finance applications

Ultimately, we want to engage the audience in a discussion on how to increase the usability of EO-derived biodiversity data and insights to support nature-friendly financial decision-making.

Co-authors: Changenet, Alexandre; Schoefield, Paul; Pellet, Cameron; Wood, Anna; Creer, Simon; Bush, Alex

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Ecosystem conservation and restoration actions requires structured means for reliable monitoring in order to ensure the credibility needed to quantify their success in and facilitate their financing. Here we present a framework for Monitoring, Reporting and Verification of Biodiversity and Ecosystem Services (MRV-BES) which would enable outcome-based payments, fostering efficient conservation and restoration. The framework builds upon previous experiences in MRV of carbon credits, making use of previous good practices and avoiding shortcomings, thus extending MRV systems so that payments for carbon removals would be just one ES among many others, providing a multidimensional consideration of BES in MRV. In our framework, additionally is proven through the construction of a reference model for the restoration action, which is compared against a business as usual model which we use as counterfactual. The difference between the reference model and the counterfactual can be used as reference levels of relative success in the restoration goals that can be employed as a common ’BES currency’ which, in comparison with an existing market (e.g. carbon credits) can be employed to give monetary values to all the BES involved. The amount of effort employed in the monitoring needs to be accounted for in the valuing of BES credits, so that dedicating resources in reliable monitoring would bring a monetary revenue that encourages its investment. For this reason, the framework is based in the principle of conservativeness in MRV, for which payments are to be granted on the basis of the most conservative evidence available. This principle of conservativeness ensures that intensive monitoring reducing the uncertainty in estimates of BES indicators can pay off for its own investment. In the context of the multidimensionality of BES credits this is of particular importance because increasing the number of ES under consideration also increases the confidence in the success of the restoration action, and thus our MRV-BES framework also encourages the multidimensional character of BES to be monitored and accounted for. Our MRV-BES framework also allows to take into consideration synergies and trade-offs among diverse ES. In SUPERB project we apply the MRV-BES framework to 12 demo areas across with diverse ecosystem restoration actions in Europe. These projects include a large number of restoration goals and ecosystem services involved, with monitoring methods including remote sensing techniques involving LiDAR and multispectral drones plus mobile laser scanning, DNA metabarcoding of airborne and soil arthropods plus soil fungi, bioacoustics of bats and birds, and citizen-science assessments of ground vegetation. The MRV-BES framework provided common means for reporting the success at these 12 demo areas, given this diversity of goals and techniques involved. We advocate for payment for outcome schemes, and for that reason these MRV-BES systems need to be underpinned by bundle agreements with defined spatio-temporal bounds. Our MRV-BES framework is in principle meant for the scale of individual conservation and restoration actions under voluntary markets. Nonetheless, public sector regulation and monitoring of a network of reference and counterfactual sites could enable scaling of restoration efforts, which would enable this MRV-BES framework to also be used to prove progress toward restoration and conservation policy target in national reporting.

Oil palm plantations (OPP) are a threat to biodiversity: at least 53 mammal, 50 bird and 23 amphibian species might be threatened by OPP globally according to the Red List of the International Union for the Conservation of Nature (IUCN). However, this number is likely underestimated, since the IUCN Red List does not code threats specifically for OPP, and there are no global spatial analyses assessing how exposed is biodiversity to this threat. Using a recently published OPP map based on remote sensing data, we provide the first global spatially explicit analysis of how much species are exposed to OPP. We also analyse how much protected areas, countries and ecoregions are exposed to OPP. For each feature of interest (species, protected areas, countries or ecoregions), we calculate exposure as the percentage of the feature’s range overlapping with OPP, and further distinguish exposure from industrial and small holders plantations. By highlighting which species, countries and ecoregions are the most exposed to OPP, our work contributes to identify the species or areas for which conservation actions should be prioritized to limit the impact of OPP on biodiversity. We also stress out how much of OPP exposure occurs within species, countries or ecoregions’ protected range, and consequently discuss the role of protected areas in mitigating threats from OPP. While our analyses depict a worrying situation regarding the exposure of biodiversity to OPP, the impact of alternative oil production scenarios on biodiversity still need to be explored.

BirdWatch, funded under the Horizon Europe Program, focuses on improving the state of biodiversity of the EU's agricultural landscape, in line with the EU Green Deal, the EU Biodiversity Strategy for 2030, and the Farm to Fork Strategy.

