AI’s environmental impact has quickly become a major global concern. In 2025, AI systems are estimated to produce between 32.6 and 79.7 million tonnes of CO2 emissions each year, roughly equal to the annual emissions of New York City.
In addition, Data centers powering AI used about 415 TWh of electricity in 2024, around 1.5% of global electricity demand, and this could rise to 945 TWh by 2030. AI is growing so fast that its energy use and carbon footprint are increasing faster than the tech industry’s shift toward cleaner energy.
In this article, we explore the latest AI carbon emissions statistics, including data on electricity use, emissions, water consumption, and the environmental impact of large-scale AI systems.
Key AI Carbon Emissions Statistics
- AI systems are estimated to emit 32.6 to 79.7 million tonnes of CO2 annually in 2025.
- At the midpoint estimate, AI emissions are roughly equal to the annual carbon footprint of New York City.
- AI-related emissions already account for more than 8% of global aviation greenhouse gas emissions.
- Global data center electricity demand is projected to rise from 415 TWh in 2024 to 945 TWh by 2030.
- AI workloads already make up around 24% of server-level electricity use in data centers.
- By 2030, data centers could consume around 3.7% of global electricity demand.
- A single ChatGPT query may use 5× more electricity than a standard web search.
- Training GPT-3 required around 1,287 MWh of electricity and produced roughly 502 metric tons of CO2.
- AI systems are estimated to consume around 765 billion liters of water in 2025 for cooling and operations.
- Researchers estimate AI applications could help reduce global emissions by 3.2 to 5.4 billion tonnes of CO2-equivalent annually by 2035 through efficiency gains in energy, transport, and industry.
AI Carbon Footprint in 2025
As artificial intelligence scales across industries, its energy use is rising, bringing more attention to its environmental cost. The “AI carbon” footprint refers to the emissions produced by training and running AI systems in data centers and cloud infrastructure. While still a small share of global emissions, studies show its impact is growing quickly and becoming easier to measure.
AI Carbon Emissions Scale
A 2025 peer-reviewed study by Dutch researcher Alex de Vries-Gao (published in Patterns) provides one of the first detailed estimates separating AI’s environmental impact from general data center emissions. The findings show that AI is becoming a significant contributor to global emissions:
- AI systems are estimated to produce 32.6 to 79.7 million tonnes of CO2 per year in 2025.
- At the midpoint of this range, AI’s emissions are comparable to the entire annual CO2 output of New York City (~52.2 million tonnes in 2023).
- These emissions already account for over 8% of global aviation-related greenhouse gases.
- According to reports, if growth continues unchecked, high-emitting AI systems alone could reach up to 102.6 million tonnes of CO2-equivalent per year.
Share of Global AI Carbon Emissions
While AI-related emissions are rising quickly, their overall share of global emissions is still relatively small but increasingly significant:
- The 166 digital companies reporting emissions data were responsible for about 0.8% of global energy-related CO2 emissions in 2023. The 164 companies reporting electricity usage consumed around 581 TWh of electricity, equal to 2.1% of global demand, with just 10 companies accounting for roughly half of that consumption.
- Within the ICT sector, data centers are the largest source of emissions (45%), followed by communications networks at 24%.
Overall, AI’s footprint is still a fraction of global emissions, but its rapid growth rate and concentration in a few large players make it an increasingly important driver of digital-sector energy demand.
The Energy Root of AI Carbon Emissions
AI’s carbon footprint is primarily driven by its heavy electricity consumption. Behind every AI model are large-scale data centers that require continuous power for computation, storage, and cooling. As AI adoption accelerates, these facilities are expanding rapidly and becoming one of the fastest-growing sources of electricity demand.
AI Carbon Growth Driven by Data Center Electricity Demand
AI’s carbon footprint is largely driven by its growing electricity demand, which is concentrated in data centers, the core infrastructure that powers AI systems. According to Statista, in 2024 global data centers consumed around 415 TWh of electricity, a figure expected to surge to nearly 945 TWh by 2030 as AI workloads expand.
