Sust Global's acquisition by ISS STOXX in August 2025 brings geospatial AI into the heart of the firm's sustainability solutions offering, with founder Josh Gilbert – now ISS STOXX's head of geospatial strategy – leading the work to deliver investors advanced tools for assessing and managing asset-level climate and nature risks.
Environmental Finance: How has the integration of Sust Global into ISS STOXX accelerated the original vision you had for Sust Global?
Josh Gilbert: The acquisition has been an acceleration of the vision we always had to connect the latest climate science and geospatial data science with the financial sector. We wanted to build tools that allow investors to understand emergent risks – particularly physical climate risks. Our expertise was rooted in geospatial and climate science; now, with ISS Stoxx's expertise, we can accelerate our learning across the financial sector. But the core mission has remained unchanged.
The acquisition enables us to go faster, continuing to advance our strengths in geospatial analytics and physical climate risk while integrating with the broader suite of sustainability capabilities at ISS STOXX.
EF: What does geospatial analytics reveal about risk and opportunities that traditional financial analysis misses?
JG: I have always questioned the assumptions underpinning financial and economic models. Traditional financial analysis is inherently reductionist: you strip out noise to make models manageable. But in doing so, you often leave out crucial variables – particularly those linked to the environment.
Natural disasters are happening more frequently and with greater severity. Traditional models haven't captured that well. Geospatial analytics lets us integrate 'spatio-temporal data' – data over space and time – into financial decision-making. This allows investors to bring physics-based models into financial models, creating a more realistic picture of risks and opportunities.
EF: What is your unique modelling approach?
JG: We integrate a range of different data sources. For example, we integrate publicly available Global Circulation Models (forward-looking climate projections) with satellite-derived observational data and other multimodal datasets. Using AI and machine learning, we blend these sources to create models that are scientifically rigorous yet accessible to financial professionals. The result is that investors, who don't necessarily have PhDs in geospatial science, can use validated, back-tested models seamlessly within their workflows.
EF: One of your flagship tools is the Regen Atlas. How does it work, and why is its forward-looking design potentially a game changer for carbon projects?
JG: Regen Atlas was co-funded by the European Space Agency and aims to answer a fundamental question in the natural capital space: will forests and blue carbon projects sequester the carbon they promise to, especially in a changing climate?
Most monitoring, reporting, and verification (MRV) systems look at the past and present, not the future. That's the gap we're trying to plug. For example, California's carbon trading system had a wildfire risk buffer pool intended to last a century, but it was depleted in just 10 years. This highlights why forward-looking analysis is critical – this type of analytics is crucial for building trust and transparency in carbon projects.
Regen Atlas uses Sust Global's proprietary wildfire models – fusing climate scenarios from the Intergovernmental Panel on Climate Change (IPCC), satellite-derived data, and AI-driven modelling to assess sequestration risk. Importantly, it doesn't just drop a pin on a map. Forests or mangroves are vast, so our polygon pipeline analyses entire spatial footprints. This combination of granularity and breadth allows us to identify which parts of a forest are at higher or lower risk, while providing additional nuanced, contextual information for investors and project developers.
EF: Could this technology extend beyond forests and carbon to broader land-use management?
JG: Absolutely. While Regen Atlas was scoped for forests and mangroves, the same building blocks can be applied to regenerative agriculture, soil health, or water risk. It provides a jumping-off point for understanding these other elements of nature and biodiversity risk as well.
EF: Who are the main users of this data?
JG: With ISS STOXX, our universe expands dramatically. We're now part of a platform serving thousands of investors globally. Nature data, biodiversity metrics, and physical climate risk are becoming essential inputs for a much broader set of clients. As nature increasingly becomes treated as an asset class, our integration with ISS STOXX opens powerful new applications within the broader nature offering.
EF: How do your tools align with reporting frameworks such as the Taskforce on Nature-related Financial Disclosures (TNFD)?
JG: At Sust Global, most of our customer base worked with the Task Force on Climate-related Financial Disclosures (TCFD) and Sustainable Finance Disclosure Regulation (SFDR) reporting frameworks, but TNFD and other nature and biodiversity frameworks are gaining traction.
The set of capabilities we are building will enable us to play a key role in meeting new disclosure frameworks and market guidance. For example, as we developed the Regen Atlas project, Verra introduced new guidelines around forward-looking risk requirements for carbon projects, and our capabilities fit neatly alongside such requirements.
But reporting is only half the story. Our North Star is embedding this analysis directly into investment workflows. Sustainability teams need to comply with disclosure frameworks, but portfolio managers and asset teams need actionable insights as they make decisions. Our tools are designed for both compliance and dynamic investment use.
EF: Why is it critical to view climate and nature risks as core investment risks, not just sustainability issues?
JG: To drive meaningful change, investors need to treat these risks as integral, not as side issues. If we keep climate and nature risks in a sustainability silo, we limit their impact. Framing them as core investment risks will be essential to changing investment behaviour.
Ultimately, physical climate risk is physics-based. It's happening now, with growing frequency and severity. Long-term investors like pension funds and infrastructure players understand this, but even five-to-seven-year funds are now being affected.
Climate risk is further along the adoption curve – thanks to TCFD, for example. But nature risk is still lagging. We've seen this arc with physical climate risk: an initial focus on reporting, then a broader integration into investment. I think we're at that same inflection point with biodiversity and natural capital.
EF: How are your products being integrated into ISS STOXX's broader sustainability offerings?
JG: First, we're building a geospatial asset database, enabling asset-to-issuer-level mapping. This makes our physical risk models usable by a much wider range of investors and the financial community, not just those focused on real assets.
Second, Sust Global spent years finessing its physical risk and geospatial models. Now, those models can sit alongside ISS's carbon footprinting, scenario analysis, and Sustainability datasets. That creates a comprehensive product – not just a specific solution to a specific problem. We have built a robust, flexible platform and many existing and future ISS products will now be delivered through it.
EF: Looking ahead, what excites you most about the future of geospatial analytics in finance?
JG: Without doubt, geospatial AI – and specifically geospatial foundation models. We've seen massive advances in how we process Earth observation data, building 'digital satellites' of the planet. Companies like Clay, IBM, and Google are making breakthroughs that pack enormous amounts of spatial and temporal data into vector embeddings.
As a point of reference, in geospatial AI, we're currently in a similar place to 'the GPT-2 moment' in large language models (LLMs). GPT-3 was the breakthrough that unlocked the value of LLMs for the wider market. Geospatial AI is on the cusp of its own GPT-3 moment. The pace of progress in just the past six months has been extraordinary, faster than most of us expected.
The difference is that geospatial AI is grounded in physics and the real world, not just text. LLMs hallucinate because they're built on words; geospatial models are rooted in measurable realities around nature, cities, populations – and how those things are changing. That makes it incredibly compelling for finance and beyond.
For more information, see: www.sustglobal.com and www.issgovernance.com/sustainability
