The dominant conversation around artificial intelligence and climate change often defaults to a binary discussion: either AI is a savior that will optimize renewable grids and build more accurate climate models, or it’s a villain that will guzzle energy and water while spreading climate disinformation.
This black-and-white thinking is not new to debates around how cutting-edge innovations can impact our climate and natural surroundings. Technologies that were initially touted as silver bullets were later shown to have unintended consequences for ecosystems and vulnerable communities; while others that were categorically seen as enemies turned out to be critical in enabling breakthrough solutions.
But we can’t make the same mistake when it comes to climate change and AI. Like Dr. Maria João Sousa, Executive Director of Climate Change AI says “AI-for-climate is inherently multi-faceted and interdisciplinary, and its evolution requires a plural environment rather than a single, dominant technological pathway.”
Paradoxes and nuance aren’t new in building alliances and deploying climate solutions. Case in point,Texas produces the most wind-powered energy generation of any state in America. Remove it from the equation, and renewable production (and oil) in the U.S. plummets. China has invested more in renewable energy than any country in the world by a long shot, but it’s also the largest GHG emitter of any country.
There are also parallels between climate change and AI - human-made, seemingly unstoppable forces, driven by the wealthiest countries, spreading rapidly while humanity attempts to keep up. In many cases, we are all already living in or soon will be living in a world shaped by AI and climate change. In the words of Francisco Martin-rayo, Founder and CEO, reach 25% in 2026. But there is worry that the supply of clean energy will struggle to keep up with data centers‘ power needs. Data centers require uninterrupted power and in areas with limited renewable energy capacity or grid constraints, companies may rely on natural gas or coal. And this is happening already. For example, Meta’s new $10 billion AI data center in Louisiana will be powered by three new natural gas plants, and Chevron is building natural gas power plants to supply data centers directly.
Yet, AI’s climate and environmental impact extends beyond its energy and water footprint. The hardware needed to run AI-specialized chips, servers, cooling systems-has a meaningful “embodied” carbon cost from manufacturing, shipping, and disposal. Frequent hardware upgrades generate e-waste and emissions that may not be captured in corporate sustainability reports.
In parallel, many are concerned that AI’s deployment can reinforce global inequities around climate impact and solutions. Many AI climate tools are trained on data from wealthy geographies,making them less effective-or even counterproductive-in the Global South,where climate vulnerability is highest. Although the 74 lowest-inco
AI Offers Powerful tools to Combat Climate Change, But Energy Use Remains a Concern
Artificial intelligence is emerging as a critical tool in the fight against climate change, offering solutions for emissions reduction, adaptation, and environmental monitoring. While the technology itself consumes significant energy, experts believe its potential to accelerate the transition to renewable energy and improve resource management outweighs the drawbacks.
AI is already demonstrating its value in several key areas. Google’s DeepMind,for example,uses AI to predict wind power output 36 hours in advance,increasing grid value by 20%. AI-driven solar tracking can boost panel efficiency by up to 20%, and smart grids are leveraging AI to balance electricity demand and storage, lessening the need for fossil fuel-powered peaker plants.
Beyond energy,AI is improving emissions monitoring. Platforms like Climate Trace utilize AI-powered satellite surveillance to track methane leaks and deforestation, creating detailed emissions inventories to hold polluters accountable. Hendrik Tiesinga, Founder and CEO of Climate Edge, emphasizes this point: “Yes, AI draws substantial power – but it’s also accelerating the buildout of renewables. The same demand that worries people can drive economies of scale in clean energy and push costs down even faster.”
AI’s applications extend to adaptation and resilience. Machine learning models are now predicting wildfires, floods, and heatwaves with greater accuracy, allowing for earlier evacuations and more effective resource allocation. Platforms like Climawise connect global adaptation strategies to local risks,helping cities develop tailored responses to climate impacts.
“AI’s real power is hyper-customized insight at scale, at very low cost. For smallholder farmers, that can mean the difference between survival and collapse – optimizing water, fertilizer, and timing in ways that simply weren’t possible even a few years ago.”
– Martin-Rayo
In Bangladesh, AI-enhanced flood alerts preserved $200 million in crops during the 2024 monsoon season, demonstrating the technology’s real-world impact. AI is also being used to optimize agricultural practices, helping farmers reduce emissions and resource waste while increasing yields.
conservation efforts are also benefiting from AI. Algorithms analyze sensor data to track coral reef health, detect illegal fishing, and forecast deforestation risks in areas like the Amazon rainforest. AI-powered land-use planning models integrate data on soil,elevation,and infrastructure to predict habitat shifts and inform conservation strategies.
According to Andrew manning, AI provides the ability to measure and understand climate change in unprecedented detail. “From a climate outlook, it comes down to a simple principle: you can’t fix what you can’t measure. Large Earth observation models give us the ability to ask climate questions we’ve never been able to ask before and get answers without spending millions retraining bespoke models every time.”
Large urban areas are seen as ideal testing grounds for many of these AI-powered climate solutions, given their concentration of climate risk, inequality, and infrastructure.
