As the governor of the Bank of England, Andrew Bailey, claims AI is likely to displace people from jobs in a similar way seen during the Industrial Revolution, we ask the question ‘will AI play a major role in solving the world’s climate and nature crises, and if so, how?’
The answer to the first part of the question is almost certainly ‘yes’, AI is likely to play a major role in tackling the climate and nature crises, but as a force multiplier rather than a producer of a ‘silver bullet’.
Is AI the intelligent climate and nature solution?
Cutting emissions faster and more cheaply
AI can forecast power grid demand and balance supply, making it easier to integrate variabledelivery renewables such as wind and solar into the power delivery system.
AI can also drive smart energy management systems in buildings, to cut heating, cooling, and process energy.
In the field of transport, AI can optimise routes, manage traffic, and support logistics in reducing fuel use and tackling congestion.
Accelerating the development of clean technologies
AI has already proven it can dramatically accelerate R&D (Research & Development). In essence, it can shorten the time from laboratory discovery to deployment. This is a critical consideration given the pressing climate and nature timelines.
AI may help in the discovery of materials that will help deliver better batteries, solar panels, and carbon capture. AI can also be of critical value in climate modelling to test scenarios more quickly, and more precisely. It is also likely to be a major contributor to work relating to fusion technologies, hydrogen development, and carbon storage optimisation.
Monitoring and protecting nature
AI can improve our ability to see environmental damage early. Satellites and AI are able to detect deforestation, illegal mining, and fishing in almost real-time.
Furthermore, acoustic and photographic AI can track wildlife populations and add predictive capability.
Ecosystem modelling using AI can predict collapse risks and guide conservation. This is especially important in places where enforcement capacity is limited.
Climate adaptation and resilience
AI can help societies cope with unavoidable impacts. Whilst adaptation does not stop climate change, it can save lives and ecosystems by informing:
a. Early warning systems for floods, heatwaves, wildfires, and storms.
b. Precision agriculture that uses less water, fertiliser, and land.
c. Urban planning to reduce heat stress and flood risk.
Improved policy and finance decision-making
AI can improve targeting and accountability, and reduce the opportunity for greenwashing and capital misallocation by:
1. Identifying high-impact climate investments.
2. Stress-testing climate risks in financial systems.
3. Tracking whether climate pledges are being met.
Many major climate science programmes, research projects, and collaborative initiatives already actively use AI. These include:
The AI4 Climate programme from the UK Met Office
This UK-government-funded initiative forms part of the National Capability AI programme, applying machine learning to climate science. The programme uses AI to:
a. Making global projections locally relevant.
b. Creating hybrid models that combine physics and AI to improve accuracy and speed.
c. Forecast climate impacts at urban and regional levels.
Destination Earth (DestinE), from the European Union
This EU flagship programme is building a digital twin of the Earth - a high-fidelity AI-powered simulation environment to model climate change, extreme weather, and environmental systems. Its aim is to help policymakers simulate future risks and test mitigation and adaptation responses at scale.
The AI Climate Institute
Launched at COP30 in 2025, the AI Climate Institute aims to build capacity for AI use in climate action (especially in developing countries), support applications such as water management, disaster risk reduction, and environmental monitoring, and cultivate ethical, energy-efficient AI for climate resilience.
AI for Climate and Nature from the University of Cambridge
This interdisciplinary programme uses AI to integrate diverse environmental data such as satellite imagery, biodiversity records, and climate models, to:
a. Monitor forests and ecosystems.
b. Predict climate impacts on biodiversity.
c. Inform land use and conservation decision-making
Microsoft AI for Earth
Microsoft’s long-running programme funds and supports AI applications in climate modelling. The programme also supports biodiversity monitoring, water systems, and agriculture, as well as providing the Planetary Computer platform with data and tools for climate and environmental research across many countries.
Google DeepMind Weather Lab
DeepMind has developed an AI weather prediction system that improves forecasts of tropical cyclones and other extreme weather events, using many decades of climate data.
X (Alphabet) Project Bellweather
The Bellweather project from X Development applies AI to earth observation data in order to forecast natural disasters such as wildfires, floods, and hurricanes, and to assess infrastructure risk, and improve planning.
Whilst not always represented by formal programmes, there are also a host of other collaborations and efforts using AI in some form.
These include Climate TRACE being expanded with AI and satellite data. This important initiative detects greenhouse gas and particulate sources globally, thereby increasing the transparency of emissions data.
There are also advance AI models for sea-surface temperature reconstruction, and self-evolving AI agents for climate modelling.
Amongst this enormously promising AI-fuelled landscape there are, however, a number of caveats. Firstly, AI itself consumes energy, currently in significant quantities. Training large models consumes large amounts of electricity and without widespread use of clean power this could add to emissions.
Then there is political will. AI can show where emissions come from, and which solutions might work best, but it cannot force governments or companies to act. Only policy, regulation and social pressure can achieve that.
Nature is also extremely complex. AI models ecosystems well, certainly better than previous technologies, but data gaps remain and ecosystems have ‘tipping points’ that are hard to predict. In this context, AI cam support, but should not replace ecological expertise and indigenous knowledge.
Returning to the original question whether AI will play a major role in solving the world’s climate and nature crises, the answer is that AI can certainly significantly accelerate solutions by helping to cut waste and emissions, speed up clean technology innovation, protect ecosystems through better monitoring, and make adaptation faster and smarter.
However, AI is unlikely to solve climate change alone. Its benefits depend on clean energy, good governance, and strong institutions. When we think of AI in a climate and nature context, it should be as a powerful additional engine that determines how fast we move, rather than necessarily where we choose to go.
Related content
