A WWF-World Bank report has identified three “major data barriers” to spatial finance becoming more mainstream — lack of regular and granular asset level data, the lack of supply chain data and the poor adaptation of environmental data in financial applications.
The report, Spatial Finance: Challenges and Opportunities in a Changing World, highlights how the financial sector can benefit from the emerging field of spatial finance and calls on it to engage in a strong dialogue with data experts in these fields to develop solutions.
Spatial finance is a geospatial-driven approach designed to provide insights relevant to Environmental, Social and Governance into a specific commercial asset, a company, a parent company, a portfolio or national level scorings.
The report identifies five tiers of spatial finance, not including supplementary supply chain assessments.
- Tier 0 (Country Level): Summed or aggregated scores for countries, based on Tier 3 and 4 data.
- Tier 1 (Portfolio Level): Summed or aggregated scores for portfolios, based on Tier 2 company scores.
- Tier 2 (Parent/Company level): Summed or aggregated scores for a parents companies, based on Tier 3 and 4 results.
- Tier 3 (Asset Level): Assessment of the asset — GIS overlaps, remote sensing, plus Tier 4
- Tier 4 (Sub-asset Level): Assessment within asset — IOT, smart meters, traditional ESG reporting etc.
With atmospheric CO2 levels at all-time high, three-quarters of land on Earth and about two-thirds of the marine environment altered by human activity, there is an urgent need for steps to avert the biodiversity and climate crisis and alleviate the ensuing social issues. A key step in achieving this will be to accurately measure and limit the negative climate and environmental impacts of the commercial players. Spatial finance offers a novel opportunity to increase the transparency and accountability around the impact of a company’s operations, the report notes.
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The joint report, which came out on December 1, tackles the Environment pillar of the ESG — so far a critically underexplored issue for the financial industry. While identifying the key barriers, it also describes the potential solutions within the emerging field of ‘spatial finance’ that complements existing ESG data streams, and outlines for the first time a robust taxonomy and hierarchy for spatial finance. The report explains how discrete forms of technology, approaches and data can be considered within a single consistent framework. Using this framework, spatial finance then can provide insights at differing scales for different applications from the asset-scale for project finance, to company-scale for investment, to country-scale for sovereign debt.
What is spatial finance?
Spatial finance is “the independent assessment of the location of a company’s or a country’s assets and infrastructure using ground data, remote sensing observations and modelled insights”. Use of spatial technologies such as GIS and remote sensing makes it possible to assess the observational data (such as environmental, climate, governance and social) of an asset. In the next level, the findings can be integrated with assets at subsidiary, parent company, national or sector level to provide “insights at scales relevant to different financial applications, ranging from project finance to sovereign debt”.
The footprint of a company whose environmental impact is primarily created through its supply chains can be defined by running a spatial finance assessment that includes all the suppliers’ assets as well as its own direct assets. For instance, the footprint of a car manufacturer can be understood by running a spatial finance assessment of the company’s physical assets — factories, headquarters etc — and then a second assessment of its supply chain assets.
Courtesy: Spatial Finance: Challenges and Opportunities in a Changing World Report
The report highlights that environmental non-profit players – since they are the primary holders of climate and environmental data — have a responsibility to engage in this space to ensure the best data is available and is applied correctly to provide robust spatial finance insights. In the end, such a holistic climate and environmental data portfolio should become a global public good.
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By drawing on the previously uncaptured data and approaches, spatial finance offers a novel data source to gain additional ESG relevant insights. The report underlines four specific areas where spatial finance has immense potential:
- High frequency data on the climate and environmental performance of assets, week on week, month on month;
- Qualitative data, factual numbers driven by remote sensing or other means;
- Comparability of assets and company by applying globally consistent observational data against assets;
- Consistency across multiple scales, from assessing project finance issues to sovereign debt.
Acknowledging that spatial finance is currently best positioned to provide insights on Environment in ESG because of availability of data within that space, the report predicts that this is likely to change with an increasing number of data studies attempting to qualify social concerns. There is a need for FIs to advise on specific needs, while data experts need to advise on the best current possible data solutions, and what might be done to further refine future products to better meet needs. This continuing iteration is particularly vital, because to date no environmental or climate data has been generated specifically for application within spatial finance, notes the report.
There exists a number of challenges, but the report identifies most as data related and not technological. There is a need to improve observational datasets and develop methods to encourage and/or require actors to disclose their ownership structure, asset data and supply chains.
The report identifies six areas as major gaps that need redressal:
- Lack of Asset Data
- Improving Tier 3 Climate and Environmental Observational Data
- Tracking Parent Company and Company Trees
- Benchmarking Scoring Methodologies
- Supply Chain Asset Assessment
- Complexities of Tier 4 Data
The document provide insights into cutting-edge developments within the field, illustrated with case studies from start-ups, such as Bluefield which is into satellite-based methane detection, and perspectives of established financial data providers like Bloomberg and S&P Global, and practitioners and data providers, to explore the potential future developments.
