Google's Carbon Emissions Surge 51% Since 2019, Driven by AI's Growing Energy Demands

Google's carbon emissions just went up again

Google's carbon emissions jumped yet again as the company continues to push ahead in AI. The company's 2025 sustainability report emphasizes that its "ambition-based emissions" grew 11 percent last year to reach 11.5 million metric tons of carbon dioxide pollution, marking a 51 percent increase compared to 2019. That puts Google farther away from its goal of slashing planet-heating pollution in half by 2030 compared to a 2019 baseline. But what it calls "ambition-based" emissions excludes certain categories of supply chain pollution it considers out of its control or"peripheral" to the core business of Alphabet, Google's parent company. That includes certain purchased goods and services and food programs. A table buried in the report's appendix shows that its total emissions actually reached 15,185,200 metric tons of carbon dioxide. That's roughly equivalent to the emissions from nearly 40 gas-fired power plants over a year. Google's report also highlights a 12 percent reduction in carbon emissions from data centers, even though its energy usage continues to rise thanks to AI. Despite the decrease, carbon emissions from data centers are still far higher when compared to 2019. In its report, Google cites several factors outside its "direct control" that are making it more challenging to achieve its climate goals. That includes the "rapid evolution of AI" that could make the company's "future energy needs and emissions trajectories more difficult to predict." It also calls out changes to climate and energy-related policies, "slower-than-needed" deployment of carbon-free energy technologies, and a lack of carbon-free energy solutions in certain markets. "These external factors could affect the cost, feasibility, and timeline of our progress -- and navigating them requires flexibility," Google writes. AI is driving up energy usage at other companies like Microsoft and Meta, and the technology is on track to consume more power than Bitcoin by the end of this year. Though AI companies like DeepSeek are aiming to create more energy-efficient models, that prospect isn't stopping companies like Meta from building a massive, gas-powered data center in Louisiana. President Donald Trump has also signed an executive order promoting the use of coal to power AI data centers.

Google's emissions up 51% as AI electricity demand derails efforts to go green

Google's carbon emissions have soared by 51% since 2019 as artificial intelligence hampers the tech company's efforts to go green. While the corporation has invested in renewable energy and carbon removal technology, it has failed to curb its scope 3 emissions, which are those further down the supply chain, and are in large part influenced by a growth in datacentre capacity required to power artificial intelligence. The company reported a 27% increase in year-on-year electricity consumption as it struggles to decarbonise as quickly as its energy needs increase. Datacentres play a crucial role in training and operating the models that underpin AI models such as Google's Gemini and OpenAI's GPT-4, which powers the ChatGPT chatbot. The International Energy Agency estimates that datacentres' total electricity consumption could double from 2022 levels to 1,000TWh (terawatt hours) in 2026, approximately Japan's level of electricity demand. AI will result in datacentres using 4.5% of global energy generation by 2030, according to calculations by the research firm SemiAnalysis. The report also raises concerns that the rapid evolution of AI may drive "non-linear growth in energy demand", making future energy needs and emissions trajectories more difficult to predict. Another issue Google highlighted is lack of progress on new forms of low-carbon electricity generation. Small Modular Reactors (SMRs), miniature nuclear plants that are supposed to be quick and easy to build and get on the grid, have been hailed as a way to decarbonise datacentres. There were hopes that areas with many datacentres could have one or more SMR and that would reduce the huge carbon footprint from the electricity used by these datacentres, which are more in demand due to AI use. The report said these were behind schedule: "A key challenge is the slower-than-needed deployment of carbon-free energy technologies at scale, and getting there by 2030 will be very difficult. While we continue to invest in promising technologies like advanced geothermal and SMRs, their widespread adoption hasn't yet been achieved because they're early-stage, relatively costly, and poorly incentivised by current regulatory structures." It added that scope 3 remained a "challenge", as Google's total ambition-based emissions were 11.5m tons of CO₂-equivalent gases, representing an 11% year-over-year increase and a 51% increase compared with the 2019 base year. This was "primarily driven by increases in supply chain emissions" and scope 3 emissions increased by 22% in 2024. Google is racing to buy clean energy to power its systems, and since 2010, the company has signed more than 170 agreements to purchase over 22 gigawatts of clean energy. In 2024, 25 of these came online to add 2.5GW of new clean energy to its operations. It was also a record year for clean energy deals, with the company signing contracts for 8GW. The company has met one of its environmental targets early: eliminating plastic packaging. Google announced today that packaging for new Google products launched and manufactured in 2024 was 100% plastic-free. Its goal was to achieve this by the end of 2025. In the report, the company also said AI could have a "net positive potential" on climate, because it hoped the emissions reductions enabled by AI applications would be greater than the emissions generated by the AI itself, including its energy consumption from datacentres. Google is aiming to help individuals, cities and other partners collectively reduce 1GT (gigaton) of their carbon-equivalent emissions annually by 2030 using AI products. These can, for example, help predict energy use and therefore reduce wastage, and map the solar potential of buildings so panels are put in the right place and generate the maximum electricity.