Within this ecosystem, AI already accounts for about 24% of server-level electricity use and roughly 15% of total data center energy consumption. Although data centers currently represent only about 1.5% of global electricity demand, this share is projected to rise to 3.7% by 2030.
| Particulars | Figure | Year |
| Global data center electricity use | 415 TWh | 2024 |
| Projected global data center electricity use | 945 TWh | 2030 |
| AI share of data center server electricity | 24% | 2024 |
| AI share of total data center energy use | 15% | 2024 |
| Data centers’ share of global electricity | ~1.5% | 2024 |
| Projected share of global electricity | ~3.7% | 2030 |
Electricity demand from data centers has been growing at around 12% annually between 2017 and 2023, roughly four times faster than global electricity demand. By 2030, usage is projected to reach 945 TWh, a level comparable to Japan’s total annual electricity consumption.
U.S. Data Centers and the Rising AI Carbon Load
The United States is the global hub of AI infrastructure, hosting a large share of the world’s leading data centers and cloud computing facilities. As AI adoption accelerates, the country is experiencing a sharp rise in electricity demand driven by expanding digital infrastructure.
- U.S. data centers consumed 147 TWh of electricity in 2023.
- Electricity demand from U.S. data centers is expected to more than quadruple between 2023 and 2030, adding over 450 TWh of additional consumption.
- By 2030, data centers could account for around 11.7% of total U.S. electricity demand.
- The U.S. is projected to contribute nearly half of the global growth in data center electricity demand.
Goldman Sachs Forecast
Goldman Sachs Research projects a sharp rise in global electricity demand from data centers, largely driven by the rapid expansion of AI workloads. As AI adoption accelerates, this surge is expected to become one of the key drivers of global power consumption growth over the coming decade.
- Global data center power demand is expected to increase by 50% by 2027 compared to 2023 levels.
- By the end of the decade, demand could rise by as much as 165% versus 2023.
- The growth is primarily driven by expanding AI computing workloads and model training needs.
- By 2030, AI alone could account for around 4.5% of global electricity generation.
AI Carbon Emissions Across Training and Usage
AI’s carbon footprint is not only driven by infrastructure but also by the computation required to build and run models. Emissions come from two main phases: training, which is energy-intensive but temporary, and inference, which is continuous and scales with every user query.
AI Carbon from Training Large Models
Training large AI models requires huge amounts of computing power, which leads to significant one-time carbon emissions. The larger and more advanced the model, the more electricity it needs, and the higher its environmental impact during the training phase.
- Training GPT-3 (175 billion parameters) used about 1,287 MWh of electricity and produced roughly 502 metric tons of CO2, similar to the yearly emissions of about 112 cars.
- A University of Massachusetts study found that training a single AI model can emit over 284 metric tons of CO2 (626,000 pounds), roughly equal to the lifetime emissions of five cars.
- GPT-4 is estimated to require around 50× more electricity than GPT-3 for training due to its much larger size and complexity.
- Very large models such as GPT-4 and Gemini Ultra are believed to consume millions of kilowatt-hours, resulting in thousands of tonnes of CO2 emissions during training.
The Power of Location: A Case Study
The environmental impact of training AI models is not only determined by their size, but also by where the computing takes place. The source of electricity, whether clean or fossil fuel based can significantly change the total carbon emissions.
- Reports suggest that BLOOM (176 billion parameters) by Hugging Face was trained on a French nuclear-powered supercomputer and used 433 MWh of electricity, producing only 25 metric tons of CO2.
- In contrast, GPT-3 (similar scale) consumed 1,287 MWh of electricity and produced about 502 metric tons of CO2, nearly 20 times higher emissions.
- This difference highlights that clean energy sources can dramatically reduce AI’s carbon footprint, even when model sizes are similar.
| Model | Parameters | Electricity Used | CO2 Emissions |
| BLOOM (Hugging Face) | 176B | 433 MWh | 25 metric tons |
| GPT-3 | 175B | 1,287 MWh | 502 metric tons |
The Always-On AI Carbon Problem
Unlike training, which happens once, inference is continuous. Every time a user asks a question or generates content, the model runs computations that consume electricity. As AI usage grows globally, this ongoing demand becomes a major part of its total carbon footprint.
| Particulars | Data |
| Share of AI energy use (inference vs training) | ~60% inference / 40% training |
| Electricity use per ChatGPT query vs web search | ~5× higher |
| AI interaction energy vs Google search | Up to 10× higher |
| GPT-3 daily inference emissions | ~22.7 kg CO2 (50 lbs) |
- Google estimates that around 60% of AI energy use comes from inference, while 40% is from training.
