AI’s computational power and its ability to integrate vast amounts of data from climate and meteorological research are expected to have a profound impact on many countries’ climate efforts. The U.S.’s (re)positioning on global climate commitments, together with the energy consumption of AI data centers, increases the need for climate transition, but in practice, it will especially change how global climate efforts are implemented.
This blog post is the fourth in the series on AI’s disruption of innovation
- Where does AI impact today?
- What is new and disruptive about AI?
- AI’s effect on robotics and experimental automation
- AI’s effect on climate and meteorological research
- AIs impact on social sciences
- AI’s effect on teaching, learning, and psychotherapy
- AI’s impact on biotechnology and pharmaceuticals
- AI’s effect on materials research and quantum chemistry
- AI’s effect on theoretical physics and mathematics
- What should investment strategies in AI take into account?
Global Climate Action Is Changing Even as the Need Increases
In 2024, global investments in climate-related energy transitions reached USD 2.1 trillion. That is a doubling within six years, and growth will generally continue, as more countries now pursue strategic autonomy and self-sufficiency. Energy is the foundation for almost all economic activity.
Yet global warming, and therefore climate change, continues to rise. This development will persist both because U.S. energy transition efforts are expected to decline (loss of state subsidies, withdrawal from COP21, etc.) and because U.S. electricity consumption will increase sharply, especially due to data centers. As a result, global investments in climate adaptation are rising, and so are the global costs of repairing climate-related damage.
Accurate Climate Forecasts Are Essential, And AI Is Central
This increases the value of climate and meteorological data for climate models and weather forecasts. There is a need for faster and more detailed simulations, leveraging deep learning through broader integration of data. This can help target and optimize investments in climate adaptation.
AI’s morphogenic and self-learning nature enhances machine learning from the integration of atmospheric, oceanographic, geological, and biological data. This development will accelerate as data from drones, ocean buoys, ground stations, and satellites make it possible to monitor, for example, methane emissions, ocean temperatures, and to map deforestation, glacier melt, and more.
It also makes it possible to automate and optimize the restructuring of energy grids, agriculture, and water supply systems. For example, FourCastNet, developed by NVIDIA and Lawrence Berkeley National Lab, has created a weather model capable of simulating atmospheric dynamics 45,000 times faster than traditional models such as ECMWF. This makes it possible to generate climate forecasts in seconds instead of days.
Carbon Removal Will Continue and Grow in Importance
Carbon removal will generally continue, even though the U.S. has (again) withdrawn from COP21. The principles in COP21 regarding responsibility, duty, compensation, etc. formed the basis of the carbon removal market. This was also a key reason for Trump’s wish to exit COP21. Both the EU and China are expected to continue their efforts. Moreover, countries already experiencing the most severe climate changes generally want to increase their own efforts as well. This affects both the transition of electricity production and actual carbon removals.
- In 2024 the market amounted to just under USD 3 billion and is still expected to increase thirtyfold over the next ten years. The EU’s CBAM mechanism (Carbon Border Adjustment Mechanism) will, among other things, enter fully into effect with payments starting at New Year.
This creates commercial opportunities in both carbon capture and storage, whether it occurs:
- Artificially via CCUS, DAC, BECCS, mineral carbonation, etc.
- Naturally via carbon sequestration (reforestation, regenerative agriculture, restoration of wetlands, biochar, etc.)
- Hybrid approaches such as the “holy grail”: artificial photosynthesis.
Here, AI is the decisive enabler, because these potentials require new technologies. This in turn demands the integration of massive datasets, new approaches to “naturalness logic,” and enormous simulations with reinforcement learning.
The Same Will apply to Energy Transition, But Now Driven by Strategic Autonomy
The energy transition will generally continue, but in the future it will be driven especially by the ambition of securing nations’ strategic autonomy. This development is reinforced by geopolitical tensions (the war in Ukraine, the Taiwan conflict, etc.). AI simulations are particularly critical for:
- Solar energy: Development of perovskite and tandem solar cells with much higher efficiency and durability than today, as well as the integration of solar cells into general building materials such as facades, roofs, and load-bearing surfaces like roads and rail tracks.
- Geothermal energy i.e. both mapping underground reservoirs (to reduce drilling depth) and managing geothermal energy production.
- Nuclear energy: Especially the development of small modular reactors (SMRs) based on MSR technology. Thorium-based plants have significant potential on a 5–10 year horizon due to scalability (with China and Denmark taking lead positions). Moreover, AI is critical in plasma control for fusion reactors.
- Fusion energy: Development is also accelerating. For instance, Google DeepMind has created AI models for plasma control in Tokamak reactors using reinforcement learning.
- Energy storage: Especially in the production of new cathode and anode composites. This can phase out, for example, graphite, where China holds (near) global monopoly on refining. In addition, AI enables improved models for forecasting energy demand.
Clean water supply is also an increasing risk for many countries
Water security and purification, where the struggle for clean drinking water and water management will rise significantly over the next 5–10 years. This applies particularly to water from Tibet, from Ethiopia and from Anatolia. Armed conflicts are expected to arise, and therefore the value of securing groundwater as well as water purification is increasing. This affects the value of AI systems for, among other things:
- Early warning, e.g. pattern analysis of pollution signatures and integration of climate data
- Water purification, such as filtration, desalination, reverse osmosis, UV treatments, and chemical neutralization
- Infrastructure protection, both physical (leakages, etc.) as well as cyber- and operations-based.
All the Big Tech companies are focused on the area, and so are many startups
From the Big Tech side, especially NVidia and Google DeepMind, as well as IBM and Microsoft, are focused on climate and meteorological data. In addition, there is a very large number of startups. There will in particular be commercial potential in scalable technologies for energy production and (perhaps especially) for water purification. The field is often fragmented and therefore suitable for both passive and active investments.