Artificial Intelligence (AI) is rapidly diffusing into every sector of the economy and daily life. Its proliferation is often framed within the prevailing narrative of ‘AI for good,’ including the promise of AI as a tool to address global challenges such as climate change. Yet this optimistic framing overlooks a growing and underexamined reality: the extent to which AI itself can contribute to climate risks.
Current concerns about AI’s adverse climate impacts are largely confined to operational energy and water use by data centers. While these impacts are important, they represent only a fraction of the ways in which AI can influence climate outcomes. AI systems will reshape behavior, affect infrastructure, and alter economic and political dynamics in ways that may work against decarbonization efforts. The risks extend from individual choices and system-level efficiencies to public trust in technologies and climate governance.
Recognizing and identifying these varied risk pathways is essential if we are to ensure AI supports—rather than undermines—climate action. Drawing on a broad taxonomy of AI-related risks, our recent analysis has mapped dozens of potential linkages between AI and climate vulnerabilities, capturing key dimensions of AI-related risks including misinformation, discrimination, privacy and security, malicious use, human-computer interaction failures, and broader socioeconomic harms. These include links between AI risks and the building blocks of net-zero energy pathways, including: 1) sectoral energy demand and electricity supply networks; 2) low-carbon technology deployment and behavioural shifts; 3) climate policy; and 4) climate governance institutions (see full map here). While these links are still emerging, they highlight the urgent need to account for AI’s indirect and systemic impacts on climate goals.
This article outlines several key areas where AI contributes to climate risk—directly, indirectly, or through unintended consequences. Understanding these systemic risk linkages is essential to align AI development with climate change mitigation goals, or those goals will be pushed out of reach.
Direct energy impacts and emissions
AI-driven efficiency gains, while potentially reducing resource use, often trigger rebound effects. These occur when improved efficiency lowers costs, which in turn spur increased consumption, ultimately offsetting any environmental benefits. Efficiency and productivity gains from many different AI applications that reduce frictions or transaction costs can lead to a surge in energy-hungry activity. This is evident in contexts from e-commerce and advertising to freight logistics and buildings’ energy performance, as well as AI data centers themselves.
Moreover, AI-powered platforms frequently shape consumer behavior through automated nudging, prioritizing engagement, the interests of AI developers and deployers, over sustainability. For instance, ChatGPT automatically provides follow-up suggestions in chats that prompt people to continue using it for more queries or more image generation, stimulating rather than managing demand. Such systemic incentivization of energy-intensive behaviour is at odds with climate objectives.
Another risk is the growing reliance on agentic AI, capable of making decisions on behalf of users. In contexts like travel, healthcare, and finance, such systems risk misalignment with users’ values. For example, an AI agent tasked with booking your next travel destination might optimize for comfort or price, rather than low-carbon options.
Cybersecurity threats to low-carbon technologies
AI significantly escalates cybersecurity risks. Researchers widely anticipate an increase in AI-based hacking data breaches from automated propagation and attack capability. This presents an obstacle to the deployment of smart, networked low-carbon technologies, including electric vehicles, smart building systems, and grid-responsive technologies such as heat pumps, that need to be adopted at scale and where perceived security risks could seriously delay progress.
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Furthermore, the integration of AI into increasingly decentralized and digital systems introduces vulnerabilities that could trigger cascading failures across critical infrastructure, including energy networks, industrial sectors, and agriculture. These systemic risks are often glossed over by the hype surrounding AI as a sustainability solution in the major carbon-emitting sectors.
Climate mis- and disinformation may also promote climate scepticism, dissuade people from adopting low-carbon choices and behaviours, or worse, convince people to reject climate action altogether. A climate denial think tank managed to do just that by creating and spreading an image of a dead whale in front of wind turbines, claiming that offshore wind was responsible. Social media campaigns of this nature risk creating a feedback loop between scientific denialism and political inaction.
Socioeconomic disruption and indirect climate harms
As a general-purpose technology, AI’s societal impacts are transformative and diffuse, affecting employment, income distribution, and social cohesion. A great deal of research has been focused on AI’s impact on jobs, skills, wages, and widening inequalities, as well as the risks for discrimination, surveillance, and civil liberties. While such issues are widely recognized, including in legislation like the EU AI Act and governance discussions focused on AI red lines, their indirect implications for climate action remain underexplored.