- A single ChatGPT query can use about 5× more electricity than a standard web search.
- The International Energy Agency (IEA) estimates that AI interactions like ChatGPT may consume up to 10× the energy of a traditional Google search.
- GPT-3’s daily inference activity has been estimated to produce about 22.7 kg (50 pounds) of CO2 per day.
Big Tech Carbon Surge Driven by AI Expansion
As AI grows quickly, big tech companies are building large data center networks to power it. This has led to higher electricity use and more emissions, making Big Tech a major contributor to AI-related carbon output.
Year-over-Year Emissions Increase (2020-2023)
According to the ITU Greening Digital Companies 2025 report, between 2020 and 2023, the world’s largest AI-driven tech companies saw a sharp rise in their operational emissions. As AI workloads expanded and data center usage grew, emissions increased by an average of around 150% across major players like Amazon, Microsoft, Meta, and Google.
| Company | Emissions Increase |
| Amazon | +182% |
| Microsoft | +155% |
| Meta | +145% |
| Alphabet (Google) | +138% |
| Average | +150% |
How Major Tech Companies Are Driving AI Emissions
As artificial intelligence expands rapidly, major technology companies are building large-scale data center infrastructure to support growing computational demands. This has led to significant increases in electricity use and carbon emissions, making Big Tech a key driver of global AI-related environmental impact.
Google:
Google’s carbon emissions have increased in recent years, mainly due to the rapid expansion of data centers needed to support AI services. As AI demand grows, the company’s overall environmental footprint has continued to rise despite earlier sustainability commitments.
- Google’s total emissions reached 11.5 million metric tons of CO2-equivalent in 2024, an 11% increase from the previous year.
- Since 2019, Google’s overall carbon footprint has increased by 51%, largely driven by AI-related data center growth.
- In 2024, Google missed its key net-zero targets and ended its long-standing carbon-neutral operations status (held since 2007).
- Scope 3 emissions increased by 22% in one year and now represent the largest share of Google’s total emissions.
Microsoft:
Microsoft’s carbon emissions have increased in recent years as the company rapidly expands its cloud and AI infrastructure. Despite its long-term climate goals, rising demand for data centers and AI services has significantly increased its overall footprint.
- Microsoft’s total emissions have risen by 23.4% since 2020, despite its commitment to become carbon negative by 2030.
- Scope 3 emissions (supply chain and indirect emissions) now account for about 97% of the company’s total carbon footprint.
- The company plans to invest around $80 billion in data center infrastructure in fiscal year 2025, largely to support growing AI workloads.
Big Tech vs. Countries:
The electricity consumption of major tech companies is now large enough to be compared with that of entire countries. As AI-driven data centers expand, their energy use is reaching national-scale levels, highlighting the growing impact of Big Tech on global power demand.
- Data centers operated by Google and Microsoft consumed around 24 TWh of electricity in 2023.
- This level of consumption is higher than the entire annual electricity usage of Jordan.
AI Carbon and the Hidden Water Footprint
Along with carbon emissions, AI also has a major but less visible environmental impact through water usage. Data centers use large amounts of water mainly for cooling servers, and this demand is increasing quickly as AI infrastructure expands.
- AI-related systems are estimated to have consumed around 765 billion liters of water in 2025, more than the total global consumption of bottled drinking water.
- A medium-sized data center uses about 110 million gallons of water per year, while large facilities can consume up to 5 million gallons per day.
- On average, 1 kilowatt-hour of data center energy requires around 2 liters of water for cooling.
- Google reported using 6.4 billion gallons of water in a single year across its data centers and offices.
- Over the past three years, more than 160 new AI data centers have been built in the U.S., a 70% increase, with many located in already water-stressed regions.
- A Cornell University study estimates that by 2030, AI growth could require 731 to 1,125 million cubic meters of water annually, equal to the yearly household water use of 6 to 10 million Americans.
