The surge in asset allocation towards sustainable investment funds over the past couple of years has been unequivocally demand driven, supported by regulation. It has not really been accompanied by any major technical or technological breakthrough on the supply side. I’d argue that despite the wave of interest in ESG, we are not that much better today at assessing the sustainability or otherwise of one investment over another, then we were a decade ago. And this is despite an extraordinary profusion in what might be described as sustainability data (earth observation, climate models, survey results etc.) over that period.
The demand-supply gap matters, because the concept of a climate emergency has moved with extraordinary speed from fringe to mainstream to zeitgeist. No self-respecting politician will fly to Davos next year without a net-zero carbon emissions strategy under their arm. But ultimately it is in the capital allocation process that the rubber hits the road. Coalitions such as the Net Zero Asset Managers Initiative have emerged in response to this reality. Investors are committing to actions tomorrow that will require insights on sustainability that they do not have access to today.
Data vendors have responded to the market opportunity with alacrity. New products that for example attempt to downscale global climate models into wildfire hazard predictions are being launched with almost metronomic frequency, despite the increasingly strident questions being asked about the utility of these ‘climate analytics’ services. Meanwhile, providers of ‘traditional’ ESG data (such as self-reported questionnaire responses) reinforce their offerings by pointing to the alpha generation that ESG strategies deliver. Here too, the evidence may not stand up to scrutiny (tl;dr it often looks like correlation over causation).
So the current situation is this. Market demand for sustainability insight has never been stronger — and will only grow from here. Meanwhile the supply of data available for analysis is growing at breakneck speed. But when it comes to transforming data from analysis to decision ready insight, there is a missing link.
At our company OxEO, our founding premise is that the missing link is location — or more precisely, geospatial specificity. A company’s prospects of transitioning to net zero, or managing its ESG exposure more broadly, is highly dependent on where its operating assets are located. By assets, we mean factories, farms, warehouses, mines, data centres, buildings and so forth. And by location, we don’t just mean which country they’re in. We mean exactly where they are located.
Take water as an example. For assets where it’s a critical input, such as a bottling plant, water scarcity is a key operating risk. And for assets where it’s a significant output, such as a mine, water quality is a key regulatory risk. These risks are often best evaluated in the context of the local watershed. This requires information about the asset’s location (and of course, information about the watershed).
And yet, try and get hold of a list of location-specific assets operated by the world’s 5 biggest bottlers or mining companies. It’s remarkably difficult, despite them being public companies, with external investors. This is partly a historical legacy. When I was starting out as a fund manager, nearly 30 years ago, I’d never have thought to ask for this list. What would I have done with the information, after all? But things are different now. Through satellites, we can unlock location-specific insight that was unimaginable back then. Maybe it just takes time for corporate disclosure to catch up, or maybe it needs a nudge. We’re yet not sure, but at OxEO we’re determined to find out.
For climate data to be insightful, it needs to support decision making. Water availability and quality are frequently correlated to temperature and rainfall patterns, both of which are becoming less predictable due to climate change. Earth observation gives us a historic record of changing water extents that now goes back nearly five decades. Climate models will help us predict how these patterns will change 50 years in the future.
Combined with knowledge on the specific assets affected, investors can make refined judgments on capital allocation. The challenge comes in synthesising observed, modelled and asset level data at scale — to render timely and decision-ready insight across thousands of assets in near real-time. That’s the business we’re building at OxEO.
Results from OxEO's collaboration with the ESA Phi-Lab and the World Food Programme as part of the EO & AI for SDGs Innovation Initiative.
Candid reflections on the year so far
Alex and Lucas explain why water stress is such a pressing problem - and what OxEO is doing to address it.