AI’s Power Hunger Grabs Attention—Its Climate Wins Deserve More – CleanTechnica

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Last Updated on: 3rd March 2025, 10:45 pm

Artificial intelligence is often criticized for its energy intensity, with large-scale models like GPT-4 consuming tens of gigawatt-hours during training. However, it’s important to place this within the broader context of energy consumption and AI use case trends. I published a couple of times on demand to provide context, and now it’s time to talk about AI use cases again.

While data centers accounted for about 4.4% of U.S. electricity demand in 2022 — vastly lower in other countries as the USA has 45% of the world’s data centers, at least for now — overall grid demand has remained relatively stable for the past 25 years — even as digital activity has exploded. In reality, AI’s energy footprint is evolving, and advances in hardware and optimization techniques are rapidly improving efficiency.

For instance, NVIDIA’s Blackwell GPU architecture, launched in 2024, delivers up to a 30-fold performance increase for AI inference while cutting energy consumption by as much as 25 times compared to its predecessor. These innovations suggest that AI’s initial energy surge will taper off as the technology becomes more efficient. Meanwhile, the benefits AI brings to climate solutions — through precision forecasting, industrial decarbonization, and real-time emissions tracking — far outweigh its current energy costs. AI isn’t just a consumer of electricity; it’s an enabler of a more sustainable future.

I say again because in 2020 I published a 151-page report on machine learning and cleantech, explaining the core aspects of the technology from my perspective as a technology professional who has engaged in major transformation and innovation programs on multiple continents, often including various AI technologies, and gathering together the use cases being explored at the time. This won’t be nearly as comprehensive a review, simply because it’s likely impossible to figure out even 10% of the use cases where this core technology is being applied. No, this is a short survey of bigger hitters in the space.

Artificial intelligence is transforming the way renewable energy is integrated into power grids, making wind and solar generation more predictable and reliable. One of the biggest challenges with renewables is their variability — wind doesn’t always blow, and the sun doesn’t always shine. To address this, AI-driven forecasting is helping grid operators anticipate energy output with greater accuracy, reducing the need for fossil fuel backup and improving grid stability. Google DeepMind, in collaboration with Google’s wind farms, has used AI models to boost wind energy predictability by 20%, allowing operators to schedule power delivery more efficiently.

More accurate forecasting reduces wasted energy, minimizes reliance on carbon-intensive peaker plants, and makes renewable power more competitive with conventional energy sources. As AI continues to refine weather predictions and optimize grid management, it will play an increasingly critical role in scaling up renewables and accelerating the shift to a low-carbon economy. Not in the United States without paying a premium for private services, of course, because the slashing of budgets for the NOAA appears to be intentionally designed to force Americans and American firms to lean on for-profit weather corporations, in addition to destroying climate science in the country.

Artificial intelligence is playing a crucial role in modernizing electricity grids, making them more adaptive and efficient in handling the growing share of renewable energy. Traditional grids were designed for notionally always-on power sources like coal and gas, but the rise of predictable but variable wind and solar requires real-time adjustments to balance supply and demand. AI-driven smart grid solutions from companies like Autogrid and Schneider Electric analyze vast amounts of data to optimize energy flows, prevent overloads, and improve stability. By dynamically adjusting power distribution, AI helps reduce energy waste and reliance on fossil fuel backup systems.

AI-enabled grid management can cut carbon dioxide emissions by improving efficiency and reducing curtailment — when excess renewable energy is wasted due to grid constraints. A study by the International Energy Agency (IEA) estimates that smarter grid management could reduce annual global power sector emissions by up to 2 gigatons of CO₂ by 2050. Additionally, by enabling better demand response programs, AI allows utilities to shift energy consumption away from peak times, lowering the need for high-emission peaker plants. As the energy transition accelerates, AI-powered grids will be essential for maximizing renewable adoption and driving down emissions.

Artificial intelligence is transforming the construction and heavy industry sectors, helping to cut emissions from some of the most carbon-intensive materials—cement and steel. AI-driven process optimization is already making an impact in steel production, with companies like Boston Metal refining molten oxide electrolysis to eliminate coal from the process, reducing emissions by nearly 2 metric tons of CO₂ per ton of steel. Similarly, Carbicrete is using AI to optimize carbon-negative concrete production, replacing traditional cement with steel slag while permanently sequestering CO₂ during curing. These innovations could significantly reduce emissions from industries that together account for 15% of global CO₂ output.

Beyond materials manufacturing, AI is also reshaping how buildings are designed, reducing the demand for cement and steel in the first place. Generative AI and finite element analysis are enabling architects and engineers to create structures that use fewer resources while maintaining strength and durability. By simulating stress loads and optimizing material placement, AI can design buildings that require 30-50% less steel and concrete, dramatically lowering embedded carbon. As these technologies scale, they offer a dual benefit — reducing the emissions associated with material production while making construction more efficient.

