Are Investors Missing Biodiversity Risk?

Hello and welcome to the weekly edition of Sustainability Now. The show where we explore how the environment, our society and corporate governance effects are affected by our economy. I am Gabriela de la Serna and I am your host for today's episode. When it comes to physical climate risk, we've come a long way. Today, if we're concerned about something like wildfires, we can get very specific. We can identify which assets are exposed, where they're located and start to estimate the impact on operations or revenues. But when it comes to biodiversity risk, getting to that same level of detail has taken much longer. We've known that these risks are financially material for a while, but measuring them in a precise way has been a challenge. But that is starting to change now. So on today's episode, we are unpacking what's held by the diversity risk data back. What's different now and why investors should be paying close attention. So let's jump right in. What does this blind spot invite diversity risk data actually look like in practice? Let's take water. Many companies depend on a stable supply of clean water for their operations. For example, manufacturing, agriculture, or energy companies. But as ecosystems degrade, the impact of water-related risks don't always show up immediately. And that's part of what makes it so difficult to measure. And here, there's both a conceptual challenge and a data challenge. First, let's go with the conceptual side. And let's take a concrete example to bring this to life. Think about an oil and gas refining company. Its operations rely heavily on access to water, both for cooling systems but also for processing raw materials. Now if water availability starts to decline or water quality deteriorates, that might not immediately show up in the company's financial results. But over time, it can start to disrupt operations by forcing slowdowns or by requiring alternative water sources, all of which will cost the company money. So even when the risk is there, it's not always easy to connect what's happening in the natural environment to what's happening in a company's operations. And then there's a data challenge. Because that water stress risk we just talked about will vary a lot depending on the exact location of a company's facilities. And so, refining companies can face very different levels of water stress risk depending on where the facilities are located in regions prone to water shortages or areas where water is more abundant. But typically, investors don't have visibility at that level. So instead, they historically have been relying on proxies. So I sat down with my colleague, Bettina Mayer. One of the authors of a report published by MSEI in partnership with WWF titled "Identifying Nature Related Risks from Global Profolios to Global Risks". I asked her to walk me through how biodiversity data has evolved. Where were we before and where are we now? Yeah, so previously, I mean, the big challenge is at the conceptual, but also at the data side to really make this connection of companies' activities and the nature and biodiversity. And previously, this connection has been done often at considering where our company's headquartered and what's the kind of the state of nature, the state of biodiversity in that country. So really at the very core level and neglecting the companies usually operate internationally and also the biodiversity nature varies a lot throughout the country. And so we were super happy that at MSEI, we started to collect more and more information about where exactly, meaning really at the facility level, two companies operate. And so this gives you the possibility to also combine this with nature and biodiversity data that is very granular. And really gives you this kind of local view on it. And thanks to the partnership with WWF, they brought in kind of the nature perspective or the nature expertise that really helped to bridge that side of what is then the potential impact companies can have on their local surroundings or environment, but also what's their dependency on local ecosystems. And so bringing together the data we have at MSEI, giving a very kind of granular view on companies' activities with these expertise from WWF, we can now have this very kind of location-based view on companies' dependencies on nature or their impacts on nature and aggregate it back up to the company level while really being based on this location-specific information. We started off exploring this data to see whether they also confirm what's expected from the kind of more traditional sector level analysis and expertise. And it's nice to see that the really location-based information confirm that certain sectors are highly dependent or impactful on nature, such as material sector or energy. And that certain risks are really very widespread, basically sector-agnostic. For example, climate-related dependencies, but also water scarcity being very widely spread across the sectors. And then we were diving into it. So what can we see from our data now that is different from previous analysis because we have more granular data. And it was very interesting to see that when we look at our analysis that we have a very different view on what companies we consider as being high risk or exposed to high risk versus if we were to do that analysis based only on companies headquarters. And we were very surprised that the numbers are actually quite high in terms of difference. For example, we found that about a third of all companies within AQIMI are considered as low or only medium risk if we only consider the headquarter. However, when we look at our metrics, which consider all assets and facilities, they are then exposed to high or very high risk. That means that these 33% of companies are basically overlooked with these more traditional headquarter-based metrics and only appear on the screen for high or very high nature risks if taking into account this facility-level information. Before we go further, let me quickly unpack a couple of things. Let me just mention, when she talks about screening for high risk, she's referring to looking across a broad universe of companies and identifying which ones are exposed to higher levels of nature-related risks. In this case, the universe she's referring to is called the MSEI AQIMI, a global index covering thousands of companies. And so what we're seeing is that once you move away from those broad country-level assumptions and start looking at where companies actually operate, the picture begins to change. And in many cases, it changes quite significantly. And this is because ecosystems and nature-related risks can vary drastically, even within a single country. Let's take the US, for example. If you look at the country-level averages, the US country average against the war availability risk assessment is a two out of five, so relatively low risk. But this country average hides that many Western states face severe water stress, while other states in the East have much more abundance supply. And we know that many global companies have their production spread across different countries, or even different regions, sometimes in areas where some risks are much higher than in others. So when you take both of those things into account, so how much risk can vary across locations, where companies are actually operating, you start to see a very different picture. Not because the risk wasn't there before, but because we weren't measuring it with enough precision to see it clearly. But if I'm an investor, I'm not just interested in where a risk sits. I would also be keen to understand how much of a company's business depends on those locations that face the highest level of nature-related risks. And this is where things start to get even more interesting. Here's Bettina. So looking at the geospatial data that we have combining the WWS methodology with our MSCI geospatial data, it did a risk level of each of the facilities of a given company. But naturally, not all of these facilities have the same relevance, both because manufacturing processes might be more relevant and less easily replaceable than some administrative office-related activities. But also given that not all of these facilities generate the same amount of revenue for a new company. So I looking at the state I really want to understand, okay, which of these assets that are exposed to high risks are now also relevant for companies. To make this a little bit more concrete, let's go back to our imaginary global all-en-gas refining company. It might have offices in major cities like, let's say, London or Houston and a few key refineries where most production happens. Now both types of facilities could be exposed to water-related risks, but the impact is not going to be the same. If water stress affects an office location, then we know that the disruption is likely to be manageable. But if it affects a key refineries, say in a region like the Permian Basin, where we know that water availability is already under pressure, that can disrupt operations, slow production, and ultimately have a direct impact on revenue. And so as an investor, this is the kind of nuance I'm interested in. And here, Bettina tells me why. So as an investor looking at my portfolio and I have these hundreds of companies, I want to be able to identify the companies that are exposed very strongly to specific risks or exposed to across many different risks. And whenever you've identified companies that are potentially exposed to high risk in their relevant operational sites, that you then can go back to the tier of patient information and try understanding where does the first come from. And this would also allow, then, for example, to identify geographic concentration of risk. So seeing that all the high risk assets of a given company are located in a similar area would further amplify the risk, because it's more likely that they're all exposed to the materialization of a risk at the same time. Whereas if these high risk assets are more separated across different locations, the actual realization of the risk event, let for example, think about the drought event, is less likely to happen at the same time, and so the risk is more diversified. So it was really about from providing company level information that allows a simple way to identify companies for which you then really want to look deeper into their operational facilities, where are they located, what type of risk. And why are they exposed to high risk? And that is it for the week. A massive thanks to Athena for her take on the news with her sustainability twist, and thanks to you as well for listening and sticking around. The research that we discussed on today's show is freely available on MSCI's website. If you like this episode, don't forget to subscribe and maybe even share it with a friend or colleague. That's all from me. Thanks again and catch you next time. 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