AI-induced socioeconomic harms—such as inequality, reduced autonomy, and increased surveillance—can indirectly undermine climate efforts. These effects can reduce an individual’s agency and capabilities to act on climate and diminish civic engagement more broadly. At a higher level, they contribute to public distrust, weaken institutional legitimacy, and sap the social consensus and collective action necessary to pursue shared climate goals that protect the global commons.
A call for climate-responsive AI governance
These examples outlined above are only a subset of a broader landscape of climate-related AI risks, documented in our larger database. As AI becomes more embedded in daily life and critical infrastructure, so too does its influence on systems, behaviours, and policies that shape our climate outcomes. The impacts of AI on climate are dense, complex, and diffuse, extending way beyond its energy use in data centers.
Recognizing these systemic linkages is crucial if we want to align AI development with climate goals. It is also the first step towards developing a more robust regulatory and governance framework with appropriate risk mitigation strategies. A first step is to identify who should take on which risks, and then identify how they should be tackled. AI and climate are both global collective problems that require collective actions; only when we acknowledge the extent of the impacts of AI on the climate can we start addressing them.
Stony Brook University Receives $13.77M NSF Grant to Deploy a National Supercomputer to Democratize Access to Artificial Intelligence and Research Computing
This funding will be used to procure and operate a high-performance, highly energy-efficient computer designed to handle the growing needs of artificial intelligence research and other scientific fields that require large amounts of memory and computing power. By making this resource widely available to researchers, students, and educators across the country, the project will expand access to advanced tools, support groundbreaking discoveries, and train the next generation of scientists.
The new system will utilize low-cost and low-energy AmpereOne® M Advanced Reduced Instruction Set Computer (RISC) Machine processors that are designed to excel in artificial intelligence (AI) inference and imperfectly optimized workloads that presently characterize much of academic research computing. Multiple Qualcomm® Cloud AI inference accelerators will also increase energy efficiency, enabling the use of the largest AI models. The AmpereOne® M processors, in combination with the efficient generative AI inference performance and large memory capacity of the Qualcomm Cloud AI inference accelerators, will directly advance the mission of the NSF-led National Artificial Intelligence Research Resource (NAIRR).
This is the first deployment in academia of both of these technologies that have transformed computing in the commercial cloud. The new IACS-led supercomputer will efficiently execute diverse workloads in an energy- and cost-efficient manner, providing easily accessible, competitive and consistent performance without requiring sophisticated programming skills or knowledge of advanced hardware features.
“This project employs a comprehensive, multilayered strategy, with regional and national elements to ensure the widest possible benefits,” said IACS director Robert J. Harrison. “The team will collaborate with multiple initiatives and projects, to reach a broad audience that spans all experience levels from high school students beginning to explore science and technology to faculty members advancing innovation through scholarship and teaching.”
“The University at Buffalo is excited to partner with Stony Brook on this new project that will advance research, innovation and education by expanding the nation’s cyber-infrastructure to scientific disciplines that were not high performance computing-heavy prior to the AI boom, as well as expanding to non-R1 universities, which also didn’t have much of high-performance computing usage in the past,” says co-principal investigator Nikolay Simakov, a computational scientist at the University at Buffalo Center for Computational Research.
“AmpereOne® M delivers the performance, memory and energy footprint required for modern research workloads—helping democratize access to AI and data-driven science by lowering the barriers to large-scale compute,” said Jeff Wittich, Chief Product Officer at Ampere. “We look forward to working
with Stony Brook University to integrate this platform into research and education programs, accelerating discoveries in genomics, bioinformatics and AI.”
“Qualcomm Technologies is proud to contribute our expertise in high-performance, energy-efficient AI inference and scalable Qualcomm Cloud AI Inference solutions to this groundbreaking initiative,” said Dr. Richard Lethin, VP, Engineering, Qualcomm Technologies, Inc. “Our technologies enable seamless integration into a wide range of applications, enabling researchers and students to easily leverage advanced AI capabilities.”
Nationally and regionally, this funding will support a variety of projects, with an emphasis on fields of research that are not targeted by other national resources (e.g., life sciences and computational linguistics). In particular, the AmpereOne® M system will excel on high-throughput workloads common to genomics and bioinformatics research, AI/ML inference, and statistical analysis, among others. To help domain scientists achieve excellent performance on the system, software applications in these and related fields will be optimized for Ampere hardware and made readily available. This award reflects NSF’s statutory mission and that this initiative has been deemed worthy of support through evaluation using the foundation’s intellectual merit and broader-impacts review criteria.