I pulled out these examples in my deep and broad dives into steel and cement in the past two years, resulting in my decade by decade scenarios of steel and cement demand, supply and emissions through 2100.

Artificial intelligence is revolutionizing carbon accounting, providing companies with real-time insights into their emissions and helping them develop more effective climate strategies. AI-powered platforms like Persefoni and Watershed analyze vast amounts of data from energy use, supply chains, and transportation to give businesses an accurate picture of their carbon footprint. Unlike traditional manual reporting, which can be time-consuming and error-prone, AI enables automated tracking and identifies areas where emissions can be cut more efficiently. This is critical as governments and investors demand greater transparency on corporate sustainability efforts.

According to Watershed, its clients have reduced millions of metric tons of CO₂ by optimizing operations and shifting to cleaner energy sources. AI tools also play a key role in Scope 3 emissions reporting, which covers indirect emissions from supply chains — often the largest share of a company’s footprint. By providing more accurate emissions data, AI helps firms set credible reduction targets, comply with stricter climate regulations, and access sustainable finance opportunities.

Artificial intelligence is transforming extreme weather prediction, providing earlier and more accurate warnings for hurricanes, floods, and wildfires — events that are becoming more frequent and severe due to climate change. AI-powered platforms like Google’s FloodHub and IBM’s The Weather Company use machine learning to analyze vast amounts of meteorological data, satellite imagery, and historical weather patterns to improve forecasting. Google’s FloodHub, for example, can predict river floods seven days in advance, giving communities critical time to prepare. Similarly, AI-driven wildfire models help firefighters anticipate fire spread and allocate resources more effectively, reducing damage and loss of life.

More precise forecasting allows governments and disaster relief agencies to respond faster, potentially saving thousands of lives and billions in economic losses each year. The World Meteorological Organization estimates that improved early warning systems could reduce disaster-related economic damages by 30%. AI-driven climate modeling also helps policymakers and urban planners design more resilient infrastructure, reducing long-term climate risks. Once again, this is becoming less likely in the United States even as the rest of the world moves forward rapidly.

Artificial intelligence is becoming a critical tool for cities and businesses looking to safeguard infrastructure against the growing threats of climate change. AI-powered platforms like Jupiter Intelligence and One Concern analyze vast datasets — from satellite imagery to historical weather patterns — to assess risks from heat waves, rising sea levels, and extreme storms. By modeling future climate scenarios, these tools help city planners and utility operators make informed decisions about where to strengthen flood defenses, reinforce power grids, and redesign urban landscapes to withstand more frequent and severe weather events.

AI-enabled digital twins of existing infrastructure, like Trace Intercept‘s water solutions — full disclosure: I’m a founder — and Buzz Solution’s electricity and distribution solutions, enable asset managers to optimize maintenance and investment in the face of a rapidly changing climate.

AI-driven infrastructure planning can prevent billions of dollars in damage from climate-related disasters, reducing the need for carbon-intensive rebuilding efforts. The World Bank estimates that every dollar invested in climate-resilient infrastructure saves $4 in future damages and economic losses. Moreover, AI-assisted planning enables cities to design cooler, more energy-efficient urban environments, reducing emissions from air conditioning and improving overall sustainability.

Artificial intelligence is reshaping agriculture by making farming more efficient and sustainable, reducing emissions while improving yields. AI-driven precision agriculture helps farmers optimize fertilizer use, water consumption, and soil health, cutting waste and lowering the environmental footprint of food production. Companies like Indigo Ag and Regrow Ag use AI to analyze satellite imagery, soil data, and weather patterns, allowing farmers to apply fertilizers and irrigation only where needed. This reduces the overuse of nitrogen fertilizers, a major source of nitrous oxide (N₂O) emissions, which are nearly 300 times more potent than CO₂ as a greenhouse gas.

The Food and Agriculture Organization (FAO) estimates that more efficient fertilizer use alone could cut global agricultural emissions by up to 20%. AI is also driving the adoption of regenerative farming practices, such as cover cropping and no-till farming, which enhance soil carbon sequestration. Regrow Ag’s AI-powered platform, for example, has helped farmers store millions of tons of CO₂ in soil, effectively turning farmland into a carbon sink.

Artificial intelligence is revolutionizing the fight against deforestation, enabling real-time monitoring of illegal logging and land degradation. AI-powered satellite analytics from Global Forest Watch and Planet process vast amounts of imagery to detect changes in forest cover, allowing governments and conservation groups to intervene before large-scale damage occurs. By analyzing satellite data with machine learning, these systems can identify illegal deforestation within days instead of months, significantly improving enforcement efforts in regions like the Amazon, Southeast Asia, and the Congo Basin.

