Climate

GeonatIQ’s work in climate AI spans subsurface, market, and policy-driven systems, with a focus on turning complex climate data into decision-relevant outputs.

On the physical side, we have developed AI tools to analyse seismic and subsurface data for properties such as porosity, supporting applications including carbon capture and underground storage (CCUS). This included large-scale screening of the US onshore to identify optimal regions for capturing CO₂ from industrial sources and injecting it into the subsurface, as well as offshore work in Europe where our models identified over 500 Mt of potential CO₂ storage capacity in offshore Germany. These systems are designed to support site screening, risk reduction, and injection optimisation, rather than replacing geoscientific judgement.

On the market and policy side, we have developed AI models to predict EU ETS carbon prices, reflecting the growing intersection between climate policy, energy markets, and financial decision-making. Other projects include AI systems that analyse large-scale news and policy data to extract market-relevant context rather than simple sentiment. Instead of reducing articles to positive or negative scores, these models interpret regulatory changes, macroeconomic developments, energy market events, and climate policy narratives as structured signals, such as expected price direction, magnitude, time horizon, and volatility impact. These contextual embeddings are aligned with numerical market data to improve forecasting and decision support, particularly in carbon and energy markets.

Together, this work shows how AI can translate climate, policy, and energy complexity into actionable intelligence for storage developers, traders, policymakers, and investors.