The awarded funds are primarily for purchase of the supercomputer and first year activities, with additional funds to be provided for operations over five years, subject to external review.
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About the U.S. National Science Foundation (NSF)
The U.S. National Science Foundation (NSF) is an independent federal agency that supports science and engineering in all 50 states and U.S. territories. NSF was established in 1950 by Congress to:
Promote the progress of science.
Advance the national health, prosperity and welfare.
Secure the national defense.
NSF fulfills its mission chiefly by making grants. NSF’s investments account for about 25% of federal support to America’s colleges and universities for basic research: research driven by curiosity and discovery. They also support solutions-oriented research with the potential to produce advancements for the American people.
About Stony Brook University
Stony Brook University is New York’s flagship university and No. 1 public university. It is part of the State University of New York (SUNY) system. With more than 26,000 students, more than 3,000 faculty members, more than 225,000 alumni, a premier academic healthcare system and 18 NCAA Division I athletic programs, Stony Brook is a research-intensive distinguished center of innovation dedicated to addressing the world’s biggest challenges. The university embraces its mission to provide comprehensive undergraduate, graduate and professional education of the highest quality, and is ranked as the #58 overall university and #26 among public universities in the nation by U.S. News & World Report’s Best Colleges listing. Fostering a commitment to academic research and intellectual endeavors, Stony Brook’s membership in the Association of American Universities (AAU) places it among the top 71 research institutions in North America. The university’s distinguished faculty have earned esteemed awards such as the Nobel Prize, Pulitzer Prize, Indianapolis Prize for animal conservation, Abel Prize, Fields Medal and Breakthrough Prizes in Mathematics and Physics. Stony Brook has the responsibility of co-managing Brookhaven National Laboratory for the U.S. Department of Energy — one of only eight universities with a role in running a national laboratory. In 2023, Stony Brook was named the anchor institution for The New York Climate Exchange on Governors Island in New York City. Providing economic growth for neighboring communities and the wider geographic region, the university totals an impressive $8.93 billion in increased economic output on Long Island. Follow us on Facebook https://www.facebook.com/stonybrooku/ and X @stonybrooku.
Funding for a three-year research project slated to help develop a continuously updated database of available farmland.
RAINY RIVER — An ongoing research and innovation project that’s received provincial funding aims to use artificial intelligence to help create a database of available farmland.
The initiative is the brainchild of the Northern Ontario Farm Innovation Alliance, a not-for-profit that focuses on the agriculture sector.
“Our overall goal is to be able to create an online mapping tool that links both Crown land and private land that is available as a starting place for farmers who are looking for land,” Emily Seed, the alliance’s executive director, said in an interview with Newswatch.
“So, they know where to go looking and then they can then take the next steps in following up — whether that be an application to access that land or whether it be working with a realtor, or whatever that might look like.”
Access to usable farmland has been a longstanding issue when attempting to expand and better agriculture across northern Ontario, Seed said.
The AI component will be implemented both in the database’s front and back ends, she said. Users will be able to use a feature like a chatbot to help with searches and, behind the scenes, it will scrape data from sources like open-source databases and public information released by realtors to constantly update the tool.
That will “keep that information up-to-date and relevant in real time, so that it’s not just a standalone static tool,” Seed said.
In Northwestern Ontario, most agricultural land is southwest of Thunder Bay and in the Rainy River District.
“There’s a lot of barriers around things like Crown land access and then being able to find private land can also be a challenge,” she said. “This project is looking at how can we create some linkages in there to create long-term sustainability and make sure that people are able to find land available for agricultural use.”
“Trying to fill in some of those gaps when we’re talking about agricultural expansion and land use for agriculture in northern Ontario.”
The roughly $50,000 funding commitment from the provincial Ontario Agri-food Research Initiative will help support the project until 2027.
Seed said a publicly-available resource will likely be online more towards the end of the scheduled timeline.
“As we go through this project, we very much anticipate it to morph as we go and see what’s actually applicable,” she said. “AI is fairly new to us on that end, so we’re working with a few different developers and experts in the area to help us sort of navigate that side of things.”
“We will be working hard on it on the back end to morph it into something that’s a usable tool.”
Artificial intelligence is revolutionizing weather prediction around the world, as evidenced by the successful prediction this spring of a delayed onset of the monsoon in northeastern India.
The prediction gave millions of smallholder farmers the option of postponing planting to take better advantage of the rains or to plant different crops. Based on a preliminary phone survey, many farmers adjusted their planting as a result.