Deforestation accounts for nearly 10% of global CO₂ emissions, as forests act as major carbon sinks. According to Global Forest Watch, AI-assisted monitoring has helped reduce tree cover loss in monitored areas, preventing millions of tons of CO₂ from being released. By catching illegal activity early and supporting reforestation efforts, AI is playing a vital role in preserving biodiversity, protecting Indigenous lands, and ensuring forests continue to absorb carbon in the fight against climate change.

Artificial intelligence is becoming a crucial tool in the fight against methane emissions, one of the most potent greenhouse gases contributing to climate change. Companies like GHGSat and Kayrros use AI-powered satellite imaging to detect methane leaks from oil and gas operations, agriculture, and landfills with unprecedented accuracy. These systems analyze infrared satellite data to pinpoint leaks down to the source, allowing companies and regulators to take swift action. GHGSat’s technology, for example, can detect emissions as small as 100 kg of methane per hour, providing real-time insights that were previously impossible with conventional monitoring methods.

Methane has almost 90 times the warming potential of CO₂ over a 20-year period, and human-caused methane emissions contribute to nearly 30% of global warming. AI-driven detection has already led to the identification and mitigation of massive leaks — Kayrros estimates that rapid detection could prevent the release of millions of tons of methane annually, equivalent to taking millions of cars off the road. This may not matter in the USA, where methane leakage mitigation programs are under the gun with the current administration, but it matters globally.

Artificial intelligence is making electric vehicle (EV) charging smarter and more efficient, helping to reduce grid strain while maximizing renewable energy use. Companies like Twaice and FreeWire Technologies use AI-driven analytics to optimize charging schedules, ensuring EVs charge when electricity demand is low or when surplus renewable energy is available. AI also predicts battery degradation, extending battery life and improving overall efficiency. By dynamically managing charging patterns, these systems prevent grid overloads and reduce reliance on fossil-fuel-powered peaker plants.

EV charging optimization can cut peak electricity demand by up to 30%, reducing the need for high-emission backup power. A study by the National Renewable Energy Laboratory (NREL) found that smart charging could reduce EV-related grid emissions by as much as 20%, especially when paired with renewable energy.

Artificial intelligence is accelerating the search for low-carbon materials, enabling breakthroughs in next-generation batteries, catalysts, and carbon-negative alternatives. Machine learning models can analyze millions of chemical compounds in a fraction of the time it would take using traditional methods, identifying materials that improve energy efficiency and reduce emissions. DeepMind’s AlphaFold, originally designed for protein folding, is now being applied to material science, helping researchers predict molecular structures for sustainable alternatives in industries like energy storage and construction.

AI-driven material discovery is already leading to faster development of solid-state batteries, which could improve EV efficiency and cut reliance on lithium mining. AI is also helping develop carbon-negative cement alternatives, which could reduce emissions from cement production — currently responsible for 8% of global CO₂ emissions. By streamlining research and bringing low-carbon innovations to market faster, AI is playing a crucial role in decarbonizing key sectors and accelerating the transition to a sustainable economy.

Artificial intelligence is transforming ocean conservation by enabling real-time monitoring of coral reefs and marine biodiversity. AI-powered computer vision, used by initiatives like The Allen Coral Atlas, analyzes satellite imagery and underwater photos to detect early signs of coral bleaching, track reef health, and map changes in marine ecosystems. By automating what was once a labor-intensive process, AI allows scientists and conservationists to respond more quickly to environmental threats, improving efforts to protect vulnerable marine habitats.

Coral reefs support 25% of all marine life and act as natural carbon sinks, but rising ocean temperatures and acidification are causing mass bleaching events at an alarming rate. AI-driven monitoring has already helped pinpoint over 90% of the world’s coral reefs, giving researchers critical data to guide restoration efforts and policy interventions. By enhancing conservation strategies and informing sustainable ocean management, AI is playing a crucial role in preserving marine biodiversity and mitigating the impacts of climate change on the world’s oceans.

While AI’s energy demand is often scrutinized, its value in driving climate action far outweighs its footprint. AI optimizes renewable energy integration, enhances grid efficiency, and accelerates industrial decarbonization in sectors like cement and steel. It revolutionizes climate risk assessment, speeds up low-carbon materials discovery, and strengthens conservation efforts by monitoring deforestation and ocean health. Smart carbon accounting, methane leak detection, and precision agriculture further showcase AI’s role in cutting emissions at scale. As innovations in AI hardware continue to improve efficiency, its climate benefits — measured in gigaton-scale emissions reductions — position it as an essential tool in the fight against climate change.