Leveraging Copernicus satellite data, the project assesses agricultural areas to identify their suitability for farmland birds and strategises ways to enhance ecological conditions. As indicator species, birds offer insights into overall biodiversity health, contributing to a broader understanding of ecosystem well-being.

The project employs species distribution modeling to link bird occurrence data with habitat requirements, establishing models that gauge habitat suitability and the likelihood of an area being suitable for specific bird species. Satellite data are used to quantify essential environmental descriptors such as structural variability, land cover type, crop type, mowing intensity and soil moisture. These parameters are then fed into the habitat models to assess landscape suitability.

Knowing the state of habitat suitability and the habitat requirements, BirdWatch identifies which of the agroecological schemes under the EU’s Common Agricultural Policy (CAP), have to be applied to improve the farmland conditions. The agri-environmental schemes are selected in such a way to ensure that they are not in conflict with any spatial or ecological requirements.

Here, BirdWatch uses spatial optimisation, taking into account both the ecological requirements and the economic and operational constraints of the farmers who need to implement the agri-environmental measures as part of their obligations under the CAP.

Benefiting from Copernicus program's high temporal resolution, BirdWatch evaluates the success of agri-environmental measures and makes adjustments as needed.

Upon project completion, the service will be accessible through a web-based GIS application in the project regions of Flanders, Germany, Lithuania, and South Tyrol.

Closing the global biodiversity finance gap requires innovative financial mechanisms that value the preservation and restoration of healthy ecosystems. We introduce our Landler.io platform that enables asset-grade nature investment portfolios; along with “Biodiversity Units” -- a conservation focused, scalable and accessible monitoring framework designed to quantify ecological integrity across diverse ecosystems.

Methodology: By integrating a top-down remote sensing approach with bottom-up observations of species occurrences, our method achieves a balance between scientific rigor and practical accessibility. Key elements are habitat intactness, connectivity and species presence, which are scored annually and serve as the biophysical underlying for all investments.

Habitat intactness quantifies anthropogenic pressures, such as deforestation, infrastructure or cropland development and is monitored using remote sensing. Connectivity is included to value the ecological contribution in the regional context. The habitat perspective is complemented by in-situ monitoring of selected indicator species as proxies for ecosystem functioning. We utilize camera trap and acoustics surveys, as well as direct species observations. With this, our framework allows for rigorous monitoring that is feasible for large actors but keeps the entry bar low enough to enable the participation of community projects, too.

Platform: Our platform provides the connecting interface between investors and land stewards. It displays monitoring outcomes in an accessible, transparent and auditable form. Moreover, it establishes the market where investors can fund the protection and restoration of high-integrity ecosystems and land-stewards receive financial incentives for maintaining or enhancing the ecological integrity of their properties – hence, enabling conservation as an economically viable from of land-use.

Here, we discuss our work with major conservation actors from small-scale restoration areas to large-scale national parks; all of which generated first successful transactions. With our platform, we are hoping to foster the uptake of biodiversity finance approaches with a particular focus on catalysing private sector investment.

We argue that nature-related risks, particularly exposure to biodiversity loss, constitute a systemic financial risk. To assess these risks, we utilise Earth observation (EO) data for biodiversity risk assessments of financial issuers impacted by nature-related risks. Unlike ratings-based assessments, our approach is forward-looking, objective, non-manipulable, and independent of potentially biased, self-reported disclosures from financial issuers. We introduce the Biodiversity Geospatial Risk Impact Framework (BGRIF), a methodology for assessing geographic-based biodiversity-induced financial systemic risk using satellite imagery. Our method builds on the System of Environmental Economic Accounts Ecosystem Accounting (SEEA EA) framework, proposing indicators to evaluate the condition of ecosystem services in specific geographic locations linked to the activities of financial issuers. By employing the cascade model from ecology, we connect industrial activities with their dependence on ecosystem services using the Exploring Natural Capital Opportunities, Risks, and Exposure (ENCORE) database, estimating biodiversity risk exposure at the industry level. Furthermore, we analyse the interdependencies and systemic risks between industries operating within the European NUTS2 regions, linking them through inter-regional trade flows, which act as mechanisms for transferring biodiversity risks. Using a core-periphery model, we examine how these trade connections shape the distribution of biodiversity risk across European regions. While our primary focus is on assessing biodiversity risk at the regional level, the methodology is adaptable to corporate issuers by aligning risk assessments with the geographic locations of their assets and supply chains.