This AI-based weather model — a collaboration between the University of California, Berkeley, and the University of Chicago — paves the way for much better forecasts for hundreds of millions of farmers across the tropics and global south whose livelihoods depend on timing crop planting with the monsoon’s arrival. Nearly two-thirds of the world’s population live in regions of the tropics impacted by monsoon rains, whose arrival each year is being affected by climate change.
“This program harnesses the revolution in AI-based weather forecasting to predict the arrival of continuous rains, empowering farmers to plan agricultural activities with greater confidence and manage risks. We look forward to continuing to improve this effort in future years,” said Pramod Kumar Meherda, additional secretary at the Indian Ministry of Agriculture and Farmers’ Welfare.
The success of this AI prediction project — the largest targeted dissemination of AI weather forecasts to date — required a herculean effort by atmospheric scientists, AI experts, India’s Ministry of Agriculture and Farmers’ Welfare and a global nonprofit that supports smallholder farmers. Key to these predictions were daily climate data compiled and made publicly available by the U.S. National Oceanic and Atmospheric Administration.
To make the actual predictions, UChicago AI expert Pedram Hassanzadeh teamed up with Berkeley atmospheric scientist William Boos to evaluate and use global AI weather models that were developed independently by Google and the European Centre for Medium-range Weather Forecasts (ECMWF). Both of those models have been trained on 40 years of global climate data. To localize the models to India and correct biases in their predictions, the UC Berkeley and UChicago teams used statistics from 100 years of rainfall data from the India Meteorological Department.
A time-lapse video of the arrival of the Indian monsoon in the spring of 2025, showing how the rains (blue) stalled over southern India before proceeding northward. The AI-based weather model predicted the stall and the subsequent resumption of the northward progression of the rains.
Courtesy of the Human-Centered Weather Forecasts Initiative at the University of Chicago
The monsoon-onset forecasts, which differed for different regions, were delivered weekly to about 38 million farmers across 13 states in central and northeastern India — most of the core monsoon zone. These forecasts provided predictions up to four weeks in advance for the arrival of monsoon rains in particular regions, something that had not been done before in 150 years of monsoon forecasting, Boos said. Current numerical models, based on the physics of the atmosphere, typically provide reasonably accurate rainfall predictions no more than five days out.
When the monsoon hit southern India in early June, the AI-based model predicted that it would stop temporarily, something that was not predicted by any other available forecast. That’s what actually happened — it stalled for 20 days.
“Demonstrating that the long lead-time precipitation forecasts made by these AI models are of practical use in a tropical region where people live is a major step forward — no one really knew that before we did this work,” said Boos, a UC Berkeley professor of earth and planetary science.
Parasnath Tiwari, a farmer from Madhya Pradesh, received the forecast on his phone and was able to prepare earlier, he said. He decided to switch the types of crops he planted to more lucrative ones because the message gave him confidence that the season would be long enough.
“Before this, I mostly relied on my own experience and local knowledge to know when the monsoon would arrive,” said Tiwari. “The forecast about the arrival of the monsoon was accurate…. I have increased trust in the forecast, and I will rely on the information shared by scientists in the future.”
A false monsoon could mean disaster
Farmers in each region of northeastern India were updated on a mostly weekly basis between May and July about the probability that the true monsoon would start within a certain window of time. In a typical year, the monsoon arrives between June 15 and June 30 in the south and proceeds northward, bringing steady rain to most of the country by July. The AI model predicted the nearly three-week stall, which the Indian government communicated to the farmers.
The AI-based weather prediction model produced monsoon forecast maps like these every week, beginning May 20. The model divides India into a grid and estimates the likelihood that the monsoon rains will start in the next 1, 2, 3 or 4 weeks (bar chart) in each grid square. Each square is color coded according to which 2-week period had the highest combined probability of rain onset. A simpler message – which 2-week period is most likely to see the onset of rains – was communicated to smallholder farmers each week. The May 27 forecast, for example, shows that the monsoon rains have already arrived in the 3 southernmost regions (gray) but predicts that it will take 3 weeks — until June 18 — to move farther north (light orange) and at least one more week after that to reach the northernmost regions (yellow). The normal monsoon usually proceeds steadily northward, but this unexpected 20-day pause was accurately called by the AI model.
Courtesy of the Human-Centered Weather Forecasts Initiative at the University of Chicago
“We actually gave farmers probabilistic forecasts, telling them how likely it was that monsoon rains would start in a particular week,” Boos said. “By field-testing the SMS messages with farmers in advance, our team was able to tailor the language of the message so that they understood what was being predicted and the level of certainty of the prediction.”
