The importance of spatial data

Over the past year we have seen the emergence of nature as a source of strategic risk and opportunity for corporates. New standards, like SBTN, are producing guidance on setting targets. Meanwhile, reporting and risk management frameworks like TNFD, are building on the success of their climate counterparts. And regulation is following suit, in the next two years it will be mandatory for many corporates to start reporting nature specific impacts.

In short, to meet these evolving expectations on nature, action from enterprise and finance is needed now. But what does this mean in practice?

Getting to grips with climate required an understanding of the activities occurring across a value chain. From this, tonnes of CO2e could be calculated or estimated. Nature adds another requirement- the locations of these activities.  While a tonne of carbon emitted in one location is broadly equivalent to any other - our relationship with nature is always location specific.

Let’s look more closely at some examples of why this matters, first with dependencies, and then with impacts:

• When we look at a dependency on nature, we are trying to understand what ecosystem services are required for a particular economic activity, and whether their supply will be secure into the future. Freshwater withdrawal is a good example of this - simply looking at how much water a process requires is not enough to understand the risk (or opportunity) associated with it. We also need to know the water supply within the catchment area.

• Location specific data is equally important for understanding our impacts on nature. Quantities of land use change, fertiliser application, or water abstraction must be married with their location to appreciate the significance of the impact. Is the ecosystem protected or important for rare species, is the watercourse under pressure from adjacent users? All of this insight comes from location specific data.

Tracking our impacts and dependencies on nature then, requires the following:

1. Non-spatial asset level data: What activities are occurring (direct and/or indirect)
2. Spatial asset level data: Where are they occurring
3. Geospatial data: Information describing the state of nature in those locations

Sounds simple, and yet,  when it comes to collecting this information, we bump into a number of different challenges.

First on asset level data:

The data may not be available, this is particularly true when considering supply chains or portfolios. Either the information is not collected, or it is very time consuming to gather from relevant sources. Second, where data is available, it may not be complete or not stored in the accessible formats -  paper records are still surprisingly common!

Geospatial data:

Again, the data may not yet exist. One of many examples of this is global models of species distribution. Many are spatially biased to Europe and North America, and rely on people recording species encounters in these locations (biasing the data to natural areas near towns and cities). Further complicating matters, the information collected may be in different formats, recorded at different spatial resolutions, or capture different aspects of nature. Finally, there may not be the necessary skills internally to analyse and process geospatial data once collected.

At natcap, we’re working to address both sides of this puzzle. Leveraging third party datasets and our proprietary geospatial models, we aim to provide you with the insight you need to understand the state of nature globally. Likewise our natcap platform automates collection of your own asset level data where it's available. Where it isn’t, we develop rigorous estimations to produce a representative picture of your impacts and dependencies on nature.

Get in touch with us to explore how we might be able to support your own work on nature.

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