It has been a busy spring for OxEO. We completed the UKSA’s Leo programme, graduated from the Creative Destruction Lab’s space stream, and we now have a first customer. With deliverables due this the autumn, we need some working capital and are raising our first external investment round. This is the first in a series of five short posts written to support that effort. If you are a qualified investor and would like to see our deck, please get in touch.
We are interested in real assets. They appear under ‘fixed capital formation’ in the national accounts, and include buildings, farms, factories and infrastructure. Real assets have a sustainability footprint: they impact GHG emissions, land use, water use and biodiversity. Climate change adds an additional layer of uncertainty.
Annual investment in fixed capital formation has more than doubled in the last two decades. Even accounting for the effects of the pandemic, global investment in real assets in 2021 alone will have a US$ 20 trillion+ sustainability footprint (chart). For context, that is more than 10 times the total value of assets under management in ESG funds globally — even after recent record-breaking inflows. If you believe, as we do, that sustainability ultimately comes down to capital allocation, then real assets are where the action is.
But for capital allocation to be sustainable, decision-makers need to be able to place impacts in context. What difference does it make to land use, water use, GHG emissions or biodiversity if an asset is located in one place instead of another? What are the risk interdependencies, and how are likely consequences affected by climate variability?
Despite the boom in sustainable investment as an asset class, there are no contextual datasets available today that provide decision-makers with the insights that they need to assess these impacts systematically, and at scale. This is the problem we solve.
OxEO generates the context needed to systematically assess the sustainability of real assets. We identify and measure the most relevant sustainability risks, based on each asset’s unique attributes and geospatial location. We use remote sensing, machine learning and climate science to produce contextual risk scores at any scale — from the asset on up. We exploit three trends: i) growth in low cost, high-resolution earth observation data, ii) location and ownership data on real assets inexorably becoming a public good, and iii) a new generation of climate models to enhance predictive analysis.
We are rooted in the research traditions of a world-class university. Since last summer, we have worked with some of the brightest and best researchers to challenge and improve our process. And while much of our patent-pending work is at the edge of the envelope, we never attempt to say more than the best science will allow us to.
We believe that the current profusion in ESG data products on offer to investment managers — over 160, according to Environmental Finance –reflects the difficulty of evidencing causal relationships between sustainability information and future share price performance. This difficulty is often, and erroneously, obscured by calls for “better data”. In contrast, it is easy to understand that the geospatial location of one asset compared to another directly affects the size and shape of their respective sustainability footprints.
The thesis behind our mission statement (sustainability by place, from space) is that if decision-makers had better contextual information, they would make more sustainable allocation decisions. Even if this did not amount to $20 trillion each year, it could still mobilise more real-world impact than every ESG fund combined. We want to help shape this transformation.