How AI Carbon Could Help Cut Global Emissions
While AI is increasing energy use and emissions in the short term, it also has the potential to support climate solutions. When applied in energy, transport, and industrial systems, AI can improve efficiency and help reduce overall greenhouse gas emissions.
Emission Reduction Potential
- A 2025 study by the Grantham Research Institute estimates that AI applications in energy, transport, and food systems could reduce global emissions by 3.2 to 5.4 billion tonnes of CO2-equivalent per year by 2035.
- This potential reduction could outweigh the additional emissions from data centers and AI systems.
- PwC modeling suggests that AI-driven efficiency gains could offset the extra energy demand from data centers, making AI’s net impact on emissions neutral or slightly positive.
- Another PwC study (cited by the World Economic Forum) estimates that AI could reduce global greenhouse gas emissions by 0.1% to 1.1% between 2024 and 2035.
- Earlier research from PwC and Microsoft projects that AI applications could reduce emissions in North America by around 6.1% by 2030 under a business-as-usual scenario.
Sector-specific Applications
- In renewable energy, AI can improve the performance of solar and wind systems by increasing efficiency and boosting output by up to 20% through better grid management.
- AI-based energy systems can predict electricity demand and optimize renewable energy distribution, improving grid stability.
- In buildings, combining AI with energy-efficient policies and clean energy could reduce emissions by around 40% by 2050 compared to current trends.
AI Carbon Mitigation Pathways
AI’s rapid growth is creating major sustainability challenges, especially around energy use, carbon emissions, and water consumption. Yet researchers believe the AI carbon footprint can still be significantly reduced through cleaner energy, smarter infrastructure, and more efficient AI systems.
The Energy Challenge
One of the biggest obstacles to sustainable AI growth is the speed at which new data centers are being built. Global demand for AI computing power is rising faster than clean energy infrastructure can expand. As a result, many new facilities still rely heavily on fossil fuel-powered electricity.
Researchers at MIT warn that current data center expansion cannot be sustained cleanly because renewable energy deployment is not keeping pace with demand. Although technologies like Small Modular Reactors (SMRs) are being explored as long-term low-carbon power sources for data centers, deployment remains slow and far behind projected AI energy needs.
Pathways to Reduce AI’s Environmental Impact
Despite these challenges, researchers argue that AI’s environmental footprint can be significantly reduced through coordinated action across infrastructure, energy systems, and model design.
A Cornell University roadmap study found that strategic interventions could reduce AI data center carbon emissions by roughly 73% and water consumption by about 86% compared with worst-case growth scenarios.
Key mitigation strategies include:
- Smarter data center siting: Building facilities in regions with abundant renewable energy and lower water stress can dramatically reduce environmental impact. Researchers highlight areas in the U.S. Midwest “windbelt,” including Texas, Nebraska, and South Dakota, as more sustainable locations.
- Advanced cooling technologies: Direct-to-chip and immersion cooling systems can reduce water consumption by up to 52% compared with conventional cooling methods.
- Right-sizing AI models: Many tasks do not require massive general-purpose language models. Smaller, task-specific models consume far less energy while still delivering strong performance for targeted applications.
- Grid decarbonization: Transitioning electricity grids toward renewable and low-carbon energy sources remains the single most important lever for reducing AI-related emissions.
- Greater transparency and reporting: At present, there are few standardized requirements for companies to disclose AI-related emissions or resource use. Researchers argue that better reporting standards are essential for accurate measurement, accountability, and climate planning.
Lack of Transparency in AI Emissions
One of the biggest obstacles to reducing AI’s environmental impact is the lack of standardized reporting. Most major AI companies, including OpenAI, do not publicly disclose detailed data on AI-related energy use or carbon emissions.
The International Telecommunication Union (ITU) has called for industry-wide standards that would require companies to transparently report AI-specific electricity consumption, emissions, and environmental impacts.
Wrapping Up
AI’s carbon footprint is still small compared to total global emissions, but it is growing very quickly. As AI use increases, data centers will need more electricity and water, putting more pressure on energy systems and climate goals.
AI could also help reduce emissions by improving efficiency in areas like energy, transport, and industry. In the future, the environmental impact of AI will depend on how fast companies move toward cleaner energy, better infrastructure, and more transparent reporting of AI-related emissions.