Boos studies atmospheric dynamics, primarily the atmospheric wind patterns that deliver water in the form of monsoon rains to Central America, South America, Africa, Northern Australia and South Asia. The onset of these monsoons is important to farmers because, unlike in the U.S., the majority of farmers planting wheat, rice and other staple crops have small plots and cannot afford to irrigate if the rains fail.
“The classic catastrophe scenario is that you get a wet spell, it rains for a few days, they plant their seeds, they’re like, ‘Hooray, the rainy season has arrived,’ and then there’s 15 days of dryness afterward and all the seeds dry out and die,” Boos said. “They just spent an enormous amount of their savings to buy seed stock and plant it, and it died, and that’s a huge loss.”
Based on an analysis led by UChicago Nobel Prize-winning economist Michael Kremer, one of the leaders of the project, the researchers concluded that farmers in rural India could benefit economically from a better prediction of when the annual rains would truly begin. AI-based weather prediction models seemed like the place to start.
“We have been going through an AI-driven revolution since 2022, and AI models have shown promise for many one- to two-week forecasting applications. But their ability to predict complex phenomena — like the monsoon — was unclear, and frankly, unexpected,” Hassanzadeh said. The first revolution, beginning in the 1950s, focused on physics-based models and numerical simulations on supercomputers. This second revolution is being powered by AI models trained on observation-based data and capable of being run on a laptop.
Boos and Hassanzadeh tested more than half a dozen of the current AI weather prediction models that make predictions a month out and also predict rainfall, and chose the two best: Google’s NeuralGCM, for neural general circulation model, and the AI Forecasting System (AIFS) created by ECMWF.
Boos said that many of these models have been shown to predict global aspects of the climate as well as or better than earlier physics-based models, but few have been tasked with predictions of the seasonal onset of rains in a specific region.
Because each model had different strengths and weaknesses, the team mathematically blended Google’s NeuralGCM, ECMWF’s AIFS and historical rainfall data from the India Meteorological Department.
This blend produced a probabilistic model with a 30-day lead time, “merging multiple AI models and statistical methods to produce useful forecasts targeted at agriculture,” Boos said. “Forecasts of the start of sustained monsoon rains have historically been difficult or impossible to deliver locally with this much lead time, especially on such a large scale.”
Delivering the message
The Ministry of Agriculture and Farmers’ Welfare delivered the forecasts to the farmers directly using its SMS texting platform. The Government of Odisha also partnered with the research team to reach nearly 1 million more through a voice messaging platform. Precision Development (PxD), a global nonprofit supporting smallholder farmers in digital advisory services, led message design and testing.
Farmers throughout northwestern India received weekly forecasts about the arrival of the monsoon in the spring of 2025. Planting crops with the arrival of the monsoon is an annual ritual that can be upended when rains suddenly stop.
Photo courtesy of Precision Development, PxD
The project leaders concluded that farmers responded to these weather forecast messages. Based on early results from a phone survey, up to 55% recalled receiving weather forecasts on their phones, and among those who remembered specifically the monsoon onset forecasts, nearly half reported using the information to adjust their planting decisions. A majority of farmers also shared these messages with other farmers, suggesting an even greater reach and impact.
“I shared the monsoon arrival forecasts with other farmers in my locality. We usually talk to each other and share useful information that we come across,” Tiwari said. “Some farmers have benefited from the information I shared about the arrival of the monsoon. I feel that others will also start relying on this information and trust it for their agricultural decision-making.”
“Disseminating AI weather forecasts has an incredibly high return on investment, likely generating more than $100 for farmers for each dollar invested by the government,” said Kremer, co-director of UChicago’s Human-Centered Weather Forecasts Initiative. “India is leading the way in using AI to improve people’s lives across many sectors, including agriculture.”
The effort was partially supported by catalytic funding from AIM for Scale, a global initiative backed by the Gates Foundation and the United Arab Emirates, which works to scale up evidenced-backed, cost-effective agricultural innovations for the benefit of farmers in low- and middle-income countries. The researchers behind the project are now working with AIM for Scale to start similar programs in other low- and middle-income countries and to train government meteorologists in the global south on how to use AI models effectively.
“One of the things we would like to do for future years, hopefully for next year, is to be able to predict dry spells throughout the entire summer, issuing predictions of the likelihood of a dry period occurring within the next two to three weeks,” Boos said.
