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Introduction
In 2024, our planet experienced its hottest year on record. This is a sobering milestone, marked by unprecedented heatwaves, floods and wildfires. Greenhouse gas concentrations hit all-time highs, highlighting the urgency of the climate crisis. The previous record-holder? Just one year earlier.
The Doomsday Clock is at 89 seconds to midnight and climate change is one of the most critical factors contributing to this. With time running out to limit global warming, humanity is desperately turning to every tool available for solutions. A perhaps unexpected ally is emerging in this fight in the form of AI. Together with machine learning, AI is seen as having “potentially transformative” capabilities in areas like climate and weather forecasting. From optimising renewable energy to predicting natural disasters, AI is now being leveraged as a powerful tool to help curb emissions and build resilience against climate change’s impacts.
Some of the major sustainability-related topics in which AI is already leaving its digital fingerprints are related to optimising renewable energy, enhancing resource management and environmental monitoring.
Here are three areas where AI is already making a measurable difference: energy, agriculture and environmental monitoring.
Optimising Renewable Energy
One of the most impactful uses of AI is in the optimisation of renewable energy. Because sources like wind and solar are sporadic, balancing their output with demand is a complex challenge. Machine learning models can forecast energy production and adjust systems in real time to smooth out this variability. A notable example comes from Google DeepMind’s work on wind farms: by training neural networks on weather forecasts and turbine data, they can predict wind power output 36 hours in advance. This “foresight” powered by AI allowed Google to schedule energy deliveries more efficiently, boosting the value of its wind energy by roughly 20% compared to not using AI-driven scheduling. Such improvements make clean power more reliable and easier to integrate into the grid. Similar AI techniques are being used to optimise solar panel output, control smart grids and even cut energy waste in buildings and data centres. The result is that renewable energy systems produce more usable power with less fluctuation, accelerating the transition to a low-carbon energy future.
Sustainable Agriculture
Farmers are also deploying AI to produce more food with fewer resources. Agriculture is both victim and contributor to climate change. It suffers from changing weather patterns while accounting for significant greenhouse emissions. AI offers tools to make farming more efficient and sustainable. For instance, machine learning models can analyse soil conditions, weather data and crop health images to inform precision agriculture: delivering water, fertilisers or pesticides only when and where needed. A striking real-world example is the use of AI-powered robotics in weed control. Blue River Technology’s See & Spray system (acquired by John Deere) uses computer vision to distinguish weeds from crops in real time as a tractor moves through the field. The robot sprayer then targets only the weeds with herbicide. Because the machine “sprays exactly where the weeds are found,” this approach reduces herbicide use by 90%, avoiding thousands of gallons of chemical spray. This not only cuts farmers’ costs and greenhouse emissions from chemical production, but also reduces pollution and soil degradation. Similarly, AI-driven decision tools can help farmers predict pest outbreaks or drought stress and adjust strategies proactively. By optimizing resource use and protecting yields, AI is helping agriculture become more climate-smart.
Environmental Monitoring
AI is revolutionising how we monitor Earth’s vital signs: our forests, oceans, wildlife and atmosphere. High-resolution data from satellites, drones and sensor networks can now be analysed by AI to detect environmental changes that humans might miss. A case in point is deforestation monitoring. The Amazon rainforest, for example, lost approximately 3 million hectares to deforestation from 2022 to 2023 (about 10,000 acres per day), a rate that is nearly impossible to track from the ground. In response, organizations in Latin America launched Project Guacamaya, a partnership leveraging cloud computing and AI to protect the Amazon. This joint effort between a Colombian research center, environmental institutes, and Microsoft’s AI for Good lab uses AI models to analyze daily satellite images, camera-trap photos, and even audio recordings of the rainforest. The AI can flag signs of illegal logging, mining, or habitat loss far faster than traditional monitoring. It even uses bioacoustics to hear chainsaws or distressed animal calls as early warnings. By identifying deforestation hotspots quickly, authorities can intervene before too much damage is done. As one leader involved noted, such technology is a“game-changer in saving the Amazon”. Beyond forests, similar AI systems are used to monitor air pollution in cities, track endangered wildlife (using pattern recognition on photos and audio), and measure ocean health (e.g. spotting algae blooms or illegal fishing via satellite). This intelligent eyes-on-the-earth approach gives environmental stewards unprecedented ability to observe and protect the planet’s natural resources in real time.
Conclusion
AI will not single-handedly solve climate change, but it is already proving to be a powerful tool in humanity’s response. From stabilising renewable energy to making farming more efficient and tracking ecosystems in real time, these technologies are pushing the boundaries of what is possible. Their impact, however, will depend on how wisely and widely they are deployed. If paired with strong climate policies and global cooperation, AI could help shift the trajectory toward a more sustainable and livable future.