OxEO is raising its first external investment round. This is the third 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.
I am a non-technical founder. I have a reasonable understanding of the science behind what we are doing, and of the innovation that drives our process. But more important, in Lucas Kruitwagen I have a co-founder who publishes on these things in the most prestigious peer-reviewed journals in the world. It is fair to say that we take on different responsibilities within the company.
Lucas and I met in Oxford back in 2015. We were both part of the university’s School of Geography and the Environment; a department that, amongst other things, leads world-class research on climate modelling. It was a good place to meet: models are important to our business. Our aim is to generate the context that is needed to systematically assess the sustainability footprint of real assets such as factories, farms and mines. This requires an ability to measure changes in the size and shape of assets’ sustainability footprints over time and space. We’re interested in the difference it makes to e.g. GHG emissions, land use, water use and biodiversity if an asset is located in one place instead of another. What are the risks, and what are their interdependencies? And what uncertainties are associated with these risks in future, e.g. due to climate change.
Answering these forward-looking questions requires some form of model as the past is not normally a perfect predictor of the future. Models prompt a range of responses in people. One of the questions that our geography tutors sometimes set for new undergraduates is to discuss George Box’s aphorism that “essentially, all models are wrong, but some models are useful.” Certainly, they are ubiquitous — from climate science to capital asset pricing. At OxEO, we use models parsimoniously and transparently. We acknowledge their limitations at every turn. But we are excited about the insight that models can provide in allocating trillions of dollars of capital, more sustainably.
For example, we are interested in the relationship between surface water availability and climate change. We can observe historic changes in surface water for any location, from space. And we can access the best available models to project local changes in temperature, rainfall and humidity under different warming scenarios. We can establish the relationships between these variables and water availability, and obtain a range of confidence scores to forward-looking projections. This can help us to say something about how e.g. longer dry periods and more intense wet periods in the future might affect water availability, interdependent risks and the sustainability footprint of assets in a particular location. Over time, our confidence in these projections is increasing, as we ingest more observations and access more highly resolved models.
With that said, modelled projections are only as good as the input data. We have always explicitly acknowledged this — indeed, one of our first investments as a company was to register OxEO Best GuessⓇ as a trade mark. We never attempt to say more than the best science will allow us to. And input data — earth observation, specifically — is at the core of our business. It is the area of innovation where we have patents pending. And it is what drives the insights and context that we generate to improve sustainable capital allocation.