Despite Its Steep Environmental Costs, AI Might Also Help Save the Planet

The rapid expansion of artificial intelligence has brought renewed scrutiny to its environmental footprint. Rising electricity demand, increased water use, and growing carbon emissions linked to data centers and AI workloads have made the technology a focal point in sustainability debates. Yet the environmental story of AI is not one-dimensional. Alongside its resource intensity, AI is increasingly being deployed as a tool to improve efficiency, reduce emissions, and optimize resource use across some of the most carbon- and water-intensive sectors of the global economy.

Research into emerging technologies suggests that AI’s net environmental impact will depend less on its existence and more on how it is applied. While the energy costs are real and growing, AI is already reshaping agriculture, energy systems, buildings, and transport in ways that directly support sustainability goals.

 

Precision Agriculture and Smarter Water Use

 

Agriculture accounts for close to 70 percent of global freshwater withdrawals, placing it at the center of intensifying competition for water resources. AI-driven irrigation systems are beginning to change how water is managed on farms, shifting from blanket watering to targeted, data-led decisions.

One example is Kilimo, an Argentinian climate technology company that uses machine learning, satellite imagery, and weather data to determine precisely when and where crops need water. By tailoring irrigation to actual plant demand, the system reduces overwatering and avoids unnecessary energy use associated with pumping and distribution.

In Chile’s Biobío region, farms using Kilimo’s platform have reported water savings of up to 30 percent while maintaining crop productivity. The approach also creates financial incentives for sustainability. Verified water savings are converted into credits that local companies can purchase to offset water use, allowing farmers to recover costs and often earn returns well above their initial investment.

 

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Data Centers and the Push for Energy Efficiency

 

Data centers sit at the heart of AI’s environmental challenge. In the United States alone, they consumed approximately 176 terawatt-hours of electricity in 2023, rising further in 2024. At the same time, global internet traffic has surged more than twenty-fivefold since 2010, driven by digitalization and AI adoption.

Despite this growth, data center electricity use has increased more slowly than demand for digital services. Efficiency gains, many of them AI-enabled, have helped moderate energy consumption. AI systems now monitor workloads, temperatures, cooling performance, and power use in real time, allowing operators to match computing resources to demand and avoid wasted energy.

Companies including Microsoft and Meta use AI to manage server loads and optimize cooling. Predictive analytics allow servers to enter low-power states during periods of low demand, while automated airflow controls reduce unnecessary cooling. In Germany, Equinix has applied AI-based weather-responsive cooling in Frankfurt, improving operational efficiency by nearly 10 percent.

 

Energy Systems, Fuels, and Emissions Control

 

Energy producers are also deploying AI to improve operational efficiency and cut emissions. Drones equipped with cameras and sensors now inspect pipelines and infrastructure, while AI analyzes imagery to identify corrosion, cracks, or leaks before they escalate into major failures.

At Shell, AI systems monitor methane emissions by combining concentration data with wind speed and direction to pinpoint sources of leakage. Faster detection allows targeted maintenance and operational adjustments, supporting the company’s goal of dramatically reducing methane emissions by the end of the decade. Similar optimization tools are being used to improve efficiency in liquefied natural gas operations, lowering both energy use and emissions.

Beyond fossil fuels, AI is expected to accelerate innovation in clean energy technologies. Applications range from improving the performance of solar panels and batteries to enhancing carbon capture systems. Over time, AI could also support breakthroughs in advanced biofuels, fusion research, and the monitoring of natural carbon sinks such as forests and wetlands.

 

Buildings and District Heating Optimization

 

Buildings are responsible for roughly 28 percent of global energy-related emissions, largely due to heating, cooling, and electricity use. AI-driven building management systems are beginning to address this challenge through predictive control and demand forecasting.

In Copenhagen, the municipal utility HOFOR has deployed thousands of sensors across buildings to track temperature, humidity, and energy flows. AI models forecast heating needs up to a day in advance and adjust supply accordingly. Piloted initially in schools and multifamily housing, the system has since expanded across the city, delivering energy savings of up to 25 percent and reducing peak heating demand significantly. Annual emissions reductions from participating buildings have reached around 10,000 tonnes of carbon dioxide.

On a smaller scale, AI-enabled building systems are also showing promise. Research from Lawrence Berkeley National Laboratory suggests that medium-sized office buildings in the United States could cut energy use by more than 20 percent and significantly reduce emissions through intelligent automation of heating, cooling, and appliances.

 

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Aviation and Smarter Flight Operations

 

Aviation remains one of the most difficult sectors to decarbonize, accounting for around 2 percent of global carbon dioxide emissions. Beyond fuel burn, contrails formed at cruising altitudes contribute substantially to aviation’s warming impact.

AI is being used to optimize flight paths and altitudes in real time, helping aircraft avoid atmospheric conditions that are more likely to generate contrails. Airlines are also applying AI to improve fuel efficiency by analyzing weather patterns, wind conditions, airspace constraints, and traffic flows.

In 2023, Alaska Airlines reported saving approximately 1.2 million gallons of fuel by using AI-assisted route optimization, translating into meaningful emissions reductions on longer flights.

 

A Technology of Trade-Offs and Opportunity

 

AI’s environmental impact is inherently dual. Its rapid growth places additional strain on energy and water systems, yet its ability to optimize complex processes offers powerful tools for reducing waste, cutting emissions, and improving resource efficiency. From farms and data centers to buildings and aircraft, AI is already delivering measurable sustainability benefits.

Whether AI ultimately becomes a net positive for the planet will depend on policy choices, investment priorities, and the extent to which efficiency gains are scaled faster than energy demand grows. Used deliberately and governed effectively, AI has the potential to support a cleaner and more resilient economic system rather than undermine it.