Potential artificial intelligence aimed at tackling the climate crisis is essential. It is a belief echoed by Nuria Oliver, co founder of the Ellis Foundation and MIT-trained expert, who asserts that climate action cannot proceed without AI. Though still in development, the potential unlocked by AI feels boundless, touching transportation, farming, smart construction, hurricane forecasting, and fire management. It represents a real turning point, a crossover revolution with wide-reaching implications.
The greatest beneficiaries of this technology are public administrations. Improved weather predictions, clearer insights into greenhouse gas sources, and precise tools to mitigate environmental damage from heavy polluters can help governments tailor policies to curb the climate emergency.
Artificial intelligence often gets pictured as a driver of progress, and its strength lies in handling vast data sets from the past, present, and future. More importantly, predictive models can be built from this data, enabling the detection of patterns and trends that support informed decision making. Hendrik Zimmermann, a sustainability researcher with Germanwatch, emphasizes that digital tools and large data pools must be collected and analyzed quickly to achieve emissions reduction goals through AI or machine learning techniques.
Special applications
A study summarized in MIT Technology Review, drawing on David Rolnick’s research from the University of Pennsylvania, identifies about a dozen concrete ways AI can help combat climate change. Many of these focus on energy efficiency and rely on sharpening weather forecasts. Rolnick notes that short term progress will come from researchers directing the work.
AI can improve building design to cut lighting, heating, and cooling costs
AI will forecast energy demand, closely tied to local weather, and help optimize building envelopes to reduce energy use for lighting, heating, and cooling across seasons. Satellite imagery will guide renovation priorities for properties. For example, a Berlin project at the Borderstep Institute showed predictive machine learning could cut energy use in a block of 250 apartments by at least twenty percent.
Weather forecasting will be one of the major benefits
Other sectors near the top of pollution lists stand to gain. In transportation, AI can consolidate shipments to minimize trips and optimize routes. In agriculture, algorithms can identify the best crops for a given area, guide soil regeneration, reduce fertilizer usage, and automate pest control. Industry can push forward with new materials and cleaner, more efficient energy sources.
Reduce emissions by 4% in 2030
Beyond ecological gains, the move to AI-driven environmental solutions could bolster the global economy. A PwC report funded by Microsoft suggests these technologies could add billions to global accounts by 2030 and help cut greenhouse gas emissions by several percent. Some optimistic estimates place reductions from five to ten percent, indicating AI may strike a balance between growth and sustainability.
Fire surveillance and prevention systems can be improved
Extreme weather events have grown more frequent in recent years due to climate change. Hurricanes, heavy rainfall, and wildfires can strike with little warning. AI offers multiple approaches to manage these events before, during, and after they occur. Nuria Oliver notes autonomous drones guided by AI can help prevent fires or search for survivors after floods or earthquakes, while also supporting deforestation monitoring.
Manage forest areas in cities
The Google Tree Canopy project illustrates how cities can monitor and enhance tree cover. Trees help lower street temperatures and improve urban living quality, yet many cities lack the tools to map canopy and identify where planting is most needed. Europe-based leaders such as Adam Elman explain the value of this data, with cities like Vitoria, Barcelona, and Zaragoza already leveraging the approach.
Yet the promise comes with caveats. Oliver points out that these technologies demand substantial energy input. The benefits must be weighed against the energy costs, considering the sources of that energy and the overall lifecycle of the technology as society moves into this new era.
HIGH ENERGY CONSUMPTION
AI and especially generative AI can impose a sizable energy footprint as development accelerates. Training large models consumes large data volumes and substantial power. A New York based AI developer reported a training event that used enough energy to power tens of thousands of homes for a year. Ongoing operation also contributes energy demand, with daily usage estimates for popular tools illustrating the scale of consumption. The global electricity use tied to AI could rise significantly by the late 2020s, mirroring the energy needs of several mid-sized nations.
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Professional supervision remains necessary for now. Meteorologists anticipate that AI-enabled tools will soon boost weather forecasting and risk assessment, but human expertise will still be essential to interpret results and guide decisions. Applications span real-time water and energy management, agricultural monitoring, and optimized climate control for infrastructure and transport. Real-time data from weather systems can enhance risk prediction, reduce carbon footprints, and improve efficiency compared with older, less flexible systems.
What applications does artificial intelligence have against climate change? It can support real-time monitoring of water and energy use, agriculture, and building or transport climate control. It helps sharpen risk predictions with up-to-date forecast data, while also trimming emissions through smarter, more responsive systems.
How mature is the field? AI has existed for over two decades but is only beginning to scale in many areas. Neural networks and deep learning appear across disciplines, and their broader deployment is on the horizon as models become easier to explain and apply. The potential for predicting natural disasters and improving monitoring grows as models gain accuracy and resolution.
Can AI replace meteorologists? No, not in the near term. It remains a support tool that requires professional supervision to interpret results and ensure reliability. Automation in weather forecasting may become more intuitive in the medium term, but independent scientific insight will continue to rely on human imagination and expertise.
For further details, data and quotes cited in this overview come from industry and research experts noted above.